CN114418415A - Learner-oriented self-adjusting learning data information processing system and method - Google Patents
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Abstract
The invention belongs to the technical field of learner modeling, and discloses a learner-oriented self-adjusting learning data information processing system and a learner-oriented self-adjusting learning data information processing method, wherein a theoretical model of a learner self-adjusting learning process consisting of four circulation stages is constructed; decomposing and explaining the secondary indexes to construct a self-adjusting learning index system; data acquisition and pre-processing is performed according to the proposed self-adjusting learning index system. The invention focuses on the ability cultivation of learners, and improves and optimizes the learners by combining with actual requirements on the basis of foreign classical theory so as to better adapt to the learning conditions of students in China. The invention utilizes the cooperation mode of the family and the school to ensure that the learners with low school age can obtain more feedback and help when developing self-regulation learning, and the participation and the help of parents and teachers are reflected from the excitation process of the external motivation of the learners to the maintenance process of the internal motivation, thereby guiding the students to better carry out self-regulation learning and cooperating the two parties of the family and the school to create a good co-cultivation atmosphere for the students.
Description
Technical Field
The invention belongs to the technical field of learner modeling, and particularly relates to a learner-oriented self-adjusting learning data information processing system and method.
Background
At present, under the background of education informatization 2.0, the Internet can provide convenient learning resources for learners. However, the learning behavior habits of learners in the internet background are greatly changed compared with those in the traditional learning environment, and learners also need to reasonably plan the learning mode and efficiently utilize learning resources. Therefore, improving the self-adjusting learning ability of learners becomes an important link for students to develop good habits, cultivate self-discipline quality and improve core literacy.
The cognitive ability and self-regulation ability of students in different school ages are different, and the healthy growth of students in low school ages needs the mutual care of teachers, parents and society. Teachers should carry their hands with parents to actively develop family-school co-education and jointly cultivate self-regulation learning and self-management abilities of students.
Although the experts of the relevant scholars of education and psychology at home and abroad explore and research a lot of self-regulation learning, the definition of the self-regulation learning does not form a uniform opinion due to the difference of theoretical standpoints. Self-regulation learning is a continuous and deep research of Zimmerman based on the social cognitive theory proposed by psychologist Bandaura at the end of the seventies of the 20 th century, so that the concept of self-regulation learning is enriched and developed. Zimmerman considers that learners adjust their learning behavior by constantly monitoring and adjusting their cognitive and emotional states and observing and applying various strategies while performing self-regulation learning, thereby creating and utilizing substances and social resources in the learning environment. He thinks that the learning academic cycle has three phases: first a pre-consideration phase, followed by an operational or mental control phase, and finally a self-reflexive phase. Foreign psychologist pindrich considers self-regulated learning to be divided into planning and activation phases, monitoring phases, control phases, reaction and reflexive phases. In addition to this, there are self-adjusting learning dual process models proposed by the psychologist Boekaerts in the netherlands, and self-adjusting learning information process models proposed by the psychologist Winne in canada from an information processing perspective.
The theoretical models proposed by various scholars are emphasized, and have respective characteristic advantages and disadvantages. However, these theoretical models embody the common recognition that self-adjusting learning refers to the process of controlling, responding and adjusting the learning behavior and mind of a learner to achieve the desired goal during the learning process, and mainly includes the processes of self-establishing goal, selecting a strategy to achieve the goal, and evaluating the degree of effort.
Through the above analysis, the problems and defects of the prior art are as follows:
due to the difference of theoretical positions, the definitions of the expert scholars at home and abroad for self-adjusting learning do not form a uniform opinion, and the existing scale does not provide a scheme for matching self-adjusting learning indexes in combination with the actual national conditions of China. Self-adjusting learning capabilities the existing scale measurement is measured in two dimensions, the motivation for learning and the learning strategy, but ignores the procedural data to develop self-adjusting learning. In the execution process, the existing self-adjusting learning theory is not specific enough to the guidance in the real education and teaching scene, the learner is difficult to integrate the dimensionality related to the original theory into the ordinary learning life, and in addition, schools, teachers and parents lack corresponding knowledge resources in the process of education and assisted cultivation of the self-adjusting learning capacity of the learner. When data in an index system are collected, the education informatization of partial classes in the K-12 stage and schools is uneven, and students are prohibited from entering the campus with electronic products by the middle and primary schools in a clear order, so that great difficulty exists in using an electronic file entry form.
The significance of solving the problems and the defects is as follows:
the index system focuses on the ability cultivation of learners in the process of concept conception, and fully considers the national conditions of China and the practical requirements of school child-raising environments on the basis of the foreign classical theory to improve and optimize the learners, so that the learners are better adapted to students in China. The self-regulation learning is developed according to the circulation stage in the index system for a long time, students focus on the direction concerned by the index system, the learners can be helped to form good time concept and behavior habits, the subjective initiative of the students is fully exerted, the learners are promoted to continuously improve the comprehensive quality and the comprehensive ability of the learners, and the difficulty and the challenge in the growth process can be more leisurely dealt with. In addition, the index system integrates the concept of home-school cooperation in the design process, and the learners with low school age are considered to be influenced by the physical and mental development level, so that the learners with low school age should obtain more feedback and help besides the learners. After the double reduction is performed, the post-school work time and the weekend learning time of students are reduced, the accompanying and supervising time of parents and the channel for communicating with teachers are weakened, and the paper self-management manual is used as a good carrier for self monitoring, self guiding and home-school communication of students, so that a cooperative bridge is built for creating a good home-school co-breeding atmosphere.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a learner-oriented self-adjusting learning data information processing system and method.
The invention is realized in such a way that a learner-oriented self-adjusting learning data information processing method comprises the following steps:
step one, constructing a theoretical model of the learner self-adjusting learning process consisting of four circulation stages;
step two, decomposing and explaining the secondary indexes respectively to construct a self-adjusting learning index system;
and step three, data acquisition and preprocessing are carried out according to the proposed self-adjusting learning index system.
Further, the construction of the theoretical model of the learner self-adjusting learning process consisting of four cycle phases in the first step comprises the following steps:
on the basis of exploration and research of experts of education and psychology related scholars at home and abroad on self-adjusting learning, the practical situation of basic education of China is combined, self-adjusting learning is taken as a main theoretical support, the requirements of each stage in the self-adjusting learning process are combined, other theories are used for supplementing and perfecting a theoretical model of the self-adjusting learning process of the learner, wherein the theoretical model consists of four circulation stages, and the four stages are respectively target and plan, record and analysis, thinking resistance and evaluation, adjustment and optimization. In the target and plan stage, dividing the target and plan into two secondary indexes of a self-adjusting learning motivation and a target task making condition by applying a motivation theory and a meta-cognition plan strategy; in the recording and analyzing stage, dividing the data into two secondary indexes of a target task completion condition and a time allocation condition by using a meta-cognitive monitoring strategy; in the stage of thinking and evaluation, the thinking and evaluation are divided into two secondary indexes of self evaluation and thinking and self management capability evaluation by applying a meta-cognition regulation strategy and a self-effectiveness sense theory; in the adjusting and optimizing stage, the meta-cognition adjusting strategy and the family-school cooperation strategy are used for dividing the adjusting and optimizing into two secondary indexes of parent participation adjusting and student learning adjusting.
Wherein the proposed theoretical model comprises four cycle phases: setting a target and a plan, recording and analyzing the completion condition and the time arrangement of the target plan, performing backstepping and evaluation on an analysis result, and adjusting and optimizing the target plan before according to the content summarized by backstepping; the content of each cycle stage is divided primarily, and 8 secondary indexes of a learner self-adjusting learning index system are designed.
Further, in the second step, the preliminary dividing the content of each cycle phase includes:
(1) the evaluation of the 'target and plan' comprises two contents of 'self-regulation learning motivation' and 'target task making condition'; the target task making condition is to evaluate a target plan from the aspects of total quantity, category quantity, description normalization and reasonableness, and ensure that the made target and task are in the learner ability range and are easy to operate;
(2) the evaluation of the recording and analyzing link comprises two parts of a target task completion condition and a time distribution condition. The target task completion condition is a process of tracking and self-monitoring a target plan formulated in the previous link, and includes evaluation dimensions including completion degree, completion duration and completion quality; the time allocation condition is that the time spending of the learner is tracked and recorded from the time management perspective, the activity categories which are frequently appeared in the life of the learner, such as class attendance, autonomous learning, homework, reading out of class, exercise, social entertainment and sleeping, are selected, the time spending is classified and counted, and the time allocation condition of the learner is comprehensively reflected by combining the indexes of dominant time duration statistics, time allocation rationality and the like.
(3) The links of "thinking resistance and evaluation" comprise two items of "self evaluation and thinking resistance" and "self management ability evaluation". Self-evaluation and thinking resistance are that learners evaluate their performance from two dimensions of class and thinking resistance frequency, and the classroom performance of students is reflected by recording the concentration degree, content mastery degree and classroom participation condition of each class; the number of bars, the number of words and the specific content of the backstepping record are used for reflecting the summary and thinking of the students. The self-management ability evaluation adopts the modes of student self-evaluation, teacher evaluation, parent evaluation and expert evaluation to comprehensively evaluate the results of the four aspects, and the confidence and validity of the ability evaluation is ensured.
(4) The 'adjustment and optimization' link comprises two aspects of 'parent participation adjustment' and 'student learning adjustment'. The student learning adjustment is a main form of adjustment and optimization, and the whole learning process is adjusted and optimized through adjustment on learning motivation, learning efficiency sense, cognitive strategy, management strategy and anxiety level. In the process, teachers participate in instruction, help learners to determine problems, find reasons, guide students to try appropriate solutions, and produce feedback and intervention effects. The parent participation is embodied in the form of 'daily parent return words', the state, emotion, habit and progress content of the learner are freely evaluated from the observation view of the guardian, the problems and transformation of the learner are pointed out, the encouragement and the support are provided, and the effect of adjusting feedback is achieved.
Further, in the third step, according to multiple theories of meta-cognition, motivation theory and family school co-cultivation, 8 secondary indexes are respectively decomposed and explained, and a whole self-regulation learning index system is constructed;
(1) the self-regulation learning motivation consists of self-management perception value, self-management effectiveness feeling, self-management ability and self-management motivation;
(2) the target task making condition consists of a target making total number, a target classification number, a target description normalization, a target making rationality, a weekly task making total number, a task making classification number and a task making rationality;
(3) the target task completion condition consists of task completion degree, task completion duration and task completion quality;
(4) the time allocation condition consists of the family work time, the extracurricular reading time, the sleeping time, the exercise time, the social contact time, the entertainment time, the class time, the autonomous learning time, the working day controllable time, the rest day controllable time, the task completion time, the time allocation record integrity and the time allocation record rationality;
(5) the self-evaluation and the thinking resistance consist of concentration self-evaluation, mastery self-evaluation, participation condition record number, thinking resistance record word number and thinking resistance content;
(6) the self-management ability evaluation is composed of student self-management ability self-evaluation scores, student self-management ability teacher scores, student self-management ability parent scores and student self-management ability expert scores;
(7) the parent participation adjustment is composed of the total number of parent words, the total word number of the parent words, the emotional attribute of the parent words, the integral quality of the parent words and the score of the parent participation degree;
(8) the student learning regulation is composed of academic motivation, academic effectiveness sense, high-order thinking, meta-cognition strategy, resource management strategy, academic pressure and academic performance.
Further, the self-management perception value, the self-management effectiveness sense, the self-management ability and the self-management motivation in the step (1) include:
(1.1) self-management perception value
The self-management perception value is obtained by comprehensive analysis of the dimensions of personal orientation, social orientation, self-management awareness, use willingness of self-management auxiliary tools and self-monitoring awareness in the questionnaire. The index analysis processing method comprises the following steps: the questionnaire is provided with a plurality of test questions, each question is presented in the form of a five-level scale, the five levels of completely disapproval, common, disapproval and completely disapproval correspond to the given points of 1-5 respectively, the questionnaire results filled in by the learner are collected, the average score of the questionnaire results is finally calculated, the questionnaire results are equally divided into five corresponding levels, and the level of the learner is judged.
(1.2) sense of self-management Performance
The sense of self-management performance refers to the inference and judgment of whether an individual is able to perform self-management. The analysis results were obtained based on the self-potency-sensing table in the MSLQ questionnaire. The final perception of efficacy indicates how well the learner is satisfied with his or her scheduling and management. The index analysis processing method comprises the following steps: the questionnaire has X questions, each question is presented in the form of a five-level scale, the five levels of complete disapproval, common, disapproval and complete disapproval correspond to the given points of 1-5 respectively, the questionnaire results filled by the learner are collected, the average score is finally calculated, and the average score is divided into five corresponding levels, so that the level of the learner is judged.
(1.3) self-management ability
The self-management ability calculates the factor comprehensive score of the student in the questionnaire through the factor score, and then the score is converted into a percentage system through an efficacy coefficient method, and finally a total score of four dimensions of learning motivation, meta cognition, resource management and anxiety level is formed and is used for reflecting the self-management ability score or ranking of the student. The index analysis processing method comprises the following steps: the questionnaire has X questions, each question is presented in the form of a five-level scale, the five levels of completely disapproval, common, disapproval and completely disapproval correspond to 1-5 points respectively, the questionnaire results filled by the learner are collected, the average points are finally calculated, the average points are divided into five corresponding levels, and the level of the learner is further judged.
(1.4) self-management motivation
The self-management motivation level is obtained by comprehensive evaluation of several primary dimensions such as internal motivation, external motivation, task value, learning belief control, self-management effectiveness feeling and the like.
The total number of the target formulation, the number of the target classification, the target description normalization, the target formulation rationality, the total number of the weekly task formulation, the number of the task formulation classification and the task formulation rationality in the step (2) comprise:
(2.1) Total number Targeted
The total number of targets established by the learner for achieving a certain achievement or achievement is used for reflecting the achievement or achievement that the learner wants to achieve in the aspects of learning development, reading and writing, interests and hobbies, entertainment and social interaction and other aspects, and is an important embodiment for the learner to carry out target management and self-management.
(2.2) number of target classes
The number of goals a learner has made to achieve a certain achievement or performance in the areas of learning development, reading and writing, hobbies, entertaining socialization, and others. The data processing method is that the system automatically classifies, summarizes and sums the targets.
(2.3) object description normalization
The degree of the learner's description of the self-made target according with the set principle is used for reflecting the mastery degree and the comprehension degree of the learner on the target description specification standard or rule and reflecting the language expression ability level of the learner. The data processing method is expert evaluation and subsequent automatic machine evaluation.
(2.4) rationality of object formulation
The index is the reasonable degree of the target formulated by the learner in the aspects of specificity, quantifiability, realizability and relevance, is used for reflecting the mastery degree of the learner on the rule formulated reasonably by the target, and is an important embodiment for the learner to carry out target management and self-management. The data processing method comprises the following steps: self-evaluation: once per day, the bar line plots were generated from the values recorded for ten consecutive individuals, the average for ten individuals, and the average for ten days for the total shift. And (4) expert evaluation: and (4) making quantitative evaluation by experts in a scoring and other ways on the basis of quantitative and qualitative analysis according to the data. Automatic evaluation: the algorithm is calculated according to the diversity, relevance, time period rationality and efficiency of the tasks.
(2.5) Total Numbers of weekly tasks
The weekly statistics of the index is that the learner plans the task condition of the learner to finish the set target, is used for reflecting the attitude of the learner to achieve the target of the learner and reflecting the task management level of the learner, and is an important embodiment for the learner to carry out target management, task management and self-management. The data processing method comprises the following steps: and the system automatically classifies, summarizes and sums the tasks set by the students in week units.
(2.6) task-making the number of classes
In order to achieve the set target, the learner plans the task situation of the learner in the aspects of family homework, autonomous learning, reading, exercise, entertainment, social interaction, executive task, group task and other aspects, and the task situation is used for reflecting the attitude situation of the learner in all aspects. The data processing method comprises the following steps: and the system automatically classifies, summarizes and sums the tasks set by the students in week units.
(2.7) task formulation rationality
The task formulation reasonability is the objective, moderate and rational degree of the task formulated by the learner, can reflect the reasonable degree of the task formulation, arrangement and execution of the learner, and is the important embodiment of task management and self-management of the learner. The data processing method comprises the following steps: self-evaluation: carrying out qualitative analysis on the completion condition of the self-evaluation task of the learner; and (4) expert evaluation: the expert makes quantitative evaluation in a scoring mode and the like on the basis of quantitative and qualitative analysis according to the data; automatic evaluation: the algorithm is calculated according to the diversity, relevance, time period rationality and efficiency of the tasks.
The task completion degree, the task completion duration and the task completion quality in the step (3) comprise:
(3.1) degree of completion of task
The task completion degree represents the proportion of the total number of tasks completed by the learner on the day in the total number of tasks set on the day, and is used for reflecting the task completion quantity and progress of the learner on the day and reflecting the task completion efficiency of the learner. The visual chart designed by the report mainly gives feedback to the general condition of the task completion degree of the learner on the day, so that students can intuitively know the whole condition of task completion. The variables involved are: the completion degree, the task completion amount M of the daily manual, the total task amount M of the daily manual, the learner order i and the day order j. The data processing method is the average value of the task completion degree of each person per day:
(3.2) task completion duration
The task completion time length is the time length spent by the learner to complete the task, is used for reflecting the efficiency and the speed of the learner in the task completion process, and is important embodiment for the learner to perform task management and self-management. The data processing method comprises the following steps: and calculating according to the time point recorded by the background.
The family operation time, the extracurricular reading time, the sleeping time, the exercise time, the social contact time, the entertainment time, the class time, the autonomous learning time, the working day controllable time, the resting day controllable time, the task completion time, the time distribution record integrity and the time distribution record rationality in the step (4) comprise:
(4.1) duration of Home work
The homework time is counted once a day and the working day and the rest day are distinguished, the counting mode is the sum of the homework time spent by the learner in all subjects every day, and then a homework time columnar line graph is generated, the homework time has close correlation with the learning attitude, the attention, other time distribution and other personalized features of the learner, and the method is important embodiment for the learner to carry out self management and time management. The variables correspondingly involved include: personal day work time t1, class headcount n, learner order i, day order j, subject number k, work time standard deviation S1.
The data processing method comprises the following steps:
duration of personal daily autonomous learning:
T1i，j＝t1i，j-t1i，j-1
average autonomous learning duration within ten days of the individual:
working time variance within ten days of an individual:
average autonomous learning duration per day for a class:
(4.2) extracurricular reading duration
The reading time is counted once every working day, the working day statistics and the rest day statistics are distinguished, the time spent by the learner on the reading exercise activity on the same day is counted, and then a reading time columnar line graph is generated, wherein the reading time is closely related to the literary achievement, the time distribution condition and other personalized features of the learner. The variables correspondingly involved include: reading time t2 of the person on the day, total number of people in the class n, learner order i, sequence j, and standard deviation of reading time S2.
The data processing method comprises the following steps:
the length of time the individual reads daily:
T4i，j＝t2i，j-t2i，j-1
average reading time of individual within ten days:
individual reading time variance within ten days:
average daily reading duration for the class:
(4.3) sleep time
The sleep time is counted once every working day and is distinguished from the working day and the rest day, and the sleep time is calculated according to the difference between the time recorded before the student sleeps every day and the time of getting up in the next morning, so that the sleep time is an important embodiment for the learner to carry out self-management and time management. The sleep time has close correlation with the learning attitude, the concentration, the time input and other personalized characteristics of the learner. The variables correspondingly involved include: personal sleep time t3, class headcount n, learner order i, order j, sleep standard deviation S3.
The data processing method comprises the following steps:
duration of personal sleep:
T3i，j＝t3i，j-t3i，j-1
average spontaneous sleep duration in ten days of the individual:
individual sleep variance within ten days:
average daily sleep duration for a class:
(4.4) exercise time
Exercise time is counted once per weekday and distinguished between weekday and holiday statistics, and the time spent by the learner on the athletic exercise activity on the same day is counted to generate a bar-shaped line graph of exercise time, which is closely related to the time allocation, physiological condition, and other personalized features of the learner. The variables correspondingly involved include: personal daily workout time t4, total number of classes n, learner order i, order j, workout time standard deviation S4.
The data processing method comprises the following steps:
length of individual daily exercise:
T4i，j＝t4i，j-t4i，j-1
average exercise duration for individual ten days:
T4i，j＝t4i，j-t4i，j-1
individual exercise variance over ten days:
average exercise length per day for a class:
(4.5) social duration
The social duration is counted once every working day, working day statistics and rest day statistics are distinguished, the time spent by the learner on the social activity on the current day is counted, and then a social time columnar line graph is generated, wherein the social time is closely related to the time distribution condition, social interaction and other personalized features of the learner. The variables correspondingly involved include: personal daily social time t5, class headcount n, learner order i, order j, social time standard deviation S5.
(4.6) duration of entertainment
The entertainment duration is counted once every working day and the rest day are distinguished, the time spent by the learner on the entertainment activity on the same day is counted, and a columnar line graph of the entertainment time is generated according to the time spent by the learner on the entertainment activity, and the entertainment time is closely related to the time distribution condition, the hobbies and the interests and other personalized features of the learner. The variables correspondingly involved include: personal daily entertainment time t6, class headcount n, learner order i, order j, entertainment time standard deviation S6.
(4.7) class hours
The class time is counted once every working day, the working day and the rest day are distinguished, the time spent by the learner in the course learning on the current day is counted, and a columnar line graph of the class time is generated according to the counting, wherein the class time is closely related to the concentration, the learning attitude and other personalized features of the learner. The variables correspondingly involved include: the time t7 of the individual on class, the total number n of classes, the order i of learners, the order j of days, and the standard deviation S7 of the time of class.
(4.8) autonomous learning duration
The autonomous learning duration is counted once every working day, working day statistics and rest day statistics are distinguished, the statistics of the time spent by the learner on autonomous arrangement learning on the same day is an important performance of self management of the learner. The variables correspondingly involved include: personal daily autonomous learning time t8, class headcount n, learner order (i), day order j, autonomous learning time standard deviation S8.
(4.9) workday disposable duration
The working day controllable duration is counted once every working day, and the time that the learner can independently control in the working day is counted, so that the method is an important embodiment for self-management of the learner. The variables correspondingly involved include: personal workday dominance time t9, class headcount n, learner order i, order j, workday dominance time standard deviation S9.
(4.10) day of rest controllable duration
The duration of the rest day can be controlled once every rest day, and the time that the learner can independently control in the rest day is counted, so that the method is an important embodiment for self-management of the learner. The variables correspondingly involved include: personal day of rest dominant time t10, class headcount n, learner order i, order j, day of rest dominant time standard deviation S10.
(4.11) task completion duration
The index accumulates all the time spent by the learner to complete the task, can reflect the efficiency of the learner to complete the task, the time is recorded once a day, and the learner can clearly see the time spent by the learner to complete the task every day. The variables correspondingly involved include: total time T11, task time Tn, learner order i, and sequence j. The data processing method comprises the following steps: the system collects the data of each day and inputs the data into a formula for automatic calculation.
(4.12) time allocation record integrity
The record completeness, i.e. the manual completion degree, indicates the proportion of the completion manual filling amount of the learner in the set total amount on the day, is used for reflecting the data record condition of the learner and reflecting the attitude of the learner on treating the data record, and is also beneficial to improving the optimized record mode. The variables involved are: the learner's sequence i and the learner's sequence j are recorded in the completion degree Y1, the daily manual APP record amount M, the daily manual APP requirement record amount M. The data processing method comprises the following steps:
personal time-of-day allocation record integrity:
(4.13) time distribution record rationality
The reasonability of the time distribution record is the reasonability degree of each part of the learner in learning, reading, exercising, entertainment, sleeping and the like. The time distribution capability of the learner can be reflected by calculation of several dimensions of self evaluation, teacher grading and parent grading, and the time distribution capability is an important embodiment of time management and self management of the learner. The variables involved are: self-evaluation, expert evaluation, automatic evaluation. The data processing method comprises the following steps: self-evaluation: the variables involved are: self-management satisfaction y2, learner order i, and sequence j.
Average autonomous learning duration within ten days of the individual:
and (4) expert evaluation: and (4) making quantitative evaluation by experts in a scoring and other ways on the basis of quantitative and qualitative analysis according to the data.
Automatic evaluation: the algorithm is calculated according to the diversity, relevance, time period rationality, efficiency and the like of the tasks.
The concentration self-evaluation, the mastery self-evaluation, the participation condition record number, the thought record word number and the thought content in the step (5) comprise:
(5.1) concentration self-assessment
The concentration degree represents the self-evaluation of the concentration degree and the attention concentration degree of the learner for each class, is used for reflecting the learning input of the learner in the class and laterally reflects the self-evaluation condition of the learner on the self-psychology and emotion input. The variables correspondingly involved include: personal course concentration Y1, total number of courses per day m, course sequence k, learner sequence i, and sequence j; wherein i, j, m, k belongs to N, i, j, m is more than or equal to 1, and k is more than or equal to 1 and less than or equal to m. The data processing method comprises the following steps:
individual daily curriculum concentration average:
(5.2) self-evaluation of mastery
The mastery level shows the self-evaluation of the mastery level and the attention concentration level of the learner on the learning content of each class, is used for reflecting the learning input of the learner in the class and laterally reflecting the self-evaluation condition of the learner on the understanding of the content of the class. The variables correspondingly involved include: personal curriculum mastery degree Y2, total curriculum number m per day, curriculum sequence k, learner sequence i and sequence j; wherein i, j, m, k belongs to N, i, j, m is more than or equal to 1, and k is more than or equal to 1 and less than or equal to m. The data processing method comprises the following steps:
average of individual daily class mastery:
(5.3) number of participation records
The number of times of speaking and questioning participating in the classroom in the learning process of the learner language curriculum is used for reflecting the thinking degree of the learner in the classroom and reflecting the mastering and understanding conditions of the learner on the curriculum content. The data processing method comprises the following steps: recording and summarization was done by student daily record.
(5.4) number of record of reflection
The number of the thought-back records in the daily Chinese course record of the learner is used for reflecting the thinking degree and frequency of the learner on the Chinese course, and further reflecting the attitude of the learner on the Chinese course and the learning thought-back condition. The data processing method comprises the following steps: recording and summarization was done by student daily record.
(5.5) number of stuttering record words
The word number of the backstepping record in the daily Chinese course record of the learner is used for reflecting the self-backstepping situation of the learner on the Chinese course, and further reflecting the attitude of the learner on the Chinese course and the learning backstepping situation. The data processing method comprises the following steps: recording and summarization was done by student daily record.
(5.6) reflection content
The contents of the learner for thinking against the events in the daily record are used for reflecting the specific situation of self thinking after the learner masters and understands the language course and reflecting the attitude and deep thinking degree of the learner on the language course. The data processing method comprises the following steps: and qualitatively analyzing the contents of the student's repugnance by word cloud analysis, theme analysis and the like.
The self-management ability self-evaluation score of the students, the teacher self-management ability score of the students, the parents self-management ability score of the students and the expert self-management ability score of the students in the step (6) comprise:
and (6.1) self-evaluation scores of self-management abilities of students. The self-evaluation link in self management is an important part of self education, and learners directly influence the enthusiasm of learning and participating in social activities and the interaction relationship with other people on the evaluation of self thought, motivation, behavior and personality. The index analysis processing method comprises the following steps: learners use manuals or student questionnaires to score their level of self-management based on their daily record level and progress. And (3) collecting self-evaluation results of learners, and finally calculating average scores, wherein the average scores respectively correspond to five grades from high to low: mastery stage, proficiency stage, development stage, basal stage, below average level. Further determine the learner's level.
And (6.2) student self-management ability teacher scoring. The index analysis processing method comprises the following steps: the teacher scores his/her level of self-management through a teacher questionnaire based on the learner's daily record level.
And (6.3) the student self-management ability parental rating. The index analysis processing method comprises the following steps: parents score their self-management level according to the learner's daily record level via a parental questionnaire.
(6.4) student self-management ability expert scoring. The index analysis processing method comprises the following steps: and (4) scoring the self-management ability of the students by using the experience of the expert in reading and the breadth and depth of rich knowledge.
The total number of parent words, total number of words of parent words, emotional attribute of parent words, overall quality of parent words and score of parent participation degree in the step (7) comprise:
(7.1) Total number of parent messages
The total number of the parent messages can reflect the attitude of the parents in terms of self management, child education and the like most intuitively. The family and the school form a resultant force to educate the students, so that the school can obtain more support from the family when educating the students, and the parents can also obtain more guidance from the school when educating children. The index analysis processing method comprises the following steps: parents on the statistical manual record the number of evaluations that the learner has made for daily self-management and time management according to the learner's daily record. And counting every working day without distinguishing the working day and the rest day, and analyzing by adopting a qualitative research method including topic clustering and word cloud pictures.
And (7.2) the total number of words of the parent postwords. The total words of the parent sending words are directly counted by the number of characters filled in every day.
And (7.3) the parent sending words emotional attributes. The emotion attribute of the parent sending words can reflect that the collected parent sending words are subjected to data processing through methods such as word cloud pictures and theme analysis, and the emotional tendency and the view expressed by aspects such as self management and the attribute of the parent sending words are analyzed.
And (7.4) the overall quality of the parent message. The integral quality of the parent sending words is obtained by comprehensively analyzing indexes.
And (7.5) scoring the parental participation degree. And scoring the parents for participating in student self management according to the total number, total word number, emotional attribute and overall quality of the parent sending words, and comprehensively obtaining the score by the students and experts through qualitative and quantitative analysis and evaluation.
The academic motivation, the academic effectiveness feeling, the high-order thinking, the meta-cognitive strategy, the resource management strategy, the academic pressure and the academic performance in the step (8) comprise:
(8.1) academic motivation
Learning motivation refers to a driving tendency to induce and maintain the learning behavior of students and to direct them to a certain academic target, including two components of learning needs and learning expectations. The index analysis processing method comprises the following steps: in the system, the learning motivation level of students is obtained by comprehensive evaluation of primary dimensions including internal motivation, external motivation, task value, learning belief control and self-management effectiveness.
(8.2) feeling of academic Performance
Academic performance sense refers to the inference and judgment of whether an individual has the ability to complete an academic task. The index analysis processing method comprises the following steps: in the system, the academic performance sense level of the learner is obtained by comprehensively analyzing the learning belief control, the learning motivation and the self performance sense in the questionnaire. The final effectiveness score indicates the degree of student satisfaction and level of student confidence with the learner.
(8.3) higher order thinking
The high-order thinking is the core of the high-order ability, and refers to innovation ability, problem solving ability, decision making ability and critical thinking ability. The high-order thinking ability intensively reflects the new requirements of the knowledge era on talent qualities and is the key ability for adapting to the development of the knowledge era. The method is characterized by analysis, synthesis, evaluation and creation in teaching target classification. The index analysis processing method comprises the following steps: this portion was added gradually in the later questionnaire.
(8.4) Meta-cognitive strategies
Meta-cognition strategies refer to strategies for the students to effectively monitor and control their cognitive processes and results, and generally include planning strategies, monitoring strategies, and regulatory strategies. The index analysis processing method comprises the following steps: the partial capacity level in the questionnaire was measured by the questionnaire questions related to the meta-cognitive self-regulation strategy.
(8.5) resource management policy
The resource management strategy is a strategy for assisting students in managing available environments and resources, and has an important role in the motivation of the students. Successful use of this strategy helps students adapt to the environment and adjust the environment to suit their needs. The index analysis processing method comprises the following steps: in the system, the mastery level of the strategy by students is jointly evaluated and analyzed by time in a questionnaire and learning environment management, effort degree adjustment, peer learning and academic help seeking dimensions.
(8.6) pressure on academic Press
Learning stress refers to the mental burden that a person bears during a learning activity. The study stress refers to the stress stimulus from study borne by students in the process of studying, and also refers to the abnormal reaction which can be measured and evaluated by students in the aspects of physical, psychological and social behaviors. The index analysis processing method comprises the following steps: this section is derived from a comprehensive analysis of anxiety level and stress test questions in the questionnaire.
(8.7) academic Performance
The academic achievement is generally a digital achievement obtained comprehensively by an evaluation method including diagnosis, formative performance and terminal performance after a study. The index analysis processing method comprises the following steps: in the system, the academic achievement is filled in by the learner autonomously or is butted by a database lookup table.
Further, data acquisition in the third step comprises two ways of a self-adjusting learning manual and a self-adjusting learning APP platform, and data preprocessing comprises data cleaning, data integration and data format conversion.
Index data in the self-adjusting learning index system is collected by a matched self-adjusting learning manual or a self-adjusting learning APP platform; the self-adjusting learning manual is filled by a learner and adopts a mode of manually acquiring data; the self-adjusting learning APP platform is used for automatically collecting and uploading personal data input by a user in the using process to the cloud server through APP background log data and other forms.
The collected data comprises student monitoring data, parent evaluation data and teacher evaluation data; and the collected data is cleaned, integrated and converted in a data preprocessing stage and then is stored in an analysis model, and a visual chart and diagnosis information are automatically presented by the analysis model.
Another objective of the present invention is to provide a learner-oriented self-adjusting learning data information processing system applying the learner-oriented self-adjusting learning data information processing method, which includes a theoretical model, an index system and a data acquisition and preprocessing module.
Wherein, the theoretical model is a learner self-adjusting learning process theoretical model consisting of four circulation stages; the index system is a self-adjusting learning index system; and the data acquisition and preprocessing module is used for acquiring and preprocessing data according to a self-adjusting learning index system.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
constructing a theoretical model of the learner self-adjusting learning process consisting of four circulation stages; according to various theories and in combination with actual conditions, decomposing and explaining 8 secondary indexes respectively to construct a whole self-adjusting learning index system; data acquisition and preprocessing are carried out according to the proposed self-adjusting learning index system; the data acquisition comprises two ways of a self-adjusting learning manual and a self-adjusting learning APP platform, and the data preprocessing comprises three steps of data cleaning, data integration and data format conversion.
Another object of the present invention is to provide an information data processing terminal for implementing the learner-oriented self-adjusting learning data information processing system.
By combining all the technical schemes, the invention has the advantages and positive effects that: the learner-oriented self-adjusting learning data information processing system provided by the invention is based on a learning academic cycle three-stage theory and combines the practical situation in the field of domestic education to divide self-adjusting learning into four cycle stages of 'setting target and plan, recording and analyzing, countering and evaluating, adjusting and optimizing', and provides a set of learner-oriented self-adjusting learning ability index system and a data acquisition method. In the target and plan stages of four cycle stages in an index system, an motivation theory and a meta-cognition plan strategy are applied to divide the target and plan stages into two secondary indexes of a self-regulation learning motivation and a target task making condition; in the recording and analyzing stage, dividing the data into two secondary indexes of a target task completion condition and a time allocation condition by using a meta-cognitive monitoring strategy; in the stage of thinking resistance and evaluation, the meta-cognition adjustment strategy and the self-perception theory are applied to divide the evaluation into two secondary indexes of self evaluation and thinking resistance and self management capability evaluation; in the adjusting and optimizing stage, the meta-cognition adjustment strategy and the family-school cooperation strategy are divided into two secondary indexes of parent participation adjustment and student learning adjustment.
In order to make learners better utilize the circulation stage to develop self-regulation learning, the invention further divides the second-level index into executable specific third-level indexes and summarizes the executable specific third-level indexes to form a set of index system. By collecting three-level index information of the learner in the process of developing self-regulation learning and processing corresponding data by using an analysis method, personalized guidance and feedback are provided for the learner so as to help the learner to form good time concept and form good behavior habits, and finally, the learner can develop healthily and comprehensively.
The index system focuses on the ability cultivation of learners in the process of concept conception, and is improved and optimized by combining the national conditions and the actual requirements on the basis of the foreign classical theory, so that the index system is better adapted to students in middle and primary schools in China. The self-regulation learning is developed according to the circulation stage in the index system for a long time, so that the learner can be helped to develop good time concept and behavior habits, and the learner can be effectively assisted when the learning ability of the whole life is developed.
The index system enables learners with low school age to obtain more feedback and help when developing self-regulation learning by utilizing a family-school cooperation mode in the design process, and parents and teachers help the learners to gradually transit from the excitation of an initial external motivation to the excitation and maintenance of an internal motivation in the process, so that self-regulation learning is better performed. And (4) cooperating both parties of the family and the school to create a good co-cultivation atmosphere for the students.
The index system fully considers the education informatization degree of partial classes in the K-12 stage and schools in the application process, and provides a paper self-management manual for assisting in developing self-regulation learning activities. When self-adjusting learning is developed in school with the school age of K-12, the index system is matched with rich resources such as school book courses, class course theme plans, teacher parents and post-office workers, and powerful support can be provided for schools and teachers in actual execution.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a learner self-adjusting learning evaluation method according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a learner self-adjusting learning evaluation system according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a theoretical model of learner self-adjusting learning process according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of the division of the learner's self-adjusting learning secondary metrics according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a learner-oriented system and method for processing learning data information, which are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the learner-oriented self-adjusting learning data information processing method according to the embodiment of the present invention includes the following steps:
s101, constructing a theoretical model of the learner self-adjusting learning process consisting of four circulation stages;
s102, decomposing and explaining the secondary indexes respectively, and constructing a self-adjusting learning index system;
s103, data acquisition and preprocessing are carried out according to the proposed self-adjusting learning index system.
As shown in fig. 2, the learner-oriented self-adjusting learning data information processing system according to the embodiment of the present invention includes a theoretical model, an index system, and a data acquisition and preprocessing module.
Wherein, the theoretical model is a learner self-adjusting learning process theoretical model consisting of four circulation stages; the index system is a self-adjusting learning index system; and the data acquisition and preprocessing module is used for acquiring and preprocessing data according to a self-adjusting learning index system.
The technical solution of the present invention is further described below with reference to specific examples.
Example 1
Under the background of quality education, the education field increasingly attaches importance to the cultivation of the lifelong learning ability of learners, and the self-regulation learning ability plays a crucial role in the lifelong learning process of students, so that the learning machine is worthy of wide attention of learners, education and practice workers and education researchers. The invention provides a learner-oriented self-adjusting learning ability index system and a data acquisition method based on a learning academic cycle three-stage theory and by combining with the practical situation of the domestic education field, the self-adjusting learning is divided into four cycle stages of 'target and plan, recording and analysis, thinking resistance and evaluation, adjustment and optimization'. In a target and plan stage in an index system, dividing the target and plan into two secondary indexes of a self-adjusting learning motivation and a target task making condition by applying a motivation theory and a meta-cognition plan strategy; in the recording and analyzing stage, dividing the data into two secondary indexes of a target task completion condition and a time allocation condition by using a meta-cognitive monitoring strategy; in the stage of thinking resistance and evaluation, the meta-cognition adjustment strategy and the self-perception theory are applied to divide the evaluation into two secondary indexes of self evaluation and thinking resistance and self management capability evaluation; in the adjusting and optimizing stage, the meta-cognition adjustment strategy and the family-school cooperation strategy are divided into two secondary indexes of parent participation adjustment and student learning adjustment; in order to make learners better utilize the circulation stage to develop self-regulation learning, the invention further divides the second-level index into executable specific third-level indexes and summarizes the executable specific third-level indexes to form a set of index system. By collecting three-level index information of the learner in the process of developing self-regulation learning and processing corresponding data by using an analysis method, personalized guidance and feedback are provided for the learner so as to help the learner to form good time concept and form good behavior habits, and finally, the learner can develop healthily and comprehensively.
Aiming at the existing problems, the invention provides an index system suitable for learners to self-regulate learning.
The invention is realized in this way, an index system suitable for learner self-regulation learning includes:
the method comprises the steps of firstly, on the basis of massive exploration and research of experts of home and abroad education and psychology related schoolers on self-adjusting learning, combining practical situations of basic education in China, supporting the self-adjusting learning as a main theory, combining specific requirements of each stage in the self-adjusting learning process, and perfectly designing a theoretical model of the self-adjusting learning process of learners, wherein the theoretical model comprises four circulation stages, and the four stages are respectively a target and a plan, a record and an analysis, a thinking resistance and an evaluation, an adjustment and an optimization. In the target and plan stage, dividing the target and plan into two secondary indexes of a self-adjusting learning motivation and a target task making condition by applying a motivation theory and a meta-cognition plan strategy; in the recording and analyzing stage, dividing the data into two secondary indexes of a target task completion condition and a time allocation condition by using a meta-cognitive monitoring strategy; in the stage of thinking resistance and evaluation, the meta-cognition adjustment strategy and the self-perception theory are applied to divide the evaluation into two secondary indexes of self evaluation and thinking resistance and self management capability evaluation; in the adjusting and optimizing stage, the meta-cognition adjustment strategy and the family-school cooperation strategy are divided into two secondary indexes of parent participation adjustment and student learning adjustment;
step two, decomposing and explaining 8 secondary indexes respectively according to the multiple theories and by combining with actual conditions, and constructing a whole self-adjusting learning index system;
and step three, designing a data acquisition and preprocessing method according to the self-adjusting learning index system, wherein the data acquisition comprises two ways of a self-adjusting learning manual and a self-adjusting learning APP platform, and the data preprocessing comprises three steps of data cleaning, data integration and data format conversion.
In the first step provided by the embodiment of the present invention, on the basis of a great deal of exploration and research performed by experts of education and psychology related scholars at home and abroad on self-adjusting learning, and in combination with the actual situation of basic education in China, a theoretical model of learning process suitable for self-adjustment of learners is provided, which comprises:
(1.1) the proposed theoretical model consists of four cyclic phases: firstly, setting a target and a plan, secondly, recording and analyzing the completion condition and the time arrangement of the target plan, secondly, performing thinking resistance and evaluation on an analysis result, and finally, further adjusting and optimizing the target plan before the analysis according to the contents summarized by the thinking resistance;
(1.2) preliminarily dividing the content of each cycle stage, and designing 8 secondary indexes of a learner self-regulation learning index system;
in the second step provided by the embodiment of the present invention, the preliminary division of the content of each cycle phase includes:
(1.2.1) the evaluation of "goals and plans" includes two items, namely "self-regulating learning motivation" and "goal mission planning situation". The correct learning motivation is the driving force of self-regulation learning to continuously advance, is the basis for ensuring the reasonability and the continuity of execution willingness of the target plan, and has important influence on the setting of the target and the plan. The target task making condition is to evaluate a target plan from the aspects of total quantity, category quantity, description normalization and reasonableness, and ensure that the made target and task are in the learner ability range and are easy to operate;
(1.2.2) the evaluation of the recording and analyzing link comprises two parts of a target task completion condition and a time distribution condition. The target task completion condition is a process of tracking and self-monitoring a target plan formulated in the previous link, and comprises evaluation dimensions such as completion degree, completion duration, completion quality and the like. The time allocation condition is that the time spending of the learner is tracked and recorded from the time management perspective, activities frequently occurring in the life of the learner, such as class attendance, autonomous learning, homework, extracurricular reading, exercise, social entertainment, sleeping and the like, are selected, the time spending is classified and counted, and the time allocation condition of the learner is comprehensively reflected by combining indexes such as dominable duration statistics, time allocation rationality and the like.
(1.2.3) "thinking and evaluation" link includes "self-evaluation and thinking" and "self-management ability evaluation". The self-evaluation and the thinking resistance are that the learner evaluates the performance of the learner from two dimensions of class category and thinking resistance frequency, on one hand, the classroom performance of the student is reflected by recording the concentration degree, the content mastery degree and the classroom participation condition of each class, and on the other hand, the summary and the thinking of the student are reflected by the condition of the number, the number of words, the specific content and the like recorded by the thinking resistance. The self-management ability evaluation adopts the modes of student self-evaluation, teacher evaluation, parent evaluation and expert evaluation, and comprehensively evaluates the results of the four aspects, so that the credibility of the ability evaluation is ensured.
(1.2.4) "Regulation and optimization" link includes "parental participation regulation" and "student learning regulation" two aspects. The student learning adjustment is a main form of adjustment and optimization, and means that the whole learning process is adjusted and optimized through adjustment on aspects such as learning motivation, learning efficiency feeling, cognitive strategies, management strategies, anxiety level and the like. In the process, the teacher should continuously participate in guidance to help the learner to determine problems, find reasons, guide students to try appropriate solutions, and generate feedback and intervention effects. On the other hand, because of the limitation of the learner's personal ability and development level, it is difficult to rely entirely on the feedback of the learner's supervision and school, so that it is desirable to know the most of the learner's parents to participate in and provide supervision and guidance. The parent participation is mainly embodied in the form of 'daily parent return words', the state, emotion, habit, progress and other contents of the learner are freely evaluated from the observation perspective of the guardian, the problems and the transition of the learner are pointed out, and encouragement and support are provided, so that the effect of adjusting feedback is achieved.
In the third step provided by the embodiment of the invention, the 8 secondary indexes are respectively decomposed and explained according to multiple theories such as meta cognition, motivation theory, family school co-breeding and the like, and a whole self-adjusting learning index system is constructed;
(2.1) the self-regulation learning motivation in the index system consists of self-management perception value, self-management effectiveness feeling, self-management ability and self-management motivation;
(2.2) the target task making condition in the index system consists of the total target making quantity, the target classification quantity, the target description normalization, the target making rationality, the total task making quantity (per week), the classification task making quantity and the task making rationality;
(2.3) the target task completion condition in the index system consists of task completion degree, task completion duration and task completion quality;
(2.4) the time distribution condition in the index system consists of the family operation time length, the extracurricular reading time length, the sleep time, the exercise time, the social contact time length (the data processing mode is the same as the above), the entertainment time length (the data processing mode is the same as the above), the class time length (the data processing mode is the same as the above), the autonomous learning time length (the data processing mode is the same as the above), the working day controllable time length (the data processing mode is the same as the above), the resting day controllable time length (the data processing mode is the same as the above), the task completion time length (the data processing mode is the same as the above), the time distribution record integrity and the time distribution record rationality;
(2.5) the self-evaluation and the thinking resistance in the index system consist of concentration self-evaluation, mastery self-evaluation, participation condition record number (speech, question and the like), thinking resistance record number, thinking resistance record word number and thinking resistance content (word cloud analysis and theme analysis);
(2.6) the self-management ability evaluation in the index system consists of student self-management ability self-evaluation scores, student self-management ability teacher scores, student self-management ability parent scores and student self-management ability expert scores;
(2.7) the parent participation adjustment in the index system consists of the total number of the parent words, the total word number of the parent words, the emotional attribute of the parent words, the overall quality of the parent words and the score of the parent participation degree;
(2.8) the learning regulation of students in the index system consists of academic motivation, academic effectiveness sense, high-order thinking, meta-cognition strategy, resource management strategy, academic pressure and academic achievement;
in the step (2.1) provided by the embodiment of the present invention, the self-management perception value, the self-management effectiveness sense, the self-management ability, and the self-management motivation include:
(2.1.1) self-management perception value
The self-management perception value is obtained by comprehensive analysis of dimensions such as personal orientation, social orientation, self-management consciousness, use willingness of self-management auxiliary tools, self-monitoring consciousness and the like in the questionnaire. The index analysis processing method comprises the following steps: the questionnaire is provided with a plurality of test questions, each question is presented in the form of a five-level scale, the five levels of completely disapproval, common, disapproval and completely disapproval correspond to the given points of 1-5 respectively, the questionnaire results filled in by the learner are collected, the average score of the questionnaire results is finally calculated, and the questionnaire results are equally divided into five corresponding levels, so that the level of the learner is judged.
(2.1.2) sense of self-management Performance
The sense of self-management performance refers to the inference and judgment of whether an individual is able to perform self-management. The analysis results were obtained based on the analysis of the self-potency sensorgram portion of the MSLQ questionnaire. The final perception of efficacy indicates how well the learner is satisfied with his or her scheduling and management. The index analysis processing method comprises the following steps: the questionnaire has X questions, each question is presented in the form of a five-level scale, the five levels of complete disapproval, general, disapproval and complete disapproval correspond to the given points of 1-5 respectively, the questionnaire results filled by the learner are collected, the average score is finally calculated, and the average score is divided into five corresponding levels, so that the level of the learner is judged.
(2.1.3) self-management ability
The self-management ability calculates the factor comprehensive score of the student in the questionnaire through the factor score, and then the score is converted into a percentage system through an efficacy coefficient method, and finally a total score of four dimensions of learning motivation, meta cognition, resource management and anxiety level is formed so as to reflect the self-management ability score or ranking of the student. The index analysis processing method comprises the following steps: the questionnaire has X questions, each question is presented in the form of a five-level scale, the five levels of complete disapproval, general, disapproval and complete disapproval correspond to the given points of 1-5 respectively, the questionnaire results filled by the learner are collected, the average score is finally calculated, and the average score is divided into five corresponding levels, so that the level of the learner is judged.
(2.1.4) self-management motivation
The self-management motivation level is obtained by comprehensive evaluation of several primary dimensions such as internal motivation, external motivation, task value, learning belief control, self-management effectiveness feeling and the like.
In the step (2.2) provided by the embodiment of the present invention, the total number of the target formulations, the number of the target classes, the normalization of the target description, the rationality of the target formulations, the total number of the task formulations (per week), the number of the task formulation classes, and the rationality of the task formulation include:
(2.2.1) Total number Targeted
The total number of targets formulated by the learner to achieve a certain achievement or achievement can reflect the achievement or achievement that the learner wants to achieve in the aspects of learning development, reading and writing, hobbies, entertainment, social interaction and other aspects, and is an important embodiment for the learner to carry out target management and self-management. The specific data processing method comprises the following steps: the system automatically classifies, summarizes and sums the targets.
(2.2.2) number of target classes
The number of goals a learner has made in learning development, reading and writing, hobbies, entertaining and socializing, and others to achieve a certain achievement or performance. The specific data processing method comprises the following steps: the system automatically classifies, summarizes and sums the targets.
(2.2.3) object description normalization
The degree of the learner's description of the self-made target according with the set principle can reflect the mastery degree and comprehension degree of the learner on the target description specification standard or rule, and can also reflect the language expression ability level of the learner. The specific data processing method comprises the following steps: and (4) evaluating by experts and automatically evaluating by a subsequent machine.
(2.2.4) target formulation rationality
The index is the reasonable degree of the target formulated by the learner in aspects of specificity, quantifiability, realizability, relevance and the like, can reflect the mastery degree of the learner on the rule formulated reasonably by the target, and is important embodiment for the learner to carry out target management and self-management. The specific data processing method comprises the following steps: self-evaluation: once per day (no divided into weekdays and weekdays), the values recorded for ten consecutive individuals, the average for ten individuals, and the average for ten days for the entire shift were generated into bar-shaped line graphs. And (4) expert evaluation: and (4) making quantitative evaluation by experts in a scoring and other ways on the basis of quantitative and qualitative analysis according to the data. Automatic evaluation: the algorithm is calculated according to the diversity, relevance, time period rationality, efficiency and the like of the tasks.
(2.2.5) task to make Total quantity (weekly)
The weekly statistics of the index is that the learner further plans the task condition of the learner in order to complete the set target, can reflect the attitude of the learner to achieve the target of the learner, can also reflect the task management level of the learner, and is an important embodiment for the learner to carry out target management, task management and self-management. The specific data processing method comprises the following steps: and the system automatically classifies, summarizes and sums the tasks set by the students in week units.
(2.2.6) task-making the number of classes
In order to achieve the set target, the learner further plans the task situation of the learner in the aspects of homework, autonomous learning, reading, exercising, entertainment, social interaction, executive task, group task and other aspects, and can reflect the attitude situation of the learner in all aspects. The specific data processing method comprises the following steps: and the system automatically classifies, summarizes and sums the tasks set by the students in week units.
(2.2.7) task formulation rationality
The task formulation reasonability is the objective, moderate and rational degree of the task formulated by the learner, can reflect the reasonable degree of the task formulation, arrangement and execution of the learner, and is the important embodiment of task management and self-management of the learner. The specific data processing method comprises the following steps: self-evaluation: and carrying out qualitative analysis on the completion condition of the self-evaluation task of the learner. And (4) expert evaluation: and (4) making quantitative evaluation by experts in a scoring and other ways on the basis of quantitative and qualitative analysis according to the data. Automatic evaluation: the algorithm is calculated according to the diversity, relevance, time period rationality, efficiency and the like of the tasks.
In the step (2.3) provided by the embodiment of the present invention, the task completion degree, the task completion duration, and the task completion quality include:
(2.3.1) task completion
The task completion degree indicates the proportion of the total number of tasks completed by the learner on the day to the total number of tasks set on the day, can reflect the task completion number and progress of the learner on the day, and can also reflect the task completion efficiency of the learner. The visual chart designed by the report mainly gives feedback to the general condition of the task completion degree of the learner on the day, so that students can intuitively know the whole condition of task completion. The variables involved are: the completion degree, the task completion amount (M) of the daily manual, the total task amount M of the daily manual, the learner order (i) and the sequence (j). The specific data processing method is the average value of the task completion degree of each person per day:
(2.3.2) task completion duration
The task completion time is the time spent by the learner in completing the task, reflects the efficiency and the speed of the learner in the task completion process, and is an important embodiment for the learner to perform task management and self-management. The specific data processing method comprises the following steps: and calculating according to the time point recorded by the background.
In the step (2.4) provided in the embodiment of the present invention, the homework duration, the extracurricular reading duration, the sleep time, the exercise time, the social duration (the same data processing manner as above), the entertainment duration (the same data processing manner as above), the class duration (the same data processing manner as above), the autonomous learning duration (the same data processing manner as above), the working day controllable duration (the same data processing manner as above), the holiday controllable duration (the same data processing manner as above), the task completion duration (the same data processing manner as above), the time distribution record integrity, and the time distribution record rationality include:
(2.4.1) duration of Home work
The homework time is counted once a day and the working day and the rest day are distinguished, the statistical mode is the sum of the homework time spent by the learner in all subjects every day, and a homework time column-shaped line graph is generated according to the sum, the homework time has close correlation with the learning attitude, the concentration, other time distribution and other personalized characteristics of the learner, and the method is important embodiment for the learner to carry out self management and time management. The variables correspondingly involved include: personal work time on day (t1), class headcount (n), learner order (i), day order (j), subject number (k), work time standard deviation (S1).
The specific data processing method comprises the following steps:
duration of personal daily autonomous learning:
T1i，j＝t1i，j-t1i，j-1
average autonomous learning duration within ten days of the individual:
working time variance within ten days of an individual:
average autonomous learning duration per day for a class:
(2.4.2) extracurricular reading duration:
the reading time is counted once every working (learning) day, the working day statistics and the rest day statistics are distinguished, the time spent by the learner on the reading exercise activity on the same day is counted, and a reading time column-shaped line graph is generated according to the counting, wherein the reading time is closely related to the literary achievement, the time distribution condition and other personalized features of the learner. The variables correspondingly involved include: reading time of the person on the day (t2), total number of classes (n), learner order (i), sequence (j), and standard deviation of reading time (S2).
The specific data processing method comprises the following steps:
the length of time the individual reads daily:
T4i，j＝t2i，j-t2i，j-1
average reading time of individual within ten days:
individual reading time variance within ten days:
average daily reading duration for the class:
(2.4.3) sleep time
The sleep time is counted once every working (learning) day and is distinguished from working day and rest day statistics, the sleep time is calculated according to the difference between the time recorded before the student sleeps every day and the time of getting up in the next morning, and the sleep time is the important embodiment of self-management and time management of learners. The sleep time has close correlation with the learning attitude, the concentration, the time input and other personalized characteristics of the learner. The variables correspondingly involved include: personal sleep time (t3), class headcount (n), learner order (i), day order (j), sleep standard deviation (S3).
The specific data processing method comprises the following steps:
duration of personal sleep:
T3i，j＝t3i，j-t3i，j-1
average spontaneous sleep duration in ten days of the individual:
individual sleep variance within ten days:
average daily sleep duration for a class:
(2.4.4) exercise time
Exercise time is counted once per work (learning) day and distinguishes between weekday and holiday statistics, and the time spent by the learner on the athletic exercise activity on that day is counted to generate a bar-shaped line graph of exercise time that is closely related to the learner's time allocation, physiological condition, and other personalized features. The variables correspondingly involved include: personal daily workout time (t4), class headcount (n), learner order (i), day order (j), workout time standard deviation (S4).
The specific data processing method comprises the following steps:
length of individual daily exercise:
T4i，j＝t4i，j-t4i，j-1
average exercise duration for individual ten days:
T4i，j＝t4i，j-t4i，j-1
individual exercise variance over ten days:
average exercise length per day for a class:
(2.4.5) social duration (data processing method as above)
The social duration is counted once every work (learning) day and the working day and the rest day are distinguished, the time spent by the learner on the social activity on the current day is counted, and a social time bar-shaped line graph is generated according to the statistic, wherein the social time is closely related to the time distribution condition, social interaction and other personalized features of the learner. The variables correspondingly involved include: personal daily social time (t5), class headcount (n), learner order (i), day order (j), social time standard deviation (S5).
(2.4.6) duration of entertainment (data processing method as above)
The entertainment duration is counted once every working (learning) day and the working day and the rest day are distinguished, the time spent by the learner on the entertainment activity on the same day is counted, and a columnar line graph of the entertainment time is generated according to the counted time, and the entertainment time is closely related to the time distribution condition, the hobbies and the interests and other personalized features of the learner. The variables correspondingly involved include: personal daily entertainment time (t6), class headcount (n), learner order (i), day order (j), entertainment time standard deviation (S6).
(2.4.7) class time (data processing method as above)
The class time is counted once every working (learning) day, the working day and the rest day are distinguished, the time spent by the learner in class learning on the same day is counted, and a columnar line graph of the class time is generated according to the counting, and the class time is closely related to the concentration, learning attitude and other personalized features of the learner. The variables correspondingly involved include: the time of class (t7), total number of class (n), learner sequence (i), sequence (j), standard deviation of class time (S7).
(2.4.8) autonomous learning duration (data processing method as above)
The autonomous learning duration is counted once every working (learning) day, working day statistics and rest day statistics are distinguished, and the statistics of the time spent by the learner on autonomous arrangement learning on the day is an important performance of self management of the learner. The variables correspondingly involved include: personal daily autonomous learning time (t8), class headcount (n), learner order (i), day order (j), autonomous learning time standard deviation (S8).
(2.4.9) the length of time the working day can be controlled (data processing method as above)
The working day controllable duration is counted once every working (learning) day, and the time that the learner can independently control in the working day is counted, so that the method is an important embodiment for self-management of the learner. The variables correspondingly involved include: individual workday dominable time (t9), class headcount (n), learner order (i), sequence (j), workday dominable time standard deviation (S9).
(2.4.10) the duration of available rest days (data processing mode as above)
The duration of the rest day can be controlled once every rest day, and the time that the learner can independently control in the rest day is counted, so that the method is an important embodiment for self-management of the learner. The variables correspondingly involved include: personal day of rest may dictate time (t10), class headcount (n), learner order (i), order (j), day of rest may dictate time standard deviation (S10).
(2.4.11) duration of completion of task (data processing method as above)
The index accumulates all the time spent by the learner to complete the task, can reflect the efficiency of the learner to complete the task, records the time once a day, and the learner can clearly see the time spent by the learner to complete the task every day, thereby being beneficial to helping the learner to more reasonably arrange the task and improving the efficiency of completing the task. The variables correspondingly involved include: total time T11, task time Tn, learner order (i), and sequence (j). The specific data processing method comprises the following steps: the system collects the data of each day and inputs the data into a formula for automatic calculation.
(2.4.12) time allocation record integrity
The record integrity, namely the manual completion degree (APP uploading integrity degree), represents the proportion of the learner's completion manual filling amount (APP uploading integrity degree) in the set total amount on the day, can reflect the data record condition of the learner, can also reflect the attitude of the learner on treating the data record, and is favorable for improving the optimized recording mode. The variables involved are: completion (Y1), daily manual (APP) record amount (M), daily manual (APP) request record amount (M), learner order (i), and sequence (j). The specific data processing method comprises the following steps:
personal time-of-day allocation record integrity:
(2.4.13) time allocation record reasonableness
The reasonability of the time distribution record is the reasonability degree of each part of the learner in learning, reading, exercising, entertainment, sleeping and the like. The time distribution capability of the learner can be reflected by calculation of several dimensions of self evaluation, teacher grading and parent grading, and the time distribution capability is an important embodiment of time management and self management of the learner. The variables involved are: self-evaluation, expert evaluation, automatic evaluation. The specific data processing method comprises the following steps: self-evaluation: the variables involved are: self-management satisfaction (y2), learner order (i), and sequence (j).
Average autonomous learning duration within ten days of the individual:
and (4) expert evaluation: and (4) making quantitative evaluation by experts in a scoring and other ways on the basis of quantitative and qualitative analysis according to the data.
Automatic evaluation: the algorithm is calculated according to the diversity, relevance, time period rationality, efficiency and the like of the tasks.
In the step (2.5) provided in the embodiment of the present invention, the concentration self-evaluation, the mastery self-evaluation, the number of participation records (speech, question, etc.), the number of backstepping records, and the backstepping content (word cloud analysis, topic analysis) include:
(2.5.1) concentration self-assessment
The concentration degree indicates the self-evaluation of the concentration degree and the attention concentration degree of each class by the learner, can reflect the learning input of the learner in the class, and can reflect the self-evaluation condition of the learner on the self-psychology and emotion input from the side. The variables correspondingly involved include: personal class concentration (Y1), total number of courses per day (m), class order (k), learner order (i), and order (j). Wherein i, j, m, k belongs to N, i, j, m is more than or equal to 1, and k is more than or equal to 1 and less than or equal to m. The specific data processing method comprises the following steps:
individual daily curriculum concentration average:
(2.5.2) self-evaluation of mastery
The mastery level indicates the self-evaluation of the learner on the mastery level and the attention concentration level of the learning content of each class, and can reflect the learning input of the learner in the class and the self-evaluation condition of the learner on the understanding of the content of the class from the side. The variables correspondingly involved include: personal class mastery (Y2), total daily class number (m), class sequence (k), learner sequence (i), and sequence (j). Wherein i, j, m, k belongs to N, i, j, m is more than or equal to 1, and k is more than or equal to 1 and less than or equal to m. The specific data processing method comprises the following steps:
average of individual daily class mastery:
(2.5.3) participating in the situation recording number of pieces (speech, question, etc.)
The times of speaking, questioning and the like of participating in the classroom in the learning process of the Chinese course of the learner can reflect the thinking degree of the learner in the classroom and can also reflect the mastering and understanding conditions of the learner on the course content. The specific data processing method comprises the following steps: recording and summarization was done by student daily record.
(2.5.4) number of record of reflection
The number of the thought-back records in the daily Chinese course record of the learner can reflect the thinking degree and frequency of the learner on the Chinese course, and further reflect the attitude of the learner on the Chinese course and the learning thought-back condition. The specific data processing method comprises the following steps: recording and summarization was done by student daily record.
(2.5.5) number of stuttering words
The word number of the backstepping record in the daily Chinese course record of the learner can reflect the self-backstepping situation of the learner on the Chinese course, and further reflect the attitude of the learner on the Chinese course and the learning backstepping situation. The specific data processing method comprises the following steps: recording and summarization was done by student daily record.
(2.5.6) backspace content (word cloud analysis, subject analysis)
The contents of the learner for thinking against the events in the daily record can reflect the specific situation of self thinking after the learner masters and understands the language course, and can reflect the attitude and deep thinking degree of the learner on the language course. The specific data processing method comprises the following steps: and qualitatively analyzing the contents of the student's repugnance by word cloud analysis, theme analysis and the like.
In the step (2.6) provided by the embodiment of the present invention, the student self-management ability self-evaluation score, the student self-management ability teacher score, the student self-management ability parent score, and the student self-management ability expert score include:
(2.6.1) self-evaluation score of self-management ability of students. The self-evaluation link in self management is an important part of self education, and learners directly influence the enthusiasm of learning and participating in social activities and influence the interaction relationship with other people on the evaluation of self thought, motivation, behavior and personality. The index analysis processing method comprises the following steps: learners use manuals or student questionnaires to score their level of self-management based on their daily record level and progress. And (3) collecting self-evaluation results of the learners and finally calculating average scores of the learners, wherein the average scores respectively correspond to five grades from high to low: mastery stage, proficiency stage, development stage, basal stage, below average level. Thereby determining the learner's level.
(2.6.2) student self-management ability teacher score. The index analysis processing method comprises the following steps: the teacher scores his/her level of self-management through a teacher questionnaire based on the learner's daily record level.
(2.6.3) student self-management ability parental rating. The index analysis processing method comprises the following steps: parents score their self-management level according to the learner's daily record level via a parental questionnaire.
(2.6.4) student self-management ability expert scoring. The index analysis processing method comprises the following steps: and (4) scoring the self-management ability of the students by using the experience of the expert in reading and the breadth and depth of rich knowledge.
In the step (2.7) provided by the embodiment of the present invention, the total number of parent words, total word number of parent words, emotion attribute of parent words, overall quality of parent words, and score of parent participation degree include:
(2.7.1) total number of parent messages
The total number of the parent messages can reflect the attitude of the parents in terms of self management, child education and the like most intuitively. The family and the school form a resultant force to educate the students, so that the school can obtain more support from the family when educating the students, and the parents can also obtain more guidance from the school when educating children. The index analysis processing method comprises the following steps: parents on a statistical manual (software) record the number of evaluations that the learner has made for his/her daily self-management and time management according to the learner's daily record. And counting every working (learning) day without distinguishing working days and rest days, and analyzing by adopting qualitative research methods such as topic clustering and word cloud pictures.
(2.7.2) total words of the native words of the parents. The total words of the parent sending words are directly counted by the number of characters filled in every day.
(2.7.3) parent message emotional property. The emotion attribute of the parent sending words can reflect that the collected parent sending words are subjected to data processing through methods such as word cloud pictures and theme analysis, and the emotional tendency and the view expressed by aspects such as self management and the attribute of the parent sending words are analyzed.
(2.7.4) parent message overall quality. The integral quality of the parent sending words is obtained by comprehensively analyzing the indexes.
(2.7.5) parent engagement score. And scoring the parents for participating in student self management according to the total number, total word number, emotional attribute and overall quality of the parent sending words, and comprehensively obtaining the score by the students and experts through qualitative and quantitative analysis and evaluation.
In the step (2.8) provided by the embodiment of the present invention, the academic motivation, the academic effectiveness sense, the high-order thinking, the meta-cognitive policy, the resource management policy, the academic pressure, and the academic achievement include:
(2.8.1) academic motivation
Learning motivation is a driving tendency to initiate and maintain the learning behavior of students and to direct them to a certain academic target. It usually contains two components, learning need and learning expectation. The index analysis processing method comprises the following steps: in the system, the learning motivation level of students is obtained by comprehensive evaluation of several primary dimensions, such as internal motivation, external motivation, task value, learning belief control, self-management effectiveness sense and the like.
(2.8.2) academic proficiency. Academic performance sense refers to the inference and judgment of whether an individual has the ability to complete an academic task. The index analysis processing method comprises the following steps: in the system, the academic performance sense level of the learner is comprehensively analyzed and obtained from the aspects of learning belief control, learning motivation, self performance sense and the like in the questionnaire. The final effectiveness score indicates the degree of student satisfaction and level of student confidence with the learner.
(2.8.3) higher-order thinking
The high-order thinking is the core of the high-order ability, and mainly refers to innovation ability, problem solving ability, decision making ability and critical thinking ability. The high-order thinking ability intensively reflects the new requirements of the knowledge era on talent quality and is the key ability for adapting to the development of the knowledge era. It is expressed in analysis, synthesis, evaluation and creation in the classification of teaching targets. The index analysis processing method comprises the following steps: this portion was added gradually in the later questionnaire.
(2.8.4) Meta-cognitive strategies
Meta-cognition strategies refer to strategies for the student's effective monitoring and control of his cognitive processes and outcomes, which typically include planning strategies, monitoring strategies (attention strategies), and regulatory strategies. The index analysis processing method comprises the following steps: the partial capacity level in the questionnaire was measured by the questionnaire questions related to the meta-cognitive self-regulation strategy.
(2.8.5) resource management policy
The resource management strategy is a strategy for assisting students in managing available environments and resources, and has an important role in the motivation of the students. Successful use of this strategy can help students adapt to the environment and adjust the environment to suit their needs. The index analysis processing method comprises the following steps: in the system, the mastery level of the strategy by students is jointly evaluated and analyzed by several dimensions of time in questionnaires, learning environment management, effort degree adjustment, peer learning, academic help seeking and the like.
(2.8.6) pressure in academic Press
Learning stress refers to the mental burden that a person bears during a learning activity. The study stress refers to the stress stimulus from study borne by students in the process of studying, and also refers to the abnormal reaction which can be measured and evaluated by students in the aspects of physical, psychological and social behaviors. The index analysis processing method comprises the following steps: this section is derived from a comprehensive analysis of anxiety level and stress test questions in the questionnaire.
(2.8.7) academic achievements
The academic achievement is generally a digital achievement obtained by integrating the academic achievement after a study in an evaluation manner such as diagnosis, formative performance, terminal performance and the like. The index analysis processing method comprises the following steps: in the system, the academic achievement is filled in by the learner autonomously or is butted by a database lookup table.
The embodiment of the invention provides an index system suitable for self-adjusting learning of learners, wherein a data acquisition and preprocessing method is designed according to the self-adjusting learning index system, the data acquisition comprises two ways of a self-adjusting learning manual and a self-adjusting learning APP platform, and the data preprocessing comprises three steps of data cleaning, data integration and data format conversion:
(3.1) index data in the self-adjusting learning index system is collected by a matched self-adjusting learning manual or a self-adjusting learning APP platform, wherein the index data is filled by a learner and then manually acquired; in the latter, personal data is input by a user in the using process and automatically collected and uploaded to a cloud server in forms of APP background log data and the like. The collected data generally comprises the aspects of student monitoring data, parent evaluation data, teacher evaluation data and the like;
and (3.2) the acquired data are cleaned, integrated and converted in a data preprocessing stage and then are stored in an analysis model, and a visual chart and diagnostic information are automatically presented by the analysis model.
Example 2
(1) Self-management manual target plan setting part
The manual is introduced by introduction instructions and tips; then, the user is required to set own target and plan, and the recording part of the indexes comprises the weekly task collection box, the standing self flag and the like.
(2) Self-management handbook review part of the reverence
The section is obtained by the review and the rethink of the learner for the time of week planning, the task arrangement and the actual implementation situation, and the evaluation and the suggestion are made to the learner by the parents and the teacher, so that the learner can conveniently summarize the experience and the training.
(3) Questionnaire before and after self-management manual annex
The questionnaire is compiled based on an MSLQ scale and a time management tendency scale by combining the set of index system and the actual condition of a project, the data of the same learner are analyzed by utilizing the questionnaire measured before and after, and the data result is provided for parents and teachers as reference so as to more pertinently develop guidance for the learner.
In order to help students in the school to improve self-regulation learning capacity, 195 total students in the four classes of the seven grades are selected in HR middle school, a self-management manual which is manufactured by taking a self-regulation learning index system as a core is used in 26 months of 5 in 2021, and self-regulation learning and self-management capacity of the students is improved in the aspects of time planning, behavior habit management and development and the like.
Before the time management manual is involved, the school actively cooperates with the introduction work of the project team, brings the moral qualities of self management, strict law and the like into the school cultural construction, and integrates the culture of self-adjusting learning ability into the school culture, the learning idea and the moral education. The school teacher is actively matched with each other, students and parents are mobilized in the forms of theme teams, parent teams and the like, a class incentive mechanism and a learning group are established, a large amount of early-stage guidance work is conducted on the students, the students are helped to be familiar with the autonomous learning skills, and the autonomous learning and self-management atmosphere is formed at any time in the ordinary classroom. Projects were supported from the beginning by schools, parents and students.
The time management manual used by the school is introduced by introduction instructions and tips skills; the recording part comprises a weekly task collecting box, a daily recording part, a weekly retrospective backstep part and the like; wherein, the daily record part of the manual comprises daily plan record, daily time management monitoring record, time management self-evaluation, parent evaluation, teacher evaluation and the like; the beginning and appendix sections of the manual also include the questionnaire (corresponding to before and after measurements, respectively) in the present set of indicators.
In the using process, students are required to fill out a time management manual every day; students develop the reflexes and summaries in the class by week, and exchange and study in the class through group discussion; the teacher can develop personalized guidance at any time according to the problems discovered at ordinary times except daily scoring and weekly feedback of organization; parents evaluate the self-management condition of students every day and keep close communication with schools; in addition, each class is also provided with 2 teaching-assistant teachers by the project team, the daily work of students is graded in an online batch improvement way and fed back one by one through 'nailing' software, and the teaching-assistant teachers cooperate with the master teachers of the class to conduct personalized guidance on the students with weak self-management and self-regulation learning abilities, so that a good co-education atmosphere is formed with the parents and teachers of the school.
The introduction of the time management manual has positive effects on students in the seven-year class of the school by combining the analysis condition of the manual in the previous stage and the interview feedback to the students, parents and teachers, such as: parents plan and guide students more; the students plan the academic more carefully, and the students can more reasonably arrange the daily time expenditure of themselves; the students can understand the guiding and guiding significance of the target tasks more deeply, and the like.
By now, the project has provided six different self-management manuals for students of the seventh grade (now eight grades); parents, executive and lessee teachers TIPS; PPT and planning scheme of the team and meeting class; self-manage the relevant explanation video; self-management strategies and methods; feedback guidance and other matching resources for student time management records.
TABLE 1 learner self-adjusting learning index system
To evaluate the effect of filling in time management manuals and other activities, we investigated the time management tendencies of students before and after 2 months of use of the manuals, and performed a forward and backward analysis of the investigated data using paired sample T-test. The results show that three dimensions related to time management show significant differences in front and back measurement, wherein the front measurement mean value of the time value sensing dimension is 3.88, and the back measurement mean value is 4.03, which indicates that students can learn the importance of time more deeply after using a time management manual, and can feel the precious value of time more effectively. The post-measurement data of the time monitoring view and the time efficiency sense are slightly lower than that of the pre-measurement data, which shows that compared with the data before the activity is implemented, part of students find that the self-control ability of the students on the time and the self-confidence of the students on the time management are reduced, and the development of the time management activity is presumed to lead the students to know the time management more comprehensively, so that the shortcomings of the students are realized.
This speculation is also confirmed by subjective subject data, and in the former test, the description of the students on their time management conditions mostly stays in the description of the homework time and the entertainment time, and the planning is very simple, for example, a student mentions that the students are all ' finish homework and play ' first '. In the latter, more classmates refer to "reasonably set goals and plans", "summarize time of day usage", and so on. Students further understand the complete time management methods of planning, monitoring, summarizing, and retrying.
To further verify the validity of the above guess and the activity, we investigated the interest of the student in the activity, participation and activity experience. Survey results show that more than 74% of classmates consider time management other self-management skills useful and important, 58.3% of classmates represent a strong willingness to participate in time management activities, 76.4% of classmates participate in the filling of time management manuals seriously, and 77.4% of students do not reject or wish to continue to use the time management handbook to improve their abilities.
In addition, more than 45% of students find time management activities, so that the time management capability, the thinking resistance and the attention of the students are improved, the time arrangement of the students is more reasonable, and the work and rest are more regular; about 40% of classmates, a 2 month period of time to manage activities and manual filling helps one develop a daily exercise and reading habit.
The above data illustrate that the activity of the present invention in SZ HR as the first stage of self-regulating learning activity focuses on guidance and regulation in time management and achieves good results in this regard. The activity can effectively improve the time utilization rate, the efficiency, the concentration degree, the thinking resistance summary and other abilities of students, and has a certain effect on the aspects of developing good habits and forming healthy work and rest.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A learner-oriented self-adjusting learning data information processing method is characterized by comprising the following steps:
step one, constructing a theoretical model of the learner self-adjusting learning process consisting of a plurality of circulation stages;
step two, decomposing and explaining the secondary indexes respectively to construct a self-adjusting learning index system;
and step three, data acquisition and preprocessing are carried out according to the proposed self-adjusting learning index system.
2. The learner-oriented self-adjusting learning data information processing method according to claim 1, wherein the plurality of loop stages in the first step includes: setting a target and a plan, recording and analyzing the completion condition and the time arrangement of the target plan, performing thinking resistance and evaluation on the analysis result, and adjusting and optimizing the target plan before according to the contents summarized by the thinking resistance; preliminarily dividing the content of each cycle stage, and designing 8 secondary indexes of a learner self-regulation learning index system;
in the target and plan stage, dividing the target and plan into two secondary indexes of a self-adjusting learning motivation and a target task making condition by applying a motivation theory and a meta-cognition plan strategy;
in the recording and analyzing stage, dividing the meta-cognitive monitoring strategy into two secondary indexes, namely a target task completion condition and a time allocation condition;
in the stage of thinking resistance and evaluation, the principle of the meta-cognition adjustment strategy and the self-perception theory are divided into two secondary indexes of self-evaluation and thinking resistance and self-management capability evaluation;
in the adjusting and optimizing stage, the meta-cognition adjustment strategy and the family-school cooperation strategy are divided into two secondary indexes of parent participation adjustment and student learning adjustment.
3. The learner-oriented self-adjusting learning data information processing method according to claim 1, wherein the preliminary division of the contents of each loop stage in the second step comprises:
(1) the evaluation of the target and the plan comprises two contents of self-regulation learning motivation and target task making condition; the target task making condition is to evaluate a target plan from the aspects of total quantity, category quantity, description normalization and reasonableness;
(2) the evaluation of the recording and analyzing links comprises target task completion conditions and time distribution conditions; the target task completion condition is a process of tracking and self-monitoring a target plan formulated in the previous link, and comprises a completion degree, a completion duration and a completion quality evaluation dimension; the time allocation condition is that the time spending of the learner is tracked and recorded from the time management perspective, the activity categories of the learner in life including class, autonomous learning, homework, reading out of class, exercise, social entertainment and sleeping are selected, the time spending is classified and counted, and the time allocation information of the learner is comprehensively reflected by combining the domination duration counting and the time allocation rationality index;
(3) the thinking resistance and evaluation comprise self evaluation and thinking resistance and self-management ability evaluation; self-evaluation and thinking reflect the classroom performance of students by recording the concentration degree, content mastery degree and classroom participation condition of each class; the number, the number of words and the specific content of the backstepping records are used for reflecting the summary and the thinking of the students; the self-management ability evaluation adopts the modes of student self-evaluation, teacher evaluation, parent evaluation and expert evaluation to comprehensively evaluate the results of the four aspects;
(4) the adjustment and optimization comprise parental participation adjustment and student learning adjustment; the student learning adjustment is a main form of adjustment and optimization, and the whole learning process is adjusted and optimized through adjustment on learning motivation, learning efficiency feeling, cognitive strategies, management strategies and anxiety level; in the process, teachers participate in guidance to help learners to determine problems and find reasons, and guide students to try appropriate solutions to produce feedback and intervention effects; the parent participation is embodied in the form of daily parent sending words, and the state, emotion, habit and progress content of the learner are freely evaluated from the observation view of the guardian, so that the problems and the transition of the learner are pointed out.
4. The learner-oriented self-adjusting learning data information processing method according to claim 1, wherein in the third step, 8 secondary indexes are respectively decomposed and explained according to multiple theories of meta-cognition, motivation theory and family school co-education, so as to construct a whole self-adjusting learning index system;
(1) the self-regulation learning motivation consists of self-management perception value, self-management effectiveness feeling, self-management ability and self-management motivation;
(2) the target task making condition consists of a target making total number, a target classification number, a target description normalization, a target making rationality, a weekly task making total number, a task making classification number and a task making rationality;
(3) the target task completion condition consists of task completion degree, task completion duration and task completion quality;
(4) the time allocation condition consists of the family work time, the extracurricular reading time, the sleeping time, the exercise time, the social contact time, the entertainment time, the class time, the autonomous learning time, the working day controllable time, the rest day controllable time, the task completion time, the time allocation record integrity and the time allocation record rationality;
(5) the self-evaluation and the thinking resistance consist of concentration self-evaluation, mastery self-evaluation, participation condition record number, thinking resistance record word number and thinking resistance content;
(6) the self-management ability evaluation is composed of student self-management ability self-evaluation scores, student self-management ability teacher scores, student self-management ability parent scores and student self-management ability expert scores;
(7) the parent participation adjustment is composed of the total number of parent words, the total word number of the parent words, the emotional attribute of the parent words, the integral quality of the parent words and the score of the parent participation degree;
(8) the student learning regulation is composed of academic motivation, academic effectiveness sense, high-order thinking, meta-cognition strategy, resource management strategy, academic pressure and academic performance.
5. The learner-oriented information processing method for learning data by self-regulation according to claim 4, wherein the self-management-aware value, the self-management-effective feeling, the self-management-ability, and the self-management motivation in the step (1) include: self-management perception value, self-management effectiveness sense, self-management ability and self-management motivation;
the total number of the target formulation, the number of the target classification, the target description normalization, the target formulation rationality, the total number of the weekly task formulation, the number of the task formulation classification and the task formulation rationality in the step (2) comprise: the total number of target formulation, the number of target classification, the normalization of target description, the rationality of target formulation, the total number of weekly task formulation, the number of task formulation classification, and the rationality of task formulation.
6. The learner-oriented self-adjusting learning data information processing method according to claim 4, wherein the task completion degree, the task completion time length, and the task completion quality in the step (3) include:
(3.1) task completion:
the task completion degree represents the proportion of the total number of tasks completed by the learner on the day in the total number of tasks set on the day, and is used for reflecting the task completion number and progress of the learner on the day and reflecting the task completion efficiency of the learner; the visual chart designed by the report mainly gives feedback to the general condition of the task completion degree of the learner on the day, so that students can intuitively know the whole condition of task completion; the variables involved are: the completion degree, the task completion amount M of the daily manual, the total task amount M of the daily manual, the learner order i and the sequence j; the data processing method is the average value of the task completion degree of each person per day:
(3.2) task completion duration
The task completion time is the time spent by the learner to complete the task, is used for reflecting the efficiency and the speed of the learner in the task completion process, and is important embodiment for the learner to perform task management and self-management; the data processing method comprises the following steps: calculating according to the time point recorded by the background;
the family operation time, the extracurricular reading time, the sleeping time, the exercise time, the social contact time, the entertainment time, the class time, the autonomous learning time, the working day controllable time, the resting day controllable time, the task completion time, the time distribution record integrity and the time distribution record rationality in the step (4) comprise:
(4.1) Home work duration: the family homework time is counted once a day and the working day and the rest day are distinguished, the counting mode is the sum of the homework time spent by the learner in all subjects every day, and then a homework time columnar line graph is generated, the homework time has close correlation with the learning attitude, the attention, other time distribution and other personalized characteristics of the learner, and the method is important embodiment for the learner to carry out self-management and time management; the variables correspondingly involved include: personal current day work time t1, class headcount n, learner order i, sequence j, subject sequence number k, work time standard deviation S1;
the data processing method comprises the following steps:
duration of personal daily autonomous learning:
T1i，j＝t1i，j-t1i，j-1；
average autonomous learning duration within ten days of the individual:
working time variance within ten days of an individual:
average autonomous learning duration per day for a class:
(4.2) extracurricular reading time: reading time is counted once every working day, working day statistics and rest day statistics are distinguished, the time spent by a learner on reading exercise activities on the same day is counted, a reading time columnar line graph is further generated, and the reading time is closely related to the literary achievement, the time distribution condition and other personalized features of the learner; the variables correspondingly involved include: reading time t2 of the person on the day, total number of people n in the class, sequence i of the learner, sequence j of the person, and standard deviation of reading time S2;
the data processing method comprises the following steps:
the length of time the individual reads daily:
T4i，j＝t2i，j-t2i，j-1；
average reading time of individual within ten days:
individual reading time variance within ten days:
average daily reading duration for the class:
(4.3) sleep time: the statistics are carried out once every working day and the statistics of the working day and the rest day are distinguished, the sleeping time is obtained by calculation according to the difference value of the recorded time before the student sleeps every day and the time when the student gets up in the next morning, and the statistics are important embodiment for the learner to carry out self management and time management; the sleep time has close correlation with the learning attitude, the concentration, the time input and other personalized characteristics of the learner; the variables correspondingly involved include: personal sleep time t3, class headcount n, learner order i, order j, sleep standard deviation S3;
the data processing method comprises the following steps:
duration of personal sleep:
T3i，j＝t3i，j-t3i，j-1；
average spontaneous sleep duration in ten days of the individual:
individual sleep variance within ten days:
average daily sleep duration for a class:
(4.4) exercise time: counting once every working day and distinguishing working day statistics and rest day statistics, counting the time spent by the learner on the physical exercise activity on the same day, and generating an exercise time columnar line graph according to the time, wherein the exercise time is closely related to the time distribution condition, the physiological condition and other personalized characteristics of the learner; the variables correspondingly involved include: personal daily exercise time t4, class headcount n, learner order i, order j, exercise time standard deviation S4;
the data processing method comprises the following steps:
length of individual daily exercise:
T4i，j＝t4i，j-t4i，j-1
average exercise duration for individual ten days:
T4i，j＝t4i，j-t4i，j-1
individual exercise variance over ten days:
average exercise length per day for a class:
(4.5) social duration: counting once every working day and distinguishing working day statistics and rest day statistics, and counting the time spent by the learner on social activities on the same day so as to generate a social time columnar line graph, wherein the social time has close correlation with the time distribution condition, social interaction and other personalized features of the learner; the variables correspondingly involved include: personal daily social time t5, class headcount n, learner order i, order j, social time standard deviation S5;
(4.6) length of entertainment time: counting once every working day and distinguishing working day statistics and rest day statistics, counting the time spent by the learner on the entertainment activities on the same day, and generating an entertainment time columnar line graph according to the counting, wherein the entertainment time is closely related to the time distribution condition, the interest and hobbies and other personalized features of the learner; the variables correspondingly involved include: personal daily entertainment time t6, class headcount n, learner order i, order j, entertainment time standard deviation S6;
(4.7) class hour length: counting once every working day and distinguishing working day and rest day statistics, counting the time spent by the learner in the course learning on the same day, and generating a cylindrical line graph of the lesson time, wherein the lesson time has close correlation with the concentration, learning attitude and other personalized features of the learner; the variables correspondingly involved include: the individual daily class time t7, class headcount n, learner order i, day order j, class time standard deviation S7;
(4.8) autonomous learning period: the statistics is carried out once every working day and the statistics of working days and rest days are distinguished, the statistics of the time spent by learners on autonomous arrangement learning on the same day is an important performance of self management of learners; the variables correspondingly involved include: the personal daily autonomous learning time t8, the total number n of classes, the learner order i, the sequence j and the standard deviation of the autonomous learning time S8;
(4.9) workday disposable duration: the statistics is carried out once every working day, and the statistics of the time which can be independently controlled by the learner in the working day is an important embodiment for the learner to carry out self-management; the variables correspondingly involved include: the dominant time of individual workday t9, the total number of classes n, the learner order i, the order j, and the dominant time standard deviation of workday S9;
(4.10) day of rest may dictate length: the statistics is carried out once every rest day, and the statistics of the time of the learner in the independent control of the rest day is an important embodiment for the learner to carry out self-management; the variables correspondingly involved include: the personal day of rest dominant time t10, the total number of people in class n, the learner order i, the order j, the day of rest dominant time standard deviation S10;
(4.11) task completion duration: the index accumulates all the time spent by the learner to complete the task, and is used for reflecting the efficiency of the learner to complete the task, the time is recorded once a day, and the learner can clearly see the time spent by the learner to complete the task every day; the variables correspondingly involved include: total time T11, task time Tn, learner order i, and sequence j; the data processing method comprises the following steps: the system collects data of each day and inputs the data into a formula for automatic calculation;
(4.12) time allocation record integrity: the record integrity, namely the manual completion degree, indicates the proportion of the completion manual filling amount of the learner in the set total amount on the day, is used for reflecting the data record condition of the learner and reflecting the attitude of the learner on treating the data record, and is also beneficial to improving the optimized recording mode; the variables involved are: the method comprises the following steps of (1) completing degree Y1, daily manual APP record quantity M, daily manual APP requirement record quantity M, learner sequence i and sequence j; the data processing method comprises the following steps:
personal time-of-day allocation record integrity:
(4.13) time allocation record reasonableness: the reasonable degree of each part of the learner in learning, reading, exercising, entertainment and sleeping; the system is calculated and deduced by several dimensionalities of self evaluation, teacher grading and parent grading, is used for reflecting the time distribution capacity of the learner, and is an important embodiment of time management and self management of the learner; the variables involved are: self-evaluation, expert evaluation and automatic evaluation; the data processing method comprises the following steps: self-evaluation: the variables involved are: self-management satisfaction y2, learner order i, sequence j;
average autonomous learning duration within ten days of the individual:
and (4) expert evaluation: the expert makes quantitative evaluation in a scoring mode and the like on the basis of quantitative and qualitative analysis according to the data;
automatic evaluation: calculating an algorithm according to the diversity, relevance, time period rationality and efficiency of tasks;
the concentration self-evaluation, the mastery self-evaluation, the participation condition record number, the thought record word number and the thought content in the step (5) comprise:
(5.1) concentration self-assessment: the concentration degree represents the self-evaluation of the concentration degree and the attention concentration degree of the learner on each class, is used for reflecting the learning input of the learner in the class and laterally reflecting the self-evaluation condition of the learner on the self-psychology and emotion input; the variables correspondingly involved include: personal course concentration Y1, total number of courses per day m, course sequence k, learner sequence i, and sequence j; wherein i, j, m, k belongs to N, i, j, m is more than or equal to 1, and k is more than or equal to 1 and less than or equal to m; the data processing method comprises the following steps:
individual daily curriculum concentration average:
(5.2) self-rating of mastery degree: the mastery degree represents the self-evaluation of the mastery degree and the attention concentration degree of the learner on the learning content of each class, is used for reflecting the learning input of the learner in the course and laterally reflecting the self-evaluation condition of the learner on the understanding of the content of the course; the variables correspondingly involved include: personal curriculum mastery degree Y2, total curriculum number m per day, curriculum sequence k, learner sequence i and sequence j; wherein i, j, m, k belongs to N, i, j, m is more than or equal to 1, and k is more than or equal to 1 and less than or equal to m; the data processing method comprises the following steps:
average of individual daily class mastery:
(5.3) number of participation records: the number of times of speaking and questioning participating in the classroom in the learning process of the learner Chinese course is used for reflecting the thinking degree of the learner in the classroom and reflecting the mastering and understanding conditions of the learner on the course content; the data processing method comprises the following steps: recording and summarizing through daily records of students;
(5.4) number of records: the number of the thought-back records in the daily Chinese course record of the learner is used for reflecting the thinking degree and frequency of the learner on the Chinese course, and further reflecting the attitude of the learner on the Chinese course and the learning thought-back condition; the data processing method comprises the following steps: recording and summarizing through daily records of students;
(5.5) number of stuttering record words: the word number of the backstepping records in the daily Chinese course record of the learner is used for reflecting the self-backstepping situation of the learner on the Chinese course, and further reflecting the attitude of the learner on the Chinese course and the learning backstepping situation; the data processing method comprises the following steps: recording and summarizing through daily records of students;
(5.6) the contents of the thinking: the contents of the learner for thinking against the events in the daily record are used for reflecting the specific situation of self thinking after the learner masters and understands the language course and reflecting the attitude and deep thinking degree of the learner on the language course; the data processing method comprises the following steps: performing qualitative analysis on the thought-back content of the student through word cloud analysis and theme analysis;
the self-management ability self-evaluation score of the students, the teacher self-management ability score of the students, the parents self-management ability score of the students and the expert self-management ability score of the students in the step (6) comprise: self-management ability self-rating scores of students, self-management ability teacher scores of students, self-management ability parent scores of students and self-management ability expert scores of students;
the total number of parent words, total number of words of parent words, emotional attribute of parent words, overall quality of parent words and score of parent participation degree in the step (7) comprise: the total number of parent words, the total number of words of parent words, the emotional attribute of parent words, the overall quality of parent words and the score of parent participation;
the academic motivation, the academic effectiveness feeling, the high-order thinking, the meta-cognitive strategy, the resource management strategy, the academic pressure and the academic performance in the step (8) comprise: academic motivation, academic effectiveness, high-order thinking, meta-cognitive strategies, resource management strategies, academic pressures and academic achievements.
7. The learner-oriented self-adjusting learning data information processing method according to claim 1, wherein the data acquisition in step three includes two approaches of a self-adjusting learning manual and a self-adjusting learning APP platform, and the data preprocessing includes data cleaning, data integration and data format conversion;
index data in the self-adjusting learning index system is collected by a matched self-adjusting learning manual or a self-adjusting learning APP platform; the self-adjusting learning manual is filled by a learner and adopts a mode of manually acquiring data; the self-adjusting learning APP platform is used for automatically collecting and uploading personal data input by a user in the using process to the cloud server in forms of APP background log data and the like;
the acquired data comprises student monitoring data, parent evaluation data and teacher evaluation data; and the collected data is cleaned, integrated and converted in a data preprocessing stage and then is stored in an analysis model, and a visual chart and diagnosis information are automatically presented by the analysis model.
8. A learner-oriented self-adjusting learning data information processing system to which the learner-oriented self-adjusting learning data information processing method according to any one of claims 1 to 7 is applied, the learner-oriented self-adjusting learning data information processing system including a theoretical model, an index system, and a data acquisition and preprocessing module;
wherein, the theoretical model is a learner self-adjusting learning process theoretical model consisting of four circulation stages; the index system is a self-adjusting learning index system; and the data acquisition and preprocessing module is used for acquiring and preprocessing data according to a self-adjusting learning index system.
9. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
constructing a theoretical model of the learner self-adjusting learning process consisting of four circulation stages; according to various theories and in combination with actual conditions, decomposing and explaining 8 secondary indexes respectively to construct a whole self-adjusting learning index system; data acquisition and preprocessing are carried out according to the proposed self-adjusting learning index system; the data acquisition comprises two ways of a self-adjusting learning manual and a self-adjusting learning APP platform, and the data preprocessing comprises three steps of data cleaning, data integration and data format conversion.
10. An information data processing terminal for implementing the learner-oriented self-adjusting learning data information processing system according to claim 7.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106203635A (en) * | 2016-06-29 | 2016-12-07 | 北京师范大学 | A kind of on-line study behavior puts into data collection and transmission and method |
| KR20190075727A (en) * | 2017-12-21 | 2019-07-01 | 한국교육학술정보원 | Method for planning portfolio using learner analysis data on the basis of mobile terminal and service |
| CN110473125A (en) * | 2019-08-09 | 2019-11-19 | 孙明亮 | Students'Management System |
| CN111832524A (en) * | 2020-07-23 | 2020-10-27 | 山东理工大学 | Video learning behavior analysis method in SPOC environment based on time dimension |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106203635A (en) * | 2016-06-29 | 2016-12-07 | 北京师范大学 | A kind of on-line study behavior puts into data collection and transmission and method |
| KR20190075727A (en) * | 2017-12-21 | 2019-07-01 | 한국교육학술정보원 | Method for planning portfolio using learner analysis data on the basis of mobile terminal and service |
| CN110473125A (en) * | 2019-08-09 | 2019-11-19 | 孙明亮 | Students'Management System |
| CN111832524A (en) * | 2020-07-23 | 2020-10-27 | 山东理工大学 | Video learning behavior analysis method in SPOC environment based on time dimension |
Non-Patent Citations (1)
| Title |
|---|
| 戴妍;: "远程教育中自我调节学习的困境与出路――基于远程教育信息交互模式的思考", 现代远距离教育, no. 02, 15 April 2013 (2013-04-15) * |
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