Application of MOOC Data Based on Autonomous Intelligent Robot System in Students' Learning Behavior

In the traditional teaching classroom, teachers' supervision has an important influence on students to a large extent. The MOOC (massive open online courses) platform requires learners to conduct self-supervision to improve their self-regulation ability. It also requires learners to actively participate in course activities to improve the learning effect of MOOC. In the autonomous intelligent intelligent robot system, the cognitive model is required to be able to classify and identify the perception information of the robot, realize action decision-making, and at the same time, it can feedback and control the robot to obtain the information of the students' MOOC learning progress, and can accept the students' MOOC learning behavior signals to amend the decision.This paper preliminarily analyzed the factors that affected the learning effect. Based on the preliminary analysis of the influencing factors of MOOC learning behavior, through the comparative analysis of the existing learning behavior analysis framework, an operational learning behavior analysis framework for MOOC courses has been designed. The types of MOOC learning behaviors and associated variables and indicators were identified. By constructing the MOOC learning behavior analysis framework, the learning behavior patterns of MOOC learners were mined. The system log data of the MOOC platform and the basic information of learners were collected. The indicators that need to be studied in the learning behavior analysis framework were screened out. Based on learner behavior data, MOOC learners' cumulative video viewing time distribution and video viewing number distribution were explored, and learner behavior features were extracted. Further, the learning behavior patterns were classified based on behavior preference and learning motivation, respectively. Differences between the learning characteristics of different types of learners were analyzed. Among them, 40 learners believed that the quality of the learning resources of the MOOC platform based on the autonomous intelligent robot system was very important in the process of autonomous learning, accounting for an average of about 32.6%. This paper helps to improve students' autonomy in learning and stimulate students' enthusiasm for learning.

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Publication Details

Journal
Informatica
Published
2024-09-06
DOI
https://doi.org/10.31449/inf.v48i13.5828
Citations
1
Primary Topic
Advanced Sensor and Control Systems
Type
article
Field-Weighted Citation Impact
0.33

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article

Application of MOOC Data Based on Autonomous Intelligent Robot System in Students' Learning Behavior

Jimei Gao
1 citations
Informatica
Advanced Sensor and Control Systems
0.33
article

Application of MOOC Data Based on Autonomous Intelligent Robot System in Students' Learning Behavior

Jimei Gao
article en
1 citations

Abstract

In the traditional teaching classroom, teachers' supervision has an important influence on students to a large extent. The MOOC (massive open online courses) platform requires learners to conduct self-supervision to improve their self-regulation ability. It also requires learners to actively participate in course activities to improve the learning effect of MOOC. In the autonomous intelligent intelligent robot system, the cognitive model is required to be able to classify and identify the perception information of the robot, realize action decision-making, and at the same time, it can feedback and control the robot to obtain the information of the students' MOOC learning progress, and can accept the students' MOOC learning behavior signals to amend the decision.This paper preliminarily analyzed the factors that affected the learning effect. Based on the preliminary analysis of the influencing factors of MOOC learning behavior, through the comparative analysis of the existing learning behavior analysis framework, an operational learning behavior analysis framework for MOOC courses has been designed. The types of MOOC learning behaviors and associated variables and indicators were identified. By constructing the MOOC learning behavior analysis framework, the learning behavior patterns of MOOC learners were mined. The system log data of the MOOC platform and the basic information of learners were collected. The indicators that need to be studied in the learning behavior analysis framework were screened out. Based on learner behavior data, MOOC learners' cumulative video viewing time distribution and video viewing number distribution were explored, and learner behavior features were extracted. Further, the learning behavior patterns were classified based on behavior preference and learning motivation, respectively. Differences between the learning characteristics of different types of learners were analyzed. Among them, 40 learners believed that the quality of the learning resources of the MOOC platform based on the autonomous intelligent robot system was very important in the process of autonomous learning, accounting for an average of about 32.6%. This paper helps to improve students' autonomy in learning and stimulate students' enthusiasm for learning.

InformaticaVol. 48(13)
Government of Jiangsu Province
Openalex Percentile: Top 42%
Advanced Sensor and Control Systems
0.33
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