Student Performance Prediction by LMS Data and Classroom Videos

This paper conducts research on predicting academic performance based on the learning management system (LMS) data and classroom videos. Except for the interactive data of the LMS, our research introduces classroom videos to expand learning behavioral variables. Through the correlation analysis with the final exam scores, we select 6 of these variables as predictors. Then, the prediction results of different predictor combinations are compared and the result shows that the prediction based on LMS predictors and video predictors achieves the best accuracy of 89.7%, which is higher than the accuracy of using LMS predictors or video predictors individually. The predictors obtained through computer processing can be used for automatic performance prediction.

[1]  Robert D. Hoge,et al.  Predicting Academic Achievement from Classroom Behavior , 1979 .

[2]  Mukesh Kumar,et al.  Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques , 2017 .

[3]  Juan Alfonso Lara,et al.  Data mining for modeling students' performance: A tutoring action plan to prevent academic dropout , 2017, Comput. Electr. Eng..

[4]  J. Allen,et al.  Observations of Effective Teacher–Student Interactions in Secondary School Classrooms: Predicting Student Achievement With the Classroom Assessment Scoring System—Secondary , 2013, School psychology review.

[5]  C. Abraham,et al.  Psychological correlates of university students' academic performance: a systematic review and meta-analysis. , 2012, Psychological bulletin.

[6]  Carlos Delgado Kloos,et al.  Student Behavior and Interaction Patterns With an LMS as Motivation Predictors in E-Learning Settings , 2010, IEEE Transactions on Education.

[7]  Khaled Shaalan,et al.  Factors Affecting Students’ Performance in Higher Education: A Systematic Review of Predictive Data Mining Techniques , 2019, Technology, Knowledge and Learning.

[8]  Rianne Conijn,et al.  Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS , 2017, IEEE Transactions on Learning Technologies.

[9]  Sellappan Palaniappan,et al.  Student Academic Performance Prediction by using Decision Tree Algorithm , 2018, 2018 4th International Conference on Computer and Information Sciences (ICCOINS).

[10]  Ram Mohana Reddy Guddeti,et al.  Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks , 2019, Education and Information Technologies.

[11]  Ram Mohana Reddy Guddeti,et al.  Unobtrusive Behavioral Analysis of Students in Classroom Environment Using Non-Verbal Cues , 2019, IEEE Access.

[12]  Joseph A. Cobb,et al.  Relationship of Discrete Classroom Behaviors to Fourth-Grade Academic Achievement. , 1972 .

[13]  Wei Xu,et al.  A classroom concentration model based on computer vision , 2019, ACM TUR-C.

[14]  Sara E. Harris,et al.  Research and Teaching: A New Tool for Measuring Student Behavioral Engagement in Large University Classes. , 2015 .

[15]  Marc A. Brackett,et al.  Classroom emotional climate, student engagement, and academic achievement , 2012 .

[16]  James D. McKinney,et al.  Relationship Between Classroom Behavior and Academic Achievement. , 1975 .

[17]  Dinesh Babu Jayagopi,et al.  Predicting student engagement in classrooms using facial behavioral cues , 2017, MIE@ICMI.