TY - GEN
T1 - Correlation of grade prediction performance with characteristics of lesson subject
AU - Sorour, Shaymaa E.
AU - Luo, Jingyi
AU - Goda, Kazumasa
AU - Mine, Tsunenori
PY - 2015/9/14
Y1 - 2015/9/14
N2 - Learning analytics is valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities. Analyzing comment data written by students after each lesson helps to grasp their learning attitudes and situations. They can be a powerful source of data for all forms of assessment. In the current study, we break down student comments into different topics by employing two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA), to discover the topics that help to predict final student grades as their performance. The objectives of this paper are twofold: First, determine how the three time-series items: P-, C- and N-comments and the difficulty of a subject affect the prediction results of final student grades. Second, evaluate the reliability of predicting student grades by considering the differences between prediction results of two consecutive lessons. The results obtained can help to understand student behavior during the period of the semester, grasp prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.
AB - Learning analytics is valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities. Analyzing comment data written by students after each lesson helps to grasp their learning attitudes and situations. They can be a powerful source of data for all forms of assessment. In the current study, we break down student comments into different topics by employing two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA), to discover the topics that help to predict final student grades as their performance. The objectives of this paper are twofold: First, determine how the three time-series items: P-, C- and N-comments and the difficulty of a subject affect the prediction results of final student grades. Second, evaluate the reliability of predicting student grades by considering the differences between prediction results of two consecutive lessons. The results obtained can help to understand student behavior during the period of the semester, grasp prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.
UR - http://www.scopus.com/inward/record.url?scp=84961761032&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961761032&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2015.24
DO - 10.1109/ICALT.2015.24
M3 - Conference contribution
AN - SCOPUS:84961761032
T3 - Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015
SP - 247
EP - 249
BT - Proceedings - IEEE 15th International Conference on Advanced Learning Technologies
A2 - Chen, Nian-Shing
A2 - Liu, Tzu-Chien
A2 - Kinshuk, null
A2 - Huang, Ronghuai
A2 - Hwang, Gwo-Jen
A2 - Sampson, Demetrios G.
A2 - Tsai, Chin-Chung
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015
Y2 - 6 July 2015 through 9 July 2015
ER -