TY - GEN
T1 - Learning trend analysis and prediction based on knowledge tracing and regression analysis
AU - Cai, Yali
AU - Niu, Zhendong
AU - Wang, Yingwang
AU - Niu, Ke
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Estimating students’ knowledge is a fundamental and important task for student modeling in intelligent tutoring systems. Since the concept of knowledge tracing was proposed, there have been many studies focusing on estimating students’ mastery of specific knowledge components, yet few studies paid attention to the analysis and prediction on a student’s overall learning trend in the learning process. Therefore, we propose a method to analyze a student’s learning trend in the learning process and predict students’ performance in future learning. Firstly, we estimate the probability that the student has mastered the knowledge components with the model of Bayesian Knowledge Tracing, and then model students’ learning curves in the overall learning process and predict students’ future performance with Regression Analysis. Experimental results show that this method can be used to fit students’ learning trends well and can provide prediction with reference value for students’ performances in the future learning.
AB - Estimating students’ knowledge is a fundamental and important task for student modeling in intelligent tutoring systems. Since the concept of knowledge tracing was proposed, there have been many studies focusing on estimating students’ mastery of specific knowledge components, yet few studies paid attention to the analysis and prediction on a student’s overall learning trend in the learning process. Therefore, we propose a method to analyze a student’s learning trend in the learning process and predict students’ performance in future learning. Firstly, we estimate the probability that the student has mastered the knowledge components with the model of Bayesian Knowledge Tracing, and then model students’ learning curves in the overall learning process and predict students’ future performance with Regression Analysis. Experimental results show that this method can be used to fit students’ learning trends well and can provide prediction with reference value for students’ performances in the future learning.
KW - Knowledge tracing
KW - Learning performance prediction
KW - Learning trend analysis
KW - Student modeling
UR - http://www.scopus.com/inward/record.url?scp=84949990646&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-22324-7_3
DO - 10.1007/978-3-319-22324-7_3
M3 - Conference contribution
AN - SCOPUS:84949990646
SN - 9783319223230
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 41
BT - Database Systems for Advanced Applications - DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Revised Selected Papers
A2 - Ishikawa, Yoshiharu
A2 - Nutanong, Sarana
A2 - Liu, An
A2 - Qian, Tieyun
A2 - Cheema, Muhammad Aamir
PB - Springer Verlag
T2 - 2nd International Workshop on Semantic Computing and Personalization, SeCoP 2015, 2nd International Workshop on Big Data Management and Service, BDMS 2015 held in conjunction with 20th International Conference on Database Systems for Advanced Applications, DASFAA 2015
Y2 - 20 April 2015 through 23 April 2015
ER -