Extracting access patterns with hierarchical latent tree analysis: An empirical study on an undergraduate programming course

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Students commonly access learning resources with online systems nowadays. The access logs of those learning resources potentially provide rich information for course instructors to understand students. However, when there are many learning resources, the insights on students could be obscured by the large amount of unprocessed log data in its raw form. In this paper, we propose to extract the access patterns using a recently proposed method for topic modeling. The method, named hierarchical latent tree analysis, can capture co-occurrences of access to learning resources and group related learning resources together. We further show a way to consider the access periods during a course from the log data to reflect different behaviors of the students. We empirically test the proposed method using the log data captured in an undergraduate programming course with 63 students. We evaluate the performance of the proposed method based on the task for predicting students’ scores. The regression model built on the features extracted by the proposed method shows significantly better performance over three baseline models. We further demonstrate meaningful insights on the student behaviors can be obtained from the extracted access patterns. Copyright © 2019 Springer Nature Switzerland AG.
Original languageEnglish
Title of host publicationIntegrated uncertainty in knowledge modelling and decision making: 7th International Symposium, IUKM 2019, Nara, Japan, March 27–29, 2019, proceedings
EditorsHirosato SEKI, Canh Hao NGUYEN, Van-Nam HUYNH, Masahiro INUIGUCHI
Place of PublicationCham
PublisherSpringer
Pages380-392
ISBN (Electronic)9783030148157
ISBN (Print)9783030148140
DOIs
Publication statusPublished - 2019

Fingerprint

Computer programming
Students
Online systems

Citation

Poon, L. K. M. (2019). Extracting access patterns with hierarchical latent tree analysis: An empirical study on an undergraduate programming course. In H. Seki, C. H. Nguyen, V.-N. Huynh, & M. Inuiguchi (Eds.), Integrated uncertainty in knowledge modelling and decision making: 7th International Symposium, IUKM 2019, Nara, Japan, March 27–29, 2019, proceedings (pp. 380-392). Cham: Springer.

Keywords

  • Access pattern extraction
  • Hierarchical latent tree analysis
  • Log analysis
  • Probabilistic graphical models
  • Educational data mining