Using Student Logs to Build Bayesian Models of Student Knowledge and Skills

EasyChair Preprint 284

Abstract

Recent work on Intelligent Tutoring Systems have focused on more complicated knowledge domains, which poses challenges in automated assessment of student performance. In particular, while the system can log every user action and therefore keep track of the student’s solution state, it is unable to determine the hidden intermediate steps leading to such state or what the student is trying to achieve. In this paper, we show that this information can be acquired through data mining, along with the type, frequency and context of errors that students made. Our technique has been implemented as part of the student model in a tutor that teaches red-black trees. The system has been evaluated on three semesters of student data and analysis of the results shows that the proposed framework of error analysis can help the system in predicting student performance with good accuracy and the instructor in determining difficulties that students encounter, both individually and collectively as a class.

Keyphrases: Bayesian Student Model, Bayesian learning, Bayesian model, Bayesian network, Binary Search Tree, Intelligent Tutoring System, Post-test, Pre-test, Predicting Student Performance, Student logs, context rule node, data structures, error analysis, error context, extended error context, red-black tree, student model, student performance, student rule mastery, total accuracy, tutoring system

@booklet{EasyChair:284,
  author    = {Huy Nguyen and Chun Wai Liew},
  title     = {Using Student Logs to Build Bayesian Models of Student Knowledge and Skills},
  doi       = {10.29007/mdtf},
  howpublished = {EasyChair Preprint 284},
  year      = {EasyChair, 2018}}