Work

Knowledge Tracing Within Single Programming Exercise Using Process Data

Year: 2018

Type: article

Abstract: Knowledge tracing is a core technology in many intelligent learning systems. In this paper, we propose a novel knowledge tracing method that predicts learner’s knowledge state within a single programming exercise. Given a programming task, a student’s intermediate solution is represented by an abstract syntax tree and evaluated by computing its tree edit distance to the best solution. With the measure of solution quality, the learning trajectory of each student can be encoded as a real-valued sequence. Using the mean value of the sequence as a primary feature, we developed a logistic regression model to predict students’ knowledge state. We compared our method with three popular models on a large-scale dataset collected from a classic block-based programming task. The experimental results suggest that the proposed method that captures features derived from student's problem-solving processes can significantly improve the prediction performance.

Source: International Conference on Computers in Education

Authors: JIANG, Bo, YE, Yun, ZHANG, Haifeng

Institutions: Zhejiang University of Technology, Carnegie Mellon University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

Funders: National Natural Science Foundation of China, Zhejiang University of Technology

Award: G1747098832