Work

Integrating Expert Knowledge With Automated Knowledge Component Extraction for Student Modeling

Year: 2025

Type: conference-paper

Abstract: Knowledge tracing is a method to model students' knowledge and enable personalized education in many STEM disciplines such as mathematics and physics, but has so far still been a challenging task in computing disciplines.One key obstacle to successful knowledge tracing in computing education lies in the accurate extraction of knowledge components (KCs), since multiple intertwined KCs are practiced at the same time for programming problems.In this paper, we address the limitations of current methods and explore a hybrid approach for KC extraction, which combines automated code parsing with an expert-built ontology.We use an introductory (CS1) Java benchmark dataset to compare its KC extraction performance with the traditional extraction methods using a state-of-the-art evaluation approach based on learning curves.Our preliminary results show considerable improvement over traditional methods of student modeling.The results indicate the opportunity to improve automated KC extraction in CS education by incorporating expert knowledge into the process.

Source: ACM Conference on User Modeling, Adaptation and Personalization (UMAP)

DOI: 10.1145/3699682.3728348

Authors: Rafaella Sampaio de Alencar, Mehmet Arif Demirtas, Adittya Soukarjya Saha, Yang Shi, Peter Brusilovsky

Institutions: University of Pittsburgh, University of Illinois Urbana-Champaign, North Carolina State University, Utah State 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: hybrid

Funder: National Science Foundation