Automated Domain Question Mapping (DQM) with Educational Learning Materials

TL;DR

This paper presents an innovative method for automatically constructing Domain Question Maps (DQMs) from educational materials, addressing challenges in concept identification and limited labeled data to enhance personalized learning.

Contribution

The study introduces a novel approach for creating DQMs that align questions with learning objectives, improving knowledge representation and adaptive learning capabilities.

Findings

Effective generation of educational questions

Discernment of hierarchical relationships among questions

Facilitation of personalized and adaptive learning

Abstract

Concept maps have been widely utilized in education to depict knowledge structures and the interconnections between disciplinary concepts. Nonetheless, devising a computational method for automatically constructing a concept map from unstructured educational materials presents challenges due to the complexity and variability of educational content. We focus primarily on two challenges: (1) the lack of disciplinary concepts that are specifically designed for multi-level pedagogical purposes from low-order to high-order thinking, and (2) the limited availability of labeled data concerning disciplinary concepts and their interrelationships. To tackle these challenges, this research introduces an innovative approach for constructing Domain Question Maps (DQMs), rather than traditional concept maps. By formulating specific questions aligned with learning objectives, DQMs enhance knowledge representation and improve readiness for learner engagement. The findings indicate that the proposed method can effectively generate educational questions and discern hierarchical relationships among them, leading to structured question maps that facilitate personalized and adaptive learning in downstream applications.