Automated Domain Question Mapping (DQM) with Educational Learning Materials
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Automated Bias Assessment in AI-Generated Educational Content Using CEAT Framework
Recent advances in Generative Artificial Intelligence (GenAI) have transformed educational content creation, particularly in developing tutor training materials. However, biases embedded in AI-generated content--such as …
FairnessRetrieval-augmented GenerationAn Ontology for Representing Curriculum and Learning Material
Educational, learning, and training materials have become extremely commonplace across the Internet. Yet, they frequently remain disconnected from each other, fall into platform silos, and so on. One way to overcome this…
Pre-Training With Scientific Text Improves Educational Question Generation
With the boom of digital educational materials and scalable e-learning systems, the potential for realising AI-assisted personalised learning has skyrocketed. In this landscape, the automatic generation of educational qu…
Language ModelingLanguage ModellingLarge Language ModelQuestion Generation+1QuesGenie: Intelligent Multimodal Question Generation
In today's information-rich era, learners have access to abundant educational resources, but the lack of practice materials tailored to these resources presents a significant challenge. This project addresses that gap by…
Reinforcement LearningQuestion GenerationEDUQA: Educational Domain Question Answering System using Conceptual Network Mapping
Most of the existing question answering models can be largely compiled into two categories: i) open domain question answering models that answer generic questions and use large-scale knowledge base along with the targete…
Answer GenerationOpen-Domain Question AnsweringQuestion AnsweringRetrieval