A Semantic Approach to Multi-parameter Personalisation of E-Learning Systems

Al-Chalabi, Humam K. Majeed; Apoki, Ufuoma Chima

doi:10.1007/978-3-030-68527-0_24

Cited by 2 publications

(1 citation statement)

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“…The article in [6] tries to enhance the use of digital resources for students and instructors. The research papers in [7] and [8] address the learning objects that can be used in different contexts using web3. Finally, the article in [9] suggests collaborative learning to help instructors engage students in generating and evaluating questions.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Automatic Question Generation from Program Codes

Alshboul,

Baksa-Varga

2024

IJACSA

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Generating questions is one of the most challenging tasks in the natural language processing discipline. With the significant emergence of electronic educational platforms like elearning systems and the large scalability achieved with elearning, there is an increased urge to generate intelligent and deliberate questions to measure students' understanding. Many works have been done in this field with different techniques; however, most approaches work on extracting questions from text. This research aims to build a model that can conceptualize and generate questions on Python programming language from program codes. Different models are proposed by inserting text and generating questions; however, the challenge is understanding the concepts in the code snippets and linking them to the lessons so that the model can generate relevant and reasonable questions for students. Therefore, the standards applied to measure the results are the code complexity and question validity regarding the questions. The method used to achieve this goal combines the QuestionGenAi framework and ontology based on semantic code conversion. The results produced are questions based on the code snippets provided. The evaluation criteria were code complexity, question validity, and question context. This work has great potential improvement to the e-learning platforms to improve the overall experience for both learners and instructors.

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Section: Introductionmentioning

confidence: 99%