A Question-Answer Generation System for an Asynchronous Distance Learning Platform

Autor/inn/en	Wang, Hei-Chia; Maslim, Martinus; Kan, Chia-Hao
Titel	A Question-Answer Generation System for an Asynchronous Distance Learning Platform
Quelle	In: Education and Information Technologies, 28 (2023) 9, S.12059-12088 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wang, Hei-Chia) ORCID (Maslim, Martinus)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-023-11675-y
Schlagwörter	Automation; Artificial Intelligence; Computer Assisted Testing; Asynchronous Communication; Distance Education; Sentences; Multiple Choice Tests + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Distance study; Distance learning; Fernunterricht; Sentence analysis; Satzanalyse; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren
Abstract	Distance learning frees the learning process from spatial constraints. Each mode of distance learning, including synchronous and asynchronous learning, has disadvantages. In synchronous learning, students have network bandwidth and noise concerns, but in asynchronous learning, they have fewer opportunities for engagement, such as asking questions. The difficulties associated with asynchronous learning make it difficult for teachers to determine whether students comprehend the course material. Motivated students will consistently participate in a course and prepare for classroom activities if teachers ask questions and communicate with them during class. As an aid to distance education, we want to automatically generate a sequence of questions based on asynchronous learning content. In this study, we will also generate multiple-choice questions for students to answer and teachers to easily correct. The asynchronous distance teaching-question generation (ADT-QG) model, which includes Sentences-BERT (SBERT) in the model architecture to generate questions from sentences with a higher degree of similarity, is proposed in this work. With the Wiki corpus generation option, it is anticipated that the Transfer Text-to-Text Transformer (T5) model will generate more fluent questions and be more aligned with the instructional topic. The results indicate that the questions created by the ADT-QG model suggested in this work have good fluency and clarity indicators, showing that the questions generated by the ADT-QG model are of a certain quality and relevant to the curriculum. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01