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ERIC Number: EJ1394628
Record Type: Journal
Publication Date: 2023-Oct
Pages: 24
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1360-2357
EISSN: EISSN-1573-7608
Available Date: N/A
Research on the Predictive Model Based on the Depth of Problem-Solving Discussion in MOOC Forum
Li, Jiansheng; Li, Linlin; Zhu, Zhixin; Shadiev, Rustam
Education and Information Technologies, v28 n10 p13053-13076 Oct 2023
A discussion forum is an indispensable part of a massive open online course (MOOC) environment as it enables knowledge construction through learner-to-learner interaction such as discussion of solutions to assigned problems among learners. In this paper, a machine prediction model is built based on the data from the MOOC forum and the depth of discussion of solutions to assigned problems on the topic among students was analyzed. The data for this study was obtained from Modern educational technology course through Selenium with Python. The course has been offered to a total of 11,184 students from China seven times since February, 2016. The proposed model includes the formula of the depth of problem-solving discussion in MOOC forum and its prediction probability. The efficiency of the prediction model and the most important factor of the depth of problem-solving discussion in MOOC are explained in the paper. Based on the results, useful suggestions for effective teaching in MOOC forums are provided in the article.
Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link-springer-com.bibliotheek.ehb.be/
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: China
Grant or Contract Numbers: N/A
Author Affiliations: N/A