Mooc Evaluation System Based on Deep Learning
Bibliographic Data
| ID | 21729402 |
|---|---|
| Authors | Jian-Wei Tzeng (0000-0001-5507-6852, National Tsing Hua University), Chia-An Lee (0000-0001-9473-6411, National Tsing Hua University), Nen-Fu Huang, Nen‐Fu Huang (0000-0003-0231-0232, National Tsing Hua University), Hao-Hsuan Huang (National Tsing Hua University), Chin-Feng Lai (0000-0001-7138-0272, National Cheng Kung University) |
| Year | 2022 |
| Volume | 23 |
| Issue | 1 |
| Pages | 21-40 |
| Publication date | 2022-02-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The International Review of Research in Open and Distributed Learning (JOURNAL) |
| Journal identifiers | ISSN: 1492-3831 • E-ISSN: 1492-3831 |
| Publisher | Athabasca University Press (PUBLISHER • CA) |
| DOI | 10.19173/irrodl.v22i4.5417 |
| OpenAlex | W4213209796 |
| Language | EN |
| Citations received | 5 |
| References cited | 7 |
Massive open online courses (MOOCs) are open access, Web-based courses that enroll thousands of students. MOOCs deliver content through recorded video lectures, online readings, assessments, and both student–student and student–instructor interactions. Course designers have attempted to evaluate the experiences of MOOC participants, though due to large class sizes, have had difficulty tracking and analyzing the online actions and interactions of students. Within the broader context of the discourse surrounding big data, educational providers are increasingly collecting, analyzing, and utilizing student information. Additionally, big data and artificial intelligence (AI) technology have been applied to better understand students’ learning processes. Questionnaire response rates are also too low for MOOCs to be credibly evaluated. This study explored the use of deep learning techniques to assess MOOC student experiences. We analyzed students’ learning behavior and constructed a deep learning model that predicted student course satisfaction scores. The results indicated that this approach yielded reliable predictions. In conclusion, our system can accurately predict student satisfaction even when questionnaire response rates are low. Accordingly, teachers could use this system to better understand student satisfaction both during and after the course
Big data · Deep learning · Distance education · Massive open online course · Mathematics education · Multimedia · Online learning · Pedagogy · World Wide Web · Computer Science · Online Learning and Analytics · Psychology · Artificial Intelligence
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Course evaluation modeling using multi-level regression
What drives art education massive open online courses continuance intention? Exploring determinants from value–software–hardware–design perspective
Application and theory gaps during the rise of Artificial Intelligence in Education
Evaluation of Student Feedback Within a Mooc Using Sentiment Analysis and Target Groups
What predicts student satisfaction with Moocs
Identity and deception in the virtual community
Motivation in a Mooc
Let’s not forget
What "Doesn't" Matter in College
| Citations per year | 1.25 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |