Using Few-Shot Learning Materials of Multiple SPOCs to Develop Early Warning Systems to Detect Students at Risk
Bibliographic Data
| ID | 21729294 |
|---|---|
| Authors | Yung-Hsiang Hu (0000-0003-4950-048X, National Yunlin University of Science and Technology, corresponding author) |
| Year | 2022 |
| Volume | 23 |
| Issue | 1 |
| Pages | 1-20 |
| Publication date | 2022-02-01 |
| Peer Reviewed | Yes |
| 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.v22i4.5397 |
| OpenAlex | W4213313344 |
| Language | EN |
| Citations received | 3 |
| References cited | 8 |
Early warning systems (EWSs) have been successfully used in online classes, especially in massive open online courses, where it is nearly impossible for students to interact face-to-face with their teachers. Although teachers in higher education institutions typically have smaller class sizes, they also face the challenge of being unable to have direct contact with their students during distance teaching. In this research, we examined the online learning trajectories of students participating in four small private online courses that were all taught by one teacher. We collected relevant data of 1,307 students from the campus learning management system. Subsequently, we constructed 18 prediction models, one for each week of the course, to develop an EWS for identifying students in online asynchronous learning at risk of failing (i.e., students who fail their final examination). Our results indicated that the fifth-week model successfully predicted student performance, with an accuracy exceeding 83% from the eighth week onward
Asynchronous communication · Asynchronous learning · At-risk students · Cooperative learning · Distance education · Face-to-face · Mathematics education · Medical education · Multimedia · Online learning · Synchronous learning · Teaching method · Warning system · Computer Science · Medicine · Online and Blended Learning · Online Learning and Analytics · Psychology · Artificial Intelligence
Mining LMS data to develop an “early warning system” for educators
Learning analytics should not promote one size fits all
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
Learning from Imbalanced Data
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Data mining in course management systems
Early warning system as a predictor for student performance in higher education blended courses
Retention in Online Courses
| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |