Predicting student satisfaction and perceived learning within online learning environments
Bibliographic Data
| ID | 23326287 |
|---|---|
| Authors | Emtinan Alqurashi (0000-0003-4738-4331, Temple University, corresponding author) |
| Year | 2019 |
| Volume | 40 |
| Issue | 1 |
| Pages | 133-148 |
| Publication date | 2019-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Distance Education (JOURNAL) |
| Journal identifiers | ISSN: 0158-7919 • E-ISSN: 1475-0198 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/01587919.2018.1553562 |
| OpenAlex | W2903274144 |
| Language | EN |
| Citations received | 92 |
| References cited | 27 |
Student satisfaction is used as one of the key elements to evaluate online courses, while perceived learning is considered as an indicator of learning. This study aimed to explore how online learning self-efficacy (OLSE), learner–content interaction (LCI), learner–instructor interaction (LII), and learner–learner interaction (LLI) can predict student satisfaction and perceived learning. A total of 167 students participated in this study. Regression results revealed that the overall model with all four predictor variables (OLSE, LCI, LII, and LLI) was significantly predictive of satisfaction and perceived learning. The study found that LCI was the strongest and most significant predictor of student satisfaction, while OLSE was the strongest and most significant predictor of perceived learning. However, LLI was not predictive of student satisfaction and perceived learning. This study suggests that instructors employ strategies that enhance students’ OLSE, LCI, and LII. Research is needed to understand how LLI fosters student learning and satisfaction.
Distance education · Educational technology · Machine learning · Mathematics education · Multimedia · Online learning · Regression analysis · Applied Psychology · Communication in Education and Healthcare · Computer Science · Innovative Teaching and Learning Methods · Online and Blended Learning · Psychology
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| Unique citing works | 92 |
|---|---|
| Citations per year | 15,33 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 78 |