Academic success online: The mediating role of self-efficacy on personality and academic performance
Bibliographic record
Abstract
Academic success in any context is dependent upon a student's belief in their ability to succeed. While learning online, a students’ self-efficacy is affected by their confidence in their ability to interact within the online environment. With the proliferation of personalized learning and the growth of Massive Open Online Courses, this growing trend is a shift in focus from the centralized brick-and-mortar locus of control, to one of enabling student choice and agency for how, when, and where they learn. In the pre-pandemic setting, this research study examined the personality types of students enrolled in eight sections of four online courses in educational technology, and the role self-efficacy for learning online played in their academic performance. Key findings reveal that personality affects learners’ academic achievement is moderately significant, self-efficacy for online learning affects learners’ academic achievement in a small but significant way, and student conscientiousness and academic performance were significantly and fully mediated by self-efficacy for learning online while controlling for gender and English language proficiency. There were no mediation effects with the other personality traits. A discussion around learning design strategies is provided. The authors recommend that institutions adopt more flexible learning options for teaching and learning that include both online and blended learning options that provide student’s choice and agency over the learning experience but also enable the institution to be better equipped for what the uncertain future of education holds.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".