Does Context Matter? Investigating Factors Related to Students’ Academic Achievement in Classroom and Online Courses
Bibliographic record
Abstract
The aim of this thesis is to broaden the understanding of the relationships between target psychological constructs (i.e., self-regulation, motivation, self-theory of intelligence) and grades in a classroom setting as well as whether those target constructs are predictive of online course grades.We do so by collecting information from a large sample of undergraduate university students enrolled in either a traditional classroom or an online course and by analyzing the data using hierarchical regression analysis.In the present thesis, we demonstrate the strength of self-regulation and motivational constructs to positively influence classroom grades and present evidence that the constructs accounting for a significant amount of variance in classroom course grades are not applicable for explaining variance in online course grades.We discuss the implication of our findings as well as study limitations and point out potential direction for future work.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".