Self-Reported Perceptions of Online Learning and Observed Online Learning Behaviors: To What Extent Are They Consistent?
Bibliographic record
Abstract
This study investigates to what extent there is an association between students’ self-reported perceptions of online learning and observed online learning behaviors recorded by the learning analytic data. The participants were 319 undergraduates studying an engineering course in an Australian university. Data analyses were conducted using cluster analyses, Hidden Markov Model, one-way ANOVAs, and a cross-tabulation. The relations between students’ self-reported perceptions and their academic learning outcome show that those with positive perceptions tended to have higher scores. The relations between observational online learning behaviors and their academic learning outcome demonstrate that students with most learning sessions achieved more highly. The cross-tabulation finds a significant association between the cluster membership generated by by the self-reported perceptions and observational online learning behaviors. Amongst students who had most study sessions characterized by high percentages of reading and formative states and low percentage of summative states, the proportion of those with positive perceptions (40.2%) was significantly higher than those with negative perceptions (20.0%). Of students who had the least study sessions represented by moderate reading and summative states, and low formative states, the proportion of students with positive perceptions (3.0%) was significantly lower than the proportion of students having negative perceptions (8.7%).
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".