What If It’s All an Illusion? To What Extent Can We Rely on Self-Reported Data in Open, Online, and Distance Education Systems?
Bibliographic record
Abstract
Online surveys are widely used in social science research as well as in empirical studies of open, online, and distance education. However, students’ responses are likely to be at odds with their actual behavior. In this context, we examined the discrepancies between self-reported use and actual use (i.e., learning analytics data) among 20,646 students in an open, online, and distance education system. The ratio of consistent responses to each of the 11 questions ranged from 43% to 70%, and the actual access to learning resources was significantly lower than self-reported use. In other words, students over-reported their use of learning resources. Females were more likely to be consistent in their responses. Frequency of visits to the open, online, and distance education system, grade point average, self-reported satisfaction, and age were positively correlated with consistency; students’ current semester was negatively correlated with consistency. Although consistency was not maintained between actual use and self-reported use, consistency was maintained between some of the self-report questionnaires (i.e., use vs. satisfaction). The findings suggested that system and performance data should be considered in addition to self-reported data in order to draw more robust conclusions about the accountability of open, online, and distance education systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".