How do students want to learn in online distance education? Profiling student preferences
Bibliographic record
Abstract
How do (potential) students differ in their preferences for the organization of online and distance courses and programs, can these differences be grouped into preference profiles, and are there any associations between these profiles and variables, such as achievement and dropout, that are relevant for the promotion and design of online and distance teaching? In this study, three groups (enrolled students, N=1939; prospective students, N=296, people in the target group of the course or program, N=255) completed a survey consisting of 28 items with which to identify their preferences. Various significant differences in preferences between the groups were found in the item scores. Exploratory factor analysis resulted in five meaningful factors that were used to create 32 preference profiles that are identified by the dichotomized scores on the factors. In this way, the profiles conserve their dimensional relationship instead of presenting profiles as distinct types. The factors in which student preferences differ are: collaboration (group work versus self-study), pacing (fixed time schedule versus flexibility in time and tempo), the degree to which the study has a practical orientation, the degree of proactive (versus reactive) teaching and a preference for indepth learning versus superficial learning. Significant associations have been found between preference profiles and the discipline in which the student group studies, the type of program (e.g., bachelor, master), and the number of study points obtained in the last year per discipline. The results indicate that the enrolled students are more aligned to the characteristics of the teaching-learning process than the other two groups.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".