Profiles of Online Students and the Impact of Their University Experience
Bibliographic record
Abstract
In recent decades, there has been a steady growth in the population who enter higher education in both brick-and-mortar and, in particular, online universities. This has led to an increase in heterogeneous student profiles in a relatively short period of time. The purpose of this paper was to explore the student profiles at a university that gives all its courses online, namely the Universitat Oberta de Catalunya (UOC), and analyse students’ perceptions of their university experience. With this goal in mind, we constructed a student typology based on their social conditions and backgrounds using multiple correspondence analysis. Subsequently, an analysis of variance (Kruskall-Wallis test) was run to detect whether there were any differences in students’ perceptions of the impact of their university experience (N = 1850). Although the prevailing profile of students in the online university continues to reflect students with responsibilities outside of the university (e.g., work and/or family), new profiles have been observed, made up of younger students without any work or family responsibilities. In turn, younger students’ distinct perceptions of their university experience has been observed, depending on student profiles, with older students having more intrinsic perceptions, focused on learning and the acquisition of theoretical knowledge.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".