Exploring students’ learning experience in online education: analysis and improvement proposals based on the case of a Spanish open learning university
Bibliographic record
Abstract
Not surprisingly, the number of online universities continues to expand-especially in Covid-19 times. These institutions all offer "online education" with diverse institutional, technological, and pedagogical processes. However, a fundamental element has to do with the experience of the students, and how they adapt to the educational model of the online university in which they are studying. In this article, we present the main results of the case-study developed in one of the most historical and relevant virtual universities in an international context. We have explored and analysed the process of adaptation to the educational model by the student body, and their perceptions of their interactions with the pedagogical, institutional, and technological elements designed to support their learning. Qualitative and quantitative methods are used to gather and analyse the data. From 1715 students who participated in the survey and the perceptions of 30 students individually interviewed, the results show positive evaluations regarding the integration and adoption of technological competencies, and also, that the online education generally serves as a responsive model to the emergent needs of the learner. However, the results also show that students have important concerns regarding the pedagogical and institutional support provided.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".