Determinants of university students' satisfaction with information technology based classroom use by pandemic Covid19
Bibliographic record
Abstract
Educational activities have changed due to the pandemic and it was necessary to respond by giving continuity to the formative processes with virtual platforms as it happened at the Universidad Nacional del Centro del Perú - School of Social Work. The objective was to know the level of satisfaction of students in the use of the virtual platform chosen, using the Questionnaire for the evaluation of learning environments in distance education (Sp-DELES) of 42 questions with dimensions: teacher support, student interaction and collaboration, personal relevance, real learning, active learning, autonomy and satisfaction. A total of 213 students were consulted, the instrument was distributed digitally and anonymously. For the descriptive analysis, the results were ranked in three response categories: agree, indifferent and disagree; the Autonomy dimension was valued with 69.01% agreement and 9.39% disagreement. The dimension with the lowest appreciation was Satisfaction with 25.35% agreement and 28.64% disagreement. Structural equation modeling was performed with SmartPLS, obtaining that Faculty support (FS) and Active learning (AL) have a direct relationship with Satisfaction (S) in the use of the virtual platform with beta values (β) of 0.279 and 0.872 respectively. Contrarily, Real learning (RL) and Autonomy (A) have an inverse relationship with Satisfaction (S) with beta values (β) of -0.025 and -0.122 respectively. The study reflects that there is no relationship between Interaction and Communication among Students (ICS), Personal Relevance (PR) and Satisfaction (S) as the p-values were greater than 0.05. The value of the coefficient of determination (R2) was 0.778 being the representative model. The study is non-experimental, descriptive, explicative and cross-sectional.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".