Using Experiential Learning to Improve Student Attitude and Learning Quality in Software Engineering Education
Bibliographic record
Abstract
Experiential learning (EL) has great potential to transform students’ learning experience. Few studies, however, have focused on the use of EL in computer science education. The purpose of this study was to examine students' experiences with EL in computer science. Data were collected to examine the influence of EL on students' attitudes and quality of learning. The antecedent variables included student involvement, learning expectancy, instructor impact, course structure, and prior experience. PLS-SEM with PLSc was used to test generated hypotheses. The findings indicated that student involvement positively correlated with attitudes and learning expectancy. Instructor impact is positively associated with student involvement, quality of learning, and attitudes. Prior experience positively correlated with learning expectancy. Finally, course structure positively moderated the relationship between student involvement and learning expectancy. It is concluded that EL is a promising pedagogy to improve student attitudes and quality of learning in software engineering education.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".