Brick and Mortar Education vs. SCORM-based Education in Computer-programming Courses: A Comparative Study
Bibliographic record
Abstract
Online education has positively influences student performance during universities lockdown nowadays due to COVID-19, in fact both educators and students have proven their ability to develop their teaching skills by emerging several technological tools. This article analyses the performance of two cohorts of students, the first cohort was taught traditionally while the other was taught online, the scope of this study is the students enrolled in programming languages at the Faculty of Computer Science and Information Technology at Jerash University, the study was carried out between the years 2017 - 2020. 1210 students have participated in the study. This study investigates a comparative study between different methods of delivering programming-languages courses over the 3-year period, the study also aims to shed light on the impact of traditional methods on delivering computer-programming courses and how it could be improved by emerging a SCORM learning multimedia and other learning modules, activities and resources. Result shows that online delivering of courses with the use of SCORM and other tools improves students’ scores and performance slightly, the article concludes that emerging technology to learning can improve the students' creativity, understanding and performance overall.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".