Educational Technology Undergraduates’ Performance in a Distance Learning Course Using Three Courseware Formats
Bibliographic record
Abstract
Most educators’ inability to provide learning contents that suit different learning styles has caused a lot of problems in terms of performance. Thus, to cater to students’ preferences in terms of access to learning contents, the distance learning regulatory body in Nigeria emphasized that course materials should be developed in mixed-media formats. This study was carried out to compare the effects of printed, video, and Moodle-based courseware on educational technology students’ achievement, retention, and satisfaction in a distance learning course. A quasi-experimental design was employed for the study involving 108 participants from three experimental groups. The learning content and instruments, subjected to validation and reliability tests, where values of 0.78 and 0.86 were obtained using the Pearson product moment correlation and Cronbach’s alpha for achievement and satisfaction inventory, respectively, were administered within a four-week period. Data collected were analyzed using descriptive and inferential statistics. Findings indicated that the printed, video, and Moodle-based courseware formats improved students’ achievement with mean gain scores of 47.92, 40.89, and 43.03, respectively. A significant difference was observed in the achievement (F (2,104) = 8.67, p < 0.05), retention (F (2,104) = 29.406, p < 0.05), and satisfaction scores (F (2,104) = 5.662, p < 0.05) of the three groups. Open and distance learning administrators in Nigeria are recommended to produce and deploy printed, video, and Moodle-based formats of courseware to meet different students’ learning preferences.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".