Investigating African 'Digital-Immigrant' Students' Reactions to Moodle Resources
Bibliographic record
Abstract
In this study, we investigated the reactions and perceptions of ‘digital immigrant’ students to the adoption of blended learning combining the Moodle VLE and traditional face-to-face instructional delivery method on EAP courses in a Nigerian university of technology. Data sets from extractable online logs for activities, discussion board interaction and two online surveys are triangulated by focus group discussion responses. The data revealed that students’ use of the online components of the courses are high and perceptions of the various values such as relevance, reflective thinking, interactivity, tutor support, interpretation, learning experience and benefit are very positive, and are in the range of 60s to 90s in percentage points. However, peer to peer interaction while positive is not as high, indicating the additional work that need be done in addition to the challenges of infrastructure and cost that students would want addressed. Implications of the findings include the potentials of blended learning in difficult academic contexts and subject areas, the relevance of social interaction platforms in language learning and other subject areas, and the crucial role technology can play in large class contexts. Key words: Digital immigrant; Moodle; Blended learning; Interaction; Critical thinking; Learner autonomy
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".