Bibliographic record
Abstract
Mobile technology use is a major issue in higher education institutions, and one that is increasing daily. While the new generation of students (the “digital natives”) move across programs and courses, their learning expectations have started to emerge. It is with these expectations and needs in mind that educators around the world are recognizing the advantages of using mobile technologies to engage with students and make learning a more collaborative, interactive activity that can be engaged in at anytime, anywhere. Using a case study approach, this chapter explores the challenges of transforming static curricula into a mobile experience, and the ways in which these challenges were overcome within a polytechnic institution where hands-on learning takes place inside the classroom or the lab. In addition to presenting a literature review on the use of mobile technologies for teaching and learning, and an analysis of the relevance of connectivism theory to analyze students learning in the digital age, this chapter also includes an analysis of student surveys and interviews, as well as further opportunities for research.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".