E-Learning Challenges for Polytechnic Institutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it