Bibliographic record
Abstract
iPad technology emerged in the Spring of 2010. A tablet mobile technology, the iPad has generated considerable interest in higher education for teaching and learning. Any technology should be incorporated in teaching and learning because of its suitability to the learning event and context. Research on the use of iPads is needed to determine the success factors, potential pitfalls, and benefits. Students and instructor in a fourth year English directing class used iPads for course-related activities for one semester. Results of this case study's video interview with the instructor, and co-investigator, are presented in this poster. The instructor, Yorick, discusses the benefits of the iPad and apps chosen, challenges encountered and how these were dealt with, and provides recommendations for others preparing to implement iPad technology in their networked learning course in higher education. The iPad increased communication, through sharing and collaboration, it was concise, portable, and made resources affordable and more accessible. Three terms are attributed to the iPad: organization, efficiency, and enthusiasm.
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.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.122 | 0.037 |
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".