Using Smartphone Apps for Learning in a Major Korean University
Bibliographic record
Abstract
Are students from one of the high tech universities in the world fully aware of their permanent linkage to the global learning network, the World Wide Web? In their pockets, backpacks, and purses are the latest smartphones loaded will countless apps. But, how aware are these students of the use they put them to as tools for learning? One class at Seoul National University undertook a study of this question as part of its collective learning class in lifelong learning. Both the process of the class and the outcomes of the research reveal much of how the practices of learning are changing in a dynamic, globally-linked university. Forty graduate students in engineering and education were interviewed about how they use smartphone apps for learning and which apps they consider useful for learning. Their answers are reported and a comparison is made between the students in the two disciplines. The surprising outcome of our research is that the definition of learning is in transition. Learning moves from learning in a classroom towards learning within a communication-technology-based network of students, professors, and information.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".