UNDERSTANDING THE CONTEXTUAL ROLE THAT MODALITIES PLAY IN JUST-IN-TIME MOBILE LEARNING WHILE CARRYING OUT MECHANICAL TASKS
Bibliographic record
Abstract
Paper-based user manuals that provide assembly and disassembly instructions often do so with a combination of diagrams supported with textual information that clarifies how to perform the tasks. Mobile devices are emerging as a multimedia platform for providing on-demand training due to their portability. Mobile devices have limited screen size; as a result, the text instructions associated with the diagrams can produce clutter and occlusion on the screen. Also, too much information if fed through a single sensory channel (visual) may result in excessive cognitive load on the working memory of the human brain, thus hindering the learning process. In this work, two user studies were conducted to investigate the tradeoffs of using text, voice, and a combination of both modalities on the learning experience in a just-in-time mobile learning scenario. In such a scenario end-users are managing two very visual tasks at the same time; i.e., the primary task of carrying out the assembly/disassembly job and the secondary task of learning how to perform the task.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".