A Cognitive Style Perspective to Handheld Devices: Customization vs. Personalization
Bibliographic record
Abstract
Handheld devices are widely applied to support open and distributed learning, where students are diverse. On the other hand, customization and personalization can be applied to accommodate students’ diversities. However, paucity of research compares the effects of customization and personalization in the context of handheld devices. To this end, we developed a customized digital learning system (CDLS) and personalized digital learning system (PDLS), which were implemented on the handheld devices and tailored to the needs of students with diverse cognitive styles. Furthermore, we conducted two empirical studies to examine the effects of cognitive styles on the use of the CDLS and PDLS. More specifically, Study 1 identified the preferences of each cognitive style group, which were employed to develop the PDLS in Study 2, which investigated how students with different cognitive styles react to the CDLS and the PDLS. The results from these two studies showed that student in the CDLS and those in the PDLS obtained similar task scores and post-test scores. However, Serialists with the PDLS could more efficiently complete the tasks than those with CDLS. Additionally, Holists more positively perceived the PDLS than Serialists.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".