A Typology and Hierarchical Framework of Technology Use in Digital Natives' Learning.
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
The technological capability of digital natives is thought to have considerable implications on the way they communicate, socialize, think and learn. Some researchers have even suggested that fundamental changes to the educational system are required to cater for the needs of this new cohort of learner, although such claims have little empirical support. In this study, we adopt a structural approach to the investigation of the digital natives’ motivations for using technologies in learning. Based on in-depth interviews with 16 digital natives, a cluster analysis was used to segment respondents into two distinct groups: independent learners and traditional learners. Interpretive Structural Modelling (ISM) was used to develop a hierarchical structural model of technology use motivations for each group. The results show that these two groups are driven to achieve the same learning goals by different paths. Implications are drawn for both educators and managers from both research and practical perspectives.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it