Personalising Learning in Maltese - Exploring International Students’ Expectations Towards Ġabra Online Lexicon
Bibliographic record
Abstract
The rapid incorporation of digital technologies in formal education has led to significant transformations, albeit not always yielding the anticipated productivity. This study investigates the attitudinal changes towards an online Maltese lexicon, Ġabra, among 46 adult learners of Maltese as a foreign language. By utilising a composite analytical model, the study is grounded in the theoretical frameworks of Technological Frames of Reference, the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT). These frameworks guided the tracking of the development of respondents’ attitudes over a five-month research period. The survey results gathered at the onset and conclusion of the study highlighted a perceptual discrepancy between the initial expectations towards an e-dictionary and the actual usage experience of the Ġabra platform. As the students gained more experience with the platform, their attitudes towards Ġabra underwent a shift. Despite some initial expectations not being fulfilled, the majority of students ultimately found Ġabra to be a beneficial tool for learning Maltese. The students’ feedback about the Ġabra online dictionary can potentially pave the way for a more personalised learning experience of the Maltese language.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".