The Use of ICT in Public and Private Institutions of Higher Learning, Malaysia
Bibliographic record
Abstract
This study examines the extent of ICT utilization among the members of Faculty A of four public higher learning institutions (IPTA) and seven private higher learning institutions (IPTS) in Northern Malaysia. Its focus is on a) to investigate the extent of ICT resources provided by universities authorities, b) focus on types and extent of ICT usage in daily activities, c) to explore the ICT proficiencies level and d) to investigate the level of ICT integration in teaching activities. A total of 76 responses out of 77 from IPTA and only 105 out of 108 responses of IPTS are usable for further analysis in this study. Findings indicate that in the IPTA, though the facilities provided are not as plenty as in IPTS, the level of usage is quite encouraging. While in the IPTS, the levels of ICT usage among the educators are still not satisfactorily. Results also indicated that usage frequencies are more prone on informative in nature, besides integrating computer technology. Furthermore, the study also indicates that there were considerable differences in the use of ICT by educators in their perceived proficiencies and integrating computer technology. This study could be improved by expanding the total sampling population to all faculties in both universities. Methods of analysis could also be varied beyond the descriptive analysis done. Factors that could hinder the level of ICT usage by the educators could also be studied.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".