ICT FOR ACADEMIC RECORDS MANAGEMENTIN COLLEGES OF EDUCATION IN KWARA STATE, NIGERIA
Bibliographic record
Abstract
Information and communication technologies have been described as ubiquitous and its application in every aspect of organisational endeavour cannot be underestimated. This prompted the research question of how do tertiary institutions use ICTs in the process of record management of students. Descriptive survey design method was adopted sampling one-quarter of the entire population of study. The questionnaire pre-test result yielded an overall reliability coefficient of α = 0.84 above 60% coefficient of determination. The total population was 695, while total sample was 174 with 134 returned questionnaire copies found useful making 74.7 % of the total sample. Research questions and hypotheses were analysed using descriptive and inferential statistics. Hypotheses were tested at 0.05 level of significant. The findings showed that there was a high level of availability of ICTs resources and a great impact of use of ICT on students’ academic records management. Also, ICTs was found to be highly beneficial when used for students’ academic records management and there are high challenges identified. The study therefore recommended that ICT infrastructures like cloud storage and other relevant software should be enhanced to improve record management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".