Assessment of ICT Skills Relevant for Effective Learning Possessed by Undergraduate Students at University of Nigeria
Bibliographic record
Abstract
This study examined the information and communication technology (ICT) skills relevant for effective learning possessed by undergraduate students. The study made use of descriptive survey research design. The study participants were 320 undergraduate students of university of Nigeria. The instrument used for data collection was “Relevant ICT Skills for Effective Learning among Undergraduate Students Questionnaire” (RISELUSQ). The reliability of the instrument was determined using Cronbach-Alpha method and a reliability coefficient of 0.84 was obtained. Four research questions and two hypotheses guided the study. The data collected were subjected to analysis, the mean and standard deviation were used to answer the research questions while the hypotheses were tested using t-test and ANOVA at 0.05 level of significance. The findings revealed that undergraduate students possessed the relevant ICT skills that will enhance their learning and that the ICT skills of the male students were not different from that of the female. The findings also showed that the ICT skills of undergraduate students differ based on their educational level, particularly, between the 100 level and 500 levels. Equally, the finding reveals that the students face some ICT challenges like non-functional/limited projectors in classrooms, limited e-learning facilities among others. Based on the findings, it was recommended that students be encouraged to continue to improve on their ICT skills as it will enable them strive well academically and that school authorities should ensure adequate and proper infrastructures/ICT facilities be put in place within the university environment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".