Computer Programming Competencies Required by Computer Education Graduates for Sustainable Employment
Bibliographic record
Abstract
This study identified the computer programming competencies required of computer education graduates for sustainable employment in Enugu metropolis, Enugu state, Nigeria. Three research questions and one null hypothesis guided the study. The study adopted descriptive survey research design. The population for the study was 95, which comprised of 74 computing lecturers, 6 IT programming instructors, and 15 programmers. A structured questionnaire was used for data collection. The instrument was face validated by three experts in computer programming. Cronbach Alpha statistic was used to determine the internal consistency of the instrument, yielding reliability co-efficient of 0.83. Mean and Standard deviation were used to answer the research questions while ANOVA statistic was used to test the hypothesis. The study found out that 25 hard competencies, 18 business competencies, and 19 soft competencies are required by computer education graduates for sustainable employment in programming jobs. These competencies identified include among others, ability to code, test and debug programs quickly and efficiently; ability to explore and evaluate application design considerations for multiple technologies, ability to analyze users’ needs and specifications then design, test, and develop software to meet those needs, ability to recommend software upgrades for clients’ existing programs and systems, proficiency in data mining, confidence in personal ideas but open to feedbacks, adapting to changes while remaining focused on project with topmost priority and good sense of judgment. It was therefore recommended that the identified competencies should be incorporated in the curriculum for training Computer Education graduates for sustainability in programming jobs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".