Enhancing IT Graduates’ Employability Skills for Surviving in New Normal: Perspective of IT Professionals
Bibliographic record
Abstract
This research focused on how Information Technology (IT) Higher Education Institutes (HEIs) contribute to equip IT graduate employability, from the perspective of IT industry experts in Sri Lanka. The research further identifies the mismatches between IT HEIs’ contributions to equip IT graduate employability and the industry demands after COVID 19 in Sri Lanka. Thirteen semi structured interviews were conducted via zoom platform with the industry experts representing major IT companies in Sri Lanka. The interview protocol was designed to get the expert opinion about the contribution which HEIs can make to equip future graduates with necessary employability skills in general and specifically in the new normal. According to key findings of the study, providing opportunities for industrial experience, sharing technical and practical knowledge, up-to-date syllabus, firm foundation of knowledge, trainings on online platforms and tools, developing soft skills and remote working skills of graduates such as trustworthiness, minimum supervision, stress management, self-driven, attitude, team spirit, ability to take challenges and responsibilities were identified as the areas in which HEIs could contribute towards creating smart IT graduates with a digital presence ready to be employed in new normal. The outcome of this study will strengthen HEIs with necessary requirements to upgrade their IT education quality and quantity wise as then graduates can meet industry expectations for better employability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".