Task-Based Approach to Learning Risk Management on University Business Administration
Bibliographic record
Abstract
Introduction: Lack of accountability demands in university business administration may be as a result of less attention given to genuine learning approach in university education. This study was undertaken to assess the causal effects of task-based approach to learning risk management on effective business decision making and outcome appraisal of universities in Nigeria. Method: The study was a true experimental design study aimed at Knowledge and Learning Management (KALM) in universities for enforcing business accountability. The population included 250 undergraduate final year students of educational management in public universities in Cross River State, Nigeria for 2017/2018 session. The sample consisted of 120 subjects purposely selected. A researcher constructed instrument was validated and had a reliability coefficient of 0.88 obtained using cronbach’s alpha method. Three research questions and one hypothesis guided the study. Data was analysed using mean, percentage and independent t-test analysis. Results: The results showed that task-based approach produced improvement in knowledge after learning risk management. Male and female students learned risk management in the same way for university business decision making and outcome evaluation. Discussion and conclusion: Based on the result above, task-based approach to learning risk management guaranteed development of knowledge and cognitive skills of university students in university business opportunities. The implication of this finding was that students’ learning motivation and task sharing towards knowledge retention in university business outcome could not have been achieved unless something urgent was done to address the issue in a timely manner. Therefore, university management should be able to pay particular attention to genuine learning of task processes and task strategies in risk management. More so, university teachers should be aware of the benefits of integrating learning stages of pre-task, task cycle and post task in enhancing quality decision making choices in university business offers.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".