A comparison of three delivery systems for teaching an information technology course
Bibliographic record
Abstract
An important aspect of the global, knowledge-based, technology-enabled economy is that organizations must invest in continuous training [4]. Managers, now exposed to concepts such as knowledge economy, organizational intelligence, learning organization, knowledge era, and organizational learning, [2, 3, 12], need to establish the optimum mix of continuous training activities to leverage organizational knowledge to its fullest potential. Several methods and models aimed at managing organizational knowledge at a macro level are now available [2, 3, 5, 6, 8, 9, 11]. But these methods don't address the micro level problem of planning and selecting continuous training activities while taking into account manager and employee preferences, and constraints such as budget or deliverables. The analysis of empirical data presented in this article incorporated factors, criteria, and weights into a model named Econof. Our findings provide managers with valuable tools to implement a continuous training management process aligned to organizational strategy---a step toward the development of knowledge management strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".