Use of Technology Enhanced Education to Improve Teaching and Learning Process
Bibliographic record
Abstract
Abstract—The modern world continues to change as Information Technology (IT) related applications proceed to evolve at a rapid pace. The need for IT continues to grow as we begin to meaningfully engage its products in both business and the academic settings. The increased need for information security, networking and database expertise after 9/11 followed by the rebound of the US economy, has resulted in the upswing of IT employment since the first quarter of 2005. Starting 2005, highly skilled IT workers are commanding ever-higher wages as the IT employment scene shifts to a more narrowly focused marketplace. According to the Yoh Index of Technology Wages reported on February 6 2006, the IT wages increased by 3.1 percent overall in the 4th quarter of 2005 over the like quarter in the previous year. However, all of the above positive social and economic reports are not yet reflected in the ongoing decrease in enrollment of IT graduate and undergraduate college students. In this paper, we present our work on developing universal accessible content for the IT minor courses. We also discuss the effective teaching and learning methods for this generation along with planning for such challenges from an institutions perspective. Index Terms—Educational technology, learning systems. I.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".