Information Technology Tools Analysis in Quantitative Courses of IT-Management (Case Study: M.Sc. - Tehran University)
Bibliographic record
Abstract
The purpose of this study was to determine the most suitable ICT-based education and define the most suitable e-content creation tools for quantitative courses in the IT-management Masters program. ICT-based tools and technologies are divided in to three categories: the creation of e-content, the offering of e-content, and access to e-content. In this study the first two categories are considered for on-campus education and virtual education (both synchronous and asynchronous).In the comparisons, eight modes of delivery styles were verified using two methods; first they were compared two by two in an ordinal questionnaire measured by an Eigenvector technique. Next they were compared by a single-weighted method. The results were then agreed upon by experts using a personal approach in group decision making. The most effective ICT-based education was defined as on-campus education and the Collaborative Learning Environment with Virtual Reality (CLE-VR) received the highest level for virtual education because it highlighted the social presence, and then synchronous and asynchronous virtual education. Slides of Microsoft PowerPoint ranked in the upper-level for smart boards for on-campus education but they ranked in the same level for virtual education.E-content creation tools were measured by an interval questionnaire using a semi-metric scale of [0-100] by the single-weighted method. The results of second section found that the most suitable tool for creating e-content are Microsoft Office PowerPoint. Questionnaire analysis revealed a preference for social interaction in studying quantitative courses, creating, and customizing e-content as soon as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".