Evaluation of Virtual Objects: Contributions for the Learning Process
Bibliographic record
Abstract
The constant technological development in education, and the potentiality of the resources offered by Information and Communication Technologies (ICTs), are challenges faced by teaching institutions in Brazil, especially by those institutions, which by the very nature of their services intend to provide distance education courses. In such a scene, one sees the use of technology as a tool to give support and to take part in the process of teaching activities, such as the Virtual Learning Objects (VLOs), which offer an opportunity to contribute to the teaching and learning process. Considering this, the present work aims at analyzing the VLOs used in the distance education courses of Economic Sciences and of Accounting at the Universidade Federal de Santa Catarina (Federal University of Santa Catarina), under the quality criteria indicated by the Learning Object Review Instrument (LORI) methodology proposed by Nesbit, Belfer, and Vargo (2002), Nesbit, Belfer, and Leacock (2004), and Leacock & Nesbit (2007), in order to learn how to better take profit of efforts and resources.
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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 | 0.067 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".