Evaluation of Virtual Objects: Contributions for the Learning Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p class="3">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 &amp; Nesbit (2007), in order to learn how to better take profit of efforts and resources.</p>
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.
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 | 0.053 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it