L3OP: Learning Styles Application Using Learning Objects Approach
Bibliographic record
Abstract
Learning Objects is not a new approach in electronic learning system. It has been applied since more than seven years ago. It can be clearly seen in CanCore project [9] in Canada and the emerging of several standards of Learning Objects by well-known organizations such as IEEE. L3OP (Learning Objects Technology in Object-Oriented Programming ELearning System) project applies the Learning Objects concept into electronic learning system focusing on digitalizing learning styles (especially among higher education students). The electronic learning systems have been used by these students since past few generations and many of them have been enhanced. However, some students still feel that the electronic learning systems are not as effective as they may seem. The reason is varieties of traditional methods of learning styles used by these students, for example, jotting down and highlighting notes cannot be done in the electronic learning systems. For example, some students prefer to use highlight pen or marker to underline the important notes. However, the electronic learning systems can not digitize the styles. The project is only concentrated on visual type of learners. The Learning Objects approach used in this project not only to ensure the organization of the database but also the reusability of the learning content itself.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".