19. Progettare una KBC nei corsi universitari online
Bibliographic record
Abstract
In recent years, university online courses progressively increased. Different training approaches and theoretical models have been developed: in particular the focus is on the socio-cultural constructivist approach, that considers knowledge as something dynamic, developed within a community, and on a theoretical model, called Knowledge Building Community (Bereiter, 2002; Scardamalia, 2002, 2003). The need arises, just to meet this new demand, that teachers and tutors increasingly broaden the ability to design courses online. This is indeed a complex task, as it must be able to integrate into a unified framework needs and expectations of participants and the available resources. This contribution will consider the design of an online course starting from a practical experience held at the University of Valle d’Aosta. Therefore different stages and different operations will be described to design the course: setting goals, definition of units and material preparation, organization of the database and of the “views” in the Knowledge Forum, preparation of the “face to face meetings”, definition of the students’ roles and of metacognitive reflection. Such moments lead to the definition of a precise and detailed training agreement between teachers, tutors and students that will enable them to form a research community that builds knowledge, starting from a problem related to the issues under study and initiating a process of investigation.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.213 |
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".