K3 – an e-Learning Forum with Elaborated Discourse Functions for Collaborative Knowledge Management
Bibliographic record
Abstract
Abstract: The e-learning platform K3 realizes a constructivist learning model augmented with collaborative properties. K3 courses, mainly offered since 2004 at the University of Konstanz, follow the blended learning model. K3 collaborative discourse work is organized in virtual groups. All group members have to choose a role (moderator, summarizer, etc.) for a certain period and their role performance is part of their evaluation. Discourse takes place in an electronic (asynchronous) forum. Each contribution/comment must be specified according to its discourse function. These specifications structure discourse and allow selective retrieval of discourse objects. Students are encouraged to augment their contributions informationally by reference objects. A graphic interface facilitates navigation through complex discourse structures and makes them transparent. The technical basis of K3 is an open source, objectoriented client-server system for the management of the different types of K3 data. 1 Background of e-Learning in Higher Education in Europe In Europe, with some delay compared to earlier developments in the USA, Canada and in other countries, the importance of e-learning for quality and efficiency in higher education is no longer disputed. The political background in Europe for a greater awareness of the value of e-learning for higher education in general is the so
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".