Learning needs analysis of collaborative e-classes in semi-formal settings: The REVIT example
Bibliographic record
Abstract
Analysis, the first phase of the typical instructional design process, is often downplayed. This paper focuses on the analysis concerning a series of e-courses for collaborative adult education in semi-formal settings by reporting and generalizing results from the REVIT project. REVIT, an EU-funded research project, offered custom e-courses to learners in several remote European areas and received a ‘best practice’ distinction in social inclusion. These e-courses were designed and developed for the purpose of providing training in aspects of the learners’ professional domains related to the utilization of information and communication technologies. The main challenge was to prove that it is possible and economically feasible to provide meaningful training opportunities via distance education, by utilizing existing infrastructure (“revitalizing schools”) and by making use of modern digital technology affordances coupled with suitable distance learning techniques and Web 2.0 tools. ADDIE, the generic instructional systems design model, enhanced with a rapid prototyping phase, was put forth in order to allow stakeholders to interact with a prototypical e-course, which served as an introductory lesson and as a reference point, since its evaluation informed the design choices of all subsequent e-courses. The learning needs approach adopted in REVIT combined learner analysis, context analysis, and needs analysis into a coherent analysis framework in which several methods (observation, estimation, document analysis, survey, and dialogue) were exploited. Putting emphasis on the analysis phase and decoupling the design from the delivery of the e-courses facilitated adaptation and localization. Adaptation and localization issues concerning the adoption of the REVIT distance learning framework, taking into account the socio-cultural and pedagogical context, are discussed. A central result reported is that the analysis phase was crucial for the success of the whole endeavour and that carrying it out properly is not straightforward or easy. The analysis framework presented in this paper could be useful in other similar e-learning situations whose “educational-identity” also involves distance and adult learning in a semi-formal setting.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".