Encouraging students' experience-based learning through their studies in an e-learning environment: Two Schematic Storyboards
Bibliographic record
Abstract
This paper addresses the importance of students' experience-based learning in virtual settings of higher education and some practical approaches for fostering their experiential learning facilitated by e-learning platforms. These practical approaches try to extend students' learning beyond the theoretical disciplinary focus inside the universities and engage them in acquiring knowledge and more importantly skills they will need in their professional life. To do so, two schematic storyboards have been drawn based on the experiences of some Iranian experts including students, entrepreneurs and academics represented through some focus groups. The titles of these storyboards are: “Business Plan Writing”, and “Tentative start-up of a business”. Our primary focus is on defining some technical and pedagogical capabilities in an e-learning platform based on the three groups of experts' experiences as well as principals of experience-based learning, enabling both students and tutors to use them for creating a more practical, real and effective teaching-learning environment. Before drawing each storyboard four descriptive components named Functional Specifications (FS) have been written, trying for defining different aspects of the storyboards. These components have been generated based on the participants' experiences and then supports from the literature has been extracted and added to these FSs. While the paper is written from an e-learning perspective, the issues and processes raised are applicable to any higher education system that seeks to value and reward practical and experience-based education.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".