Problem Based Learning: A Facilitator of Computational Thinking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
These proceedings represent the work of contributors to 18th European Conference on e-Learning (ECEL 2019), hosted by Aalborg University, Copenhagen, Denmark on 7-8 November 2019. The Conference Co-Chairs are Rikke Ørngreen, Mie Buhl and Bente Meyer, and, all from Aalborg University, Copenhagen, Denmark. ECEL is now a well-established event on the academic research calendar and now in its 18th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research. The opening keynote presentation is given by Anthony “Skip” Baisel, from the Queen Mary University of London on the topic of Higher Education Pedagogy using Game Design. The second day of the conference will open with interactive collaborative keynote by Mie Buhl, Bente Meyer, Rikke Ørngreen, on the topic of Does IT work? Investigating factors at play in e-learning research. With an initial submission of 181 abstracts, after the double blind, peer review process there are 76 Academic research papers, 3 PhD research papers, and 26 work-in-progress papers published in these Conference Proceedings. These papers represent research from Austria, Belgium, Bhutan, Canada, Chile, China, Cyprus, Czech Republic, Denmark, France, Germany, Ghana, Greece, Greenland, Hong Kong, Ireland, Italy, Japan, Norway, Poland, Portugal, Russia, South Africa, Sweden, Taiwan, Thailand, The Netherlands, Turkey, UAE, UK and USA.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it