Bibliographic record
Abstract
The article presents the outcomes of the research project supported by Linköping University, Sweden. The research project constitutes a part of an umbrella project called Pedagogiska Utvecklingsmedel för E-lärande 2019 (Pedagogical Development Tools for E-learning 2019). The research project focuses on the International Master's Program “Gender Studies - Intersectionality and Change” offered at the Unit of Gender Studies, Department of Thematic Studies, Faculty of Arts and Sciences, Linköping University, Sweden. The main aims of the research project are to determine which teaching content, teaching methods, learning activities, teacher’s role, and students’ own strategies matter for learning i.e., for acquiring knowledge and skills/competences in an international blended, face-to-face and online, Master’s Program, and to present students’ experiences with face-to-face and online education in the Program. The project is based on qualitative, semi-structured interviews with the 2nd year students and alumni who have participated in the Program. The interviews were conducted online in November and December 2019. The article presents which content, teaching methods, learning activities, teacher’s role, and students’ own strategies matter for the acquirement of knowledge and skills by the students in blended education. It describes how Campus and online phases of the Program matter for students’ learning. Next to that, it indicates the challenges related to online study, but also educational methods that may help to overcome them.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".