Exploring Professional Development Needs and Strategies for Instructors/Faculty Facilitating ePortfolios Online
Bibliographic record
Abstract
This qualitative study sampled 30 university websites across Canada to identify which Canadian universities offer ePortfolio activities to students and faculty members. The researchers from Athabasca University identified eight institutions as offering ePortfolio activities and aimed to explore how faculty or instructors of such ePortfolio activities were selected and what professional development (PD) opportunities were available to them. The study included 11 participants from the eight Canadian universities identified during our search of university websites mentioning ePortfolios. Through a descriptive and interpretive process in a series of 60–90-minute interviews with faculty, educational developers, and instructional designers at the identified universities, the four researchers explored the type of professional development offered to faculty members who are involved or will be involved in ePortfolio use and program integration. The main focus of their interviews was on 1) the nature and type of development offered; 2) how it has been organized; 3) to what extent it has been effective; 4) how faculty members are chosen to teach ePortfolio courses; 5) what lessons have been learned about these factors; and 6) what recommendations are offered or proposed by PD developers, facilitators, and faculty participants. Given that the use of ePortfolios is a rapidly emerging pedagogy in higher education, it is not surprising, perhaps, that faculty development has not kept pace with the practice of ePortfolio introduction. Preliminary results have revealed variations of ePortfolio use (or lack thereof) in higher education. The findings have also revealed the need for a Canadian ePortfolio community to enable practitioners, proponents, and researchers to build on each other’s knowledge, share experiences, and engage in the dissemination of open education resources housed in ePortfolio projects.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".