Portfolios in Design and Technology Education: Investigating Differing Views
Bibliographic record
Abstract
In many professions, portfolios constitute a primary method of documenting proficiency, skill, style and talent by showing examples of actual work.However, the multiple purposes of portfolios in design and technology education have given rise to problems.The conversion of a portfolio into a product has become a significant problem, as have the constraints imposed by examining bodies.This paper will describe a research study that investigated the use of portfolios in professional practice, initial teacher education and secondary design and technology education.Separate focus group interviews were conducted with professional designers, teacher educators and secondary school teachers of design and technology education in both England and Canada.Questions asked of participants focused on definitions and the advantages and disadvantages of using a portfolio, as well as the particular purposes of portfolios in the context of the professional work of each group.Audiotapes of the interviews were transcribed verbatim.Analysis of the data involved thematic analysis and concept analysis.Preliminary analysis of the data has identified that professionals use four types of folio, each for a quite different purpose.These findings have given rise to questions about how these four types of folio could be used to enhance teaching, learning and assessment in design and technology education, and to what extent the adoption of these four types of folio could resolve the conflict between the portfolio as a teaching and learning tool and the portfolio as an assessment instrument.
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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".