Exploring Teacher Candidates’ Experiences of using Design Thinking for Curriculum Planning and Instruction: A Case Study.
Bibliographic record
Abstract
Teacher collaboration for pedagogical activities such as curriculum planning and problem solving enhances teacher learning and skill development (Darling-Hammond, 2012). Thus, exposing new teachers to situations where they can develop the knowledge and skills to solve pedagogical problems is an important support. In this study we explored teacher candidates’ (TC) experiences of using design thinking (DT) (Brown, 2008) for curriculum planning and the development of problem solving mindsets. DT has been used in primarily with in-service teachers, but there is little documented evidence of using DT with pre-service teachers. Our study was guided by the primary research questions: What is the nature of TCs experiences of using a DT process for curriculum planning? What are TCs perceptions of using DT with adolescent students? How do TCs perceive the use of DT to conceptualize and solve challenges in their professional lives? TCs were recruited from a course in the Faculty of Education at Queen’s University entitled Teaching Grades 7 & 8. We collected and analyzed 25 final course reflections which reflected the above research questions. We report on the preliminary findings from our first rounds of data analysis and explicate the benefits, challenges, and potentials for using DT with TCs.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".