Engaging Students in Qualitative Research Practice and Understanding through Constructionist Perspectives: Socially Constructed Qualitative Research Pedagogies
Bibliographic record
Abstract
A growing number of books have explored how teachers teach students' qualitative research in second or foreign language classroom contexts step by step. It is rare, however, to find a book on research methods that includes a social constructionist approach to reflective and experiential learning for conducting qualitative research to help students become good qualitative researchers. To fill this void, Janet C. Richards, Audra Skukauskaitė, and Ron Chenail wrote a book entitled Engaging Students in Socially Constructed Qualitative Research Pedagogies (2022). This book is an important, forward-thinking, and useful resource for the rapidly expanding field of qualitative research pedagogy. In addition, it is applicable to a broader range of active teaching in higher education. The authors discuss a constructionist approach to education. The book is separated into three sections, each of which describes a unique strategy or method for actively incorporating students into qualitative research. Chapter authors with backgrounds in six countries (the United States, Lithuania, Canada, Israel, China, and Russia) present imaginative, practical, and theoretical methods for actively incorporating students in the research learning process. The book will be of interest to students, who will appreciate the inclusion of student projects and authentic scenarios through which instructors assist student learning and qualitative research. The book will also be interesting for instructors who want to improve their pedagogy and find new ways to teach.
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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 | 0.055 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".