Connecting Inquiry, Research, and Technology: The Multigenre Digital Inquiry Project
Bibliographic record
Abstract
This article describes research on a Multigenre Digital Inquiry Project (MDIP), a technology-infused project designed to provide an opportunity for students to inquire about a topic of interest and share their research using 21st century technologies. Instead of composing a research paper or literature review, students designed a website with pieces written in multiple genres to share their learning, including at least two pieces created using digital tools. In this article, the authors share the design of the MDIP and how it was implemented in three teacher education courses. Data analysis aimed to understand how pre-service teachers engaged in this project and reflected on their learning. Using themes from the analysis of students’ end-of-semester reflections and memos written about the pieces included in the projects, the authors share how students valued support in various areas including technology, how they expanded their views of writing and genre, and how their experiences illustrated academic, personal, and pedagogical growth. Ultimately, students learned from this challenging, yet rewarding experience. Finally, the authors share suggestions for others interested in incorporating a MDIP in their work.
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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.029 |
| Research integrity | 0.003 | 0.006 |
| 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".