Using Think Alouds, Think Afters, and Think Togethers to Research Adolescents’ Inquiry Experiences
Bibliographic record
Abstract
This article presents three research methods—Think Alouds, Think Afters, and Think Togethers—as ways of gathering data to describe the experiences of adolescents during instructional activities. These verbal report methods were used in two studies that examined the information-seeking processes of adolescents in Inuvik, Northwest Territories and Beaumont, Alberta. The first study revealed that participants needed both mediation (instruction and support) and practice to develop the skills and strategies needed for full-text searching of electronic encyclopedias. The second study revealed that students needed mediation (instruction and support) throughout an inquiry-based learning experience and that using Kuhlthau’s (1993) Information Search Process model as a guide for cognitive and affective mediation was useful. The Think Alouds, Think Afters, and Think Togethers allowed the researcher to collect data about the adolescents’ experiences of information-seeking; the data-gathering processes also provided the participants with a deeper understanding of their own experiences of instructional activities. I conclude the article with recommendations to enhance researchers’ use of verbal report methods with adolescents.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".