The Trouble With Think Alouds: Generating Data Using Concurrent Verbal Protocols
Bibliographic record
Abstract
Verbal protocol analysis is a methodology that is frequently used in cognitive psychology and education. The use of this method in library and information studies, however, is still very limited. Verbal protocol analysis aims to find cognitive processes while solving a problem. However, concurrent verbal protocols have been seen to cause problems when the task involves a high cognitive load, when the information is difficult to verbalize because of its form, i.e., visual data, or when the processes are automatic for the participants. This paper looks at studies using concurrent verbal protocols and summarizes the findings of this research. Then, this paper compares and contrasts the analysis of 130 concurrent verbal protocols (Think Alouds) gathered from twelve junior high school students from Inuvik, Canada. These Think Alouds are from a case study of the information-seeking processes of junior high students when accessing information from CD-ROM encyclopedias. Preliminary analysis indicates that several of the participants experienced difficulty with Think Alouds. A discussion of possible reasons for these difficulties will be included.
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 | 0.178 | 0.492 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".