Reading Actively Online: An Exploratory Investigation of Online Annotation Tools for Inquiry Learning / La lecture active en ligne: étude exploratoire sur les outils d'annotation en ligne pour l'apprentissage par l’enquête
Bibliographic record
Abstract
This study seeks to design and facilitate active reading among secondary school students with an online annotation tool – Diigo. Two classes of different academic performance levels were recruited to examine their annotation behavior and perceptions of Diigo. We wanted to determine whether the two classes differed in how they used Diigo; how they perceived Diigo; and whether how they used Diigo was related to how they perceived it. Using annotation data and surveys in which students reported on their use and perceptions of Diigo, we found that although the tool facilitated individual annotations, the two classes used and perceived it differently. Overall, the study showed Diigo to be a promising tool for enhancing active reading in the inquiry learning process. Cette étude vise à concevoir et à faciliter la lecture active chez les élèves du secondaire grâce à l’outil d'annotation en ligne Diigo. Deux classes avec des niveaux de rendement scolaire différents ont été retenues afin qu’on examine leur manière d’annoter et leur perception de Diigo. Nous avons voulu déterminer si les deux classes diffèrent dans leur façon d’utiliser Diigo, leur perception de Diigo, et si leur manière d’utiliser Diigo était liée à leur perception. En utilisant les données d'annotation et d'enquêtes dans lesquelles les élèves relataient leur utilisation et leur perception de Diigo, nous avons constaté que, même si l'outil a facilité les annotations individuelles, les deux classes l’ont utilisé et perçu différemment. Dans l'ensemble, l'étude a montré que Diigo est un outil prometteur pour l'amélioration de la lecture active dans le processus d'apprentissage par enquête.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".