The digital reading path: researching modes and multidirectionality with iPads
Bibliographic record
Abstract
Abstract This paper reports a study that examines the integration of tablet technologies such as iPads into literacy lessons to investigate how reading and meaning‐making occur within this digital medium. Specifically in this paper, we discuss the concept of reading paths as applied to physical and cognitive planes of meaning‐making. The paper reports on data collected as part of a Social Sciences and Humanities Research Council (SSHRC) funded project involving researchers from Canada, the United States and Australia. The study is currently under way in schools in the three different countries where the researchers are observing students in classrooms in primary and secondary schools. The research is designed with a mixed methods approach coding video footage of dyads to enable close study of their interaction during literacy tasks incorporating iPads. Our findings show that the affordances of touch technology allow for multimodal, multidirectional reading paths. By tracking students' interactions with the digital platform through touch, it is possible to see navigation as evidence of the relationship between material and cognitive processes, which fosters metatextual awareness. These aspects of modes and new literacies construct a dynamic materiality for students' reading and writing. As a result, we propose that current awareness of the mode of gesture needs to be expanded to take into account haptic ways of learning.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".