Do “Interactive” Educational Technologies Promote Interactive Literacy Instruction?
Bibliographic record
Abstract
This article explores the concept of lesson interactivity within six primary and elementary teachers’ use of whole-class and personal digital devices over multiple lessons including Interactive Whiteboards, data projectors, laptop computers, and others. The analysis focuses on the differences between technical vs pedagogic interactivity (Smith, Higgins, Wall, & Miller, 2005) where technical interactivity refers to direct tactile interaction with technology and pedagogic interactivity refers to the interaction between teachers, students, and lesson content which may occur with or without technology use. Technical interactivity varied in duration between teachers and lessons, but teachers’ use of whole-class devices typically exceeded students’ use. Use of personal devices by students was infrequent, and often supported the content displayed on a whole-class device. In terms of pedagogic interactivity facilitated by technology use, the most frequent activities were teacher-directed questioning and guided practice, during which the teachers had a correct answer or method in mind. Use of deeper pedagogic interaction through discussion, student inquiry, or research were not observed. Teachers expressed that they faced barriers to interactive technology use including program and resource constraints as well as lack of teacher comfort with technology. This research was conducted following Tri-Council guidelines for the Ethical Conduct of Research Involving Humans. It has passed the research ethics board of two universities and two school districts.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".