8. Chromebooks vs. Printed Books – Exploring Influences on Students’ Reading Comprehension and Dictionary-use
Bibliographic record
Abstract
The digital age is an era beginning in the 1980s in societies wherein the retrieval, management, and transmission of information using digital technology is a principal activity (Flewitt, Messer, & Kucirkova, 2015). In recent years, digital technology has been rapidly incorporated into Canadian schools, inspiring a debate concerning how educators should use newly emerging digital technology in the classroom, and ultimately whether digital media platforms should be accepted as a replacement for print-based media platforms at all. This research project uses quantitative methods and a within-subjects research design to compare fifth grade Eastern Ontarian students’ frequency of dictionary-use and reading comprehension scores when reading a Chromebook and using an online dictionary, in contrast to when reading a printed book and using a printed dictionary. It was hypothesized that students would achieve higher reading comprehension scores and demonstrate more frequent dictionary-use when reading with a Chromebook and online dictionary than when reading a printed book and using a printed dictionary. This was due to the reportedly more ergonomic nature of digital media platforms (Dundar & Akcayir, 2012), and to the unique set of skills acquired by many children growing up during the digital age (Steeves, 2014). It was found that the participants used the online dictionary significantly more frequently than the printed dictionary, but no significant difference was found between participants’ reading comprehension scores in the two conditions. The results of this research project may have implications for the pedagogical tools and practices used in local Eastern Ontarian elementary school classrooms.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".