Separating the Medium from the Message: Effects of Web- Versus Pencil and Paper- Delivery of the ABRACADABRA Intervention on Literacy, Motivation and Self-Esteem
Bibliographic record
Abstract
This study reports a randomized control trial intervention investigating the impact of delivery format (computer versus paper) on students’ reading and spelling skills, reading motivation and self-esteem using a web-based early literacy tool, A Balanced Approach for Children Designed to Achieve Best Results for All (ABRACADABRA) alongside a paper version of this tool. Based on critiques of technology by Clark (1983) and the Time-Displacement Hypothesis of technology (Vandewater, Bickham, & Lee, 2006), we predicted negative effects of technology on reading, spelling, and reading-related motivation, and self-esteem at post-test. The ABRACADABRA intervention was supplemental, delivered in three weekly 15-minute supplemental reading sessions for eight weeks. Results first showed no difference in the pace and depth of delivery across format and also showed comparable improvements in participants’ reading and spelling at post-test in both the computer-based and paper ABRACADABRA instruction conditions and little evidence of difference by medium of intervention delivery on reading motivation, self-esteem, and enjoyment. It was concluded that the computer-based intervention does not have negative effects over its paper counterpart on students’ literacy skills, and related literacy percepts, and provide no support for the Clark or Time-Displacement Hypotheses in this context.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".