Assessing fidelity of implementation to a technology‐mediated early intervention using process data
Bibliographic record
Abstract
Abstract Background Process data, data generated by a user's interaction with a web‐based application, is an emerging tool in educational research. The current study explores using process data as a measure of implementation fidelity to a randomized control trial (RCT) of the Read It Again Mobile (RIA‐M) curricular supplement. Objectives To determine the extent to which teachers implemented RIA‐M and to assess the utility of using process data in the assessment of fidelity. Methods The RCT involved 30 pre‐kindergarten classrooms with a sample of n = 216 students. RIA‐M provides a curricular supplement which teachers may incorporate into classroom instruction and is delivered via a tablet computer. Pre and post literacy assessments are used to determine treatment effect. Process data, produced from teacher interactions with the tablet, and classroom observations are used to assess fidelity. Results and Conclusions Our findings indicate no difference between treatment and control students in the RCT. Yet, we find that process data provides unique fidelity information concerning treatment exposure, adherence, and quality of program delivery. Specifically, process data indicated that teachers did not demonstrate the same level of fidelity that was captured in classroom observations. This finding provides some evidence for the absence of an intervention effect. Major Takeaways The current study improves our understanding of how web‐based interventions may be assessed for implementation fidelity using process data. Further, process data offers a potentially reliable and scalable measure of fidelity for other web‐based educational interventions.
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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 | 0.100 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".