Why this app: Can a video‐based intervention help parents identify quality educational apps?
Bibliographic record
Abstract
Abstract Researchers recommend that parents look for five benchmarks as indicators of quality educational apps (ie, scaffolding, curriculum, development team, feedback, learning theory), yet results show that parents undervalue some of these benchmarks. The current study examined if a short video‐based intervention would enhance parents' value‐judgements of apps featuring the five educational benchmarks. In original and modified app experiments ( n = 100; n = 101), parents of children 4–11 years old were randomly assigned to watch either a 9‐minute video that detailed how the five benchmarks augment learning, or a 2‐minute control video. Parents evaluated 10 simulated apps containing either benchmarks or buzzwords. The original app experiment shows that a brief intervention can help parents identify quality educational apps via the benchmarks, but the modified app experiment suggests it only works if developers are using specific keywords in app descriptions. Helping parents select quality educational apps is more complicated than simply telling them what to look for. Practitioner notes What is already known about this topic Parents have a main role in selecting apps and deciding on how often children use them; however, they have difficulty evaluating an app's educational potential in a market mixed with high‐ and low‐quality products that lacks a standard for including educational apps on the App Store. There are five research‐based benchmarks that are indicators of quality educational apps. These include apps created by an interdisciplinary development team , having a guiding curriculum with a clear purpose, including scaffolding and feedback , and being based on a learning theory . However, parents are not valuing all of these educational benchmarks equally. Educational videos disseminated via YouTube have become an established medium to enhance people's knowledge. Mayer's cognitive theory of multimedia learning, which suggests that people learn better when there is both auditory and visual information given together, is a framework used to design educational videos. What this paper adds This study leveraged a useful, accessible medium (ie, educational YouTube videos) to make research on educational apps accessible enough such that it could influence parents' app selection. Parents of children aged 4–11 years‐old viewed either an educational intervention‐ or control‐video and assessed educational apps through measures that replicate how consumers evaluate apps on the App Store (ie, their willingness to download the app, how much they would pay, their rating, and ranking). Original and modified app experiments demonstrate that a brief, educational video designed using key features from Mayer's multimedia theory can improve parents' app selection. In the original app experiment, parents in the intervention group are valuing the guiding curriculum and development team benchmarks over others, which may be due to the structure of the intervention video (ie, worked‐examples immediately after pre‐training of benchmarks). In the modified app experiment, parents in the intervention group did not differ from the control group in their evaluations of the different benchmarks apart from rating the app with learning theory higher and ranking the guiding curriculum benchmark lower. The higher rating of learning theory may be a result of the language change in the simulated app to include more user‐friendly terms. The unexpected ranking result may be due to a limitation with the measure. Building on prior research on the educational benchmarks, these experiments show that parents are valuing them over buzzwords; however, even when shown an educational video, they are not valuing them all equally. Implications for practice and/or policy An educational video based on the five educational benchmarks could be used as a tool to easily disseminate knowledge to enhance parents' educational app selection. The experiments shed light on the complexity of knowledge dissemination to the public. While there is evidence for the use of research‐based educational videos to disseminate knowledge to parents when selecting educational apps, the App Store descriptions should include accessible language that makes the research‐based benchmarks easy to select. This study also provides suggestions for improving the development of educational videos.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".