Why this app? How parents choose good educational apps from app stores
Notice bibliographique
Résumé
Abstract Educational apps can be considered a dominant medium for providing educational content to children. Parents are major stakeholders and mediators in the selection of apps (Dias & Brito, 2021). It is critical to know how they choose apps for their children and understand what indicates a quality educational app, as well‐designed apps can support and enhance children's learning process. An online study with parents was conducted to identify parents' most dominant needs while selecting apps for their children. Parents' app selection behaviour was investigated leveraging Uses and Gratifications theory. Parents viewed 10 mock math apps that replicated the App Store presentation format. Five apps included educational benchmarks (eg, feedback) and five contained educational buzzwords (eg, interactive). Immediately following each app, parents provided value judgements of the app by stating whether they would download the app or not, rating it on a 5‐point‐scale, stating how much they would be willing to pay, and explaining why they chose to download the app or not. Results from paired‐samples t ‐tests, and repeated‐measures ANOVAs indicated that parents value educational benchmarks over buzzwords suggesting that parents are primarily seeking apps that meet their children's educational needs. Parents' app needs seem to align with some of the research on what makes a good educational app. Practitioner notes What is already known about this topic Touch screen devices can enhance learning outcomes for children, if well designed educational applications are used (Camilleri & Camilleri, 2019; Cohen et al., 2011). Five educational benchmarks have been identified as indicators of app quality that parents can use to distinguish well designed apps (Dubé et al., 2020); having a development team that involves educators, possessing a guiding curriculum (Vaala et al., 2015), being based on a learning theory (Kebritchi & Hirumi, 2008), containing scaffolded learning, and providing feedback (Callaghan & Reich, 2018; Cayton‐Hodges et al., 2015). Uses and Gratifications theory suggests that people use the media to satisfy their psychological needs and to achieve their personal goals (Katz et al., 1973). What this paper adds The study used Uses and Gratifications theory to identify parents' most dominant needs while selecting apps for their children. With the assumption that parents select apps based on their anticipated gratifications or parental need fulfilment (Broekman et al., 2016, 2018). Different features of the apps are presented in the forms of images and text descriptions in the App Store. The study investigated which features parents value when selecting apps from the App Store by including educational benchmarks and buzzwords in the images and text descriptions of the apps. Parents valued educational benchmarks over buzzwords. Thus, parents' app needs seem to align with the research‐based signifiers of app quality. Parents valued apps that feature development team, scaffolding, and guiding curriculums more than those with central learning theories and feedback. Development team had the highest download frequency and rating while learning theory had the lowest download frequency and rating. Parents were willing to pay more for the development team app and the least for ones containing feedback. The learning theory app was ranked the highest while the development team app received the lowest ranking from parents. Implications for practice and/or policy Including research‐based educational benchmarks in the apps and their app store descriptions promotes a research‐based framework for developing and identifying quality apps. Research‐based educational benchmarks could be used to determine a set of evidence‐based guidelines to assist app developers in the process of developing and presenting apps.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».