Graduate student learning decisions, motivations and reactions to nudge designs in a public health core curriculum
Notice bibliographique
Résumé
Purpose Learning analytics are often used as proxies for student engagement. More qualitative data on how post-secondary students engage with course elements are needed to guide the design, development and deployment of learning analytics information, particularly in the use of nudge techniques. Design/methodology/approach In the context of a graduate-level quantitative course within a public health core curriculum, the following research questions were explored: What do students cite as their motivations when making decisions about whether, when or how to engage with course content and learning supports? and What are student reactions to visual prompts designed to activate these motivations? This qualitative study included two phases of interviews: (1) in-depth interviews with screen sharing as students interacted with the learning management system and (2) in-depth interviews as students reviewed pairs of visual prompts that could potentially be used as behavioral nudges. Findings The study found that student motivations when making decisions about course content and learning supports principally fell into three categories: learning, doing and performing and that all participants attributed their visual prompt preferences to personal motivations or self-perceptions as learners. Research limitations/implications We acknowledge the limitations for external validity and generalizability of the findings in this study. The goal of this formative design research was not to assess the relationship between study habits and motivations and learning outcomes; rather, it was to provide insight to researchers and practitioners seeking to develop, test or employ nudges based on learner study habits. We also acknowledge the small sample size for Phase 2. The aim of Phase 2 was not to identify emergent themes through content analysis but to explore student reactions to nudges mapped to the Damgaard and Nielsen (2018) typology as part of investigating its salience in applications informed by Phase 1 learner study habits. Practical implications Insights from this study could not only be used to design engagement-focused interventions to be applied in education but also in sectors such as training or organizational development. Educators could incorporate the study’s findings to create more engaging learning environments or curricula, fostering active participation and improved learning outcomes and inform policies in education, public programs or workforce development by encouraging evidence-based engagement practices. Originality/value The motivation categories that emerged here – learning, doing and performing – are consistent with studies delving into motivational constructs in education like expectancy value theory, self-regulation and achievement orientation (Ames and Archer, 1988; Pintrich and De Groot, 1990; Wigfield, 1994) and can be leveraged to design interventions that increase engagement, which has been shown in previous work to be lower than hoped (Garbers et al., 2022) to support student educational outcomes.
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Comment cette classification a été obtenuedéplier
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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».