An Intervention to Support Higher Education Teachers’ Teaching Processes and Well-Being: Protocol for an Intervention Study
Notice bibliographique
Résumé
BACKGROUND: Higher education (HE) teachers are experiencing numerous pressures in their work, such as increased workload, rising student numbers, and declining job resources, making their well-being a crucial issue. Previous studies indicate that adopting a learning-focused approach to teaching (LFT) correlates positively with HE teachers' self-efficacy beliefs and positive emotions. Moreover, there is growing evidence that mindfulness-based interventions can enhance teachers' well-being and teaching processes. OBJECTIVE: This study aims to describe the design of an intervention developed for higher education teachers to support their teaching processes and well-being. The aim of the intervention is to help teachers reflect on their own teaching, offer tools to use learning-focused teaching methods, and increase teachers' ability to define the problem and use learning-focused teaching methods using guided reflection and mindfulness-based practices. METHODS: We developed an intervention in which the teachers participate in 4 group meetings and 2 individual guided reflection sessions (before and after all the group meetings). All group meetings and guided reflection sessions were conducted online via Zoom (Zoom Video Communications). Between the group meetings, the participants independently complete self-study assignments and mindfulness-based exercises available on the Moodle platform. In the guided reflection sessions, the teachers reflect on their previously video-recorded teaching situation together with a researcher. During the video-recorded teaching situation, the teacher wears a Moodmetric smart ring measuring the teacher's arousal level, and episodes with different arousal levels (high, low, and changing) are presented to the participants in the guided reflection sessions. To examine the relations between higher education teachers' teaching processes and well-being, and the intervention's effects, we collect longitudinal data before and after the intervention with various methods (eg, experience sampling, interviews, and surveys). RESULTS: The recruitment of the intervention participants took place in the fall of 2023 from 9 HE institutions in Finland. Altogether, 56 teachers participated in the first part of the intervention (baseline measurement and guided reflection), and 37 participants completed the intervention in spring 2024 by participating in the second guided reflection session and data collection phase. In addition to these 37 participants, 7 teachers who were not able to record their teaching in the second data collection phase but who had still participated in the group meeting and done mindfulness practices, were interviewed about their experiences. The data collection is still ongoing, and additional data will be collected during the academic year 2024-2025. CONCLUSIONS: This study aims to contribute valuable insights into the relations between higher education teachers' teaching processes and well-being, and how an intervention consisting of guided reflection, group meetings, and mindfulness-based practices may enhance teachers' awareness of how their teaching is related to well-being and ways to influence it. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65428.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,008 | 0,004 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,064 | 0,011 |
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 source (Gemma direct ou Codex distillé), 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 ».