An Intervention to Support Higher Education Teachers’ Teaching Processes and Well-Being: Protocol for an Intervention Study
Bibliographic record
Abstract
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.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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".