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- Ahmadi, Asghar, et al.
(author)
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A Classification System for Teachers’ Motivational Behaviors Recommended in Self-Determination Theory Interventions
- 2023
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In: Journal of Educational Psychology. - Washington, DC : American Psychological Association (APA). - 0022-0663 .- 1939-2176. ; 115:8, s. 1158-1176
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Journal article (peer-reviewed)abstract
- Teachers’ behavior is a key factor that influences students’ motivation. Many theoretical models have tried to explain this influence, with one of the most thoroughly researched being self-determination theory (SDT). We used a Delphi method to create a classification of teacher behaviors consistent with SDT. This is useful because SDT-based interventions have been widely used to improve educational outcomes. However, these interventions contain many components. Reliably classifying and labeling those components is essential for implementation, reproducibility, and evidence synthesis.We used an international expert panel (N = 34) to develop this classification system. We started by identifying behaviors from existing literature, then refined labels, descriptions, and examples using the Delphi panel’s input. Next, the panel of experts iteratively rated the relevance of each behavior to SDT, the psychological need that each behavior influenced, and its likely effect on motivation. To create a mutually exclusive and collectively exhaustive list of behaviors, experts nominated overlapping behaviors that were redundant, and suggested new ones missing from the classification. After three rounds, the expert panel agreed upon 57 teacher motivational behaviors (TMBs) that were consistent with SDT. For most behaviors (77%), experts reached consensus on both the most relevant psychological need and influence on motivation. Our classification system provides a comprehensive list of TMBs and consistent terminology in how those behaviors are labeled. Researchers and practitioners designing interventions could use these behaviors to design interventions, to reproduce interventions, to assess whether these behaviors moderate intervention effects, and could focus new research on areas where experts disagreed. © 2023 American Psychological Association
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| 2. |
- Ahmadi, Asghar, et al.
(author)
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A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity
- 2021
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In: Psychosocial Intervention. - Madrid : Colegio Oficial de Psicologos. - 1132-0559 .- 2173-4712. ; 30:3, s. 139-153
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Research review (peer-reviewed)abstract
- Many psychological treatments have been shown to be cost-effective and efficacious, as long as they are implemented faithfully. Assessing fidelity and providing feedback is expensive and time-consuming. Machine learning has been used to assess treatment fidelity, but the reliability and generalisability is unclear. We collated and critiqued all implementations of machine learning to assess the verbal behaviour of all helping professionals, with particular emphasis on treatment fidelity for therapists. We conducted searches using nine electronic databases for automated approaches of coding verbal behaviour in therapy and similar contexts. We completed screening, extraction, and quality assessment in duplicate. Fifty-two studies met our inclusion criteria (65.3% in psychotherapy). Automated coding methods performed better than chance, and some methods showed near human-level performance; performance tended to be better with larger data sets, a smaller number of codes, conceptually simple codes, and when predicting session-level ratings than utterance-level ones. Few studies adhered to best-practice machine learning guidelines. Machine learning demonstrated promising results, particularly where there are large, annotated datasets and a modest number of concrete features to code. These methods are novel, cost-effective, scalable ways of assessing fidelity and providing therapists with individualised, prompt, and objective feedback. © 2021 Colegio Oficial de la Psicología de Madrid
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| 3. |
- Noetel, Michael, et al.
(author)
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Prediction Versus Explanation in Educational Psychology : a Cross-Theoretical Approach to Using Teacher Behaviour to Predict Student Engagement in Physical Education
- 2023
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In: Educational psychology review. - New York, NY : Springer. - 1040-726X .- 1573-336X. ; 35:3
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Research review (peer-reviewed)abstract
- Educational psychology usually focuses on explaining phenomena. As a result, researchers seldom explore how well their models predict the outcomes they care about using best-practice approaches to predictive statistics. In this paper, we focus less on explanation and more on prediction, showing how both are important for advancing the field. We apply predictive models to the role of teachers on student engagement, i.e. the thoughts, attitudes, and behaviours, that translate motivation into progress. We integrate the suggestions from four prominent motivational theories (self-determination theory, achievement goal theory, growth mindset theory, and transformational leadership theory), and aim to identify those most critical behaviours for predicting changes in students’ engagement in physical education. Students (N = 1324 all from year 7, 52% girls) from 17 low socio-economic status schools rated their teacher’s demonstration of 71 behaviours in the middle of the school year. We also assessed students’ engagement at the beginning and end of the year. We trained elastic-net regression models on 70% of the data and then assessed their predictive validity on the held-out data (30%). The models showed that teacher behaviours predicted 4.39% of the variance in students’ change in engagement. Some behaviours that were most consistently associated with a positive change in engagement were being good role models (β = 0.046), taking interest in students’ lives outside of class (β = 0.033), and allowing students to make choices (β = 0.029). The influential behaviours did not neatly fit within any single motivational theory. These findings support arguments for integrating different theoretical approaches, and suggest practitioners may want to consider multiple theories when designing interventions. More generally, we argue that researchers in educational psychology should more frequently test how well their models not just explain, but predict the outcomes they care about. © 2023, Crown.
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