The EduFlow Model: A Contribution Toward the Study of Optimal Learning Environments

Heutte, Jean; Fenouillet, Fabien; Kaplan, Jonathan; Martin‐Krumm, Charles; Bachelet, Rémi

doi:10.1007/978-3-319-28634-1_9

Cited by 42 publications

(46 citation statements)

References 28 publications

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“…Workers are among the categories less present in quantitative research on optimal experience, but they cover an area of increasing interest for researchers of the flow [ 64 ]. We have therefore chosen to use a third sub-sample of workers, identifying the subjects among social workers (psychologists, psychotherapists, educators and teachers), which in literature are considered to be professions chosen in part because they are a possible source of positive psychological experience [ 65 ] [ 66 ] This has also enabled us to maintain a sufficiently uniform sub-sample. The resulting data shows, first, that you can experience flow through any type of experience.…”

Section: Discussionmentioning

confidence: 99%

Measuring Dispositional Flow: Validity and reliability of the Dispositional Flow State Scale 2, Italian version

Riva

Talò

et al. 2017

PLoS ONE

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ObjectiveThe aim of this study is to evaluate the psychometric properties of the Italian version of the Dispositional Flow Scale-2 (DFS-2), for use with Italian adults, young adults and adolescents.MethodIn accordance with the guidelines for test adaptation, the scale has been translated with the method of back translation. The understanding of the item has been checked according to the latest standards on the culturally sensitive translation. The scale thus produced was administered to 843 individuals (of which 60.69% female), between the ages of 15 and 74. The sample is balanced between workers and students. The main activities defined by the subjects allow the sample to be divided into three categories: students, workers, athletes (professionals and semi-professionals).ResultsThe confirmatory factor analysis, conducted using the Maximum Likelihood Estimator (MLM), showed acceptable fit indexes. Reliability and validity have been verified, and structural invariance has been verified on 6 categories of Flow experience and for 3 subsamples with different with different fields of action. Correlational analysis shows significant high values between the nine dimensions.ConclusionsOur data confirmed the validity and reliability of the Italian DFS-2 in measuring Flow experiences. The scale is reliable for use with Italian adults, young adults and adolescents. The Italian version of the scale is suitable for the evaluation of the subjective tendency to experience Flow trait characteristic in different contest, as sport, study and work.

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Section: Discussionmentioning

confidence: 99%

Measuring Dispositional Flow: Validity and reliability of the Dispositional Flow State Scale 2, Italian version

Riva

Talò

et al. 2017

PLoS ONE

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“…evolution of performances, of the users. The results indicated that PEANUT had a beneficial impact on the perception that users had of their ability to learn and memorize how to use MT-BCI, which is a component of the user-experience [197]. PEANUT also had a different influence on MT-BCI performances depending on the level of autonomy of the participants.…”

Section: Supportive Feedbackmentioning

confidence: 95%

A review of user training methods in brain computer interfaces based on mental tasks

et al. 2021

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Mental-Tasks based Brain-Computer Interfaces (MT-BCIs) allow their users to interact with an external device solely by using brain signals produced through mental tasks. While MT-BCIs are promising for many applications, they are still barely used outside laboratories due to their lack of reliability. MT-BCIs require their users to develop the ability to self-regulate specific brain signals. However, the human learning process to control a BCI is still relatively poorly understood and how to optimally train this ability is currently under investigation. Despite their promises and achievements, traditional training programs have been shown to be sub-optimal and could be further improved. In order to optimize user training and improve BCI performance, human factors should be taken into account. An interdisciplinary approach should be adopted to provide learners with appropriate and/or adaptive training. In this article, we provide an overview of existing methods for MT-BCI user training-notably in terms of environment, instructions, feedback and exercises. We present a categorization and taxonomy of these training approaches, provide guidelines on how to choose the best methods and identify open challenges and perspectives to further improve MT-BCI user training.

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“…Specifically, the extent of students' concentration, enjoyment, and interest in learning activities has been shown to predict learning and achievement outcomes (Reeve, 2013). For example, learning is dependent on focused concentration in the present moment (Heutte, Fenouillet, Kaplan, Martin-Krumm, & Bachelet, 2016). Numerous studies including those from neuroscience relate interest to learning, including attention, curiosity, continuing inquiry, and school achievement (Renninger & Hidi, 2015).…”

Section: Associations Between Student Engagement and Learningmentioning

confidence: 99%

The influence of the high school classroom environment on learning as mediated by student engagement

Shernoff

Ruzek

Sinha

2016

School Psychology International

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Classroom learning environments are frequently assumed to exert their influence on learning indirectly, via student engagement. The present study examined the influence of environmental challenge and support on learning in high school classrooms, and the potential for student engagement to act as a mediator in this relationship. Data were collected in seven classrooms in six different subjects in several US high schools. The 104 students in these classes participated in the Experience Sampling Method (ESM) and reported records (N ¼ 254) of engagement, learning, and related experiential variables. Measures of the learning environment were also rated from video footage. Variations in the learning environment observed and rated from video were linked to students' realtime reactions to instruction synchronously. Results indicated that environmental support, but not environmental challenge, was significantly related with perceived learning. Multi-level path analyses revealed that the association between environmental supports and learning was mediated by student engagement. This mediating relationship held specifically for two components of environmental support: Motivational supports and supportive relationships. Implications are discussed for the benefit of practicing school psychologists, including strategies for facilitating motivational and relational support to enhance student engagement.

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The EduFlow Model: A Contribution Toward the Study of Optimal Learning Environments

Cited by 42 publications

References 28 publications

Measuring Dispositional Flow: Validity and reliability of the Dispositional Flow State Scale 2, Italian version

Measuring Dispositional Flow: Validity and reliability of the Dispositional Flow State Scale 2, Italian version

A review of user training methods in brain computer interfaces based on mental tasks

The influence of the high school classroom environment on learning as mediated by student engagement

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