Proposal for a conceptual evolution of the flow in education (EduFlow) model

答辩人 比例(比率) 背景(考古学) 数学教育 心理学 认知 计算机科学 流量(数学) 体验式学习 教育学 数学 量子力学 生物 物理 古生物学 神经科学 法学 政治学 几何学
作者
Jean Heutte,Fabien Fenouillet,Charles Martin‐Krumm,Ilona Boniwell,Mihály Csíkszentmihályi
出处
期刊:Le Centre pour la Communication Scientifique Directe - HAL - Diderot [Centre National de la Recherche Scientifique]
被引量:17
摘要

Background Over the past 35 years, researchers have developed and validated new measurement instruments for flow in a variety of contexts (Moneta, 2012), but to our knowledge, before Heutte et al.’s recent works (2014a), no specific self-report tool was designed to assess flow in the educational context. When constructing the Flow in Education Scale (EduFlow Scale), Heutte and his colleagues collapsed flow experiential dimensions in a quest for a more compact measurement. Thus, EduFlow Scale is a 12-item scale reducing respondent burden, and differentiating four dimensions of flow that are relevant to cognitive processes: Cognitive absorption, Time transformation, Loss of self-consciousness, and Autotelic experience—well-being. The EduFlow Scale was tested in various educational contexts with students ranging from primary school to university including face-to-face learning (Fenouillet et al., 2014) and online learning (Heutte et al., 2014a; Heutte et al., 2014b), with strong criterion-related validity. The EduFlow Scale has three advantages: it is suited to flow measurement in various educational contexts; it is both multidimensional and short; it differentiates between four dimensions of flow that are related to cognitive process. Aims The present study aims to implement the EduFlow Scale in a large-scale project in France and to further improve the scale, both conceptually and psychometrically, as one important flow component (cf. immersion) is lacking from the instrument. Methods This contribution focuses on a comparative study of flow in two Massive Open Online Courses (each MOOC brings over 10,000 participants), and in classrooms (over 1,000 teachers), via several online surveys. Results The analysis of concurrent validity tests suggests a conceptual evolution of the EduFlow model. In the EduFlow Scale-2, two items from the original scale were modified in order to better improve measurement of immersion. In addition, the name of one conceptual dimension of the EduFlow model was changed. Confirmatory factor analyses demonstrated the good fit indexes of the EduFlow-2 model: CFI=.99; TLI=.98; RMSEA=.053; SRMR = .03. Thus, in the EduFlow-2 model, the four dimensions it enables are: Cognitive control; Immersion and time transformation; Loss of self-consciousness; Autotelic experience—well-being. According to the evolution of our model, cognitive absorption is conceptualized via the first three dimensions: Cognitive control, Immersion and time transformation, and Loss of self-consciousness. Discussion This conceptual evolution of the EduFlow Model should better contribute to the study of cognitive activities in various environments, including learning within organizational contexts (Heutte, 2015). Exploration at the personal, collective and institutional levels can broaden our understanding of flow in education by accounting for the intricate relations of factors within processes that are context bound, such as learning and knowledge creation. Keywords: Conceptual modelization of flow; education; scale development; scale validation References Fenouillet, F., Martin-Krumm, C., Heutte, J., & Besancon, M. (2014). An urgent call for change: Flow, motivation and well-being in French School students. 7th ECPP, Amsterdam, the Netherlands. Heutte, J. (2015). Proposal for a pedagogical toolkit for monitoring the optimal learning environnement. 4th European Flow-Researchers’ Network Meeting, Braga, Portugal. Heutte, J., Fenouillet, F., Boniwell, I., Martin-Krumm, C., & Csikszentmihalyi, M. (2014a). Optimal learning experience in digital environments: theoretical concepts, measure and modelisation, Symposium Digital Learning in 21st Century Universities, Georgia Institute of Technology (Georgia Tech), Atlanta, GA. Heutte J., Kaplan J., Fenouillet F., Caron P.-A., & Rosselle, M. (2014b). MOOC User Persistence. In L. Uden, J. Sinclair, Y.-H. Tao, & D. Liberona (Ed.), Learning Technology for Education in Cloud., Communications in Computer and Information Science Vol. 446, 13 24. Springer. Moneta G.B. (2012). On the Measurement and Conceptualization of Flow. In Engeser, S. (Ed.), Advances in. Flow Research (pp. 23-50). New York: Springer.

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