SMSLEFA: An alternative synergistic multilayered analysis of students’ learning engagement in EFL context

Didi Suherdi

Abstract


Students’ learning engagement (SLE) has been the focus of educational research at least since the 1990s. Studies have been conducted using various methods and data analysis approaches and frameworks. However, reviews on related literature show that thus far there is no synergistic multilayered framework of analysis that has been developed and utilized. In the meantime, understanding SLE using discrete and separated framework is by no means conclusive. Hence, it is reasonable to argue that synergistic multilayered framework is imperative if conclusive result is being targeted. For this purpose, the writer has developed an alternative framework, called SMSLEFA, standing for Synergistic Multilayered Students’ Learning Engagement Framework of Analysis. This paper will explicate how this framework of analysis works as well as describing the nature of SLE in an English as a foreign language (EFL) teaching. To achieve these objectives, a sample of EFL teaching in an Indonesian context, involving a teacher and his 24 students, has been purposively selected. The analysis shows that SMSLEFA has successfully explicated the SLE in a synergistic multilayered way and described the intricacy of the SLE in the class under study, and that SLE in the teaching-learning process has been successfully developed through the interwoven network of K1- (teachers’ explanation) and Ds1 (teachers’ invitation to perform communicative activities)-initiated exchanges, and the support of other kinds of exchanges. This interaction pattern encourages the development of synergistic combination of C1 (remembering text elements) and P2 (manipulating model texts) processes as the dominant processes, leading to the production of T (text)-level communication as the most frequently processed throughout the teaching-learning process.

Keywords


students’ learning engagement; interaction patterns; learning behavior processes; text constituent processing

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DOI: http://dx.doi.org/10.17509/ijal.v8i1.11457

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