International Electronic Journal of Mathematics Education

Deep Learning as Constructed in Mathematics Teachers’ Written Discourses
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Fauskanger J, Bjuland R. Deep Learning as Constructed in Mathematics Teachers’ Written Discourses. Int Elect J Math Ed. 2018;13(3), 149-160. https://doi.org/10.12973/iejme/2705
APA 6th edition
In-text citation: (Fauskanger & Bjuland, 2018)
Reference: Fauskanger, J., & Bjuland, R. (2018). Deep Learning as Constructed in Mathematics Teachers’ Written Discourses. International Electronic Journal of Mathematics Education, 13(3), 149-160. https://doi.org/10.12973/iejme/2705
Chicago
In-text citation: (Fauskanger and Bjuland, 2018)
Reference: Fauskanger, Janne, and Raymond Bjuland. "Deep Learning as Constructed in Mathematics Teachers’ Written Discourses". International Electronic Journal of Mathematics Education 2018 13 no. 3 (2018): 149-160. https://doi.org/10.12973/iejme/2705
Harvard
In-text citation: (Fauskanger and Bjuland, 2018)
Reference: Fauskanger, J., and Bjuland, R. (2018). Deep Learning as Constructed in Mathematics Teachers’ Written Discourses. International Electronic Journal of Mathematics Education, 13(3), pp. 149-160. https://doi.org/10.12973/iejme/2705
MLA
In-text citation: (Fauskanger and Bjuland, 2018)
Reference: Fauskanger, Janne et al. "Deep Learning as Constructed in Mathematics Teachers’ Written Discourses". International Electronic Journal of Mathematics Education, vol. 13, no. 3, 2018, pp. 149-160. https://doi.org/10.12973/iejme/2705
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Fauskanger J, Bjuland R. Deep Learning as Constructed in Mathematics Teachers’ Written Discourses. Int Elect J Math Ed. 2018;13(3):149-60. https://doi.org/10.12973/iejme/2705

Abstract

This study explores the notion of deep learning as constructed in the discourses of practicing mathematics teachers. Analyses of data from written texts show that the teachers tend to conceptualise deep learning in terms of two broad categories: students’ deep learning and the work of teaching for deep learning. In both categories, students’ previous knowledge or background knowledge, students’ thinking and their understanding, interdisciplinarity and relations to daily life are emphasised. Related to the work of mathematics teaching for deep learning, variation in mediating tools, variation in approaches to teaching, the learning objective for a lesson and the importance of applying knowledge are emphasised. Possible implications from these findings are discussed.

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