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Examining Students' Metacognitive Awareness Through Analysis of Student-generated Learning Responses
Author(s) -
Saryn R. Goldberg,
Jennifer A. Rich,
Amy M. Masnick,
Marie Paretti,
Cassandra Groen,
Benjamin Lutz,
Lisa McNair
Publication year - 2016
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/p.26795
Subject(s) - metacognition , confusion , coding (social sciences) , mathematics education , set (abstract data type) , computer science , error analysis , psychology , cognition , mathematics , statistics , neuroscience , psychoanalysis , programming language
This work-in-progress provides a preliminary exploration of students’ metacognitive monitoring abilities by analyzing written self-evaluations of statics problems. Metacognitive approaches to learning encourage students to examine their own thinking processes as a means of deepening their understanding. We used qualitative coding to analyze students’ level of metacognitive awareness regarding both their ability to solve a given problem and their ability to identify sources of error. The full data set includes 10 response sequences (homework solution and student writing about their solution) from 69 students. In this paper, we present the analysis of two of these sequences, one from early and one from later in the semester. The findings show that for both assignments, about half the students recognized their inability to solve the problems correctly, though in both cases the groups were split between those who could accurately identify one or more sources of error and those who could not. This finding points to the need for teaching practices that can help students develop the ability both to accurately assess their performance and, perhaps more importantly, identify sources of error and confusion that can then lead to successful learning.

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