Work in Progress: Students’ Reflection Quality and Effective Team Membership
Author(s) -
Saira Anwar,
Muhsin Menekşe,
Damji Heo,
Dayoung Kim
Publication year - 2020
Publication title -
papers on engineering education repository (american society for engineering education)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--31306
Subject(s) - reflection (computer programming) , self reflection , quality (philosophy) , vagueness , peer learning , computer science , teamwork , psychology , mathematics education , artificial intelligence , fuzzy logic , philosophy , epistemology , political science , psychoanalysis , law , programming language
This work in progress paper is to investigate the relationship between two self-regulation strategies as self-reflection and collaborative working in teams. Effective instructors employ various strategies to enhance students’ learning outcomes in engineering classes. The primary goal of these strategies is to involve students in the learning process actively, and to promote students’ self-regulated learning strategies. Students’ reflection and ability to work in teams are two such techniques, which could enhance the learning outcomes and self-regulation of students. In this exploratory study, we collected data from 114 First-Year Engineering students and investigated the relationship between students’ reflection quality and their becoming a better and active team member. We used CourseMIRROR mobile learning system to collect students’ reflections during an academic semester. We also evaluated each student reflection based on its quality. The reflection quality here refers to specificity or vagueness of reflections. Based on our prior research on the significance of the reflection quality on student learning [1], we developed a coding schema to specify the degree of reflection’s quality. We further used the Comprehensive Assessment of Team Member Effectiveness (CATME) for peer and self-evaluation on five dimensions. Initial findings reveal statistically significant relations between five aspects of CATME and reflection’s quality. We also conducted linear regression analyses to explore how these five CATME dimensions predict reflection quality scores.
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