Motivation is frequently studied in the context of engineering education. However, the use of the term motivation can be inconsistent, both in how clearly it is defined and in how it is implemented in research designs and practice. This systematic literature review investigates the use of motivation across recent engineering education publications. Results show that the majority of engineering education publications referencing motivation as a concept do not provide a clear definition for the term, nor do they draw upon the existing body of literature surrounding motivation. Within the publications that do draw upon motivation literature, we found that a small number of frameworks were most prominent. We believe that both of these factors potentially inhibit the understanding of motivation in engineering education. Therefore, we provide a number of suggestions for how researchers and practitioners can work to rectify these trends.
Background
The use of metacognition is critical to learning, especially in fields such as engineering that involve problem‐solving and difficult conceptual material. Due to limitations with current methodological approaches, new methods are needed to investigate engineering students' metacognitive engagement in learning situations that are self‐directed, such as study groups.
Purpose
Our purpose was to develop an approach to investigate the metacognitive engagement of undergraduate engineering students in self‐directed learning environments. The Naturalistic Observations of Metacognition in Engineering (NOME) Observational Protocol and Coding Strategy is a qualitative data collection method that allows researchers to observe the behaviors of students who are studying in groups to determine the student's engagement in different metacognitive practices. The NOME is intended to be used by researchers interested in studying online metacognitive behaviors without the direct interference of a methodological approach.
Design/Method
We observed three study groups where students were working on an engineering problem‐solving homework assignment. Using a taxonomic definition of metacognition, we coded episodes of observation transcripts to identify behaviors that represented key definitions in the taxonomy.
Results
We combined subcodes and descriptions of behaviors with key definitions to develop a coding strategy useful for future observational studies. Evidence of intercoder agreement and agreement in unitizing indicates that the coding strategy can reliably be used by multiple trained coders to identify metacognitive engagement.
Conclusions
The reliability evidence shows that the NOME may be a useful tool for researchers in engineering education interested in studying the metacognitive habits of engineering students in self‐directed study.
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