Under what conditions are graduate students most likely to learn? How do we, as teachers, best create those conditions? The answer to these questions was the focus of this study whereby 91 masters’ students identified learning tasks that were most and least engaging. A model utilizing affective, behavioral and cognitive attributes was developed to measure graduate student engagement in learning tasks. Student survey data demonstrated a direct relationship between perceived value of the learning task, perceived effort put forth in achieving the learning task and perceived student engagement in learning. Multiple regression was used to predict engagement; two attributes, value and effort, predicted 93.2% of the variance in student learning task engagement. Results derived from a repeated measures t-test indicated that students performed significantly better, as measured by grades (p = .003), on learning tasks identified as most engaging when compared to learning tasks identified as least engaging.

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Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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