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Title:
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LEARNING ENGAGEMENT, LEARNING OUTCOMES AND LEARNING GAINS: LESSONS FROM LA |
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Author(s):
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Dirk Tempelaar, Bart Rienties and Quan Nguyen |
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ISBN:
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978-989-8533-93-7 |
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Editors:
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Demetrios G. Sampson, Dirk Ifenthaler, Pedro Isaías and Maria Lidia Mascia |
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Year:
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2019 |
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Edition:
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Single |
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Keywords:
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Blended Learning, e-Tutorials, Learning Analytics, Learning Engagement, Learning Traces, Process and Product Data |
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Type:
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Full Paper |
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First Page:
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257 |
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Last Page:
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264 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Learning analytic models are built upon traces students leave in technology-enhanced learning platforms as the digital
footprints of their learning processes. Learning analytics uses these traces of learning engagement to predict performance
and provide learning feedback to students and teachers when these predictions signal the risk of failing a course, or even
dropping-out. But not all of these trace variables act as stable and reliable predictors of course performance. In previous
research, the authors concluded that trace variables of product type, such as mastery, do a better job than trace variables
of process type, such as clicks or time-on-task, in predicting performance. In this study, we extend this analysis by
focusing on learning gains rather than learning outcomes as the most important performance dimension. Distinguishing
two different levels of initial proficiency, our empirical analysis into the learning of mathematics by first-year university
students indicates that the lack of stability of the engagement types of process type is mainly explained by learning
pattern found in students of high initial proficiency. For these students, high levels of engagement lead to lower, rather
than higher, predicted learning outcomes. Amongst students with lower initial proficiency, higher levels of engagement
play a different role. |
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