research-article

Motivated Information Seeking and Graph Comprehension Among College Students

Authors:

Stephen J. Aguilar,

Clare BaekAuthors Info & Claims

LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge

Pages 280 - 289

https://doi.org/10.1145/3303772.3303805

Published: 04 March 2019 Publication History

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Abstract

Learning Analytics Dashboards (LADs) are predicated on the notion that access to more academic information can help students regulate their academic behaviors, but what is the association between information seeking preferences and help-seeking practices among college students? If given access to more information, what might college students do with it?

We investigated these questions in a series of two studies. Study 1 validates a measure of information-seeking preferences---the Motivated Information-Seeking Questionnaire (MISQ)----using a college student sample drawn from across the country (n = 551). In a second study, we used the MISQ to measure college students' (n=210) performance-avoid (i.e., avoiding seeming incompetent in relation to one's peers) and performance-approach (i.e., wishing to outperform one's peers) information seeking preferences, their help-seeking behaviors, and their ability to comprehend line graphs and bar graphs---two common graphs types for LADs.

Results point to a negative relationship between graph comprehension and help-seeking strategies, such as attending office hours, emailing one's professor for help, or visiting a study center---even after controlling for academic performance and demographic characteristics. This suggests that students more capable of readings graphs might not seek help when needed. Further results suggest a positive relationship between performance-approach information-seeking preferences, and how often students compare themselves to their peers.

This study contributes to our understanding of the motivational implications of academic data visualizations in academic settings, and increases our knowledge of the way students interpret visualizations. It uncovers tensions between what students want to see, versus what it might be more motivationally appropriate for them to see. Importantly, the MISQ and graph comprehension measure can be used in future studies to better understand the role of students' information seeking tendencies with regard to their interpretation of various kinds of feedback present in LADs.

References

[1]

Stephen J Aguilar, Steven Lonn, and Stephanie D Teasley. 2014. Perceptions and use of an early warning system during a higher education transition program. In LAK '14 Proceedings of the Third International Conference on Learning Analytics and Knowledge. New York, New York, USA, 113--117.

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