The efficacy of learning analytics is predicated on the validity of techniques used to uncover patterns about student learning and engagement, and the ways in which these patterns are communicated to various stakeholders. How students understand representations of their learning, and whether or not those representations motivate them in positive ways, is not well understood. This dissertation addresses this gap in the literature through two complementary studies.
Study 1 utilizes qualitative interviews (n = 60) to investigate how students at-risk of college failure interpret visual representations of their potential academic achievement. Findings suggest an interplay between the information communicated by visualizations and students’ own inclinations towards the information they wished to see. Visualizations showing only the participants academic information, for example, evoked statements focused on personal growth from students when they interpreted the graphs. Visualizations that cast an individual student’s performance against the class average, however, evoked maladaptive responses.
Study 2 designed and validated the Motivated Information-Seeking Questionnaire (MISQ) using a college student sample drawn from across the country (n = 551). The MISQ measures constructs that are parallel to mastery, performance-avoid, and performance approach goal orientations as theorized by Achievement Goal Theory. Confirmatory Factor Analysis (CFA) was used to internally validate the MISQ scales, resulting in validation of the performance-approach information-seeking (PAIS) and performance-avoid information-seeking (PVIS) dimensions. Results of external validation indicated that PVIS and PAIS were empirically distinguishable from performance-approach and performance-avoid achievement goal orientations. Multiple regression analysis supported the predictive power of PVIS and PAIS with regard to students’ emotional responses to certain types of visualizations and to what they attributed their success and/or failure, after controlling for relevant demographic characteristics.
Taken together, these studies increase our knowledge of the various dimensions students use while interpreting visualizations, and uncovered tensions between what students want to see, versus what it might be more motivationally appropriate for them to see. Both studies suggest three maxims for the design and use of visualizations: 1) Never assume that more information is better; 2) Anticipate and mitigate against potential harm; and 3) Always suggest a way for students to grow.PhDEducation and PsychologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133441/1/aguilars_1.pd
