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Stephen J. Aguilar

Researcher at University of Southern California

Publications -  25
Citations -  747

Stephen J. Aguilar is an academic researcher from University of Southern California. The author has contributed to research in topics: Learning analytics & Computer science. The author has an hindex of 12, co-authored 20 publications receiving 443 citations. Previous affiliations of Stephen J. Aguilar include University of Michigan.

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Journal ArticleDOI

Survey-software implicit association tests: A methodological and empirical analysis

TL;DR: A novel method for constructing IATs using online survey software (Qualtrics) is introduced and its validity is empirically assessed; it appears to be reliable and valid, offer numerous advantages, and make I ATs accessible for researchers who use survey software to conduct online research.
Journal ArticleDOI

Investigating student motivation in the context of a learning analytics intervention during a summer bridge program

TL;DR: Investigation of changes in students' academic motivation orientations over the course of one bridge program and how a learning analytics-based intervention was employed by academic advisors to inform their face-to-face meetings with students indicates that students' exposure to displays of their academic performance negatively predicts this change.
Posted ContentDOI

Survey-Software Implicit Association Tests: A Methodological and Empirical Analysis

TL;DR: The implicit association test (IAT) is widely used in psychology as discussed by the authors, but it cannot be run within online surveys, requiring researchers who conduct online surveys to rely on third-party tools.
Proceedings ArticleDOI

GradeCraft: what can we learn from a game-inspired learning management system?

TL;DR: A learning management system designed with an eye towards learning analytics, it is hoped GradeCraft will give instructors new insight into student engagement, and provide data-driven ideas about how to tailor courses to student needs.
Proceedings ArticleDOI

Perceptions and use of an early warning system during a higher education transition program

TL;DR: Findings from the implementation of a learning analytics-powered Early Warning System by academic advisors who were novice users of data-driven learning analytics tools sheds new light on how student analytic data might be incorporated into the work practices of advisors working with university students.