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• W3014938467 endingPage "103890" @default.
• W3014938467 startingPage "103890" @default.
• W3014938467 abstract "Strategies that incorporate learning analytics are continuing to advance as higher education institutions work to promote student success. Although many of the early learning analytics applications were intended to help teaching professionals to identify at-risk students, some learning analytics applications display information on course progress and performance directly to students. While positive associations between student use of learning analytics and achievement have been reported, some have expressed concern that for at-risk students, low estimated grades might induce negative emotions, which could lead to disengagement or even withdrawal. However, few studies have examined the effects of such applications on at-risk students. Elements of Success is a learning analytics platform that provides students with weekly performance feedback, including a current estimated grade. This study examined the relationship between student use of Elements of Success and academic performance among at-risk students in an introductory chemistry course. Specifically, we compared final grade outcomes and the risk of withdrawal among students who received a low estimated grade after the first midterm. Results indicated that viewing performance feedback, including a low estimated grade, was not associated with withdrawal from the course for at-risk students. Furthermore, at-risk students who used Elements of Success were found to be resilient. After controlling for prior learning outcomes, demographics, and self-reported study skills, it was found that they were more likely to earn a final passing grade (C- or above) than at-risk students who never used it. The results and limitations are further discussed." @default.
• W3014938467 created "2020-04-10" @default.
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• W3014938467 date "2020-07-01" @default.
• W3014938467 modified "2023-10-10" @default.
• W3014938467 title "Elements of Success: Supporting at-risk student resilience through learning analytics" @default.
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• W3014938467 doi "https://doi.org/10.1016/j.compedu.2020.103890" @default.
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