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

Abstract

This paper reports findings from the implementation of a learning analytics-powered Early Warning System (EWS) by academic advisors who were novice users of data-driven learning analytics tools. The information collected from these users sheds new light on how student analytic data might be incorporated into the work practices of advisors working with university students. Our results indicate that advisors predominantly used the EWS during their meetings with students-despite it being designed as a tool to provide information to prepare for meetings and identify students who are struggling academically. This introduction of an unintended audience brings significant design implications to bear that are relevant for learning analytics innovations.

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

“…While the two surveys described above represent the main data sources described in this paper, our overall design-based research program investigates how academic advisors in Bridge and other at-risk student programs use our EWS, called Student Explorer. This system was designed to provide advisors information about their students' engagement and performance to facilitate timely interventions (see Aguilar, Lonn, & Teasley, 2014;Krumm et al, 2014;Lonn, Aguilar, & Teasley, 2013;Lonn et al, 2012). Students' weekly progress updates were presented through a dashboard that provided representations of student engagement and performance that allowed the advisors to readily identify students who were succeeding in any given course, beginning to show signs of falling behind, or struggling with their coursework (see Fig.…”

Privacy-driven Design of Learning Analytics Applications – Exploring the Design Space of Solutions for Data Sharing and Interoperability

“…"Many myths surrounding the use of data, privacy infringement and ownership of data need to be dispelled and can be properly modulated once the values of learning analytics are realized" (Arnold et al, 2014, p. 259). Some authors reminded the audience that one should be mindful (of privacy) when designing user interfaces (Aguilar, 2014). In doing so, another paper pointed out that while ethics and privacy are features of educational data sciences, public entities are required to adhere to FERPA and other such regulations, whereas "in the private sector there are fewer restrictions and less regulations regarding data collection and use" (Piety et al, 2014, p. 198).…”

Developing an evaluation framework of quality indicators for learning analytics

“…As a first step, the framework's criteria and QIs were therefore transformed into a questionnaire using Google Forms 1 . For every quality indicator the questionnaire asked (1) whether that QI was present in/supported by a tool or not or whether it was not applicable, (2) in what way that QI was present in/supported by the tool, and (3) how difficult or easy (on a scale of 1 (very difficult) to 5 (very easy)) it was to judge that QI. At the end of each criterion section participants were offered an open text box asking for To find suitable LA tool candidates the submissions to the previous Learning Analytics and Knowledge conferences as well as a number of existing tools from previous project partners were browsed.…”

“…Eight prominent LA tools were then randomly selected to be used for the evaluation of the framework: Blackboard Learn 9. [9,10] and Student Explorer [1,13]. The study was conducted with members from the LACE project 8 consortium and its associated partners.…”