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

Lonn, Steven; Aguilar, Stephen J.; Teasley, Stephanie D.

doi:10.1016/j.chb.2014.07.013

Cited by 122 publications

(99 citation statements)

References 39 publications

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“…This implies that apart from a learning dashboard, online learning environments need to provide additional tools that facilitate learners to carry out all the phases of the SRL cycle, supporting learners in subsequent steps once awareness has been realised. These findings emphasise the need of designing LA dashboards as a tool embedded into the instructional design, potentially solving problems related to low uptake of LA dashboards [28].…”

Section: P R E P R I N T 4 Discussionmentioning

confidence: 99%

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Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice

Jivet

Scheffel

Drachsler

et al. 2017

Lecture Notes in Computer Science

202

205

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Abstract. It has been long argued that learning analytics has the potential to act as a "middle space" between the learning sciences and data analytics, creating technical possibilities for exploring the vast amount of data generated in online learning environments. One common learning analytics intervention is the learning dashboard, a support tool for teachers and learners alike that allows them to gain insight into the learning process. Although several related works have scrutinised the state-of-the-art in the field of learning dashboards, none have addressed the theoretical foundation that should inform the design of such interventions. In this systematic literature review, we analyse the extent to which theories and models from learning sciences have been integrated into the development of learning dashboards aimed at learners. Our critical examination reveals the most common educational concepts and the context in which they have been applied. We find evidence that current designs foster competition between learners rather than knowledge mastery, offering misguided frames of reference for comparison.

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Section: P R E P R I N T 4 Discussionmentioning

confidence: 99%

“…There is a common notion in the LA community that learning analytics research should be deeply grounded in learning sciences [28,29]. Suthers & Verbert [38] labelled LA the "middle space" as it lies at the intersection between P R E P R I N T technology and learning sciences.…”

Section: Introductionmentioning

confidence: 99%

Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice

Jivet

Scheffel

Drachsler

et al. 2017

Lecture Notes in Computer Science

202

205

View full text Add to dashboard Cite

show abstract

“…Researchers and the STEM Academy's academic advisors and leaders developed Student Explorer through a two-year collaborative effort using principles of designbased research (Cobb, Confrey, diSessa, Lehrer, & Schauble, 2003;Krumm et al, 2014). Student Explorer now also serves staff members in Summer Bridge, probation, and general engineering advising roles across campus, annually tracking over 4,500 undergraduate students (Lonn et al, 2015).…”

Section: Development Of Student Explorermentioning

confidence: 99%

“…Optimizing the learning environment in higher education, particularly across students' concurrent course loads, includes presenting patterns and indicators of student behaviour to intermediaries (e.g., academic advisors and coaches) who can act upon such information (Duval, 2011;May, George, & Prévôt, 2011). Our prior work has focused on leveraging a learning-analytics-powered early warning system, Student Explorer, to help academic advisors quickly identify students in need of academic support and allow these professionals to engage in sense-making activities that support subsequent actions (see Krumm et al, 2014;Lonn, Aguilar, & Teasley, 2015).…”

Section: Early Warning Systems Researchmentioning

confidence: 99%

Improving Early Warning Systems with Categorized Course Resource Usage

Waddington

Nam²,

Lonn³

et al. 2016

Learning Analytics

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Early Warning Systems (EWSs) aggregate multiple sources of data to provide timely information to stakeholders about students in need of academic support. There is an increasing need to incorporate relevant data about student behaviours into the algorithms underlying EWSs to improve predictors of student success or failure. Many EWSs currently incorporate counts of course resource use, although these measures provide no information about which resources students are using. We use seven years of data from seven core STEM courses at a large university to investigate the associations between student use of categorized course resources (e.g., lecture or exam preparation resources) and their final course grade. Using logistic regression, we find that students who use exam preparation resources to a greater degree than their peers are more likely to receive a final grade of B or higher. In contrast, students who use more lecture-related resources than their peers are less likely to receive a final grade of B or higher. We discuss the implications of our results for developers deciding how to incorporate categories of course resource usage data into EWSs, for academic advisors using this information with students, and for instructors deciding which resources to include on their LMS site.

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“…Programs traditionally have three components:  Academic preparation through coursework and training in study skills  Institutional acculturation and cohort development  Improvement of student self-esteem and self-efficacy 10 Aims, structures and goals of bridge programs vary, and time allotted to each component depends on the goals of the program. Some programs focus on retention and broad degree completion for specific student populations while others focus on retention in a particular program or course of study.…”

Section: Research Backgroundmentioning

confidence: 99%