A Learning Management System-Based Early Warning System for Academic Advising in Undergraduate Engineering

Krumm, Andrew E.; Waddington, R. Joseph; Teasley, Stephanie D.; Lonn, Steven

doi:10.1007/978-1-4614-3305-7_6

Cited by 90 publications

(66 citation statements)

References 19 publications

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“…These systems are generically known as early warning systems (EWSs) and usually rely on a combination of demographic datasets and data derived from academic environments to identify students who need extra support (Lonn et al 2012; Jayaprakash et al 2014). The output from EWSs typically include notifying teachers which students are at risk (and perhaps suggesting a range of ways they could further support these students to stay at university), as well as actions directly proposed to the students (Krumm et al 2014). Nowadays, this application of LA has grown to encompass a wide variety of sub-areas to provide student support through a variety of methods (Ferguson 2012b).…”

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confidence: 99%

Data-Driven Personalization of Student Learning Support in Higher Education

Liu

Bartimote-Aufflick

Pardo

et al. 2017

Studies in Systems, Decision and Control

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Abstract. Despite the explosion of interest in big data in higher education and the ensuing rush for catch-all predictive algorithms, there has been relatively little focus on the pedagogical and pastoral contexts of learning. The provision of personalized feedback and support to students is often generalized and decontextualized, and examples of systems that enable contextualized support are notably absent from the learning analytics landscape. In this chapter we discuss the design and deployment of the Student Relationship Engagement System (SRES), a learning analytics system that is grounded primarily within the unique contexts of individual courses. The SRES, currently in use by teachers from 19 departments, takes a holistic and more human-centric view of data -one that puts the relationship between teacher and student at the center. Our approach means that teachers' pedagogical expertise in recognizing meaningful data, identifying subgroups of students for a range of support actions, and designing and deploying these actions, is facilitated by a customizable technology platform. We describe a case study of the application of this human-centric approach to learning analytics, including its impacts on improving student engagement and outcomes, and debate the cultural, pedagogical, and technical aspects of learning analytics implementation.Keywords: actionable intelligence, implementation, intelligence amplification, learning analytics, personalization, student engagement. Abbreviations EDMEducational data mining EWS Early warning system LA Learning analytics LMS Learning management system SRES Student Relationship Engagement System 2 Introduction The state of data-driven student supportThe rise in use of technology mediation in learning scenarios is providing unprecedented amounts of data about how educational institutions work and how students participate in learning experiences. At the same time, learning scenarios are becoming increasingly diverse and complex. The areas of educational data mining (EDM) and learning analytics (LA) have emerged to address the issue of how to use data to improve our understanding of learning, and enhance the overall quality of the learning experience for the student. Although EDM and LA researchers and practitioners maintain a similar focus (Baker and Siemens 2014), they differ in their approach to data generated in educational settings. Researchers in EDM frequently focus their analyses on the formulation or improvement of data mining algorithms designed to detect and predict important factors in a learning scenario. LA, on the other hand, focuses on how these algorithms can be deployed and integrated in learning designs, used by teachers, and provide tangible improvements for students. However, in their initial stages, both disciplines placed their emphasis mostly on how data can be collected and used by algorithms and not so much on how these data can then lead to actions that have a positive effect on students.Prior to the availability of massive amounts of data, the area...

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mentioning

confidence: 99%

Data-Driven Personalization of Student Learning Support in Higher Education

Liu

Bartimote-Aufflick

Pardo

et al. 2017

Studies in Systems, Decision and Control

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“…Researchers tend to analyze these data sets using the computational and statistical methods emerging from the big data field to discover a new pattern of learning and teaching [15] [16]. This leads to a new field which uses the applications of data mining methods to an educational dataset called educational data mining (EDM) [17].…”

Section: E-learning and Educational Data Miningmentioning

confidence: 99%

Data Mining and Gamification Techniques in Adaptive E-Learning: Promises and Challenges

Al-Towirgi¹,

Daghestani²,

Ibrahim³

2018

IJCA

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Educational Data Mining EDM is an emerging discipline. It concerned with extracting useful information from large educational data. It serves education improving by presenting information to facilitate the process of decision making. EDM has many methods and applications the context of e-learning. Gamification is the process of using mechanics and dynamics of games onto non-game context to promote the desired behavior. An emerging type of learning method is the adaptive e-learning. This paper discusses the state of the art of EDM and gamification methods to build adaptive e-learning system.

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“…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%

“…Student Explorer is an EWS that originally provided near real-time data from the LMS at a large research university to support the existing work of academic advisors in the STEM (Science, Technology, Engineering, and Mathematics) Academy (Krumm et al, 2014). The aim of the STEM Academy is to increase the academic success of historically underrepresented students in STEM fields through a holistic student development program.…”

Section: Development Of Student Explorermentioning

confidence: 99%

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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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A Learning Management System-Based Early Warning System for Academic Advising in Undergraduate Engineering

Cited by 90 publications

References 19 publications

Data-Driven Personalization of Student Learning Support in Higher Education

Data-Driven Personalization of Student Learning Support in Higher Education

Data Mining and Gamification Techniques in Adaptive E-Learning: Promises and Challenges

Improving Early Warning Systems with Categorized Course Resource Usage

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