Using Signals for appropriate feedback: Perceptions and practices

Tanes, Zeynep; Arnold, Kimberly E.; King, Abigail Selzer; Remnet, Mary Ann

doi:10.1016/j.compedu.2011.05.016

Cited by 86 publications

(72 citation statements)

References 25 publications

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“…Although interim grades and other performance data are often ignored by, or unavailable to, LA systems (Clow 2012), large-scale analyses have shown that they can be one of the most important predictive variables in models of academic risk (Jayaprakash et al 2014). Similarly, in the context of face-to-face education, class attendance has been positively associated with improved student outcomes (Rodgers 2001;Massingham and Herrington 2006;Superby et al 2006), and although being a frequently requested 7 data source for teachers, it is notoriously difficult to collect (Shacklock 2016;Dyckhoff et al 2012). Additionally, a large proportion of meaningful studentteacher interaction and assessment may occur outside of the LMS, which is a blindspot for typical LA approaches (West et al 2015).…”

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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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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“…This objective is consistent with the determination of similar procedures in the Purdue Signals system (Tanes et al, 2011). Our second objective was to improve communication with students about their participation and performance in the course and guide them to helpful resources (e.g., online and in-person tutoring and writing services).…”

Section: Objectivesmentioning

confidence: 61%

“…The new version of the early warning system, which is integrated into the LMS via the Retention Center dashboard (a) reduces the number of steps that an instructor has to take to identify students who have fallen below the set thresholds for involvement and performance in the course, and (b) simplifies the process of sending an early warning. In their report on the determinants of success of the Purdue Signals system, Tanes, Arnold, King, and Remnet (2011) remark that reluctance on the part of instructors using the system stemmed from a lack of understanding of how the system could benefit them and not knowing the best practices related to its use. Based on this finding, and on feedback from faculty members not using the older early warning process optimally, we decided to implement the Retention Center-based early warning system in a select few courses, with the aim of determining which approaches seem to work best.…”

Section: Stakeholdersmentioning

confidence: 99%

A Resource-Constrained Approach to Implementing Analytics in an Institution of Higher Education: An Experience Report

Buerck

2014

Learning Analytics

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Academic analytics and learning analytics have been increasingly adopted by academic institutions of higher learning for improving student performance and retention. While several studies have reported the implementation details and the successes of specific analytics initiatives, relatively fewer studies exist in literature that describe the possible constraints that can preclude an academic or learning analytics initiative from succeeding fully, meeting the criteria of success as defined by the stakeholders affected by such initiatives. Our aim in this article is to describe the constraints that precluded a successful completion of our analytics initiative and how we re-envisioned our approach and scope to achieve our primary goals while operating within the constraints and tools associated with our academic environment.

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“…There is a detailed description of how detecting these situations may benefit the institutions. An example of the use of AA is the Signals project in use at Purdue University (Arnold, 2010;Tanes, Arnold, King, & Remnet, 2011). A system has been developed to monitor the events recorded in the institutional LMS and apply measures to improve student success, retention and graduation rates.…”

Section: Academic/learning Analytics and Educational Data Miningmentioning

confidence: 99%