Addressing the Challenges of Institutional Adoption

Colvin, Cassandra; Dawson, Shane; Wade, Alexandra T.; Gašević, Dragan

doi:10.18608/hla17.024

Cited by 36 publications

(52 citation statements)

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“…Concurrent with these sector-wide structural changes, there has been an increase in the range of available data sources and computational methodologies, which has led many institutions to identify LA as a strategic priority and to invest, sometimes heavily, in software solutions (Colvin et al 2016). At the time the system reported in this chapter was initially developed, LA was not a priority at our institution but there was already an active network of teaching leaders and central student support 5 staff concerned with the experience of first year students and their transition to university.…”

Section: Local Contexts Influencing Data-driven Student Supportmentioning

confidence: 99%

“…In the Australian higher education context, Colvin et al (2016) noted that sustainable LA adoption relies on (i) organization strategy that lays the groundwork for LA, (ii) implementation capability that integrates teachers' practices with data and tools, (iii) tools that address real needs, and (iv) a capacity to monitor and improve the quality and usability of implementations. Our journey with the SRES has serendipitously approached this from the bottom up and nevertheless has seen increasing adoption at our institution.…”

Section: Enabling Scaling-up Of Data-driven Student Learning Supportmentioning

confidence: 99%

“…As Colvin et al (2016) noted, "implementers require an analytic tool or combination of tools that manage data inputs and generate outputs in the form of actionable feedback. The capacity to implement is crucially linked to the quality of these tools and the data they rely on and generate… As these increasingly meet the 'real' needs of learners and educators, organizational uptake is accelerated" (p. 2).…”

Section: System Adoption Usage and Impactmentioning

confidence: 99%

“…In an Australian study, personal responses through emails, phone calls, and consultations were the preferred mechanism of data-driven actions (West et al 2015). In another study, tools that "manage data inputs and generate outputs in the form of actionable feedback" were found to be the most adoptable (Colvin et al 2016). Even large-scale implementations that involve opaque algorithms eventually involve teachers contacting students based on the outputs of these algorithms (e.g.…”

mentioning

confidence: 99%

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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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Section: Local Contexts Influencing Data-driven Student Supportmentioning

confidence: 99%

Section: Enabling Scaling-up Of Data-driven Student Learning Supportmentioning

confidence: 99%

Section: System Adoption Usage and Impactmentioning

confidence: 99%

mentioning

confidence: 99%

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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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“…The use of learning analytics for predictive purposes is projected to expand to university- and system-wide projects (Heath and Leinonen, 2016; Roberts et al, 2016). However, at the current time the application of big data to learning analytics for the purposes of learning instruction is less common (Dede et al, 2016), typically involving small-scale projects with a focus on understanding learning and teaching practices (Siemens et al, 2013; Colvin et al, 2015). The disproportionate focus on prediction over learning highlights the gap between the use of big data and learning analytics for prediction and its application to enhancing learning (Dede et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Student Attitudes toward Learning Analytics in Higher Education: “The Fitbit Version of the Learning World”

et al. 2016

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Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups (N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, “Uninformed and Uncertain,” represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; “Help or Hindrance to Learning,” “More than a Number,” and “Impeding Independence”; represented students' perceptions of the likely impact of learning analytics on their learning. “Driving Inequality” and “Where Will it Stop?” represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how “personal” vs. “collective” purposes or principles can intersect with “uniform” vs. “autonomous” activity. The findings highlight the need the need to engage students in the decision making process about learning analytics.

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