The Data-Assisted Approach to Building Intelligent Technology-Enhanced Learning Environments

Brooks, Christopher; Greer, Jim; Gutwin, Carl

doi:10.1007/978-1-4614-3305-7_7

Cited by 36 publications

(18 citation statements)

References 10 publications

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“…Specifically, Lockyer and colleagues highlight the importance of identifying ahead of time what activity patterns would be expected for successful (or unsuccessful) student engagement in the pedagogical design, and using tools such as checkpoint and process analytics to look for these at particular points during the learning activity [28]. This is important because the same pattern of activity in a system may be considered more or less productive depending on the activity design; for example a wheel and spoke social network in a discussion forum may be appropriate for a Q & A session with an instructional expert, but problematic if the goal is to build community among a group of learners [5]. By addressing the questions of interpretive frame and activity flow, Lockyer and colleague's model describes a pedagogical intervention by which teachers can engage in systematic efforts to use analytics as a productive part of their everyday teaching practice [28].…”

Section: Pedagogical Interventions For Teachersmentioning

confidence: 99%

Designing pedagogical interventions to support student use of learning analytics

Wise

2014

Proceedings of the Fourth International Conference on Learning Analytics and Knowledge

183

130

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This article addresses a relatively unexplored area in the emerging field of learning analytics, the design of learning analytics interventions. A learning analytics intervention is defined as the surrounding frame of activity through which analytic tools, data, and reports are taken up and used. It is a soft technology that involves the orchestration of the human process of engaging with the analytics as part of the larger teaching and learning activity. This paper first makes the case for the overall importance of intervention design, situating it within the larger landscape of the learning analytics field, and then considers the specific issues of intervention design for student use of learning analytics. Four principles of pedagogical learning analytics intervention design that can be used by teachers and course developers to support the productive use of learning analytics by students are introduced: Integration, Agency, Reference Frame and Dialogue. In addition three core processes in which to engage students are described: Grounding, Goal-Setting and Reflection. These principles and processes are united in a preliminary model of pedagogical learning analytics intervention design for students, presented as a starting point for further inquiry.

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Section: Pedagogical Interventions For Teachersmentioning

confidence: 99%

Designing pedagogical interventions to support student use of learning analytics

Wise

2014

Proceedings of the Fourth International Conference on Learning Analytics and Knowledge

183

130

View full text Add to dashboard Cite

show abstract

“…For example, in Figure 14, Mark responded to Mike's post. Node diameter reflects the importance of a student's posts where importance is quantified as proposed by Brooks et al (2014). nStudy creates the social learning network on the right based on the tag selected by the student in making a post.…”

Section: Analytic 61: Social Learning Network Based On the Interactmentioning

confidence: 99%

What if learning analytics were based on learning science?

Marzouk

Raković

Liaqat

et al. 2016

AJET

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Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students' decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning science that explain how students learn. We present six learning analytics that reflect what is known in six areas (we call them cases) of theory and research findings in the learning sciences: setting goals and monitoring progress, distributed practice, retrieval practice, prior knowledge for reading, comparative evaluation of writing, and collaborative learning. Our designs demonstrate learning analytics can be grounded in research on self-regulated learning and self-determination. We propose designs for learning analytics in general should guide students toward more effective self-regulated learning and promote motivation through perceptions of autonomy, competence, and relatedness.

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“…He also goes a step further to expand the notion of reflective practice as an individual activity to one in which analytics can facilitate conversation between teachers and students. A similar notion of analytics as part of reflective practice has been proposed by Brooks et al (2014) in their "data-assisted approach to building technology-enhanced learning environments" (p. 123). This work is useful in providing a general theoretical framing for learning analytics use; however, a specific vision of what the learning-analytics reflective practice cycle might look like and how it can be supported remains to be provided.…”

Section: Introductionmentioning

confidence: 89%

Developing Learning Analytics Design Knowledge in the “Middle Space”: The Student Tuning Model and Align Design Framework for Learning Analytics Use

Wise

Vytasek

Hausknecht

et al. 2016

OLJ

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This paper addresses a relatively unexplored area in the field of learning analytics: how analytics are taken up and used as part of teaching and learning processes. Initial steps are taken towards developing design knowledge for this “middle space,” with a focus on students as analytics users. First, a core set of challenges for analytics use identified in the literature are compiled. Then, a process model is presented for conceptualizing students’ learning analytics use as part of a self-regulatory cycle of grounding, goal-setting, action and reflection–the Student Tuning Model. Finally, the Align Design Framework is presented with initial validation as a tool for pedagogical design that addresses the identified challenges and supports students’ use of analytics as part of the tuning process. Together, the framework’s four interconnected principles of Integration, Agency, Reference Frame and Dialogue / Audience provide a useful starting point for further inquiry into well-designed learning analytics implementations.

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The Data-Assisted Approach to Building Intelligent Technology-Enhanced Learning Environments

Cited by 36 publications

References 10 publications

Designing pedagogical interventions to support student use of learning analytics

Designing pedagogical interventions to support student use of learning analytics

What if learning analytics were based on learning science?

Developing Learning Analytics Design Knowledge in the “Middle Space”: The Student Tuning Model and Align Design Framework for Learning Analytics Use

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