Learning Analytics Implementation Design

Wise, Alyssa Friend; Vytasek, Jovita M.

doi:10.18608/hla17.013

Cited by 51 publications

(54 citation statements)

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“…A critical issue often echoed in the LA literature is the lack of real-time interventions that align well with instructors' pedagogical needs (Harrer & Göhnert, 2015;Nguyen, Rienties, & Toetenel, 2017;Wise & Vytasek, 2017). As a result, the LA researched has yielded a limited impact so far.…”

Section: Background: Learning Design Learning Analytics and Moocsmentioning

confidence: 99%

“…Thus, even though considerable progress has been made in conceptual development and design of LA (Bakharia et al, 2016;Lockyer et al, 2013;Manolis & Karkalasi, 2017), the products generated (e.g., data, visualizations, and tools) often ignore the influence of the pedagogical context (Eradze et al, 2017;Nguyen et al, 2017) and do not align well with instructors' needs for refining their pedagogy and learning activities (Mor et al, 2015). For a greater impact on practice, LA solutions need to be implemented in a way that is sensitive to the needs of learning context for which they are intended (Harrer & Göhnert, 2015;Wise & Vytasek, 2017).…”

Section: Background: Learning Design Learning Analytics and Moocsmentioning

confidence: 99%

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Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Gómez‐Sánchez

Dimitriadis

et al. 2019

Interactive Learning Environments

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With the emergence of MOOCs, there is a growing interest in prediction research. Most existing predictive models do not consider the context for which they are intended, thus resulting in limited impact. Learning design (LD) can provide a contextual understanding for the design of predictive models in collaboration with the instructors, maximizing their potential for supporting learning. This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning LD and LA during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past MOOC, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, including the learning activity to focus on, the target variable to predict, and the practical constraints to consider. Later, two predictive models were built for the prediction task identified: LD-specific model, in which the features were based on the LD and pedagogical intentions, and a generic model, which was based on cumulative features, not informed by the LD. Although the LD-specific predictive model did not outperform the generic one, some features derived from the LD and pedagogical intentions were predictive. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor's opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the parts of the LD that need improvement. That is, the results of the LA informed back the LD, where the instructor was a mediator. The implications for improving the LD are discussed.

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Section: Background: Learning Design Learning Analytics and Moocsmentioning

confidence: 99%

Section: Background: Learning Design Learning Analytics and Moocsmentioning

confidence: 99%

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Gómez‐Sánchez

Dimitriadis

et al. 2019

Interactive Learning Environments

View full text Add to dashboard Cite

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“…This view is reinforced by recent research in implementation of LA, cf. the principle of customisation (Wise & Vytasek, 2016) and the increased sense of agency in multimodal LA customisation (Rodríguez-Triana et al, 2018). Tthe open nature of exploratory learning activities makes this a particularly strong requirement.…”

Section: Design Guidelines For La For Elesmentioning

confidence: 99%

“…Augment constructionist pedagogical strategies While the previous guidelines may be applicable and important in other contexts beyond exploratory learning, the successful involvement of teachers early in the design highlights the importance of taking into account the specific context for which LA solutions are being designed (cf. Wise & Vytasek, 2016). Many of our usage scenarios may be applicable in other blended learning scenarios, eg, with intelligent tutoring systems such as the ones described in du Boulay (2019) and Holstein et al (2017).…”

Section: Design Guidelines For La For Elesmentioning

confidence: 99%

“…Emin-Martnez et al, 2014;Mor, Ferguson, & Wasson, 2015) even in a context such as constructionist learning that is less subject to formal assessment and measurement. Our future work therefore includes investigating how such TA tools could be extended to support teachers in even more complex learning scenarios, eg, blended or collaborative learning, as well as identifying the training needs and continuous professional development opportunities to embed such tools in teachers' practice (Rodríguez-Triana et al, 2018;Wise & Vytasek, 2016).…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

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Intelligent analysis and data visualisation for teacher assistance tools: The case of exploratory learning

Mavrikis¹,

Geraniou²,

Santos³

et al. 2019

Brit J Educational Tech

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While it is commonly accepted that Learning Analytics (LA) tools can support teachers' awareness and classroom orchestration, not all forms of pedagogy are congruent to the types of data generated by digital technologies or the algorithms used to analyse them. One such pedagogy that has been so far underserved by LA is exploratory learning, exemplified by tools such as simulators, virtual labs, microworlds and some interactive educational games. This paper argues that the combination of intelligent analysis of interaction data from such an Exploratory Learning Environment (ELE) and the targeted design of visualisations has the benefit of supporting classroom orchestration and consequently enabling the adoption of this pedagogy to the classroom. We present a case study of LA in the context of an ELE supporting the learning of algebra. We focus on the formative qualitative evaluation of a suite of Teacher Assistance tools. We draw conclusions relating to the value of the tools to teachers and reflect with transferable lessons for future related work. What is already known about this topic Learning Analytics (LA) tools can support teachers' orchestration but most of the work so far has focus mostly on instructionist pedagogies or Computer‐Supported Collaborative Learning. Exploratory Learning Environments (ELEs) encourage students to freely explore a domain and follow a variety of solutions and have the potential to lead to more engagement and deeper learning. The freedom in ELEs introduces orchestration challenges that hinder the adoption of such pedagogies in classroom. What this paper adds A set of usage scenarios for LA tools for exploratory environments derived from teacher input and design and methodological guidelines based on a reflection from the study. Showcases a suite of Teacher Assistance tools purposefully designed for teachers' orchestration requirements during Exploratory Learning. Empirical evidence of the perceived usefulness of these tools and feedback from a large number of teachers thanks to a methodology of re‐using previously collected classroom data. Implications for practice and/or policy A methodology for involving teachers in the design and evaluation of LA. Highlights teacher training needs for LA. Teachers should be provided with tools that help them orchestrate different activities including ones with digital tools underpinned by a constructionist pedagogy.

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