Analyzing and Visualizing Learning Data: A System Designer's Perspective

Pelánek, Radek

doi:10.18608/jla.2021.7345

Cited by 11 publications

(8 citation statements)

References 28 publications

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“…Finally, the sixth and final paper in this special section is the practitioner report by Pelanek (2021) that examines schoolbased learning analytics from a unique angle of learning system designers. The author provides an overview and inventory of different data visualization approaches for common types of learning data, highlighting the benefits and challenges of the different techniques.…”

Section: Summary Of Special Sectionmentioning

confidence: 99%

“…The author provides an overview and inventory of different data visualization approaches for common types of learning data, highlighting the benefits and challenges of the different techniques. Pelanek's (2021) study also provides an overview of different challenges when designing analytics systems, which include dealing with bias, contract cheating and the temporal nature of learning analytics data. We hope that this contribution provides a valuable inventory for analytics system designers faced with similar challenges.…”

Section: Summary Of Special Sectionmentioning

confidence: 99%

“…Finally, we also see a strong emphasis on theory-driven research and development within the LA field, drawing from a wide range of pedagogical, sociotechnical, and psychometric models. This includes the metacognitive model of reflection by McAlpine et al (1999) utilized in the study by Cloude et al (2021), the theory of inquiry-based learning adopted by Rodríguez-Triana et al (2021), Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al, 2003), and itemresponse theory model by Pelanek (2021). While these models serve a wide range of purposes, theoretically grounding the LA research and practice is paramount to enable dialogue and collaboration with other fields and build upon a vast amount of research in education and related domains.…”

Section: Key Takeaways From This Special Sectionmentioning

confidence: 99%

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Learning Analytics for Primary and Secondary Schools

Kovanović

Mazziotti²,

Lodge

2021

JLA

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Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.

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Section: Summary Of Special Sectionmentioning

confidence: 99%

Section: Summary Of Special Sectionmentioning

confidence: 99%

Section: Key Takeaways From This Special Sectionmentioning

confidence: 99%

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Learning Analytics for Primary and Secondary Schools

Kovanović

Mazziotti²,

Lodge

2021

JLA

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“…We use data from the learning environment Umíme (umimeto.org), an online platform that covers a wide range of subjects, including Czech (for native speakers), English (as a second language), mathematics, and computer science. This learning environment is used by tens of thousands of students every day, primarily elementary and high school students; see Pelánek (2021) for more details. The data we used represents a wide range of student behaviors, ranging from concentrated practice to rapid guessing and cheating, as the environment is used for both voluntary practice and mandatory homework.…”

Section: Datamentioning

confidence: 99%

Leveraging Response Times in Learning Environments: Opportunities and Challenges

Pelánek

2023

Preprint

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Computer-based learning environments can easily collect student response times. These can be used for multiple purposes, such as modeling student knowledge and affect, domain modeling, and cheating detection. However, to fully leverage them, it is essential to understand the properties of response times and associated caveats. In this study, we delve into the properties of response time distributions, including the influence of aberrant student behavior on response times. We then provide an overview of modeling approaches that use response times and discuss potential applications of response times for guiding the adaptive behavior of learning environments.

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“…Taking a different perspective on implementation, we have also seen papers investigating hands-on issues of working with learning data. These include a Practical Report in one of the Special Sections unpacking the potential of particular kinds of analytic approaches for primary and secondary schools from a system designer perspective (Pelanek, 2021), and an analysis of the potential to re-identify de-identified data via publicly available school data (Yacobson et al, 2021). This latter concern, of course, threads through the community's long-standing attention to ethical and privacy concerns, represented in a review on the topic published this year (Cerratto Pargman, and McGrath (2021).…”

Section: Problems Solutions and Impact In The Jla Papers Of 2021mentioning

confidence: 99%

What Makes Learning Analytics Research Matter

Wise

Knight

Ochôa

2021

JLA

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The ongoing changes and challenges brought on by the COVID-19 pandemic have exacerbated long-standing inequities in education, leading many to question basic assumptions about how learning can best benefit all students. Thirst for data about learning is at an all-time high, sometimes without commensurate attention to ensuring principles this community has long valued: privacy, transparency, openness, accountability, and fairness. How we navigate this dynamic context is critical for the future of learning analytics. Thinking about the issue through the lens of JLA publications over the last eight years, we highlight the important contributions of “problem-centric” rather than “tool-centric” research. We also value attention (proximal or distal) to the eventual goal of closing the loop, connecting the results of our analyses back to improve the learning from which they were drawn. Finally, we recognize the power of cycles of maturation: using information generated about real-world uses and impacts of a learning analytics tool to guide new iterations of data, analysis, and intervention design. A critical element of context for such work is that the learning problems we identify and choose to work on are never blank slates; they embed societal structures, reflect the influence of past technologies; and have previous enablers, barriers and social mediation acting on them. In that context, we must ask the hard questions: What parts of existing systems is our work challenging? What parts is it reinforcing? Do these effects, intentional or not, align with our values and beliefs? In the end what makes learning analytics matter is our ability to contribute to progress on both immediate and long-standing challenges in learning, not only improving current systems, but also considering alternatives for what is and what could be. This requires including stakeholder voices in tackling important problems of learning with rigorous analytic approaches to promote equitable learning across contexts. This journal provides a central space for the discussion of such issues, acting as a venue for the whole community to share research, practice, data and tools across the learning analytics cycle in pursuit of these goals.

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Analyzing and Visualizing Learning Data: A System Designer's Perspective

Cited by 11 publications

References 28 publications

Learning Analytics for Primary and Secondary Schools

Learning Analytics for Primary and Secondary Schools

Leveraging Response Times in Learning Environments: Opportunities and Challenges

What Makes Learning Analytics Research Matter

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