A scoping review of empirical studies on theory-driven learning analytics

Wang, Qin; Mousavi, Amin; Lu, Chang

doi:10.1080/01587919.2021.2020621

Cited by 7 publications

(7 citation statements)

References 69 publications

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“…Recent research by Wang, Mousavi, and Lu (2022) mapped key theoretical constructs found in LA research and based on their analysis and found that most of the research in LA "were guided by the theories of self-regulated learning and social constructivism; most integrated theories into LA for better interpreting the data analysis results; and most linked theoretical constructs and log variables directly." The authors also found that researchers employed survey-instruments to measure theoretical constructs.…”

Section: Theoretical Influencesmentioning

confidence: 99%

Learning Analytics in Open, Distance, and Digital Education (ODDE)

Prinsloo

2023

Handbook of Open, Distance and Digital Education

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Data, and specifically student data, has always been an integral part of good teaching as well as providing evidence for strategic and operational planning, resource allocation, pedagogy, and student support. As Open, Distance, and Digital Education (ODDE) become increasingly datafied, institutions have access to greater volumes, variety, and granularity of student data, from more diverse sources than ever before. This provides huge opportunity for institutions, and specifically educators and course support teams, to better understand learning, and provide more appropriate and effective student support.With the emergence of learning analytics (LA) in 2011, the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs, gained momentum, both as research focus and practice. Since then, LA have become institutionalized in many higher education institutions, mostly in residential institutions located in the Global North, and established a prolific presence in research on student learning in digitized environments. While LA has become institutionalized in the Open University (UK), it remains an emerging research focus and practice in many ODDE institutions across the world.This chapter considers the implications of LA for ODDE research and practice by first providing a brief overview of the evolution of LA, and specifically the theoretical influences in this evolution. A selection of major research findings and discourses in LA are then discussed, before the chapter is concluded with some open questions for a research agenda for LA in ODDE.

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Section: Theoretical Influencesmentioning

confidence: 99%

Learning Analytics in Open, Distance, and Digital Education (ODDE)

Prinsloo

2023

Handbook of Open, Distance and Digital Education

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“…One way to do so is to call for more theoretical development; however, not just any theoretical development. LA is awash with ad-hoc theoretical models borrowed from the diverse fields that constitute its assemblage (Khalil et al, 2022;Wang et al, 2022;Ferguson et al, 2016). Developing a more comprehensive theoretical framework describing the overall ecosystem of domains and concepts that comprise LA would be more helpful.…”

Section: The Need For a Comprehensive Theoretical Frameworkmentioning

confidence: 99%

Aligning the Goals of Learning Analytics with its Research Scholarship

Ferguson,

Khosravi,

Kovanović

et al. 2023

Learning Analytics

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“…Researchers have criticized existing LA systems for their insufficient grounding in the learning sciences and called for a better balance between theory and data-driven approaches [7,8]. Most studies took a data-driven approach at the beginning of LA investigations without utilizing specific learning theories to guide their analysis.…”

Section: Insufficient Grounding In Learning Sciencesmentioning

confidence: 99%

“…However, there are still some areas for improvement in the development of LA systems relative to their theoretical grounding and design choices [7][8][9], challenges in their implementation [10], and issues in the evaluation of their effectiveness [11]. Jivet et al [12] recognize these as critical moments in the LA life cycle, which should always be informed by learning theories to produce effective LA tools.…”

Section: Introductionmentioning

confidence: 99%

Learning Analytics in the Era of Large Language Models

Mazzullo,

Bulut,

Wongvorachan

et al. 2023

Analytics

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Learning analytics (LA) has the potential to significantly improve teaching and learning, but there are still many areas for improvement in LA research and practice. The literature highlights limitations in every stage of the LA life cycle, including scarce pedagogical grounding and poor design choices in the development of LA, challenges in the implementation of LA with respect to the interpretability of insights, prediction, and actionability of feedback, and lack of generalizability and strong practices in LA evaluation. In this position paper, we advocate for empowering teachers in developing LA solutions. We argue that this would enhance the theoretical basis of LA tools and make them more understandable and practical. We present some instances where process data can be utilized to comprehend learning processes and generate more interpretable LA insights. Additionally, we investigate the potential implementation of large language models (LLMs) in LA to produce comprehensible insights, provide timely and actionable feedback, enhance personalization, and support teachers’ tasks more extensively.

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A scoping review of empirical studies on theory-driven learning analytics

Cited by 7 publications

References 69 publications

Learning Analytics in Open, Distance, and Digital Education (ODDE)

Learning Analytics in Open, Distance, and Digital Education (ODDE)

Aligning the Goals of Learning Analytics with its Research Scholarship

Learning Analytics in the Era of Large Language Models

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