Utilising Learning Analytics for Study Success: Reflections on Current Empirical Findings

Mah, Dana-Kristin; Yau, Jane Yin-Kim

doi:10.1007/978-3-319-64792-0_2

Cited by 29 publications

(24 citation statements)

References 25 publications

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“…During the track several scientific papers are briefly presented and discussed with experts. Some of this track editions have been associated to special issues in journals such as Computers in Human Behavior [2] or the International Journal of Engineering Education [3]. Several topics have been discussed during the track, some of the most significant could be: Prediction of students success or failure based in their learning and interaction evidences; tools to improve learning process, Learning Analytics and Mobile devices, Ethics about Learning Analytics, Visual Learning Analytics, Academic Analytics, Multimodal Learning Analytics, Social Network Analytics, Competence Assessment through Learning Analytics, Discussion about the quantity and quality of data to make decisions in educational contexts, Application of Learning Analytics tools and techniques, Personalization of Learning by using Learning Analytics, etc.…”

Section: Spainmentioning

confidence: 99%

“…Even though the field of learning analytics is an evolving field of both research and practice [4], there have already been some relevant efforts in terms of its development in a Swedish context. These attempts are currently expanding in higher educational settings.…”

Section: Swedenmentioning

confidence: 99%

“…Although the field of learning analytics is still in its infancy, seen from an international perspective, it has already produced innovative educational research that demonstrate the utility of learning analytics on the micro level (understanding and developing learning and teaching), on the meso level (understanding and developing single educational organizations), and on the macro level (understanding and developing on a national level or across educational organizations) [3,4,5].…”

Section: Introductionmentioning

confidence: 99%

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Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

Nouri

Ebner²,

Saqr

et al. 2019

iJAI

Self Cite

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Information and communication technologies are increasingly mediating learning and teaching practices as well as how educational institutions are handling their administrative work. As such, students and teachers are leaving large amounts of digital footprints and traces in various educational apps and learning management platforms, and educational administrators register various processes and outcomes in digital administrative systems. It is against such a background we in recent years have seen the emergence of the fast-growing and multi-disciplinary field of learning analytics. In this paper, we examine the research efforts that have been conducted in the field of learning analytics in Austria, Denmark, Finland, Norway, Germany, Spain, and Sweden. More specifically, we report on developed national policies, infrastructures and competence centers, as well as major research projects and developed research strands within the selected countries. The main conclusions of this paper are that the work of researchers around Europe has not led to national adoption or European level strategies for learning analytics. Furthermore, most countries have not established national policies for learners’ data or guidelines that govern the ethical usage of data in research or education. We also conclude, that learning analytics research on pre-university level to high extent have been overlooked. In the same vein, learning analytics has not received enough focus form national and European national bodies. Such funding is necessary for taking steps towards data-driven development of education.

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Section: Spainmentioning

confidence: 99%

Section: Swedenmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

Nouri

Ebner²,

Saqr

et al. 2019

iJAI

Self Cite

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“…Learning Analytics (LA) has the prime aim to improve the study success of students by collecting data about the way students learn, designing models that can extrapolate observed learning behaviours into predictions of learning outcomes, and provide learning feedback to students and teachers in case those predictions signal a risk of unsuccessful learning, or even drop-out (Ifenthaler, 2015;Ifenthaler, Mah, & Yau, 2019). An important source of data applied in predictive modelling is trace data generated by technology-enhanced learning environments that represent the digital footprints of the learning of students in these learning tools.…”

Section: Introductionmentioning

confidence: 99%

“…An important source of data applied in predictive modelling is trace data generated by technology-enhanced learning environments that represent the digital footprints of the learning of students in these learning tools. LA is not without challenges (Ifenthaler et al, 2019): how to define study success, and what trace variables to choose to provide reliable predictions of that study success, are amongst such challenges.…”

Section: Introductionmentioning

confidence: 99%

Learning Engagement, Learning Outcomes and Learning Gains: Lessons From La

Tempelaar

Rienties

Nguyen

2019

Proceedings of the 16th International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2019)

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Learning analytic models are built upon traces students leave in technology-enhanced learning platforms as the digital footprints of their learning processes. Learning analytics uses these traces of learning engagement to predict performance and provide learning feedback to students and teachers when these predictions signal the risk of failing a course, or even dropping-out. But not all of these trace variables act as stable and reliable predictors of course performance. In previous research, the authors concluded that trace variables of product type, such as mastery, do a better job than trace variables of process type, such as clicks or time-on-task, in predicting performance. In this study, we extend this analysis by focusing on learning gains rather than learning outcomes as the most important performance dimension. Distinguishing two different levels of initial proficiency, our empirical analysis into the learning of mathematics by first-year university students indicates that the lack of stability of the engagement types of process type is mainly explained by learning pattern found in students of high initial proficiency. For these students, high levels of engagement lead to lower, rather than higher, predicted learning outcomes. Amongst students with lower initial proficiency, higher levels of engagement play a different role.

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Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

Rodríguez,

Riaño,

García

et al. 2023

Comp Applic In Engineering

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Owing to the positive impact of technology on education, in recent years, e‐learning platforms have increased in popularity and implementation. However, different authors agree that, despite the benefits of using these platforms, one of the main problems is the distraction during the interaction between the student and the E‐Learning platform, since the students visit web resources as social networks, which is not related to their learning process. to identify patterns associated with moments of distraction that students can present on these platforms. Following the above, this study aims to apply Learning Analytics using the Density‐based spatial clustering of applications with noise (DBSCAN) clustering technique and the PrefixSpan algorithm, to identify frequent sequential patterns of interaction, before a distraction event of the student from their interaction with a virtual learning platform. From this, clustering with a Silhouette coefficient close to 0.75 is obtained, presenting good separation, high cohesion, and compaction in the generated groups. Additionally, a slight characterization of each group is achieved based on the most frequent sequential patterns of interaction. Finally, in this study, a successful integration between DBSCAN and PrefixSpan is achieved, to identify sequential interaction patterns associated with moments of distraction in E‐Learning platforms, which generates a starting point for later studies to make the sequential recommendation of educational resources from these patterns.

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Utilising Learning Analytics for Study Success: Reflections on Current Empirical Findings

Cited by 29 publications

References 25 publications

Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

Efforts in Europe for Data-Driven Improvement of Education – A Review of Learning Analytics Research in Seven Countries

Learning Engagement, Learning Outcomes and Learning Gains: Lessons From La

Application of learning analytics for sequential patterns detection associated with moments of distraction in students in e‐learning platforms

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