Introduction into Sparks of the Learning Analytics Future

Pechenizkiy, Mykola; Gašević, Dragan

doi:10.18608/jla.2014.13.8

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…As Bates (2019) argues, this evolution reflects a broader shift in societal values and needs, where education must cater to a digitally fluent population. Furthermore, the rise of big data and analytics has offered educators unprecedented insights into student learning patterns, enabling more personalized and adaptive learning experiences (Siemens & Gasevic, 2020).…”

Section: The Evolution Of Educational Paradigmsmentioning

confidence: 99%

Brave New World: Navigating New Paradigms in the Educational Landscape for the Evolving Learner in a Rapidly Changing Era

2024

IJFMR

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This literature review explores the significant transformations in the educational landscape prompted by technological advancements and changing learner profiles in the digital era. The review examines the evolution of learner profiles, highlighting the shift towards multisensory learning, digital fluency, and a preference for interactive, personalized learning experiences. It delves into integrating innovative technologies such as virtual reality (VR), augmented reality (AR), and blockchain in education and discusses their implications for teaching and learning. The review also considers the impact of neuroscience on educational practices, emphasizing the importance of understanding brain functioning and neuroplasticity in developing effective teaching strategies. The unprecedented shift to online and blended learning models, accelerated by the COVID-19 pandemic, is analyzed for its long-term effects on educational delivery and pedagogy. Additionally, the review addresses the growing emphasis on interdisciplinary learning, sustainability, mental health, and well-being within educational curricula. The implications of these transformative trends for educators, policymakers, and learners are discussed, highlighting the need for adaptive and responsive approaches to education in the face of rapid technological and societal changes. The review concludes with reflections on the future trajectory of education, anticipating continued technical integration, evolving pedagogical theories, and an increased focus on preparing learners for global challenges and lifelong learning.

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Section: The Evolution Of Educational Paradigmsmentioning

confidence: 99%

Brave New World: Navigating New Paradigms in the Educational Landscape for the Evolving Learner in a Rapidly Changing Era

2024

IJFMR

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“…Learning analytics is defined as "the use of intellectual data, learner-generated data, and analytical models to discover information and social connections, and to predict and advise learning" (Siemens 2010). Learning analytics also contribute to improve learning environments, and examine learning effectiveness in students by collecting and analyzing data to assess learning behavior and performance (Pechenizkiy et al 2014;Ogata et al, 2015). Yin et al (2019) investigate the successful learning patterns based on eBook reading logs.…”

Section: Challenges For the Web-based Application: Learning Analyticsmentioning

confidence: 99%

Visualization of Learning Log of Web-Based Application for Understanding the Structure of Geometric Proofs

Hamada

Yamada

2019

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

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Many students are not good at geometric proofs. One reason for this is that it is possible to memorize the proof procedure explained by the teacher without understanding the structure of a geometric proof. So far, we have developed a web-based application that supports the understanding of the "structure of geometric proofs" in junior high school mathematics and have verified its effectiveness. Although the results of the evaluation experiments have indicated the usefulness of this web-based application, it was not possible to identify the factors that brought about its usefulness. In addition, because the web-based application was not equipped with a log analysis function, it was not possible to grasp the aspects that students struggle with during a proof in the web-based application. In this paper, we present the results and problems of the evaluation experiment of the web-based application so far and discuss the newly added log analysis function.

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“…Learning analytics has been utilized for the purpose of understanding and optimizing learning by collecting, analyzing and reporting of student data and the contexts associated with them [3]. It incorporates multidisciplinary science that aims to support the improvement of the learning process through the collection and analysis of student behavior and performance data [4]. Besides students' behavior and performance data, some scholar utilized learning analytics based on students' emotional data and network data [5].…”

Section: Introductionmentioning

confidence: 99%

Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering

Laksitowening

Prasetya

Suwawi

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Students can have various characteristics and learning patterns. By understanding the characteristics and learning pattern of individual students, teachers can provide individualized learning strategies based on students' needs. Students' learning patterns may experience changes depending on their conditions during the learning process. If the learning pattern analysis is only run once, then the progress and changes in student learning patterns throughout the learning process cannot be recognized. On the other hand, periodical analysis is expected to describe the dynamics of student learning patterns from time to time. This research is intended for capturing students' dynamic learning pattern using Hierarchical Clustering. We clustered the learning patterns based on Learning Management Systems (LMS) activity logs. The activity log data were partitioned into several periodical datasets. The results of the periodic clustering indicated that students’ learning patterns varied from one another and changed from time to time. Most students experienced change in learning patterns throughout the semester. The analysis also indicated that learning pattern also has the potential to be improved and maintained.

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Introduction into Sparks of the Learning Analytics Future

Cited by 9 publications

References 18 publications

Brave New World: Navigating New Paradigms in the Educational Landscape for the Evolving Learner in a Rapidly Changing Era

Brave New World: Navigating New Paradigms in the Educational Landscape for the Evolving Learner in a Rapidly Changing Era

Visualization of Learning Log of Web-Based Application for Understanding the Structure of Geometric Proofs

Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering

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