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Journal articles on the topic 'Learning analytics'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Søby, Morten. "Learning Analytics." Nordic Journal of Digital Literacy 9, no. 02 (2014): 89–91. http://dx.doi.org/10.18261/issn1891-943x-2014-02-01.

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Slade, Sharon, and Paul Prinsloo. "Learning Analytics." American Behavioral Scientist 57, no. 10 (2013): 1510–29. http://dx.doi.org/10.1177/0002764213479366.

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Siemens, George. "Learning Analytics." American Behavioral Scientist 57, no. 10 (2013): 1380–400. http://dx.doi.org/10.1177/0002764213498851.

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Ifenthaler, Dirk. "Learning Analytics." Zeitschrift SEMINAR 28 (September 23, 2022): 52–63. http://dx.doi.org/10.3278/sem2203w005.

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Ebner, Martin, Kinshuk, David Wohlhart, Benham Taraghi, and Vive Kumar. "Learning Analytics." JUCS - Journal of Universal Computer Science 21, no. (1) (2015): 1–6. https://doi.org/10.3217/jucs-021-01.

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Webb, Stephen. "Learning analytics explained." Innovations in Education and Teaching International 54, no. 6 (2017): 625–26. http://dx.doi.org/10.1080/14703297.2017.1380692.

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Chatti, Mohamed Amine, Anna Lea Dyckhoff, Ulrik Schroeder, and Hendrik Thüs. "Forschungsfeld Learning Analytics." i-com 11, no. 1 (2012): 22–25. http://dx.doi.org/10.1524/icom.2012.0007.

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Summary Learning analytics has attracted a great deal of attention in technology enhanced learning (TEL) in recent years as educational institutions and researchers are increasingly seeing the potential that learning analytics has to support the learning process. Learning analytics has been identified as a possible key future trend in learning and teaching (Johnson et al., 2011). Analytics can be a powerful tool to support learning. There are, however, a number of issues that need to be addressed before starting analytics projects. In this paper, we identify various challenges and research opp

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Pistilli, Matthew D. "Learning Analytics Explained." Open Learning: The Journal of Open, Distance and e-Learning 33, no. 3 (2018): 267–69. http://dx.doi.org/10.1080/02680513.2018.1486187.

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Suzani, Mohamad Samuri. "LACLOD: Learning Analytics for Children's Logic Development." International Journal of Multimedia & Its Applications (IJMA) 13, no. 1/2 (2021): 1–14. https://doi.org/10.5281/zenodo.4899482.

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Learning Analytics for Children's Logic Development (LACLOD) is a web-based and mobile friendly learning analytic platform for assessing the logic development of children age 3 to 4 years old in TASKA PERMATA UPSI Malaysia. The platform is developed using Unity and connected through Google Analytics (GA) plugin where it tracked the user interaction for the application. LACLOD is designed only for mobile or tablet which is using Android. In this paper, the development of this learning analytic platform is presented. For evaluation of this system, observation and survey have been used, to ge

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Maseleno, Andino, Noraisikin Sabani, Miftachul Huda, Roslee Ahmad, Kamarul Azmi Jasmi, and Bushrah Basiron. "Demystifying Learning Analytics in Personalised Learning." International Journal of Engineering & Technology 7, no. 3 (2018): 1124. http://dx.doi.org/10.14419/ijet.v7i3.9789.

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This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relat

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Narayan Koranchirath, Nithin. "Impact of Machine Learning on Healthcare Analytics." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 942–47. http://dx.doi.org/10.21275/sr24210203022.

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Dauitbayeva, A. O. "MODERN METHODS OF MACHINE LEARNING AND ANALYTICS." ТЕХНИКА ҒЫЛЫМДАРЫ ЖӘНЕ ТЕХНОЛОГИЯ 7, no. 3 (2024): 12–19. https://doi.org/10.52081/tst.2024.v03.i7.039.

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The article proposes algorithms for processing big data to optimize business processes. The methods of data integration, distributed computing and machine learning for analysis and forecasting are considered. Testing on business cases has shown cost reduction and increased accuracy of solutions, confirming the practical value of the developed approaches. In the modern world, the volume of data is growing exponentially, thanks to the development of technology and the ubiquity of digital devices. Petabytes of information are generated daily: This is data from social networks, electronic devices,

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Siemens, George. "The Journal of Learning Analytics: Supporting and Promoting Learning Analytics Research." Journal of Learning Analytics 1, no. 1 (2014): 3–5. http://dx.doi.org/10.18608/jla.2014.11.2.

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The paper gives a brief overview of the main activities for the development of the emerging field of learning analytics led by the Society for Learning Analytics Research (SoLAR). The place of the Journal of Learning Analytics is identified Analytics is the most significant new initiative of SoLAR.

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Shahril Khuzairi, Nur Maisarah, Manjit Singh Sidhu, and Zaihisma Che Cob. "Learning Analytics and Teaching Analytics: The Similarities and Differences." International Journal of Humanities, Management and Social Science 3, no. 2 (2020): 52–58. http://dx.doi.org/10.36079/lamintang.ij-humass-0302.135.

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Analytics in education which constitutes of Learning Analytics and Teaching Analytics arouses great attention among researchers and practitioners in the current climate. The use of analytics in education enables educational data to be collected and analysed to serve the needs of all stakeholders to improve the educational process. The present paper gives an overview of Learning Analytics and Teaching Analytics and explores its similarities and differences, as well as the confusion that has been raised between the two defined terms. Alongside, the analytics selection flowchart presented in this

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James, Nikki. "Learning Analytics to Support Experiential Learning." Experiential Learning and Teaching in Higher Education 3, no. 3 (2022): 8. http://dx.doi.org/10.46787/elthe.v3i3.3420.

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Hernandez Leo, Davinia, María Jesús Rodríguez-Triana, Paul Salvador Inventado, and Yishay Mor. "Connecting Learning Design and Learning Analytics." Interaction Design and Architecture(s), no. 33 (June 20, 2017): 3–8. http://dx.doi.org/10.55612/s-5002-033-001psi.

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Kalshetti, Prof U. M., Keyur Kulkarni, Deepenkumar Patel, and Sharang Nimbalkar. "Students Learning Evaluation Using Learning Analytics." International Journal of Advanced Engineering Research and Science 4, no. 4 (2017): 89–92. http://dx.doi.org/10.22161/ijaers.4.4.11.

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Hansen, Randy. "Learning Analytics: Data for Learning Design." Journal of Digital Learning in Teacher Education 32, no. 2 (2016): 48–49. http://dx.doi.org/10.1080/21532974.2016.1158029.

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Joksimovic, Srecko, Dragan Gasevic, and Marek Hatala. "Learning Analytics for Networked Learning Models." Journal of Learning Analytics 1, no. 3 (2014): 191–94. http://dx.doi.org/10.18608/jla.2014.13.20.

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Teaching and learning in networked setting has attained a significant amount of attention recently. The central topic of networked learning research is human-human and human-information interactions that occur within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach in analyzing their effects. Therefore, the main goal of this research is the development of a theoretical model that allows for a comprehensive and scalable analysis of how and why learners engage into collaboration in networked communities. The pr

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Pechenizkiy, Mykola, and Dragan Gasevic. "Introduction into Sparks of the Learning Analytics Future." Journal of Learning Analytics 1, no. 3 (2015): 145–49. http://dx.doi.org/10.18608/jla.2014.13.8.

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This section offers a compilation of 16 extended abstracts summarizing research of the doctoral students who participated in the Second Learning Analytics Summer Institute (LASI 2014) held at Harvard University in July 2014. The abstracts highlight the motivation, main goals and expected contributions to the field from the ongoing learning analytics doctoral research around the globe. These works cover several major topics in learning analytics including novel methods for automated annotations, longitudinal analytic studies, networking analytics, multi-modal analytics, dashboards, and data-dri

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Knobbout, Justian, and Esther Van der Stappen. "A Capability Model for Learning Analytics Adoption: Identifying Organizational Capabilities from Literature on Learning Analytics, Big Data Analytics, and Business Analytics." International Journal of Learning Analytics and Artificial Intelligence for Education (iJAI) 2, no. 1 (2020): 47. http://dx.doi.org/10.3991/ijai.v2i1.12793.

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Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “<em>What capabilities for the successful adoption of learning analytics can be identifie

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Wang, Shouhong, and Hai Wang. "Knowledge Analytics." International Journal of Business Analytics 7, no. 4 (2020): 14–23. http://dx.doi.org/10.4018/ijban.2020100102.

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Big data has raised challenges and opportunities for the education sector. Educational analytics encompass a variety of computational techniques to process educational big data for effective teaching, learning, research, service, and administrative decision making. Learning analytics and academic analytics have been widely discussed in the literature of education; however, knowledge analytics have not been discussed in the educational analytics field. Knowledge analytics are a relatively new subject in the knowledge management area. Knowledge analytics lie outside of the definitions of learnin

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Wise, Alyssa, Yuting Zhao, and Simone Hausknecht. "Learning Analytics for Online Discussions: Embedded and Extracted Approaches." Journal of Learning Analytics 1, no. 2 (2014): 48–71. http://dx.doi.org/10.18608/jla.2014.12.4.

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This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some embedded in the learning environment to provide students with real-time information on their activity in-progress; and some extracted from the lea

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Macfadyen, Leah P., Lori Lockyer, and Bart Rienties. "Learning Design and Learning Analytics: Snapshot 2020." Journal of Learning Analytics 7, no. 3 (2020): 6–12. http://dx.doi.org/10.18608/jla.2020.73.2.

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“Learning design” belongs to that interesting class of concepts that appear on the surface to be simple and self-explanatory, but which are actually definitionally vague and contested in practice. Like “learning analytics,” the field of learning design aspires to improve teaching practice, the learning experience, and learning outcomes. And like learning analytics, this interdisciplinary field also lacks a shared language, common vocabulary, or agreement over its definition and purpose, resulting in uncertainty even about who its practitioners are — Educators? Designers? Researchers? All of th

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Zainuddin, Muhammad Izzat Izzuddin bin, and Hairulliza Mohamad Judi. "Personalised Learning Analytics: Promoting Student’s Achievement and Enhancing Instructor’s Intervention in Self-regulated Meaningful Learning." International Journal of Information and Education Technology 12, no. 11 (2022): 1243–47. http://dx.doi.org/10.18178/ijiet.2022.12.11.1745.

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Academic monitoring is implemented at higher learning institutions to allow students and instructors to communicate academically, especially learning progress. However, the system cannot monitor student performance on an ongoing basis, such as class attendance, continuous assessment records and assignment submissions. Personalised learning analytics use student-generated data and analytical models to gather learning patterns so that instructors may advise on students’ learning. Although various studies provide insight into the analytical framework of learning, attention to self-regulated meani

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Bonnin, Geoffray, and Anne Boyer. "Apport des Learning Analytics." Administration & �ducation N�146, no. 2 (2015): 125. http://dx.doi.org/10.3917/admed.146.0125.

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Ifenthaler, Dirk, and Clara Schumacher. "Learning Analytics im Hochschulkontext." WiSt - Wirtschaftswissenschaftliches Studium 45, no. 4 (2016): 176–81. http://dx.doi.org/10.15358/0340-1650-2016-4-176.

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Koh, Ying Yun Juliana, Henk G. Schmidt, Naomi Low-Beer, and Jerome I. Rotgans. "Team-Based Learning Analytics." Academic Medicine 95, no. 6 (2020): 872–78. http://dx.doi.org/10.1097/acm.0000000000003157.

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Verbert, Katrien, Erik Duval, Joris Klerkx, Sten Govaerts, and José Luis Santos. "Learning Analytics Dashboard Applications." American Behavioral Scientist 57, no. 10 (2013): 1500–1509. http://dx.doi.org/10.1177/0002764213479363.

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Keshavamurthy, Usha, and Dr H. S. Guruprasad. "Learning Analytics: A Survey." International Journal of Computer Trends and Technology 18, no. 6 (2014): 260–64. http://dx.doi.org/10.14445/22312803/ijctt-v18p155.

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31

Hershkovitz, Arnon, Simon Knight, Shane Dawson, Jelena Jovanović, and Dragan Gašević. "About "Learning" and "Analytics"." Journal of Learning Analytics 3, no. 2 (2016): 1–5. http://dx.doi.org/10.18608/jla.2016.32.1.

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This issue of the Journal of Learning Analytics features three special sections that look into topics of learning analytics for 21st century skills, multimodal learning analytics, and sharing of datasets for learning analytics. The issue also features a paper that looks at models for early detection of students at risk in tertiary education. The editorial concludes with a summary of the changes in the editorial team of the journal.

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Johannes, Paul C., Christian L. Geminn, and Maxi Nebel. "Learning Analytics nach Satzung." Datenschutz und Datensicherheit - DuD 47, no. 11 (2023): 715–20. http://dx.doi.org/10.1007/s11623-023-1849-y.

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Chahal, Ayushi, Preeti Gulia, and Nasib Singh Gill. "Different analytical frameworks and bigdata model for internet of things." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1159–66. https://doi.org/10.11591/ijeecs.v25.i2.pp1159-1166.

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Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data an

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Ngulube, Patrick, and Mthokozisi Masumbika Ncube. "Leveraging Learning Analytics to Improve the User Experience of Learning Management Systems in Higher Education Institutions." Information 16, no. 5 (2025): 419. https://doi.org/10.3390/info16050419.

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This systematic review examines the application of learning analytics to enhance user experience within Learning Management Systems in higher education institutions. Addressing a salient knowledge gap regarding the optimal integration of learning analytics for diverse learner populations, this study identifies analytical approaches and delineates implementation challenges that contribute to data misinterpretation and underutilisation. Consequently, the absence of a systematic evaluation of analytical methodologies impedes the capacity of higher education institutes to tailor learning processes

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J, Prathipa. "Stock Market Analytics: Statistical and Machine Learning Techniques." International Journal of Psychosocial Rehabilitation 24, no. 1 (2020): 1828–33. http://dx.doi.org/10.37200/ijpr/v24i1/pr200284.

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Tadi, Venkata. "Transparent Machine Learning: Building Trust in Data Analytics." International Journal of Science and Research (IJSR) 9, no. 12 (2020): 1850–57. http://dx.doi.org/10.21275/sr24709205430.

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Bozkurt, Aras, and C. Sharma Ramesh. "Exploring the learning analytics equation: What about the carpe diem of teaching and learning?" Asian Journal of Distance Education 17, no. 2 (2022): i—xiv. https://doi.org/10.5281/zenodo.7402312.

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Humans have always been lured by the idea that they can use data to understand a phenomenon and make predictions about it. Learning analytics, in this sense, promise to understand and optimize learning and the environments in which it occurs by collecting data from learners and learning contexts. In this regard, this study systematically examines research on learning analytics through bibliometric, data mining, and analytics approaches. This paper argues that research interest in learning analytics is increasing steadily; some countries, higher education institutions, and researchers have a sp

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Prof., Suman Acharya. "Crime Analytics using Machine Learning." International Journal of Inventive Engineering and Sciences (IJIES) 10, no. 3 (2023): 1–5. https://doi.org/10.35940/ijies.F7444.0310323.

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<strong>Abstract: </strong>Crime is one of the most significant and pervasive problems in our society, and preventing it is a crucial duty. A large number of crimes are perpetrated each day. Maintaining and analyzing crime data to forecast and solve crimes is the current issue. This project analyzes a large dataset of crimes and predicts future crimes based on conditions. This project uses data science and machine learning for India's crime data prediction. Thus, Decision Tree, Logistic Regression, Multi-Regression, k-NN, Lasso & Ridge, and Random Forest are all involved in the supervi

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Reinders, Hayo. "Learning Analytics for Language Learning and Teaching." JALT CALL Journal 14, no. 1 (2018): 77–86. http://dx.doi.org/10.29140/jaltcall.v14n1.225.

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Reinders, Hayo. "Learning analytics for language learning and teaching." JALT CALL Journal 14, no. 1 (2018): 77–86. http://dx.doi.org/10.29140/jaltcall.v14n1.j225.

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If only we could know what our students were up to at any given moment in class. Who is paying attention, and who is falling asleep? Who understands the past perfect and who thinks it is about something wonderful that happened yesterday? And wouldn’t it be great if we knew who is motivated and who is ready to drop out of the course? Language teachers perhaps struggle with these questions even more than teachers in other domains, because their students are not able to communicate their preferences and needs as well as l1 speakers. Learning analytics involves monitoring student engagement and co

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Chen, Weiqin. "Knowledge-Aware Learning Analytics for Smart Learning." Procedia Computer Science 159 (2019): 1957–65. http://dx.doi.org/10.1016/j.procs.2019.09.368.

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Emerson, Andrew, Elizabeth B. Cloude, Roger Azevedo, and James Lester. "Multimodal learning analytics for game‐based learning." British Journal of Educational Technology 51, no. 5 (2020): 1505–26. http://dx.doi.org/10.1111/bjet.12992.

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Pfeiffer, Anke, and Dieter Uckelmann. "Fostering Lab-Based Learning with Learning Analytics." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 14 (2022): 4–27. http://dx.doi.org/10.3991/ijoe.v18i14.35073.

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Digital learning environments, such as online laboratories offer many opportunities for collecting data for Learning Analytics (LA). This article presents a systematic literature review for LA in laboratory based learning environments for Higher Engineering Education, which yielded 23 key references. The focus of the study was formed by the following research questions (RQ): What types of data are currently collected in online laboratories (RQ 1)? How is LA used to support learning and teaching processes as well as the design of the online-laboratory environment (RQ 2)? What design recommendat

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Agus, Rahmadi, and Suzani Mohamad Samuri. "Learning Analytics Contribution in Education and Child Development: A Review on Learning Analytics." Asian Journal of Assessment in Teaching and Learning 8 (December 23, 2018): 36–47. http://dx.doi.org/10.37134/ajatel.vol8.4.

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Agus, Rahmadi, and Suzani Mohamad Samuri. "Learning Analytics Contribution in Education and Child Development: A Review on Learning Analytics." Asian Journal of Assessment in Teaching and Learning 8 (December 23, 2018): 36–47. http://dx.doi.org/10.37134/ajatel.vol8.4.2018.

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Viberg, Olga, Barbara Wasson, and Agnes Kukulska-Hulme. "Mobile-assisted language learning through learning analytics for self-regulated learning (MALLAS): A conceptual framework." Australasian Journal of Educational Technology 36, no. 6 (2020): 34–52. http://dx.doi.org/10.14742/ajet.6494.

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Many adult second and foreign language learners have insufficient opportunities to engage in language learning. However, their successful acquisition of a target language is critical for various reasons, including their fast integration in a host country and their smooth adaptation to new work or educational settings. This suggests that they need additional support to succeed in their second language acquisition. We argue that such support would benefit from recent advances in the fields of mobile-assisted language learning, self-regulated language learning, and learning analytics. In particul

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Dyulicheva, Yu Yu. "Application of Learning Analytics in Higher Education: Datasets, Methods and Tools." Vysshee Obrazovanie v Rossii = Higher Education in Russia 33, no. 5 (2024): 86–111. http://dx.doi.org/10.31992/0869-3617-2024-33-5-86-111.

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The accumulation of big educational data on the platforms of universities and social media leads to the need to develop tools for extracting regularities from educational data, which can be used for understanding the behavioral patterns of students and teachers, improve teaching methods and the quality of the educational process, as well as form sound strategies and policies for universities development. This article provides an analysis and systematization of datasets on available repositories, taking into account the learning analytics problems solved on their basis. In particular, the artic

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Mangaroska, Katerina, and Michail Giannakos. "Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning." IEEE Transactions on Learning Technologies 12, no. 4 (2019): 516–34. http://dx.doi.org/10.1109/tlt.2018.2868673.

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Tretow-Fish, Tobias Alexander Bang, and Md Saifuddin Khalid. "Methods for Evaluating Learning Analytics and Learning Analytics Dashboards in Adaptive Learning Platforms: A Systematic Review." Electronic Journal of e-Learning 21, no. 5 (2023): 430–49. http://dx.doi.org/10.34190/ejel.21.5.3088.

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This research paper highlights and addresses the lack of a systematic review of the methods used to evaluate Learning Analytics (LA) and Learning Analytics Dashboards (LAD) of Adaptive Learning Platforms (ALPs) in the current literature. Addressing this gap, the authors built upon the work of Tretow-Fish and Khalid (2022) and analyzed 32 papers, which were grouped into six categories (C1-6) based on their themes. The categories include C1) the evaluation of LA and LAD design and framework, C2) the evaluation of user performance with LA and LAD, C3) the evaluation of adaptivity, C4) the evaluat

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Klašnja-Milićević, Aleksandra, Mirjana Ivanović, and Bela Stantić. "Designing Personalized Learning Environments — The Role of Learning Analytics." Vietnam Journal of Computer Science 07, no. 03 (2020): 231–50. http://dx.doi.org/10.1142/s219688882050013x.

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Learning analytics, as a rapidly evolving field, offers an encouraging approach with the aim of understanding, optimizing and enhancing learning process. Learners have the capabilities to interact with the learning analytics system through adequate user interface. Such systems enables various features such as learning recommendations, visualizations, reminders, rating and self-assessments possibilities. This paper proposes a framework for learning analytics aimed to improve personalized learning environments, encouraging the learner’s skills to monitor, adapt, and improve their own learning. I

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