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About 10,000 papers for “Learning analytics”
Mahiéddine Djoudi, Vanda Luengo, Hassina El Kechaï et al. · SPIRE - Sciences Po Institutional REpository · 2018
L’objectif principal est de préciser les définitions dans le domaine de l’éducation des termes suivants (souvent anglais) : Learning analytics, educational data mining, deep learning , learning machine, adaptive Learning, etc.
Cited by 0
Heilala, Ville · Jyväskylä University Digital Archive (University of Jyväskylä) · 2022
Pedagogically meaningful, research-based, and ethical learning analytics could foster the values and learning aims we want to advance in our society and educational system. However, it is essential to combine knowledge of the learning sciences and computational sciences when developing and applying learning analytics. This dissertation advances ananalytics approach called student agency analytics that utilizes learning analytics methods and computational psychometrics. Student agency is a vital characteristic of a learner, especially during times of uncertainty and change. Student agency has been raised to an important position in educational policymaking, and it has been identified as an essential aspect to consider when facilitating lifelong learning. The research advances the analysis process, examines the results from the student and teacher point of view, and provides novel insights into student agency. Specifically, the research addresses the issue of how to combine theoretical knowledge of learning and analytical methods as a comprehensive process in learning analytics while taking into account teachers’ perspectives, methodological issues, and some limitations in learning analytics. The results show that i) student agency can be characterized, and different profiles can be generated using robust clustering, ii) higher course satisfaction and performance is associated with higher student agency, iii) students reporting low agentic resources experience various restrictive aspects in learning, iv) explainable artificial intelligence techniques can provide additional insight about the intricacies of student agency, and v) teachers can utilize the analytics results in professional reflection and pedagogical decision-making.
Cited by 0
Martin Drlík, Piet Kommers, Cyril Klimeš et al. · Constantine the Philosopher University in Nitra eBooks · 2024
Cited by 0Open access
Philip Henry Kerr · Cambridge University Press eBooks · 2022
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Cited by 0
Danny Yen-Ting Liu, Tim Rogers, Abelardo Pardo · ASCILITE Publications · 2015
The rise of learning analytics in the last few years has seen fervent development from institutions, researchers, and vendors. However, it seems to have had a laggard reception in higher education. Peering behind some barriers to adoption, we question whether common approaches that address the economics of low hanging fruit distract us from asking and answering deeper questions about student learning. This may lead to destructive feedback loops where learning analytics, swept by the currents of institutional agendas and cultures, does not deliver upon its promises to those who need it most - students and educators.
Cited by 6Open access
Peraya, Daniel · Archive ouverte UNIGE (University of Geneva) · 2021
Historique et définiton du concept. État de l'art, projets et perspectives en Suisse romande et au Tessin.
Cited by 0Open access
Peraya, Daniel · Archive ouverte UNIGE (University of Geneva) · 2021
Historique et définiton du concept. État de l'art, projets et perspectives en Suisse romande et au Tessin.
Cited by 0Open access
Syed Ibad Ali, Mohammad Shahnawaz Shaikh, Pabitra Kumar Nandi et al. · IGI Global eBooks · 2025
Learning analytics is a significant area of technology-enhanced learning (TEL) that has emerged during the last decade. This review of the field begins with an examination of the technological, educational, and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learning-focused perspectives, and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining, and academic analytics. Finally, it examines developing areas of learning analytics research and identifies a series of future challenges.
Cited by 6
The Center on Online Learning and Students with Disabilities · KU ScholarWorks (The University of Kansas) · 2012
Cited by 0Open access
Scheffel, Maren · DSpace (Open University in the Netherlands) · 2017
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Austin Gibbons · 2011
Computer Science educators are faced with the tradeo between the amount of time available and giving students individual attention. In many classrooms, particularly in the K-12 grades, it is not possible for
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Mackie, Kyle, Troop, Meagan · SOURCE Sheridan's Institutional Repository (Sheridan College) · 2021
This open textbook is a curation of resources related to recent learning analytics (LA) initiatives at Sheridan College, exploring LA from philosophical, methodological, instructional, and ethical points of view.\nSheridan’s work in LA aims to leverage learning data to help foster a culture of care and bolster the success of students. While these resources are grounded in the work at the College, it is our hope that they will support and inspire educators with tools and guidance on how to use data to inform and enhance teaching and learning.\nIn the pages that follow, you will find background information related to LA, recordings from Community of Practice meetings, interactive infographics, links to current research, guiding documents, and reflective worksheets.
Cited by 0
Dian Permatasari, - · Digital Library UIN Sunan Kalijaga (Sunan Kalijaga State Islamic University) · 2022
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Ice Phil, Layne Melissa, Boston Wallace · 2023
The lineage of quantitative research from classical statistics to learning analytics represents several transformations from classical statistics through artificial intelligence, machine learning, and data mining. This chapter discusses learning analytics as a tool for quality assurance with the concept of data mining. Taking the concepts of learning and discovery to the next level, learning analytics collects the data, analyzes the data, and investigates emerging patterns, revealing patterns yielding pathways to informed decision making. Various quality assurance models for higher education have been designed with high expectations and hopes that they will eventually gain universal adoption. The chapter looks at the convergence of Big Data, data mining, and learning analytics, emphasizing the elements of quality assurance that these forces seek to uncover. Access to data across institutional departments provides administrators with an overarching perspective that enables insight into how strategic decisions made at the administrative level eventually affect the student.
Cited by 0
Dirk Ifenthaler, Hendrik Drachsler · 2020
Cited by 24
Heil, Joana, Bandtel, Matthias · KITopen · 2023
update ist das Online-Format des Hochschulnetzwerk Digitalisierung der Lehre Baden-Württemberg (HND-BW) für den Austausch zu aktuellen Fragen des digitalen Lehrens, Lernens und Prüfens. Am 17.02.2023, 12:00 – 13:00 Uhr, findet die vierte Ausgabe von HND-BW up>date statt: Joana Heil (Universität Mannheim) stellt das Learning Analytics-Tool „LeAP“ vor. Joana Heil ist wissenschaftliche Mitarbeiterin am Lehrstuhl für Technologiebasiertes Instruktionsdesign an der Universität Mannheim. Ihre Hauptforschungsinteressen liegen in den Bereichen adaptives Lernen, Online-Assessment und Learning Analytics. LeAP ist eine Learning Analytics-Anwendung, welche in das Learning Management System Ilias integriert ist.
Cited by 0Open access
Shireen Panchoo · 2018
During face to face classroom interactions, learning is facilitated through the dissemination of course materials by subject specialists, in a pedagogical manner, suiting the pace of the learners. It is known that proper interactions with contents, tutors and peers encourage learning to take place. However, the learner-content process is known to be challenging for online learners who struggle alone to overcome learning difficulties, and which may lead them to drop out from the course. In absence of face to face interactions, the content, being the prime source of knowledge for distant learners, has to be up to date and comprehensive while catering for different learning styles of students. Therefore, this paper aims at investigating whether learners meet the cognitive level as required by the course's learning objectives. Through a questionnaire, 18 online learners were requested to identify their learning activities done in both asynchronous and synchronous modes as part of their learning process. Furthermore, content analysis, using both Activity Theory and the Jaillet & Panchoo grid, was done on four online tutorials to identify if learners study their course materials as desired and if their interactions are of cognitive level. A total of 761 lines of logged textual interactions was coded twice. Results show that learners go through different learning activities as imposed by the module but less emphasis is put on ensuring that learners study the course content before embarking on the assignment. Acquiring the relevant knowledge from the course materials prior to online discussions, will encourage better interactions and learning to take place in this socio-constructivism set up.
Cited by 3
Siemens, George · UTS ePRESS (University of Technology Sydney) · 2012
This presentation was given at the University of Technology Sydney as part of a forum on a data intensive university
Cited by 0
Sharon Slade, Paul Prinsloo · American Behavioral Scientist · 2013
The field of learning analytics has the potential to enable higher education institutions to increase their understanding of their students’ learning needs and to use that understanding to positively influence student learning and progression. Analysis of data relating to students and their engagement with their learning is the foundation of this process. There is an inherent assumption linked to learning analytics that knowledge of a learner’s behavior is advantageous for the individual, instructor, and educational provider. It seems intuitively obvious that a greater understanding of a student cohort and the learning designs and interventions they best respond to would benefit students and, in turn, the institution’s retention and success rate. Yet collection of data and their use face a number of ethical challenges, including location and interpretation of data; informed consent, privacy, and deidentification of data; and classification and management of data. Approaches taken to understand the opportunities and ethical challenges of learning analytics necessarily depend on many ideological assumptions and epistemologies. This article proposes a sociocritical perspective on the use of learning analytics. Such an approach highlights the role of power, the impact of surveillance, the need for transparency, and an acknowledgment that student identity is a transient, temporal, and context-bound construct. Each of these affects the scope and definition of learning analytics’ ethical use. We propose six principles as a framework for considerations to guide higher education institutions to address ethical issues in learning analytics and challenges in context-dependent and appropriate ways.
Cited by 894Open access
Duval, Erik; 16838;, Verbert, Katrien; 40828; · Lirias (KU Leuven) · 2012
This paper provides a brief introduction to the domain of ‘learning analytics’. We first explain the background and idea behind the concept. Then we give a brief overview of current research issues. We briefly list some more controversial issues before concluding.
Cited by 0Open access