SOLAR (Society for Learning Analytics Research) est un réseau interdisciplinaire de chercheurs internationaux de premier plan qui explorent le rôle et l'impact de « Ananlytics » sur l'enseignement, l'apprentissage, la formation et le développement. SoLAR continue de participer activement à l'organisation de la Conférence internationale sur le « Learning Analytics & Knowledge (LAK) » ainsi que le « Learning Analytics Summer Institute (LASI), » en lançant de multiples initiatives pour soutenir la recherche collaborative et ouverte, la promotion de la publication et de la diffusion de la recherche sur le «Learning Analytics, Elle prodigue des conseils et des expertises aux gouvernements étatiques, provinciaux et nationaux. Pour plus d’information, voir son site web au https://solaresearch.org/

Le programme Learning Analytics Community Exchange (LACE) était un projet financé par l'UE dans le cadre du 7ème programme-cadre impliquant neuf partenaires de toute l'Europe. Les partenaires de LACE sont passionnés par les perspectives actuelles et futures de l'analyse de l'apprentissage (LA) et de l'exploration de données éducatives (EDM). Le projet visait à intégrer les communautés travaillant sur le LA et l'EDM,  des écoles des universités en partageant des solutions efficaces à des problèmes réels.

En France, suite à un financement du ministère pour l'étude Learning Analytics comme solution au sein de l'enseignement supérieur français, le consortium national ESUP-Portail a mis en place la plateforme "Apereo Learning Analytics Initiative". Elle est destinée à étudier et évaluer les possibilités offertes par les Learning Analytics dans un contexte français. Les tests seront réalisées à l'université de Lorraine qui possède une plateforme de plus de 11 000 cours en ligne sur laquelle se rendent les étudiants pour suivre et participer aux cours.

L'objectif premier est de : récupérer les traces numériques laissées par ces étudiants sur cette plateforme, stocker ces traces dans une base de données No.S.Q.L, et enfin, relier ces traces à d'autres informations en lien avec leur scolarité (notes, diplômes obtenus, etc.). Un moteur d'algorithme prédictif basé sur des technologies "Big-Data" est fourni dans cette plateforme. Il permettra de traiter les informations collectées.

L’appel national à projets e-FRAN (Espaces de formation, de recherche et d’animation numérique) s’inscrit dans le cadre d’un investissement scientifique de grande ampleur pour accompagner la mise en œuvre du plan numérique pour l’éducation. 

Le projet METAL (Modèles Et Traces au service de l’Apprentissage des Langues) porté par le LORIA (Laboratoire Lorrain de Recherche en Informatique et ses Applications) vise à améliorer l’apprentissage écrit et oral des langues en analysant les traces laissées par l’élève lors de ces apprentissages et en développant de nouveaux outils permettant de personnaliser l’apprentissage en fonction des besoins et du rythme de chaque apprenant.

La conference « International Conference on Learning Analytics and Knowledge » est organisé régulièrement depuis 2011 et s’intéresse au Learning Analytics et la connaissance.

La première conférence, organisée à Alberta (USA) du 27 février au 1^er mars 2011, par George Siemens,  s’est concentré sur l'intégration des dimensions techniques et sociales et pédagogiques de l'analyse d'apprentissage.  Elle fût à l’origine d’une des tentatives de définitions du terme « Learning Analytics ». (LAK 2011).

LAK 2012 a eu lieu à Vancouver (Canada) du 29 avril au 02 mai 2012, LAK 2013 à Leuven (Belgique) du 08 au 12avril 2013, LAK 2014 à Indianapolis (USA) du 24 au 28 mars  2014, LAK 2015 à Poughkeepsie (USA) du 16 au 20 mars 2015, LAK 2016 à Edinburgh, (Royaume Uni) du 25 au 29 avril 2016 et LAK 2017, la 7^ème édition a eu lieu à nouveau à Vancouver (Canada)  du 13 au 17 mars 2017. L’édition du LAK’2018 au lieu à Sydney, NSW, Australia, du 5 au 9 mars 2018.

Conférences LAK 

• LAK'2018 (Sydney, Australia)
• LAK2017 (Vancouver, BC)
• LAK2016 (Edinburgh, UK)
• LAK2015 (Poughkeepsie, NY)
• LAK2014 (Indianapolis, IN)
• LAK2013 (Leuven, Belgium)
• LAK2012 (Vancouver, BC)
• LAK11 (Banff, AB)

LASI (Learning Analytics Summer Institute) ou l'atelier d'été sur le Learning Analytics est un événement stratégique, co-organisé par SoLAR et les institutions hôtes. Parallèlement, un réseau mondial de sections locales LASI gère ses propres ateliers. 

Le LASI se veut un regard critique sur ce qui pourrait être considéré comme une nouvelle discipline avec un potentiel  pour la recherche et la pratique en éducation. Réunir le bon groupe de personnes pour un «atelier d'été» intensif pourrait servir de tremplin intellectuel et social pour accélérer la maturation de la discipline.

Ateliers LASI

• LASI 2018 (New York City, NY)
• LASI 2017 (Ann Arbor, MI)
• LASI 2016 (Ann Arbor, MI)
• A-LASI 2015 (Sydney, NSW)
• LASI 2015 International Network
• LASI 2014 (Cambridge, MA)
• A-LASI 2014 (Sydney, NSW)
• LASI-Locals & Global Online 2014
• A-LASI 2013 (Sydney, NSW)
• LASI 2013 (Palo Alto, CA)
• LASI-Locals & Global Online 2013

Il s'agit d'une série d'événements régionaux organisés en coopération avec SoLAR pour faciliter l'échange d'informations, d'études de cas, d'idées pour les jeunes chercheurs.

Les événements Flare sont 

• SoLAR Flare Southeast US (Georgia Institute of Technology, 2018)
• LACE SoLAR Flare (OU, 2015)
• SoLAR Flare Southeast US (Emory, 2015)
• LACE SoLAR Flare (OU, 2014)
• SoLAR Flare UK (OU, 2012)
• SoLAR Flare Australia (UTS, 2012)
• SoLAR Flare USA (Purdue,

La production, la collecte, l'agrégation et le traitement à grande échelle d'informations provenant de diverses plates-formes d'apprentissage et d'environnements en ligne ont suscité des préoccupations éthiques et de confidentialité en ce qui concerne les dommages potentiels aux individus et à la société. Dans le passé, ces types de préoccupations ont eu des répercussions dans des domaines aussi divers que l'informatique, les études juridiques et les études de surveillance.

Afin de placer les questions liées à la protection de la vie privée au cœur de la conception des applications d'analyse d'apprentissage, il est nécessaire de dégager ce concept en fonction de son contexte socioculturel.

Au sein d'un consortium européen, nous cherchons à mieux comprendre les problèmes, à trouver des moyens de surmonter les problèmes et de relever les défis liés à aspects éthiques et de confidentialité de la pratique de l'analyse de l'apprentissage. Ces ateliers interactifs ont pour but de sensibiliser aux enjeux majeurs de l'éthique et de la vie privée. Il sera également utilisé pour développer des solutions pratiques pour l'apprentissage des chercheurs et des praticiens de l'analyse qui leur permettront de faire progresser l'application des technologies d'analyse de l'apprentissage.

Le Workshop Ethics and Privacy in Learning Analytics (EP4LA) organise une série d'événements depuis octobre 2014 :

• 1er EP4LA @ Utrecht, NL, 28 octobre 2014
• 2ème EP4LA @ Education Days, NL, 11 novembre 2014
• 3e EP4LA @ BDEDU, Washington, États-Unis, 11 novembre 2014
• 4ème Fondation EP4LA @ Apereo, FR, février 2015
• 5ème EP4LA @ JISC, Royaume-Uni, février 2015
• 6e EP4LA @ LAK15, États-Unis, mars 2015
• 7ème EP4LA @ LAK16, Royaume-Uni, avril 2016
• 8ème EP4LA @ Duesto, Espagne, 28 juin 2016
• 9ème EP4LA @ Tallinn, Estonie, 20 juin 2016
• Kanazawa, Japon, 14 septembre 2016
• LASI-Asie, Séoul Corée, 19 - 20 septembre 2016
• LACE Chine, Pékin, 26 - 28 septembre 2016
• 10ème EP4LA @ EC-TEL 2017, Tallinn, Estonie, 12 septembre 2017

About the Journal

Journal of Learning Analytics is a peer-reviewed, open-access journal, disseminating the highest quality research in the field. The journal is the official publication of the Society for Learning Analytics Research (SoLAR). With an international Editorial Board comprising leading scholars, it is the first journal dedicated to research into the challenges of collecting, analysing and reporting data with the specific intent to improve learning. “Learning” is broadly defined across a range of contexts, including informal learning on the internet, formal academic study in institutions (primary/secondary/tertiary), and workplace learning.

The journal seeks to connect researchers and developers with practitioners, creating and disseminating new tools and techniques, studying transformations, and providing ongoing evaluation and critique of the conceptual, technical, and practice outcomes. The interdisciplinary focus of the journal recognizes that computational, pedagogical, institutional, policy and social domains must be brought into dialogue with each other to ensure that interventions and organizational systems serve the needs of all stakeholders. The journal seeks to bring into dialogue the intersection of the fields of Education, Computation and Sensemaking.

Journal of Learning Analytics welcomes papers that either describe original research or offer a review of the state of the art in a particular area. The journal also welcomes practice-focused papers or in-progress research reports that detail Learning Analytics applications in real-world settings, provided that they offer innovative insights for advancing the field (see the About section).

Manuscripts can be submitted to the Journal of Learning Analytics any time. Only manuscripts for special sections should be submitted by the specific date as defined in the call for papers of the special issues, which are available in theAnnouncements section.

In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics.

Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world.

Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to:

• Enhance student and faculty performance.
• Improve student understanding of course material.
• Assess and attend to the needs of struggling learners.
• Improve accuracy in grading.
• Allow instructors to assess and develop their own strengths.
• Encourage more efficient use of resources at the institutional level.

Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success

Bibliographic information

Data Mining and Learning Analytics: Applications in Educational Researc
Samira ElAtia, Donald Ipperciel, Osmar R. Zaà ¯ane
John Wiley & Sons, 26 sept. 2016 - 320 pages

Addresses the impacts of data mining on education and reviews applications in educational research teaching, and learning 

This book discusses the insights, challenges, issues, expectations, and practical implementation of data mining (DM) within educational mandates. Initial series of chapters offer a general overview of DM, Learning Analytics (LA), and data collection models in the context of educational research, while also defining and discussing data mining’s four guiding principles— prediction, clustering, rule association, and outlier detection. The next series of chapters showcase the pedagogical applications of Educational Data Mining (EDM) and feature case studies drawn from Business, Humanities, Health Sciences, Linguistics, and Physical Sciences education that serve to highlight the successes and some of the limitations of data mining research applications in educational settings. The remaining chapters focus exclusively on EDM’s emerging role in helping to advance educational research—from identifying at-risk students and closing socioeconomic gaps in achievement to aiding in teacher evaluation and facilitating peer conferencing. This book features contributions from international experts in a variety of fields.

Includes case studies where data mining techniques have been effectively applied to advance teaching and learning
Addresses applications of data mining in educational research, including: social networking and education; policy and legislation in the classroom; and identification of at-risk students
Explores Massive Open Online Courses (MOOCs) to study the effectiveness of online networks in promoting learning and understanding the communication patterns among users and students
Features supplementary resources including a primer on foundational aspects of educational mining and learning analytics
Data Mining and Learning Analytics: Applications in Educational Research is written for both scientists in EDM and educators interested in using and integrating DM and LA to improve education and advance educational research.
 

Developing Effective Educational Experiences through Learning Analytics

Mark Anderson (Edge Hill University, UK) and Collette Gavan (Edge Hill University, UK)

Release Date: April, 2016|Copyright: © 2016 |Pages: 361

ISBN13: 9781466699830|ISBN10: 1466699833|EISBN13: 9781466699847|DOI: 10.4018/978-1-4666-9983-0

Description

The quality of students’ learning experiences is a critical concern for all higher education institutions. With the assistance of modern technological advances, educational establishments have the capability to better understand the strengths and weaknesses of their learning programs.

Developing Effective Educational Experiences through Learning Analytics is a pivotal reference source that focuses on the adoption of data mining and analysis techniques in academic institutions, examining how this collected information is utilized to improve the outcome of student learning. Highlighting the relevance of data analytics to current educational practices, this book is ideally designed for researchers, practitioners, and professionals actively involved in higher education settings.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

• Global Higher Education
• Knowledge Visualization
• Online Learning
• Resampling Methods
• Robotics Applications
• Student Responsibility
• Survey Development

Reviews and Testimonials

Editors Anderson and Gavan present readers with a collection of academic essays and scholarly articles that together provide a reference to the theory and practice of the contemporary development of effective learning experiences through learning analytics in a variety of educational contexts. The twelve contributions that make up the main body of the text are focused on learning analytics for leveraging pedagogically purposeful indicators of academic performance, the role of learning analytics in enhancing teaching and learning, the role of learning analytics in global higher education, effective learning strategies for the twenty-first century, and many other related topics.

Learning Analytics Explained
1st Edition
by Niall Sclater (Author)
ISBN-13: 978-1138931732
ISBN-10: 113893173X

Learning Analytics Explained draws extensively from case studies and interviews with experts in order to discuss emerging applications of the new field of learning analytics. Educational institutions increasingly collect data on students and their learning experiences, a practice that helps enhance courses, identify learners who require support, and provide a more personalized learning experience. There is, however, a corresponding need for guidance on how to carry out institutional projects, intervene effectively with students, and assess legal and ethical issues. This book provides that guidance while also covering the evolving technical architectures, standards, and products within the field.

Learning Analytics in R with SNA, LSA, and MPIA 

1st ed. 2016 , Format Kindle

de Fridolin Wild (Auteur)

This book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which combines social network analysis (SNA) with latent semantic analysis (LSA) to help create and analyse a meaningful learning landscape from the digital traces left by a learning community in the co-construction of knowledge.    

The hybrid algorithm is implemented in the statistical programming language and environment R, introducing packages which capture – through matrix algebra – elements of learners’ work with more knowledgeable others and resourceful content artefacts. The book provides comprehensive package-by-package application examples, and code samples that guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner’s journey mapped and analysed. This building block application will allow the reader to progress to using and building analytics to guide students and support decision-making in learning.

Détails sur le produit

• Format : Format Kindle
• Taille du fichier : 6489.0 KB
• Nombre de pages de l'édition imprimée : 284 pages
• Editeur : Springer; Édition : 1st ed. 2016 (4 avril 2016)
• Vendu par : Amazon Media EU S.à r.l.
• Langue : Anglais
• ASIN: B01DTX3YX4

Learning Analytics: Fundaments, Applications, and Trends: A View of the Current State of the Art to Enhance e-Learning

Alejandro Peña-Ayala

Springer, 17 févr. 2017 - 303 pages

This book provides a conceptual and empirical perspective on learning analytics, its goal being to disseminate the core concepts, research, and outcomes of this emergent field. Divided into nine chapters, it offers reviews oriented on selected topics, recent advances, and innovative applications. It presents the broad learning analytics landscape and in-depth studies on higher education, adaptive assessment, teaching and learning. In addition, it discusses valuable approaches to coping with personalization and huge data, as well as conceptual topics and specialized applications that have shaped the current state of the art.

By identifying fundamentals, highlighting applications, and pointing out current trends, the book offers an essential overview of learning analytics to enhance learning achievement in diverse educational settings. As such, it represents a valuable resource for researchers, practitioners, and students interested in updating their knowledge and finding inspirations for their future work.

Learning Analytics: Measurement Innovations to Support Employee Development 

by John R. Mattox II (Author),‎ Mark Van Buren (Author),‎ Jean Martin (Author)

ISBN-13: 978-0749476304

ISBN-10: 0749476303

The potential to improve education due to the large amounts of data on learning and learners is unprecedented and has created an information gap in understanding what to do with all the raw data.

Providing a framework for understanding how to work with learning analytics, authors John R. Mattox II and Jean Martin show L&D and HR practitioners the power that effective analytics has on building an organization and the impact this power has on performance, talent management, and competitive advantage.
 

Martin and Mattox focus on aligning training with business needs and answering the questions “Is training effective?” and “How can it improved or made more effective?” Beginning with an explanation of what learning analytics is and the business need for it, they move on to applying business intelligence principles, linking learning to impact, connecting training content with business needs, optimizing investments in learning, and placing learning development within the larger scope of talent management. Chapters include case studies from Hilton Hotels, Shell Oil, and American Express to highlight best practice and to provide examples of how companies apply various methodologies across a range of industries.

Learning Analytics in Higher Education: New Directions for Higher Education, Number 179

John Zilvinskis (Editor), Victor Borden (Editor)

ISBN: 978-1-119-44382-7

120 pages

December 2017, Jossey-Bass

Description

Gain an overview of learning analytics technologies in higher education, including broad considerations and the barriers to introducing them. This volume features the work of practitioners who led some of the most notable implementations, like:

• the Open Learning Initiative now at Stanford University,
• faculty-led projects at the University of Michigan, including ECoach and SLAM,
• the University of Maryland, Baltimore County s Check My Activity and
• Indiana University s FLAGS early warning system and e-course advising initiatives. 
  
  Readers will glean from these experiences, as well as from a national project in Australia on innovative approaches for enhancing student experience, an informed description of the role of feedback within these technologies, and a thorough discussion of ethical and social justice issues related to the use of learning analytics, and why higher education institutions should approach such initiatives cautiously, intentionally, and collaboratively. 
  
  This is the 179th volume of the Jossey-Bass quarterly report series New Directions for Higher Education. Addressed to presidents, vice presidents, deans, and other higher education decision makers on all kinds of campuses, it provides timely information and authoritative advice about major issues and administrative problems confronting every institution.

Table of Contents

EDITORS’ NOTES 5
John Zilvinskis, Victor M. H. Borden

1. An Overview of Learning Analytics 9
John Zilvinskis, James Willis, III, Victor M. H. Borden

2. Incorporating Learning Analytics in the Classroom 19
Candace Thille, Dawn Zimmaro

3. Learning Analytics Across a Statewide System 33
Catherine Buyarski, Jim Murray, Rebecca Torstrick

4. Learner Analytics and Student Success Interventions 43
Matthew D. Pistilli

5. Cultivating Institutional Capacities for Learning Analytics 53
Steven Lonn, Timothy A. McKay, Stephanie D. Teasley

6. Using Analytics to Nudge Student Responsibility for Learning 65
John Fritz

7. Ethics and Justice in Learning Analytics 77
Jeffrey Alan Johnson

8. Learning Analytics as a Counterpart to Surveys of Student Experience 89
Victor M. H. Borden, Hamish Coates

9. Concluding Thoughts 103
John Zilvinskis, Victor M. H. Borden

INDEX 109

This book explores trends in learning and knowledge analytics in open education, as explored in proceedings papers from AECT-LKAOE 2015 International Research Symposium. The chapters investigate various issues surrounding open education in all disciplines, such as learning design in open-ended learning environments, MOOCs (Massive Open Online Courses), learning analytics studies and applications, and technology and new media. The chapter authors provide guidance for how to design and develop most effective, efficient, and appealing instruction as well as suggesting learning strategies relevant to the open education era.