Cognitive versus behavioral learning analytics dashboards for supporting learner’s awareness, reflection, and learning process

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Mohamed Fahmy Yousef ◽  
Ahmed Ramadan Khatiry
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Behavioral Learning

What are the risks of Virtual Reality data? Learning Analytics, Algorithmic Bias and a Fantasy of Perfect Data

2021 ◽  
pp. 146144482110127
Author(s):  
Marcus Carter ◽  
Ben Egliston
Keyword(s):  
Virtual Reality ◽  
Learning Process ◽  
Learning Analytics ◽  
Soft Skills ◽  
Education Technology ◽  
Skills Training ◽  
Emerging Technology ◽  
Critical Issues ◽  
Vr Training ◽  
Algorithmic Bias

Virtual reality (VR) is an emerging technology with the potential to extract significantly more data about learners and the learning process. In this article, we present an analysis of how VR education technology companies frame, use and analyse this data. We found both an expansion and acceleration of what data are being collected about learners and how these data are being mobilised in potentially discriminatory and problematic ways. Beyond providing evidence for how VR represents an intensification of the datafication of education, we discuss three interrelated critical issues that are specific to VR: the fantasy that VR data is ‘perfect’, the datafication of soft-skills training, and the commercialisation and commodification of VR data. In the context of the issues identified, we caution the unregulated and uncritical application of learning analytics to the data that are collected from VR training.

scholarly journals ALAS-KA: A learning analytics extension for better understanding the learning process in the Khan Academy platform

2015 ◽  
Vol 47 ◽  
pp. 139-148 ◽  
Author(s):  
José A. Ruipérez-Valiente ◽  
Pedro J. Muñoz-Merino ◽  
Derick Leony ◽  
Carlos Delgado Kloos
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Khan Academy

scholarly journals Demystifying Learning Analytics in Personalised Learning

2018 ◽  
Vol 7 (3) ◽  
pp. 1124 ◽  
Author(s):  
Andino Maseleno ◽  
Noraisikin Sabani ◽  
Miftachul Huda ◽  
Roslee Ahmad ◽  
Kamarul Azmi Jasmi ◽  
...  
Keyword(s):  
Learning Environment ◽  
Learning Process ◽  
Learning Analytics ◽  
Educational Institutions ◽  
Learning Resources ◽  
Personalised Learning ◽  
Report Data ◽  
Overall Performance ◽  
The Relationship ◽  
Specific Learning

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 relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics. 

Integrating Assessment, Feedback, and Learning Analytics in Educational Games

2019 ◽  
pp. 127-169
Author(s):  
Yaëlle Chaudy ◽  
Thomas M. Connolly
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Assessment Tool ◽  
Educational Games ◽  
Assessment Process ◽  
Crucial Aspect ◽  
Assessment Feedback ◽  
Black Boxes ◽  
Literature Reviews

Assessment is a crucial aspect of any teaching and learning process. New tools such as educational games offer promising advantages: they can personalize feedback to students and save educators time by automating the assessment process. However, while many teachers agree that educational games increase motivation, learning, and retention, few are ready to fully trust them as an assessment tool. A likely reason behind this lack of trust is that educational games are distributed as black boxes, unmodifiable by educators and not providing enough insight about the gameplay. This chapter presents three systematic literature reviews looking into the integration of assessment, feedback, and learning analytics in educational games. It then proposes a framework and present a fully developed engine. The engine is used by both developers and educators. Designed to separate game and assessment, it allows teachers to modify the assessment after distribution and visualize gameplay data via a learning analytics dashboard.

Integrating Assessment, Feedback, and Learning Analytics in Educational Games

2022 ◽  
pp. 1803-1846
Author(s):  
Yaëlle Chaudy ◽  
Thomas M. Connolly
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Assessment Tool ◽  
Educational Games ◽  
Assessment Process ◽  
Crucial Aspect ◽  
Assessment Feedback ◽  
Black Boxes ◽  
Literature Reviews

Assessment is a crucial aspect of any teaching and learning process. New tools such as educational games offer promising advantages: they can personalize feedback to students and save educators time by automating the assessment process. However, while many teachers agree that educational games increase motivation, learning, and retention, few are ready to fully trust them as an assessment tool. A likely reason behind this lack of trust is that educational games are distributed as black boxes, unmodifiable by educators and not providing enough insight about the gameplay. This chapter presents three systematic literature reviews looking into the integration of assessment, feedback, and learning analytics in educational games. It then proposes a framework and present a fully developed engine. The engine is used by both developers and educators. Designed to separate game and assessment, it allows teachers to modify the assessment after distribution and visualize gameplay data via a learning analytics dashboard.

A Complete Validated Learning Analytics Framework

2019 ◽  
Vol 15 (3) ◽  
pp. 42-59 ◽  
Author(s):  
Ahmed Tlili ◽  
Fathi Essalmi ◽  
Mohamed Jemni ◽  
Professor Kinshuk ◽  
Nian-Shing Chen
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Data Use ◽  
Methods And Techniques ◽  
Key Issues

Advances in technology have given the learning analytics (LA) area further potential to enhance the learning process by using methods and techniques that harness educational data. However, the lack of guidelines on what should be taken into considerations during application of LA hinders its full adoption. Therefore, this article investigates the issues that should be considered during the design of LA experience from the data use perspective. The results obtained present a validated LA framework which is composed of eighteen validated key issues that should be considered by various stakeholders in their contexts to enhance designing LA experiences. This framework can also be used by researchers and practitioners to learn more about LA and its designing issues.

scholarly journals Learning as a Machine. Crossovers Between Humans and Machines

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Mireille Hildebrandt
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Active Participation ◽  
Theory And Practice ◽  
Academic Disciplines ◽  
Special Issue ◽  
Rich Diversity ◽  
Current Thinking ◽  
Multidisciplinary Nature ◽  
The Rich

This article introduces the special issue from SoLAR’s 2016 Learning Analytics and Knowledge conference. The field of learning analytics (LA) draws heavily on theory and practice from a range of diverse academic disciplines. In so doing, LA research embodies a rich integration of methodologies and practices, assumptions and theory to bring new insights into the learning process. Reflecting this rich diversity, the theme of LAK 2016 highlights the multidisciplinary nature of the field and embraces the convergence of these disciplines to provide theoretical and practical insights to challenge current thinking in the field.  This overview introduces six articles, each of which expands on an invited talk or paper from the conference, with the added goal of offering a small taste of the rich experience that comes from  active participation in the conference. 

Forschungsfeld Learning Analytics

i-com ◽  
2012 ◽  
Vol 11 (1) ◽  
pp. 22-25 ◽  
Author(s):  
Mohamed Amine Chatti ◽  
Anna Lea Dyckhoff ◽  
Ulrik Schroeder ◽  
Hendrik Thüs
Keyword(s):  
Learning Process ◽  
Learning Analytics ◽  
Future Trend ◽  
Educational Institutions ◽  
Technology Enhanced Learning ◽  
Research Opportunities ◽  
Learning And Teaching ◽  
Process Learning ◽  
Enhanced Learning

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 opportunities in the emerging area of learning analytics.

scholarly journals Adaptive Learning Supported by Learning Analytics for Student Teachers’ Personalized Training during in-School Practices

2021 ◽  
Vol 14 (1) ◽  
pp. 124
Author(s):  
Carmen Fernández-Morante ◽  
Beatriz Cebreiro-López ◽  
María-José Rodríguez-Malmierca ◽  
Lorena Casal-Otero
Keyword(s):  
Student Teachers ◽  
Learning Process ◽  
Adaptive Learning ◽  
Learning Analytics ◽  
Intelligent System ◽  
Strong Support ◽  
Educational Innovation ◽  
Initial Teacher Education ◽  
Second Phase ◽  
Sources Of Information

This paper presents the results of the second phase of the international project “Improving Educational Innovation, Competitiveness, and Quality of Higher Education through Collaboration between University and Companies (EKT)”. The use of adaptive learning supported by learning analytics is proposed as a pedagogical strategy to work on the collaborative and personalized learning process that takes place during the school placement period of initial teacher education. Learning analytics is expected to facilitate the analysis of the different sources of information and data generated in the learning process. The collected data will be centralized in a learning record store (LRS), which will serve as a repository for xAPI compatible traces from the tools that make up EKT intelligent system. The system is expected to provide a strong support to decision-making so that participant agents can collaborate, advise, and contribute to the future teacher’s personalized training according to his or her progress and the context in which the practice takes place. The need analysis of tutors in the five pilot countries is presented, which has made it possible to define the process variables that make up the learning analysis architecture of the EKT system.

scholarly journals Performance indicators in higher education with learning analytics

2017 ◽  
Vol 3 (2) ◽  
pp. 12
Author(s):  
Carlos Morais ◽  
Paulo Alexandre Alves ◽  
Luisa Miranda
Keyword(s):  
Virtual Environment ◽  
Learning Process ◽  
Learning Analytics ◽  
Teaching And Learning ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Negative Consequences ◽  
Access Frequency ◽  
The Mean ◽  
Academic Information

The regular use of virtual learning environments by educational institutions and the availability of large quantities of data regarding the teaching and learning process both challenge teachers, institutions and researchers to explore such data within the perspective of learning analytics in order to obtain indicators which might contribute to the improvement of the teaching and learning process. Consequently, such improvement may lead to students’ better learning outcomes as well as a better relationship with the institution they attend. Taking into account that student retention and dropout from degree courses before their completion have highly negative consequences for a high number of students, the aim of this research is to look into indicators associated with such problems through a mixed methodology, both qualitative and quantitative, and through procedures of analysis and research of data from databases containing academic information provided by a sample of 1,588 undergraduates. The focus of the study lies upon the relation and analysis of the following variables: students’ frequency of access per course unit to the virtual environment of the institution they attend; quantity of course units passed; and mean of the marks obtained in the course units passed. Among the results obtained, we highlight the existence of a positive degree of association with moderate correlation between the variables frequency of access per course unit to the virtual environment and the mean of the number of course units passed. Following the division of the sample into four different groups, each group corresponding to one quartile, resulting from the data regarding the frequency of accesses per course unit to the institution’s virtual environment, significant differences were found concerning the mean of the course units passed between the groups showing a higher frequency of access to the virtual learning environment and those with a lower access frequency, to the advantage of the groups showing a higher frequency of access to the virtual environment. Therefore, considering the way the groups were constituted, there is proof that the higher the frequency of access to the virtual environment is, the higher the probability of students succeeding in the course units they attend will be.

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