Learning Analytics in Augmented Reality : Blueprint for an AR / xAPI Framework

Secretan, Joshua; Wild, Fridolin; Guest, Will

doi:10.1109/tale48000.2019.9225931

Cited by 15 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the needs of the ARETE Authoring Toolkit platform and the collection of educational data, further development will utilise Augmented Reality metadata (Secretan et al, 2019) [16] and Learning Objects' metadata and methods identified within this review will be considered for analysis of the metadata provided from the Learning Management System [17].…”

Section: Discussionmentioning

confidence: 99%

Review of Learning Analytics and Educational Data Mining Applications

Mangina¹,

Psyrra²

2021

EDULEARN Proceedings

View full text Add to dashboard Cite

During the past decade the educational data research is rapidly growing. The use of technology in education has created the need to store and manage large amounts of data that come from various sources and have different formats. Educational data can be used to benefit educational systems and the science of learning. The project "Augmented Reality Interactive Educational System" (ARETE) funded by EU Programme Horizon 2020 aims to support interactive technologies for the provision of Augmented Reality (AR) content through an open source learning management system and authoring toolkit for the broader community of users. The utilisation of educational data is vital for the efficient data management and the two relevant areas of focus in review that focus on the use of educational data to support education are the Educational Data Mining (EDM) and Learning Analytics (LA). Several studies have been published recently focusing on applications using educational data, revealing that educational data analytics is an evolving science, where researchers have explored the various use cases of applying data mining and analytics techniques on the educational domain. However, there is still a need for exploring the main objectives of applying EDM and LA techniques and defining the specific problems in the educational domain they try to resolve. The aim of this analysis is to identify studies' objective trends that recent applications are trying to achieve and to identify potential research gaps. The possible correlation between the use of particular types of techniques used by the EDM/LA applications in relation to the goals they are trying to achieve is also being presented. This paper presents the review of EDM and LA empirical studies that have been published between 2016 to 2020. To gain insight into the trend direction of the different projects, the publications are clustered based on the methods applied and the purposes those studies tried to accomplish. Studies that applied more than one technique were assigned to the method groups more than once. This paper will provide an association table of EDM/LA techniques and the objectives for which they have been used, and will serve as a model for other researchers in order to choose the method for their own specific goals. Finally, the goals that recent EDM and LA applications are approaching will be presented, which can be a source of inspiration for further research questions, by providing information on areas of educational goals that remain unexplored or have not received much attention so far.

show abstract

Section: Discussionmentioning

confidence: 99%

Review of Learning Analytics and Educational Data Mining Applications

Mangina¹,

Psyrra²

2021

EDULEARN Proceedings

View full text Add to dashboard Cite

show abstract

“…One of the benefits of this can be seen in recent research that shows that the combination of physical movement and a gaming element in learning experiences for children not only enhances their ability to learn, but in fact also enhances cognitive function [2], [3]. With such new tools that can engage the body, "we need better Learning Analytics for real-time and real-world interaction" [4].…”

Section: New Ways Of Learning New Ways Of Measuringmentioning

confidence: 99%

“…In this formulation, as described by Secretan, Wild, & Guest, Performance = Action + Competence. "An action is the smallest possible step that can be taken by a user in a learning activity," they say, and "competence becomes visible when learners perform" [4].…”

Section: Augmented Reality Learning Experience Modelmentioning

confidence: 99%

Standards and affordances of 21st-century digital learning: Using the Experience Application Programming Interface and the Augmented Reality Learning Experience Model to track engagement in extended reality

Rogers¹,

Alexander²

2022

ACTA IMEKO

View full text Add to dashboard Cite

<p class="Abstract">The development of new extended reality (XR) technologies for learning enables the capture of a richer set of data than has previously been possible. To derive a benefit from this wealth of data requires new structures appropriate to the new learning activities these immersive learning tools afford. The Experience Application Programming Interface (xAPI) and the Augmented Reality Learning Experience Model (ARLEM) standards have been developed in response, and their adoption will help ensure interoperability as XR learning is more widely deployed. This paper briefly describes what is different about XR for learning and provides an account of xAPI and its structures as well as design principles for its use. The relationship between environmental context and ARLEM is explained, and a case study of a VR experience using xAPI is examined. The paper ends with an account of some of the promises for collecting data from early childhood learning experiences and an unsuccessful attempt at a study using Augmented Reality with young children.</p>

show abstract

“…In accordance with the abovementioned studies and conclusions drawn by other researchers [3,16], the greatest potential of AR is generally attributed to the visually appealing graphics and the vivid multimedia features, which offer diverse opportunities for interplay and engagement, as well as on the immediate (real-time) feedback that users receive in response to their actions. Additionally, an ongoing body of literature [7,27,28] has also provided possible solutions to enhance the potential of AR via LA. Such a combination can assist researchers and educators to recognize new ways of understanding the key concepts of action, as well as students' competencies and outcomes.…”

Section: Augmented Reality In Educationmentioning

confidence: 99%

A Learning Analytics Conceptual Framework for Augmented Reality-Supported Educational Case Studies

Kazanidis

Πέλλας

Christopoulos

2021

MTI

View full text Add to dashboard Cite

The deployment of augmented reality (AR) has attracted educators’ interest and introduced new opportunities in education. Additionally, the advancement of artificial intelligence has enabled educational researchers to apply innovative methods and techniques for the monitoring and evaluation of the teaching and learning process. The so-called learning analytics (LA) discipline emerged with the promise to revolutionize traditional instructional practices by introducing systematic and multidimensional ways to improve the effectiveness of the instructional process. However, the implementation of LA methods is usually associated with web-based platforms, which offer direct access to learners’ data with minimal effort or adjustments. On the other hand, the complex nature of immersive technologies and the diverse instructional approaches which are utilized in different scientific domains have limited the opportunities for research and development in this direction. Within these research contexts, we present a conceptual framework that describes the elements of an LA process tailored to the information that can be gathered from the use of educational applications, and further provide an indicative case study for AR-supported educational interventions. The current work contributes by elucidating and concretizing the design elements of AR-supported applications and provides researchers and designers with guidelines on how to apply instructional strategies in (augmented) real-world projects.

show abstract

Learning Analytics in Augmented Reality : Blueprint for an AR / xAPI Framework

Cited by 15 publications

References 4 publications

Review of Learning Analytics and Educational Data Mining Applications

Review of Learning Analytics and Educational Data Mining Applications

Standards and affordances of 21st-century digital learning: Using the Experience Application Programming Interface and the Augmented Reality Learning Experience Model to track engagement in extended reality

A Learning Analytics Conceptual Framework for Augmented Reality-Supported Educational Case Studies

Contact Info

Product

Resources

About