Learning Analytics: Serving the Learning Process Design and Optimization

Author(s):  
Yanyan Li ◽  
Haogang Bao ◽  
Chang Xu
2021 ◽  
pp. 146144482110127
Author(s):  
Marcus Carter ◽  
Ben Egliston

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.


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

2018 ◽  
Vol 7 (3) ◽  
pp. 1124 ◽  
Author(s):  
Andino Maseleno ◽  
Noraisikin Sabani ◽  
Miftachul Huda ◽  
Roslee Ahmad ◽  
Kamarul Azmi Jasmi ◽  
...  

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. 


2013 ◽  
Vol 33 (3) ◽  
pp. 993-999 ◽  
Author(s):  
Zhijun Zhou ◽  
Zhuo You ◽  
Zhihua Wang ◽  
Xin Hu ◽  
Junhu Zhou ◽  
...  

Author(s):  
Yaëlle Chaudy ◽  
Thomas M. Connolly

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.


2022 ◽  
pp. 1803-1846
Author(s):  
Yaëlle Chaudy ◽  
Thomas M. Connolly

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.


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