Real-time learning analytics in educational games

2013 ◽  
Author(s):  
Miroslav Minović ◽  
Miloš Milovanović
Keyword(s):  
Real Time ◽  
Learning Analytics ◽  
Educational Games

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.

Context-Based Data Model for Effective Real-Time Learning Analytics

2020 ◽  
Vol 13 (4) ◽  
pp. 790-803
Author(s):  
Kai Liu ◽  
Sivanagaraja Tatinati ◽  
Andy W. H. Khong
Keyword(s):  
Real Time ◽  
Data Model ◽  
Learning Analytics

scholarly journals Artificial intelligence, real-time feedback and workplace learning analytics to support in situ complex problem-solving: a commentary

2020 ◽  
Vol 37 (5) ◽  
pp. 267-277
Author(s):  
Maarten de Laat ◽  
Srecko Joksimovic ◽  
Dirk Ifenthaler
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real Time ◽  
Workplace Learning ◽  
Learning Analytics ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Feedback Systems ◽  
Content Type

PurposeTo help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).Design/methodology/approachComplex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).FindingsIn this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.Originality/valueThe commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

Learning Analytics and Educational Games: Lessons Learned from Practical Experience

2014 ◽  
pp. 16-28 ◽  
Author(s):  
Ángel Serrano-Lagunaa ◽  
Javier Torrentea ◽  
Borja Maneroa ◽  
Ángel del Blancoa ◽  
Blanca Borro-Escribanoa ◽  
...  
Keyword(s):  
Learning Analytics ◽  
Educational Games ◽  
Practical Experience ◽  
Lessons Learned

scholarly journals Mining interactions in immersive learning environments for real-time student feedback

2013 ◽  
Vol 29 (2) ◽  
Author(s):  
Gregor Kennedy ◽  
Ioanna Ioannou ◽  
Yun Zhou ◽  
James Bailey ◽  
Stephen O'Leary
Keyword(s):  
Real Time ◽  
Learning Environments ◽  
Learning Analytics ◽  
Learning Systems ◽  
Student Feedback ◽  
Formative Feedback ◽  
Digital Data ◽  
Online Interactions ◽  
Use Of Data ◽  
Hidden Patterns

<p>The analysis and use of data generated by students’ interactions with learning systems or programs – learning analytics – has recently gained widespread attention in the educational technology community. Part of the reason for this interest is based on the potential of learning analytic techniques such as data mining to find hidden patterns in students’ online interactions that can be meaningfully interpreted and then fed back to students in a way that supports their learning. In this paper we present an investigation of how the digital data records of students’ interactions within an immersive 3D environment can be mined, modeled and analysed, to provide real-time formative feedback to students as they complete simulated surgical tasks. The issues that emerged in this investigation as well as areas for further research and development are discussed.</p>

scholarly journals Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher–AI Complementarity

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Kenneth Holstein ◽  
Bruce M. McLaren ◽  
Vincent Aleven
Keyword(s):  
Real Time ◽  
Real World ◽  
Learning Analytics ◽  
Educational Technologies ◽  
Design Development ◽  
Awareness Tool ◽  
Classroom Evaluation ◽  
Support Teacher ◽  
K 12 ◽  
Time Awareness

Involving stakeholders throughout the creation of new educational technologies can help ensure their usefulness and usability in real-world contexts. However, given the complexity of learning analytics (LA) systems, it can be challenging to meaningfully involve non-technical stakeholders throughout their design and development. This article reports on the iterative co-design, development, and classroom evaluation of Konscia, a wearable, real-time awareness tool for teachers working in AI-enhanced K-12 classrooms. In the process, we argue that the co-design of LA systems requires new kinds of prototyping methods. We introduce one of our own prototyping methods, REs, to address unique challenges of co-prototyping LA tools. This work presents the first end-to-end demonstration of how non-technical stakeholders can participate throughout the whole design process for a complex LA system—from early generative phases to the selection and tuning of analytics to evaluation in real-world contexts. We conclude by providing methodological recommendations for future LA co-design efforts.

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