Stealth assessment strategy in distributed systems using optimal deep learning with game based learning

This chapter is an instance of the deployment of game-based learning (GBL) in an upper secondary school in the UK. A retrospective case study was conducted with sixth-form students (aged 17) and their teacher. The study examines their perceptions after the use of Spore™ in their biology lessons. The teacher integrated GBL with a dialogic teaching approach to promote deep learning among the students. A surface–deep learning matrix was created to extract the students’ attributes as deep learners and to identify the teacher’s characteristics. A comparison is presented between the perceived advantages of GBL, the normal learning approach, and how the students see teachers who use technology in teaching. The chapter focuses on the discussion of issues associated with the choice of focus group as a research method and the practice of GBL in the 14–19 context.

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Stealth Assessment Embedded in Game-Based Learning to Measure Soft Skills: A Critical Review

Advances in Game-Based Learning - Game-Based Assessment Revisited ◽

10.1007/978-3-030-15569-8_4 ◽

2019 ◽

pp. 67-83

Author(s):

Xinyue Ren

Keyword(s):

Critical Review ◽

Soft Skills ◽

Game Based Learning ◽

Stealth Assessment

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Session details: Volume I: Artificial intelligence and agents, distributed systems, and information systems: Neuro computing and deep learning and continuous-time computing track

Proceedings of the 31st Annual ACM Symposium on Applied Computing - SAC '16 ◽

10.1145/3252765 ◽

2016 ◽

Author(s):

Sander Bohte ◽

Davide Zambrano ◽

Cecilia Laschi ◽

Egidio Falotico ◽

Florian Röhrbein ◽

...

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Information Systems ◽

Distributed Systems ◽

Continuous Time

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A Hierarchical Assessment Strategy on Soft Error Propagation in Deep Learning Controller

Proceedings of the 26th Asia and South Pacific Design Automation Conference ◽

10.1145/3394885.3431573 ◽

2021 ◽

Author(s):

Ting Liu ◽

Yuzhuo Fu ◽

Yan Zhang ◽

Bin Shi

Keyword(s):

Deep Learning ◽

Error Propagation ◽

Soft Error ◽

Assessment Strategy ◽

Hierarchical Assessment

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DeepStealth: Game-Based Learning Stealth Assessment With Deep Neural Networks

IEEE Transactions on Learning Technologies ◽

10.1109/tlt.2019.2922356 ◽

2020 ◽

Vol 13 (2) ◽

pp. 312-325 ◽

Cited By ~ 7

Author(s):

Wookhee Min ◽

Megan H. Frankosky ◽

Bradford W. Mott ◽

Jonathan P. Rowe ◽

Andy Smith ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Game Based Learning ◽

Stealth Assessment

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Catastrophic co-production: A student-staff partnership for developing an educational game

International Journal for Students as Partners ◽

10.15173/ijsap.v4i2.4120 ◽

2020 ◽

Vol 4 (2) ◽

pp. 122-131

Author(s):

Nicholas Glover ◽

Kerry J. Knox ◽

Ben Kirman ◽

Matthew Topham ◽

Katylily Westbury-Hawkins ◽

...

Keyword(s):

Higher Education ◽

Deep Learning ◽

Teaching And Learning ◽

Development Process ◽

Academic Staff ◽

Development Project ◽

Educational Game ◽

Learning Tools ◽

Game Based Learning ◽

Biology Learning

We describe the production of an educational game (Catastrophic) for supporting biology learning in higher education (HE) that was developed through a partnership between students and academic staff. We consider the ways in which the development project intersects with the use of game-based learning in HE and with Students as Partners (SaP) practice. We describe the rationale for the project, discussing the use of games in the context of a shift from surface to deep learning during the transition to HE. We then reflect upon the development process and the resulting game, drawing on student and staff perspectives gathered using interviews. Finally, we make recommendations for others embarking on student-staff partnerships for the co-creation of teaching and learning tools.

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A First Step towards Integrating Educational Theory and Game Design

Advances in Game-Based Learning - Handbook of Research on Improving Learning and Motivation through Educational Games ◽

10.4018/978-1-60960-495-0.ch005 ◽

2011 ◽

pp. 98-117 ◽

Cited By ~ 5

Author(s):

Jan-Paul van Staalduinen

Keyword(s):

Deep Learning ◽

Literature Review ◽

Game Design ◽

Design Research ◽

Interactive Systems ◽

Game Based Learning ◽

Educational Impact ◽

Core Elements ◽

Game Elements

As of yet, there is no clear relationship between game elements and deep learning. This chapter used a literature review to create an overview of 25 game elements that contribute to learning. The TOPSIM game, by TATA Interactive Systems, was used in a case study to delve into the educational impact of 16 of these game elements. Using pre-game and post-game tests, it was concluded that the students learned from the game, and that they considered the following elements to contribute to their learning: ‘action-domain-link’, ‘adaptation’, ‘debriefing’, ‘conflict’, ‘control’, ‘fantasy’, ‘goals/objectives’, ‘mystery’, and ‘safety’. These results will be used in the construction of a game-based learning model that also incorporates theory on education game design, research on educational elements and principles in games, and theory on core elements that make up all games, whether educational or entertaining.

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