Bayes Academy : An Educational Game for Learning Bayesian Networks (original) (raw)
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Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns and interconnected variables. Bayesian network modeling techniques make it possible to model such problems and obtain precise predictions about the causal impact that changing the value of one variable may have on the values of other variables connected to it. But Bayesian modeling is itself complex, and has until now remained largely inaccessible to lay people. In a large scale lab experiment, we provide proof of principle that a Bayesian network modeling tool, adapted to provide basic training and guidance on the modeling process to beginners without requiring knowledge of the mathematical machinery working behind the scenes, significantly helps lay people find normative Bayesian solutions to complex problems, compared to generic t...
The Eurasia Proceedings of Educational and Social Sciences, 2022
The development and multiple variations in technology and science have endured the education. Nevertheless, education is one of the primary components that uphold the development of a country. In the meantime, diverse technologies have been introduced to blend in education. For example, Bayesian Networks is a probability-based data modelling approach that illustrates a set of variables and their conditional dependencies through a Directed Acyclic Graph (DAG). Each node formed inside the graph has a Conditional Probability Table (CPT). Therefore, the endurance of this bibliometric review is to identify peer-reviewed literature on the Bayesian network approach in education. Scopus citation databases are used in the data-gathering phase. In addition, PICOS Framework and PRISMA approach were obtained and analysed for keyword search on the research topic. This bibliographic data of articles published in the journals over ten years were extracted. R-tool and VOS viewer were used to analyse the data contained in all journals and articles. This bibliometric review shows the usage of the Bayesian network approach in education, especially in educational application development. The findings from 87 articles extracted show that teaching and learning activity delivery and educational management have improved. The findings show an increasing trend in published studies related to the Bayesian network in education. Next, the United Kingdom and the United States became highly productive countries in the publication of studies within the scope of the Bayesian network. Next, interdisciplinary became the primary choice in the publication of studies in the field of Bayesian networks. The level of predictive accuracy generated through the Bayesian network approach improves the quality of educational application development. However, the findings of previous studies indicate that there is a need to extend the Bayesian network approach in education.
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Having presented both theoretical and practical reasons for artificial intelligence to use probabilistic reasoning, we now introduce the key computer technology for dealing with probabilities in AI, namely Bayesian networks. Bayesian networks (BNs) are graphical models for reasoning under uncertainty, where the nodes represent variables (discrete or continuous) and arcs represent direct connections between them. These direct connections are often causal connections. In addition, BNs model the quantitative strength of the connections between variables, allowing probabilistic beliefs about them to be updated automatically as new information becomes available. In this chapter we will describe how Bayesian networks are put together (the syntax) and how to interpret the information encoded in a network (the semantics). We will look at how to model a problem with a Bayesian network and the types of reasoning that can be performed.
Intelligent Games for Education - An Intention Monitoring Approach based on Dynamic Bayesian Network
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Technological developments have brought about a paradigm shift in the world of education. The education system must be more open and flexible, where students can experience these opportunities according to their skill level. 21st-century education and the application of the elements of Revolution 4.0 Industry in education realize that initiative. The Bayesian Network approach is becoming one of the essential tools in the development of educational applications. Therefore, the persistence of this systematic review is to identify peer-reviewed literature on the Bayesian network approach in education. Scopus and Web of Science, and IEEE citation databases are used in the data-gathering phase. PRISMA approach and keyword search were obtained and analyzed. This bibliographic data of articles published in the journals over ten years were extracted. VOS viewer was used to analyzing the data contained in all journals and articles. This systematic review shows that the development in educati...
Degeneracy in Student Modeling with Dynamic Bayesian Networks in Intelligent Edu-Games
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EVALUATING LEARNER PROGRESS IN A PERSUASIVE SERIOUS GAME
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In a context of serious games learning, we propose a modelling of the players-learners based on Bayesian Network technology coupled with the Diagram of Influence. This modelling has for objective evaluating the effectiveness of the serious game in learning through the evaluation of the behavioural change of the players-learners. This approach was applied to a persuasive serious game of anticorruption.
Bayesian networks for student model engineering
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Bayesian networks are graphical modeling tools that have been proven very powerful in a variety of application contexts. The purpose of this paper is to provide education practitioners with the background and examples needed to understand Bayesian networks and use them to design and implement student models. The student model is the key component of any adaptive tutoring system, as it stores all the information about the student (for example, knowledge, interest, learning styles, etc.) so the tutoring system can use this information to provide personalized instruction. Basic and advanced concepts and techniques are introduced and applied in the context of typical student modeling problems. A repertoire of models of varying complexity is discussed. To illustrate the proposed methodology a Bayesian Student Model for the Simplex algorithm is developed.