Dynamic Cognitive Modeling for Adaptive Serious Games

Streicher, Alexander; Busch, Julius; Roller, Wolfgang

doi:10.1007/978-3-030-77873-6_12

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…A combination of some or all of the aforementioned data sources is then used to interpret what learners know and/or will do. The typical learning indicators that have been widely utilized in literature are learning performance [69], [71], learners' attention levels [68], emotion [72], [74], collaboration and interaction degrees [75], [76], cognitive states [77], and engagement levels [78], [79].…”

Section: A Multimodal Data Inputsmentioning

confidence: 99%

Student Modeling and Analysis in Adaptive Instructional Systems

et al. 2022

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There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-theart review of 11 years of research (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

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Section: A Multimodal Data Inputsmentioning

confidence: 99%

Student Modeling and Analysis in Adaptive Instructional Systems

et al. 2022

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“…This allows for systems which can teach and allow for improvement with a more thorough understanding of the user's behavior. For instance, cognitive architectures (see Section 3.2) can be used to define intelligent tutoring systems [122].…”

Section: Ai In Games and Teachingmentioning

confidence: 99%

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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As we discussed in Part I of this topic [30], there is a clear desire to model and comprehend human behavior. Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. In Part I, we discussed learning methods which generate a model of behavior from exploration of the system and feedback based on the exhibited behavior as well as topics relating to the use of or accounting for beliefs with respect to applicable skills or mental states of others. In this work, we will continue the discussion from the perspective of methods which focus on the assumed cognitive abilities, limitations, and biases demonstrated in human reasoning. We will arrange these topics as follows (i) methods such as cognitive architectures, cognitive heuristics, and related which demonstrate assumptions of limitations on cognitive resources and how that impacts decisions and (ii) methods which generate and utilize representations of bias or uncertainty to model human decision-making or the future outcomes of decisions.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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Modeling, Replicating, and Predicting Human Behavior: A Survey

Fuchs

Passarella

Conti

2023

ACM Trans. Auton. Adapt. Syst.

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Given the popular presupposition of human reasoning as the standard for learning and decision-making, there have been significant efforts and a growing trend in research to replicate these innate human abilities in artificial systems. As such, topics including Game Theory, Theory of Mind, Machine Learning, etc. integrate concepts which are assumed components of human reasoning. These serve as techniques to replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, allowing them not only to replicate human models as a technique to “learn”, but to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper is to provide a succinct yet systematic review of important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without necessarily learning via trial-and-error.

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Dynamic Cognitive Modeling for Adaptive Serious Games

Cited by 3 publications

References 21 publications

Student Modeling and Analysis in Adaptive Instructional Systems

Student Modeling and Analysis in Adaptive Instructional Systems

Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty

Modeling, Replicating, and Predicting Human Behavior: A Survey

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