Computers have transformed every facet of our culture, most dramatically communication, transportation, finance, science, and the economy. Yet their impact has not been generally felt in education due to lack of hardware, teacher training, and sophisticated software. Another reason is that current instructional software is neither truly responsive to student needs nor flexible enough to emulate teaching. The more instructional software can reason about its own teaching process, know what it is teaching, and which method to use for teaching, the greater is its impact on education. 

Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a student's learning needs. Dr. Woolf taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible, whether for classroom or life-long learning. The book describes multidisciplinary approaches to using computers for teaching, reports on research, development, and real-world experiences, and discusses intelligent tutors, web-based learning systems, adaptive learning systems, intelligent agents and intelligent multimedia.

*Combines both theory and practice to offer most in-depth and up-to-date treatment of intelligent tutoring systems available
*Presents powerful drivers of virtual teaching systems, including cognitive science, artificial intelligence, and the Internet
*Features algorithmic material that enables programmers and researchers to design building components and intelligent systems

Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing E-learning

We present a probabilistic model to monitor a user's emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user's emotional arousal (i.e., the state of the interaction) and its effects (i.e., bodily expressions that are known to be influenced by emotional reactions). The probabilistic model relies on a Dynamic Decision Network to leverage any indirect evidence on the user's emotional state, in order to estimate this state and any other related variable in the model. This is crucial in a modeling task in which the available evidence usually varies with the user and with each particular interaction. The probabilistic model we present is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user's learning and engagement during the interaction with educational games.

/pdf/probabilistic-assessment-of-user-s-emotions-in-educational-gg2mf76hx3.pdf

Probabilistic assessment of user's emotions in educational games

This paper investigates the problem of policy learning in multiagent environments using the stochastic game framework, which we briefly overview. We introduce two properties as desirable for a learning agent when in the presence of other learning agents, namely rationality and convergence. We examine existing reinforcement learning algorithms according to these two properties and notice that they fail to simultaneously meet both criteria. We then contribute a new learning algorithm, WoLF policy hillclimbing, that is based on a simple principle: “learn quickly while losing, slowly while winning.” The algorithm is proven to be rational and we present empirical results for a number of stochastic games showing the algorithm converges.

/pdf/rational-and-convergent-learning-in-stochastic-games-s1igz72uhe.pdf

Rational and convergent learning in stochastic games

Research into context-aware computing risks losing sight of the user. This paper discusses how different types of information about a user, ranging from information about the current context to information about the user’s long-term properties, can simultaneously be relevant to a given adaptation decision. Pointers are given to two areas of research that can help with the integration of a broader range of information into context-aware systems: research on user-adaptive systems and on decision-theoretic methods.

Modelling both the Context and the User

This paper presents the details of a student model that enables an open learning environment to provide tailored feedback on a learner's exploration. Open learning environments have been shown to be beneficial for learners with appropriate learning styles and characteristics, but problematic for those who are not able to explore effectively. To address this problem, we have built a student model capable of detecting when the learner is having difficulty exploring and of providing the types of assessments that the environment needs to guide and improve the learner's exploration of the available material. The model, which uses Bayesian Networks, was built using an iterative design and evaluation process. We describe the details of this process, as it was used to both define the structure of the model and to provide its initial validation.

Probabilistic Student Modelling to Improve Exploratory Behaviour

One feature of intelligent user interfaces is an ability to make decisions that take into account a variety of factors, some of which may depend on the current situation. This article focuses on one general approach to such decision making: predict the consequences of possible system actions on the basis of prior empirical learning, and evaluate the possible actions, taking into account situation-dependent priorities and the tradeoffs between the consequences. This decision-theoretic approach is illustrated in detail with reference to an example decision problem, for which models for decision making were learned from experimental data. It is shown how influence diagrams and methods of decision-theoretic planning can be applied to arrive at empirically well-founded decisions. This paradigm is then compared with two other paradigms that are often employed in intelligent user interfaces. Finally, various possible ways of learning (or otherwise deriving) suitable decision-theoretic models are discussed.

When actions have consequences: empirically based decision making for intelligent user interfaces

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When actions have consequences: empirically based decision making for intelligent user interfaces