Data-Driven Refinement of a Probabilistic Model of User Affect

Conati, Cristina; Maclaren, Heather

doi:10.1007/11527886_7

Cited by 38 publications

(17 citation statements)

References 8 publications

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“…The application therefore needs to build and update dynamically an integrated 'rational&emotional' model, by dealing with the innumerable sources of uncertainty that derive both from the limited accuracy of the recognition process and from the way affective and rational factors influence each other. Cognitive emotion models with different knowledge grain sizes have been proposed; these include EMA (Marsella and Gratch 2006), the model in (Conati and McLaren 2005), and Emotional-Mind (Carofiglio et al in press). These and other models tend to rely on the famous Ortony, Clore and Collin's psychological theory (Ortony et al 1988).…”

Section: Integrating Affective States Into Consistent User Modelsmentioning

confidence: 99%

Introduction to special Issue on ‘Affective modeling and adaptation’

Carberry

Rosis

2008

User Model User-Adap Inter

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Section: Integrating Affective States Into Consistent User Modelsmentioning

confidence: 99%

Introduction to special Issue on ‘Affective modeling and adaptation’

Carberry

Rosis

2008

User Model User-Adap Inter

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“…Developing models that can reliably detect differences in how students choose to use intelligent tutoring systems, and the attitudes and goals which underlie these decisions, has received considerable attention in recent years [1,3,4,7,8]. A number of models have been developed which can reliably detect specific student behaviors -from avoiding help [cf.…”

Section: Introductionmentioning

confidence: 99%

“…A number of models have been developed which can reliably detect specific student behaviors -from avoiding help [cf. 1], to gaming the system [4], to competing with other students [7]. These models have supported the development of systems that influence students to learn to use intelligent tutoring systems more effectively [2].…”

Section: Introductionmentioning

confidence: 99%

Generalizing Detection of Gaming the System Across a Tutoring Curriculum

Baker

Corbett

Koedinger

et al. 2006

Intelligent Tutoring Systems

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Abstract.In recent years, a number of systems have been developed to detect differences in how students choose to use intelligent tutoring systems, and the attitudes and goals which underlie these decisions. These systems, when trained using data from human observations and questionnaires, can detect specific behaviors and attitudes with high accuracy. However, such data is timeconsuming to collect, especially across an entire tutor curriculum. Therefore, to deploy a detector of behaviors or attitudes across an entire tutor curriculum, the detector must be able to transfer to a new tutor lesson without being re-trained using data from that lesson. In this paper, we present evidence that detectors of gaming the system can transfer to new lessons without re-training, and that training detectors with data from multiple lessons improves generalization, beyond just the gains from training with additional data.

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“…An example is the affective user model in the educational game Prime Climb, a game designed to help children learn about number factorization while being coached by an intelligent agent [42]. This affective user model has nodes such as: goal nodes (that model the objective during game playing, e.g., learn math, have fun, beat partner), action nodes (for both the player and the action), goal satisfaction nodes (to model the degree of satisfaction that each action causes), and emotional nodes that allow for the modeling of six of the 22 emotions described in the OCC theory of emotions [147], namely, joy/distress (states of the node "emotion for the game"), pride/shame (states of the node "emotion for self") and admiration/reproach (states of the node "emotion for agent").…”

Section: Context-rulementioning

confidence: 99%

“…• In [29] and [42], Conati and colleagues use an iterative design process: the initial model is developed using the researcher's intuition or the expert's opinions, and then data from real users is collected and used to refine both the parameters and the structure, either by adding new nodes (such as "general exploration" or nodes that describe attitudes towards the intelligent agent, such as "wanting help") or by modifying the direction of the links (for example, between knowledge and exploration nodes or game events and goals).…”

Section: Building Student Models By Combining the Domain Expert's Knomentioning

confidence: 99%

User Models for Adaptive Hypermedia and Adaptive Educational Systems

Brusilovsky

Millán

Lecture Notes in Computer Science

645

416

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Abstract. One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling. IntroductionAdaptive hypermedia and other adaptive Web systems (AWS) belong to the class of user-adaptive software systems [174]. One distinctive feature of an adaptive system is a user model. The user model is a representation of information about an individual user that is essential for an adaptive system to provide the adaptation effect, i.e., to behave differently for different users. For example, when the user searches for relevant information, the system can adaptively select and prioritize the most relevant items (see Chapter 6 of this book [125]). When the user navigates from one item to another, the system can manipulate the links (e.g., hide, sort, annotate) to provide adaptive navigation support (see Chapter 8 of this book [21]). When the user reaches a particular page, the system can present the content adaptively (see Chapter 13 of this book [28]). To create and maintain an up-to-date user model, an adaptive system collects data for the user model from various sources that may include implicitly observing user interaction and explicitly requesting direct input from the user. This process is known as user modeling. User modeling and adaptation are two sides of the same coin. The amount and the nature of the information represented in the user model depend to a large extent on the kind of adaptation effect that the system has to deliver. 4 P. Brusilovsky and E. Millán As mentioned in the introduction, Chapters 1 to 5 of this book are focused mostly on the modeling side of personalization, while the remaining chapters focus mostly on the adaptation side. Chapters 1 and 2 are specifically devoted to user models and user modeling. Beyond this, user modeling issues are discussed at different levels of detail in several other chapters. This chapter attempts to complement the remaining chapters in two ways. First, it provides an overview (a "big picture") of the user modeling side referring readers when necessary to additional information in chapters within this book. Second, it attempts to complement other chapters by presenting aspects that are either not covered in other chapters or covered insufficiently.To envision the big picture, this chapter follows Sleeman [175] who suggested classifying user models by the nature and form of information contained in the model as well as the methods of working with it. Following hi...

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Data-Driven Refinement of a Probabilistic Model of User Affect

Cited by 38 publications

References 8 publications

Introduction to special Issue on ‘Affective modeling and adaptation’

Introduction to special Issue on ‘Affective modeling and adaptation’

Generalizing Detection of Gaming the System Across a Tutoring Curriculum

User Models for Adaptive Hypermedia and Adaptive Educational Systems

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