Open Student Models

Open Student Models

Eshaa M. Alkhalifa (University of Bahrain, Bahrain)
Copyright: © 2009 |Pages: 5
DOI: 10.4018/978-1-60566-198-8.ch225
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Abstract

When a student makes an error, the instructor wonders what possible misconception caused that error (Self, 1990) and attempts to correct it through altering the instruction method. Consequently, student models represent the system’s assumptions of learner knowledge and preferences without giving any guarantees that this model accurately reflects any of the information it contains. These models are utilized to present the right type of materials at the right point in time in the right presentation style (Fisher, 2001) in order to achieve optimal knowledge transfer. There are two main approaches followed when modeling student knowledge. The first attempts to delve into the cognitive workings of the student’s mind and tries to best explain how the results could be obtained. Some of those who followed this approach are Martin and Vahn Lehn (1995), Langley, Wogulis, and Ohlsson (1990), Ikeda, Kono, and Mizoguchi (1993), among others. The second approach assumes the process that occurs between the “inputs” and “outputs” that occur in a “black box” scenario. The researchers who adopt this presumption attempt to formulate a mapping between the situation and student response to that situation. Some of those who are following this type of modeling include Webb, Cumming, Richard, and Yum (1991) and Webb and Kuzmycz (1996).
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Evolution Of Open Student Models

When a student makes an error, the instructor wonders what possible misconception caused that error (Self, 1990) and attempts to correct it through altering the instruction method. Consequently, student models represent the system’s assumptions of learner knowledge and preferences without giving any guarantees that this model accurately reflects any of the information it contains.

These models are utilized to present the right type of materials at the right point in time in the right presentation style (Fisher, 2001) in order to achieve optimal knowledge transfer. There are two main approaches followed when modeling student knowledge. The first attempts to delve into the cognitive workings of the student’s mind and tries to best explain how the results could be obtained. Some of those who followed this approach are Martin and Vahn Lehn (1995), Langley, Wogulis, and Ohlsson (1990), Ikeda, Kono, and Mizoguchi (1993), among others. The second approach assumes the process that occurs between the “inputs” and “outputs” that occur in a “black box” scenario. The researchers who adopt this presumption attempt to formulate a mapping between the situation and student response to that situation. Some of those who are following this type of modeling include Webb, Cumming, Richard, and Yum (1991) and Webb and Kuzmycz (1996).

Those who follow the first approach are in a sense predicting possible causes for student behavior. In order to be able to check the accuracy of the student model in representing the student’s cognitive characteristics, VanLehn and Niu (2001) conducted a study in sensitivity analysis. They found out that an intelligent interface is more likely to result in erroneous assumptions about student knowledge than a computer-aided instruction interface. They also found out that the accuracy of the model is strongly dependent on the inputs given to the modeler.

The fallibility of these modelers opened up a new avenue of research where students are allowed to see and learn from their models. This in short is an Open Student Model. Dimitrova, Self, and Brna (2000) indicate that when a student is allowed to join a discussion about his learner model, then he is engaged in the process of reflecting upon his knowledge and reconsidering the ideas and assumptions he has formed.

Misconceptions are consequently discovered by the learner and corrected. Existing approaches for involving the learner in the modeling process include open learner models (Paiva & Self, 1995), collaborative student models (Bull, Brna, & Pain, 1995), and interactive diagnosis (Dimitrova et al.,2000). These are listed in Table 1 along with their main features.

Key Terms in this Chapter

Cognition: The psychological result of perception, learning, and reasoning.

Cognitive Tool: A tool that reduces the cognitive load required by a specific task.

Mental Model: A mental recreation of the states of the world reproduced cognitively in order to offer itself as a basis for reasoning.

Student Model: Different types of information obtained and retained by a computer program or module of an education system that includes information about achievement, learning level, preferences, and so forth.

Computer-Aided Instruction Interface: A point of communication between a human and a computer that is utilized with a system that is programmed to teach learners using a computer program.

Intelligent Interface: A point of communication between a human and a computer that displays qualities that mimic traits observed in human communication such as the use of natural languages.

Cognitive Load: The degree of cognitive processes required to accomplish a specific task.

Cognitive Level: The level of cognitive functions in the order of increasing complexity of cognitive processing.

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