Development of Bayesian Networks From Use Case Diagrams for Managing the Learner Model

Development of Bayesian Networks From Use Case Diagrams for Managing the Learner Model

DOI: 10.4018/978-1-5225-7413-2.ch003
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First of all, and to clarify the purpose, it seems important to say that the work presented in this chapter lies within the framework of learner modeling in an adaptive system understood as computational modeling of the learner. One must also state that Bayesian networks are effective tools for learner modeling under uncertainty. They have been successfully used in many systems, with different objectives, from the assessment of knowledge of the learner to the recognition of the plan followed in problem solving. The main objective of this chapter is to develop a Bayesian networks for modeling the learner from the use case diagram of the unified modeling language. The prototypes and diagrams presented in this chapter are arguments in favor of the objective. The network obtained also promotes reusing learner modeling through similar systems.
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Use Case Diagram: Uml Point Of View

Concept and Definition

Use cases describe the form of actions, reactions and the behavior of a system from a user perspective. They allow defining the limits of the system and the relationship between the system and the environment.

Use cases are filling a lack of raw object methods, such as Rumbaugh (Rumbaugh et al., 1991) and Jacobson (Booch et al., 1999), which did not offer techniques, for the identification of needs. In this sense, the use cases associated with technical objects allow a comprehensive approach to the entire life cycle, from the specification to implementation.

A use case is a specific way of using a system. It is the image of system functionality, triggered in response to the stimulation of an external actor.

Key Terms in this Chapter

User Model: Is the subdivision of human-computer interaction that describes the process of building up and modifying a conceptual understanding of the user.

Bayesian Networks: Probabilistic graphical model or a type of statistical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.

E-Learning: A concept that describes the cognitive science principles of effective multimedia learning using electronic educational technology.

Learner Profile: A part of the learner model that only contain the static information of the learner that could be gathered before developing a learner model.

Diagrams: Is a symbolic representation of information according to some visualization technique.

Adaptive Hypermedia Systems: On-line information and help systems, as well as institutional information systems, that provide hyperlinks that are most relevant to the user in an effort to shape the user's cognitive load.

Unified Modeling Language: A modeling language used in the field of software engineering that aims to provide a standard way to visualize the design of a system.

Use Case Diagrams: Use case diagrams are UML diagrams describing units of useful functionality (use cases) performed by a system in collaboration with external users (actors).

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