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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
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Behavior Modification01:21

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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
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Personalized Gamification for Learning: A Reactive Chatbot Architecture Proposal.

Carina S González-González1, Vanesa Muñoz-Cruz1, Pedro Antonio Toledo-Delgado1

  • 1Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain.

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Summary
This summary is machine-generated.

Gamified learning platforms are more effective when using conversational bots for cross-platform interaction. This study presents an open student-player model with machine learning for adaptive, playful multiplication practice for children.

Keywords:
chatbotsgame learning analyticsgamificationopen learner modelingpersonalizationuser modeling

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Area of Science:

  • * Educational Technology
  • * Human-Computer Interaction
  • * Artificial Intelligence in Education

Background:

  • * Traditional learning platforms often limit user interaction to a single digital interface, reducing effectiveness.
  • * Cross-platform accessibility is crucial for engaging students in digital learning environments.
  • * Conversational bots offer a viable solution for enhancing user interaction across multiple platforms.

Purpose of the Study:

  • * To present an open student-player model for gamified learning.
  • * To describe an architecture integrating this model with a conversational bot.
  • * To develop a playful tool for children to practice multiplication tables.

Main Methods:

  • * Development of an open student-player model incorporating machine learning for online adaptation.
  • * Design of a system architecture to host the open model and chatbot.
  • * Implementation of a chatbot with a reactive architecture compatible with the overall system.

Main Results:

  • * A functional architecture integrating an adaptive student model and a conversational bot was designed.
  • * The system facilitates cross-platform interaction, enhancing user engagement.
  • * Initial validation suggests the tool can effectively encourage playful multiplication practice.

Conclusions:

  • * Integrating conversational bots into gamified learning platforms enhances cross-platform interaction and effectiveness.
  • * An open student-player model with machine learning enables adaptive and personalized learning experiences.
  • * The developed chatbot and architecture provide a promising tool for improving foundational math skills in children.