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Purposive Learning01:22

Purposive Learning

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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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Cognitive Learning01:21

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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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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Automatic Processing and Automatic Social Behavior01:28

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Information Processing Approach01:30

Information Processing Approach

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The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is...
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Related Experiment Video

Updated: Dec 8, 2025

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An intelligent tutoring system for supporting active learning: A case study on predictive parsing learning.

J J Castro-Schez1, C Glez-Morcillo1, J Albusac1

  • 1University of Castilla-La Mancha, Department of Technology and Information Systems, Escuela Superior de Informática, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain.

Information Sciences
|September 22, 2020
PubMed
Summary

This study introduces an intelligent tutoring system promoting student-led experimentation in online learning. It enhances educational solutions by allowing learners to propose and solve tasks, improving the learning process.

Keywords:
Formal language processingIntelligent tutoring systemsOnline learningPredictive parsing

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Area of Science:

  • Computer Science Education
  • Artificial Intelligence in Education

Background:

  • Technology's impact on education necessitates flexible online learning platforms.
  • Current intelligent tutoring systems offer guidance but limit student autonomy and initiative.
  • Existing systems often restrict learners to predefined tasks, hindering experimental learning.

Purpose of the Study:

  • To propose an intelligent tutoring system that fosters student-driven learning through experimentation.
  • To enable students to initiate and undertake tasks, utilizing their skills and knowledge.
  • To evaluate the system's effectiveness in improving the learning process for predictive parsing techniques.

Main Methods:

  • Development and application of a novel intelligent tutoring system.
  • Focus on encouraging students to propose and execute tasks independently.
  • Implementation within Computer Science courses over four academic terms.

Main Results:

  • The system was designed, developed, and applied to teach predictive parsing.
  • Computer Science students utilized the system over four academic years.
  • Evaluation focused on the system's suitability for enhancing the learning process.

Conclusions:

  • The developed intelligent tutoring system supports experimental learning.
  • The system empowers students to take initiative in their learning journey.
  • Further evaluation is needed to fully ascertain its impact on student learning outcomes.