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Related Concept Videos

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.
Tolman introduced the idea that behavior is influenced by...
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Associative Learning01:27

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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.
Classical conditioning, also known...
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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Purposive Learning01:22

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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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Key factors predicting problem-based learning in online environments: Evidence from multimodal learning analytics.

Xiang Wang1, Di Sun2, Gang Cheng3,4

  • 1Faculty of Education, Beijing Normal University, Beijing, China.

Frontiers in Psychology
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

Multimodal learning analytics (MMLA) using machine learning effectively predicts online problem-based learning (PBL) performance by analyzing peer interactions. Key predictors include self-regulation and engagement across PBL stages.

Keywords:
learning processmultimodal learning analyticsonline learningpeer engagementproblem-based learning

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Area of Science:

  • Educational Technology
  • Learning Analytics
  • Artificial Intelligence in Education

Background:

  • Problem-based learning (PBL) is valuable, with growing interest in integrating learning analytics (LA), particularly multimodal learning analytics (MMLA).
  • Existing research often overlooks the connection between LA findings and specific PBL phases, and the analysis of peer interaction text data.
  • The need exists to quantify peer engagement and predict performance in online PBL environments.

Purpose of the Study:

  • To employ MMLA with machine learning (ML) to analyze peer learning processes in online PBL.
  • To identify key behavioral and self-regulatory factors predicting online PBL success.
  • To address limitations in analyzing student process data and text-based interactions.

Main Methods:

  • Utilized MMLA incorporating data from online discussions, log files, reports, and questionnaires from 104 students in an online social work course.
  • Applied ML classification models to analyze text data from peer interactions.
  • Employed hierarchical linear regression to assess the predictive power of various indicators.

Main Results:

  • Self-regulation, message posting frequency, message length, and peer engagement in representation, solution, and evaluation phases significantly predicted online PBL performance.
  • Process indicators demonstrated stronger predictive validity for online PBL performance compared to other metrics.
  • The MMLA approach successfully quantified peer learning engagement and identified predictive factors.

Conclusions:

  • MMLA, combined with ML, offers a robust method for analyzing online peer learning processes in PBL.
  • Student self-regulation and engagement across PBL stages are critical determinants of online PBL success.
  • This study provides insights for developing targeted interventions to support students in online PBL environments.