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Related Concept Videos

Purposive Learning01:22

Purposive Learning

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 bonus...
Role-Based Identity01:21

Role-Based Identity

Role-based identities are central to understanding how individuals navigate social environments by adopting distinct self-conceptions aligned with various societal roles. These identities are not fixed traits but are constructed through personal actions and the social feedback individuals receive in context-specific interactions. Each social role, such as student, teacher, or friend, carries a set of expectations and norms that influence how people think, feel, and behave within that...
Cognitive Learning01:21

Cognitive Learning

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...
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
Self Within Cultural Contexts01:30

Self Within Cultural Contexts

Cultural frameworks for understanding the self are often categorized into two broad orientations: individualism and collectivism. These paradigms influence how people define themselves, relate to others, and interpret their social worlds. Each orientation offers distinct perspectives on autonomy, responsibility, and the role of the individual within a community.Individualistic CulturesIn individualistic cultures like North America and Western Europe, identity is understood as autonomous and...
Associative Learning01:27

Associative Learning

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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Related Experiment Videos

Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments.

Conglin Qiu1, Kunkun Cui2, Zilong Wang2

  • 1Korea Dongshin University, Dongshin University, Naju-si, Korea.

Big Data
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Big Data-Driven Personalized Instruction framework using Explainable Agentic Artificial Intelligence (X-AI) to enhance multicultural higher education. The X-AI approach improves learning outcomes and student engagement through adaptive, transparent, and culturally responsive educational strategies.

Keywords:
big dataexplainable agentic AIhigher educationinstructionmulticulturepersonalization

Related Experiment Videos

Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Big Data Analytics

Background:

  • Increasing diversity in higher education and the growth of educational big data necessitate intelligent personalized instruction systems.
  • Existing systems struggle with pedagogical, cultural, and cognitive variability in multicultural settings.

Purpose of the Study:

  • To propose a Big Data-Driven Personalized Instruction framework powered by Explainable Agentic Artificial Intelligence (X-AI).
  • To address variability in multicultural higher education settings through adaptive, transparent, and culturally responsive learning.
  • To ensure pedagogical trustworthiness and ethical deployment via explainability mechanisms.

Main Methods:

  • Utilizing autonomous agentic AI architectures for goal-directed learning, dynamic learner profiling, and real-time instructional adaptation.
  • Leveraging large-scale, multimodal educational data (HarvardX-MITx Person-Course Dataset).
  • Incorporating explainability mechanisms (feature attribution, causal inference, visual analytics) for transparency and bias reduction.

Main Results:

  • The X-AI framework significantly improves learning outcome prediction accuracy (F1 = 0.88) compared to traditional methods.
  • Demonstrates enhanced instructional relevance and increased student engagement in diverse higher education datasets.
  • Provides interpretable insights into learner performance, instructional recommendations, and adaptive interventions.

Conclusions:

  • The study advances a scalable, culturally responsive paradigm for personalized education by integrating big data, agentic autonomy, and explainable AI.
  • Offers practical insights for educators, institutions, and policymakers aiming for equitable, transparent, data-driven higher education transformation.
  • Highlights the potential of X-AI to navigate complexities of multicultural learning environments effectively.