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Related Concept Videos

Introduction to Motivation and Emotion01:29

Introduction to Motivation and Emotion

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Motivation is a multifaceted process that drives behavior toward fulfilling various physiological or psychological needs. This process involves initiating, guiding, and maintaining specific actions influenced by internal and external factors. For example, when someone feels hungry while watching television, hunger is a motivator, prompting the individual to get up, walk to the kitchen, and find something to eat. In this instance, hunger initiates and sustains the behavior necessary to meet the...
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Motivational Bias01:25

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Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...
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Drive-Reduction Theory: Push Theory of Motivation01:27

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Clark Hull's drive-reduction theory, introduced in the 1940s and 1950s and often termed the "push theory" of motivation, provides a framework for understanding how biological and learned drives influence behavior. Hull suggested that motivation originates from the need to alleviate physiological tension caused by unmet biological necessities. The theory proposes that when a basic need, such as hunger or sleep, goes unfulfilled, it creates an internal imbalance. This imbalance, or...
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Incentive Theory: Pull Theory of Motivation01:18

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Incentive theory, or the "pull theory" of motivation, suggests that external rewards primarily drive behavior. Individuals are motivated to engage in activities when they anticipate a desirable outcome. This is why people often work hard for promotions or study intensively to achieve high grades. These incentives can be tangible, physical rewards such as money or promotions, or intangible, non-physical rewards like praise and social recognition.
The theory differentiates between...
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Aggregates Classification01:29

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Motivational Cycle01:20

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The motivational cycle is a key concept that explains how individuals are motivated to meet their needs. At its core, the cycle revolves around four distinct stages: need, drive, goal-directed behavior, and goal achievement. These stages respond to imbalances in the body or mind, prompting actions that restore balance.
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Related Experiment Video

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Motivation Classification and Grade Prediction for MOOCs Learners.

Bin Xu1, Dan Yang2

  • 1Computing Center, Northeastern University, Shenyang 110819, China.

Computational Intelligence and Neuroscience
|February 18, 2016
PubMed
Summary

This study introduces a two-step classification method using MOOCs (Massive Open Online Courses) learner activity data to predict certification success. The approach effectively identifies learners likely to earn a certificate and detect potential cheating, such as surrogate exam-takers.

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Area of Science:

  • Educational Technology
  • Data Science
  • Machine Learning

Background:

  • Massive Open Online Courses (MOOCs) generate vast amounts of learner data, offering potential for educational insights.
  • Predicting learner behavior, like dropout or certification success, is crucial for effective online education.
  • Detecting academic dishonesty, such as surrogate exam-takers, in large-scale online courses presents significant challenges.

Purpose of the Study:

  • To develop an effective, economical, and scalable method for predicting learner certification outcomes in MOOCs.
  • To utilize student activity features for predicting whether a learner will obtain a certification.
  • To identify potential instances of academic dishonesty, including the use of surrogate exam-takers.

Main Methods:

  • A two-step classification approach was implemented: Motivation Classification (MC) and Grade Classification (GC).
  • The MC categorizes learners into three groups: certification earning, video watching, and course sampling.
  • The GC further predicts certification attainment for learners identified as potentially earning a certificate.

Main Results:

  • The proposed method demonstrates the ability to fit classification models at a fine scale.
  • Experimental results indicate the effectiveness of the two-step classification in predicting learner certification.
  • The method shows potential for identifying surrogate exam-takers, a form of academic dishonesty.

Conclusions:

  • The developed grade prediction method effectively leverages MOOC learner activity data.
  • The two-step classification system provides a scalable solution for predicting certification success.
  • This approach offers a promising tool for enhancing academic integrity in online learning environments.