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Related Concept Videos

Reinforcement Schedules01:24

Reinforcement Schedules

116
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.
Once a behavior is learned,...
116
Reinforcement01:23

Reinforcement

154
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.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
154
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

57
In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...
57

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Using Deep Reinforcement Learning to Decide Test Length.

James Zoucha1, Igor Himelfarb2, Nai-En Tang2

  • 1University of Northern Colorado, Greeley, CO, USA.

Educational and Psychological Measurement
|May 7, 2025
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Deep reinforcement learning (DRL) can optimize test length, but current chiropractic exam lengths are advised. Shorter forms maintained accuracy but not structural integrity, showing DRL

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Area of Science:

  • Artificial Intelligence in Education
  • Psychometrics and Educational Measurement
  • Computational Optimization

Background:

  • Optimizing standardized test length is crucial for efficiency and validity.
  • Current test construction methods may not fully leverage advanced computational approaches.
  • Deep Reinforcement Learning (DRL) offers a novel framework for complex optimization problems.

Purpose of the Study:

  • To investigate the efficacy of DRL in optimizing test length for the National Board of Chiropractic Examiners Part I Exam.
  • To determine if shorter test forms can maintain psychometric integrity and structural constraints.
  • To explore DRL's potential for personalized testing and adaptive item selection.

Main Methods:

  • Modeling test form construction as a combinatorial optimization problem within a Markov Decision Process.
  • Developing and applying a DRL algorithm to generate test forms from a defined item bank.
  • Evaluating test forms based on ability estimation accuracy, content representation, and item difficulty distribution.

Main Results:

  • DRL successfully identified shorter test forms with comparable ability estimation accuracy.
  • Shorter test forms generated by DRL did not consistently maintain critical structural constraints.
  • The existing test length of 240 items was deemed advisable due to constraint adherence.

Conclusions:

  • DRL is a powerful tool for exploring test length optimization but requires careful consideration of structural constraints.
  • DRL's adaptive capabilities make it suitable for future personalized and adaptive testing environments.
  • Further research should focus on expanding item banks and computational resources for enhanced DRL performance.