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Related Concept Videos

Relative Motion Analysis - Velocity01:24

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A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
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Relative Motion Analysis - Acceleration01:10

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A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
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Lesson: Translation
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Related Experiment Video

Updated: Feb 13, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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Algorithmic analysis of relational learning processes in instructional technology: Some implications for basic,

William J McIlvane1, Joanne B Kledaras1, Christophe J Gerard1

  • 1University of Massachusetts Medical School, Worcester, MA, 01655, United States.

Behavioural Processes
|March 17, 2018
PubMed
Summary

This study introduces new computer-based tools and algorithms for dynamic assessment of relational learning in children with autism spectrum disorder and intellectual disabilities, improving instructional methods.

Keywords:
Computerized algorithmic learning supportsLearning by exclusionStimulus controlStimulus equivalence

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Area of Science:

  • Behavioral psychology
  • Educational technology
  • Developmental disabilities research

Background:

  • Quantitative analyses of relational learning often rely on simple outcome measures.
  • Existing methods for assessing relational learning lack dynamic and parametric capabilities.
  • Need for advanced tools to analyze learning processes and optimize instruction.

Purpose of the Study:

  • Develop quality computer-based programmed instruction for children with autism spectrum disorders (ASD) and intellectual disabilities (ID).
  • Create formal algorithms for dynamic assessment of learner performance and instructional adjustments.
  • Enhance the efficacy and efficiency of educational interventions.

Main Methods:

  • Focus on recent studies developing computer-based programmed instruction.
  • Design and implement formal algorithms for ongoing, dynamic assessment.
  • Utilize evidence-based principles and theories of stimulus control.

Main Results:

  • Algorithms permit ongoing, dynamic assessment of learner performance.
  • Instructional procedures can be modified in real-time to optimize learning.
  • Demonstrated benefits of algorithms in illustrating possible improvements in instructional efficacy.

Conclusions:

  • Developed algorithms offer utility for basic and translational research in relational learning.
  • Algorithm development encourages parametric research and integration of new findings.
  • Algorithms can model basic behavioral processes for effective programmed instruction design.