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Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
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Ampere-Maxwell's Law: Problem-Solving01:17

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Modeling in Therapy01:26

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Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
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Enhancing a student productivity model for adaptive problem-solving assistance.

Mehak Maniktala1, Min Chi1, Tiffany Barnes1

  • 1North Carolina State University, Raleigh, NC USA.

User Modeling and User-Adapted Interaction
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for intelligent tutoring systems to predict when students need help by analyzing their hint usage. This proactive approach significantly reduces training time and improves learning outcomes in logic proof domains.

Keywords:
Adaptive supportAssistance dilemmaData-driven tutoringPropositional logicStudent modelingUnproductivity

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Human-Computer Interaction

Background:

  • Intelligent tutoring systems (ITS) aim for effective adaptive assistance.
  • Current ITS often rely on students seeking help, which may not be optimal.
  • Proactive adaptive assistance, offering unsolicited help, is gaining interest to address the 'assistance dilemma'.

Purpose of the Study:

  • To develop and evaluate a novel data-driven approach for proactive adaptive assistance in ITS.
  • To incorporate students' prior hint usage into predicting their need for help (HelpNeed).
  • To investigate the impact of this approach on learning efficiency and effectiveness in the domain of logic proofs.

Main Methods:

  • A novel data-driven method was developed to predict students' HelpNeed by integrating their hint usage patterns.
  • A controlled study was conducted using an intelligent tutor for logic proofs.
  • An adaptive hint policy based on predicted HelpNeed (incorporating hint usage) was compared against a control group without proactive interventions.

Main Results:

  • The adaptive hint policy significantly reduced student training time and improved posttest results compared to the control.
  • Incorporating students' hint usage substantially enhanced the accuracy of HelpNeed prediction.
  • This led to reduced training unproductivity, decreased help avoidance, and increased help appropriateness.

Conclusions:

  • Proactive adaptive assistance, informed by students' hint usage, is effective in ITS for open-ended domains like logic proofs.
  • This approach enhances learning efficiency and outcomes by providing timely and appropriate support.
  • The method shows promise for ITS in various domains requiring careful consideration of when and how to offer assistance.