Supervisor-worker problems with an application in education

Dorin Shmaryahu, Kobi Gal, Guy Shani

Research output: Contribution to journalArticlepeer-review

Abstract

In many e-learning settings, allowing students to choose which skills to practice encourages their motivation and contributes to learning. However, when given choice, students may prefer to practice skills that they already master, rather than practice skills they need to master. On the other hand, requiring students only to practice their required skills may reduce their motivation and lead to dropout. In this paper, we model this tradeoff as a multi-agent planning task, which we call SWOPP (Supervisor-Worker Problem with Partially Overlapping goals), involving two agents—a supervisor (teacher) and a worker (student)—each with different, yet non-conflicting, goals. The supervisor and worker share joint goals (mastering skills). The worker plans to achieve his/her own goals (completing an e-learning session) at a minimal cost (effort required to solve problems). The supervisor guides the worker towards achieving the joint goals by controlling the problems in the choice set for the worker. We provide a formal model for the SWOPP task and two sound and complete algorithms for the supervisor to guide the worker’s plan to achieve their joint goals. We deploy SWOPP for the first time in a real-world study to personalize math questions for K5 students using an e-learning software in schools. We show that SWOPP was able to guide students’ interactions with the software to practice necessary skills without deterring their motivation.

Original languageEnglish
Article number1965
Pages (from-to)1-22
Number of pages22
JournalSensors
Volume21
Issue number6
DOIs
StatePublished - 2 Mar 2021

Keywords

  • Artificial intelligence
  • Choice
  • Classical planning
  • E-learning
  • Education
  • Interactive learning
  • Multi agent planning
  • Planning

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