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Teaching Shortest Path Algorithms With a Robot and Overlaid Projections

Pavel Jolakoski, Jordan Aiko Deja, Klen Čopič Pucihar, Matjaž Kljun

TL;DR

Timmy-a GoPiGo robot augmented with projections to demonstrate shortest path algorithms in an interactive learning environment is presented, which suggests that robots offer an engaging tool for teaching advanced algorithmic concepts, but highlight the need for further methodological refinements and larger-scale studies to fully evaluate their effectiveness.

Abstract

Robots have the potential to enhance teaching of advanced computer science topics, making abstract concepts more tangible and interactive. In this paper, we present Timmy-a GoPiGo robot augmented with projections to demonstrate shortest path algorithms in an interactive learning environment. We integrated a JavaScript-based application that is projected around the robot, which allows users to construct graphs and visualise three different shortest path algorithms with colour-coded edges and vertices. Animated graph exploration and traversal are augmented by robot movements. To evaluate Timmy, we conducted two user studies. An initial study (n=10) to explore the feasibility of this type of teaching where participants were just observing both robot-synced and the on-screen-only visualisations. And a pilot study (n=6) where participants actively interacted with the system, constructed graphs and selected desired algorithms. In both studies we investigated the preferences towards the system and not the teaching outcome. Initial findings suggest that robots offer an engaging tool for teaching advanced algorithmic concepts, but highlight the need for further methodological refinements and larger-scale studies to fully evaluate their effectiveness.

Teaching Shortest Path Algorithms With a Robot and Overlaid Projections

TL;DR

Timmy-a GoPiGo robot augmented with projections to demonstrate shortest path algorithms in an interactive learning environment is presented, which suggests that robots offer an engaging tool for teaching advanced algorithmic concepts, but highlight the need for further methodological refinements and larger-scale studies to fully evaluate their effectiveness.

Abstract

Robots have the potential to enhance teaching of advanced computer science topics, making abstract concepts more tangible and interactive. In this paper, we present Timmy-a GoPiGo robot augmented with projections to demonstrate shortest path algorithms in an interactive learning environment. We integrated a JavaScript-based application that is projected around the robot, which allows users to construct graphs and visualise three different shortest path algorithms with colour-coded edges and vertices. Animated graph exploration and traversal are augmented by robot movements. To evaluate Timmy, we conducted two user studies. An initial study (n=10) to explore the feasibility of this type of teaching where participants were just observing both robot-synced and the on-screen-only visualisations. And a pilot study (n=6) where participants actively interacted with the system, constructed graphs and selected desired algorithms. In both studies we investigated the preferences towards the system and not the teaching outcome. Initial findings suggest that robots offer an engaging tool for teaching advanced algorithmic concepts, but highlight the need for further methodological refinements and larger-scale studies to fully evaluate their effectiveness.

Paper Structure

This paper contains 17 sections, 3 figures.

Table of Contents

  1. Introduction and Background
  2. Timmy and the Learning environment: Design and Features
  3. Overview
  4. Main canvas
  5. Synchronising Timmy's movements
  6. Initial Study
  7. Protocol
  8. Findings
  9. Cognitive Load across both conditions
  10. Participant Preference Findings
  11. Pilot Study
  12. Protocol
  13. Findings
  14. Reported System Usability
  15. Participant Preference Findings
  16. ...and 2 more sections

Figures (3)

  • Figure 1: (a) Full setup of the project. (b) The robot in the projected environment. (c) Participant observing the robot in the robot condition. (d) Participant interacting with the application.
  • Figure 2: The view of the application interface. On the left panel is the pseudocode. In the middle is the main canvas with the graph showing the vertices, edges and shortest path. On the right panel we see the distance and predecessor updates.
  • Figure 3: Cognitive loads of both conditions. The values are shown per participant and are not aggregated to show the varying levels across each dimension.