From Guesswork to Game Plan: Exploring Problem-Solving-Strategies in a Machine Learning Game

Abstract

Problem-solving strategies have been investigated in various informatics education contexts. However, no substantial research has yet been conducted on the problem-solving behavior of students in the field of Machine Learning (ML).This study aims to bridge this gap by analyzing the self-directed problem-solving processes employed by students in grades 8 to 10 (n = 93) when developing decision trees as classification models. A digital multi-touch puzzle game and a custom-developed toolchain were utilized to visually capture and subsequently analyze students’ gameplay behaviors using quantitative content analysis techniques.The results of this study indicate that learners within the examined age group predominantly employ exploratory problem-solving strategies in the self-directed construction of decision trees. In contrast, structured approaches are employed much less frequently and demonstrate lower persistence, yet they are significantly more correlated with successful game completion. These findings underscore the necessity of developing learning environments that promote the application and facilitate the persistence of structured problem-solving strategies, enabling learners to engage with the functioning and development of decision trees in a systematic and purposeful manner.

Authors 4

1. Clemens Witt

   Technische Universität Dresden

   Affiliation as printed

   TUD Dresden University of Technology, Germany

2. Thiemo Leonhardt Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany

3. Erik Marx corresponding

   Technische Universität Dresden · Center for Scalable Data Analytics and Artificial Intelligence

   Affiliation as printed

   Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig, Germany

   TUD Dresden University of Technology, Germany

4. Nadine Bergner Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Germany