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Data-Driven Performance Prediction in a Geometry Game Environment

Published: 09 September 2021 Publication History

Abstract

The rapid technological evolution of the last years motivated students to develop competencies and capabilities that will prepare them for an unknown future of the 21st century. In this context, teachers intend to optimise the process of learning and make it more dynamic and exciting by introducing gamification. Thus, this paper focuses on a data-driven assessment of geometry competencies, which are essential for developing problem-solving and higher-order thinking skills. We explored them in the domain of knowledge inference, whose primary goal is to predict or measure the students' knowledge over questions as they interact with a learning platform at a specific time. Hence, the main goal of the current paper is to compare several well-known algorithms applied to the data of a geometry game named Shadowspect in order to predict students' performance in terms of classifier metrics such as Area Under Curve (AUC), accuracy, and F1 score. We found Elo to be the algorithm with the best prediction power. However, the rest of the algorithms also showed decent results, and, therefore, we can conclude that all the algorithms hold the potential to measure and estimate the actual knowledge of students. In turn, this means that they can be applied in formal education to improve teaching, learning, organisational efficiency and, as a consequence, this can serve as a basement for a change in the system.

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  • (2023)Adapting Knowledge Inference Algorithms to Measure Geometry Competencies through a Puzzle GameACM Transactions on Knowledge Discovery from Data10.1145/361443618:1(1-23)Online publication date: 6-Sep-2023

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cover image ACM Conferences
GoodIT '21: Proceedings of the Conference on Information Technology for Social Good
September 2021
345 pages
ISBN:9781450384780
DOI:10.1145/3462203
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 09 September 2021

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Author Tags

  1. Computational Social Science
  2. Data mining
  3. Game-based Assessment
  4. Geometry Capabilities
  5. Knowledge Inference

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  • (2023)Adapting Knowledge Inference Algorithms to Measure Geometry Competencies through a Puzzle GameACM Transactions on Knowledge Discovery from Data10.1145/361443618:1(1-23)Online publication date: 6-Sep-2023

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