Contextualized and Personalized Math Word Problem Generation in Authentic Contexts Using Generative Pre-trained Transformer and Its Influences on Geometry Learning

IF 4 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Journal of Educational Computing Research Pub Date : 2024-05-30 DOI:10.1177/07356331241249225
Ika Qutsiati Utami, Wu-Yuin Hwang, Uun Hariyanti
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Abstract

Recently, automatic question generation (AQG) has been researched extensively for educational purposes. Existing approaches generally lack relevant information on the authentic context and problem diversity with various difficulty levels, so we proposed a new AQG system for generating contextualized and personalized mathematic word problems (MWP) in authentic contexts using the Generative Pre-trained Transformers (GPT). Our proposed system comprises (1) authentic contextual information acquisition through image recognition by TensorFlow and augmented reality (AR) measurement by AR Core, (2) a personalized mechanism based on instructional prompts to generate three different difficulty levels for learners’ different needs, and (3) MWP generation through GPT with authentic contextual information and personalized needs. We conducted a quasi-experiment with the participation of 52 students to evaluate the effectiveness of the proposed system on geometry learning performance. Further, the learning behaviors were analyzed in the aspects of authentic context, mathematics, and reflective behavior. The findings showed better results in geometry learning performances from students who learned with contextualized and personalized MWPs than those who were taught without contextualization and personalization on MWPs. Moreover, it was found that student’s ability to comprehend the practical situation or scenario presented in a problem (problem context understanding) and students’ ability to recognize relevant information from the problem context (identifying contextual information) significantly improved their learning performance. Moreover, students’ ability to apply math concepts and solve medium-level MWP also contributes to the improvement of learning performance. Meanwhile, learners showed positive perceptions toward the proposed system in facilitating geometry learning. Therefore, it is useful to promote an authentic context setting for mathematical problem-solving.
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使用生成式预训练变换器在真实情境中生成情境化和个性化数学单词问题及其对几何学习的影响
最近,出于教育目的对自动问题生成(AQG)进行了广泛研究。因此,我们提出了一种新的自动问题生成系统,利用生成预训练变换器(GPT)在真实语境中生成语境化和个性化的数学文字问题(MWP)。我们提出的系统包括:(1)通过 TensorFlow 的图像识别和 AR Core 的增强现实(AR)测量获取真实的情境信息;(2)基于教学提示的个性化机制,针对学习者的不同需求生成三种不同难度的问题;(3)通过 GPT 生成具有真实情境信息和个性化需求的 MWP。我们进行了一项有 52 名学生参与的准实验,以评估所提出的系统对几何学习成绩的影响。此外,我们还从真实情境、数学和反思行为等方面分析了学生的学习行为。研究结果表明,使用情境化和个性化的多工平台学习几何的学生,其几何学习成绩优于未使用情境化和个性化多工平台的学生。此外,研究还发现,学生理解问题中呈现的实际情况或情景的能力(问题情境理解)和学生从问题情境中识别相关信息的能力(识别情境信息)显著提高了他们的学习成绩。此外,学生应用数学概念和解决中等水平数学问题的能力也有助于提高学习成绩。同时,学习者对拟议系统在促进几何学习方面表现出积极的看法。因此,促进数学问题解决的真实情境设置是有益的。
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来源期刊
Journal of Educational Computing Research
Journal of Educational Computing Research EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
11.90
自引率
6.20%
发文量
69
期刊介绍: The goal of this Journal is to provide an international scholarly publication forum for peer-reviewed interdisciplinary research into the applications, effects, and implications of computer-based education. The Journal features articles useful for practitioners and theorists alike. The terms "education" and "computing" are viewed broadly. “Education” refers to the use of computer-based technologies at all levels of the formal education system, business and industry, home-schooling, lifelong learning, and unintentional learning environments. “Computing” refers to all forms of computer applications and innovations - both hardware and software. For example, this could range from mobile and ubiquitous computing to immersive 3D simulations and games to computing-enhanced virtual learning environments.
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