The Effect of AI-Based Systems on Mathematics Achievement in Rural Context: A Quantitative Study

IF 5.1 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2024-11-19 DOI:10.1111/jcal.13098

Rashmi Khazanchi, Daniele Di Mitri, Hendrik Drachsler

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引用次数: 0

Abstract

Background

Despite educational advances, poor mathematics achievement persists among K-12 students, particularly in rural areas with limited resources and skilled teachers. Artificial Intelligence (AI) based systems have increasingly been adopted to support the diverse learning needs of students and have been shown to enhance mathematics achievement through personalized learning experiences by adapting to student's needs. Existing studies have documented the positive impact of AI-based systems on mathematics achievement. However, there is a lack of research that has studied AI-based systems on students' mathematics achievement and engagement in rural settings. The present study addresses the existing literature gap by focusing on the AI-based Edmentum Exact Path, analyzing its effectiveness in enhancing mathematics achievement and engagement among 8th-grade students in rural schools in the Southern United States. By examining both cognitive and affective engagement, along with students' mathematics achievement, this research explores the role of AI-based systems in student learning.

Objective

This study aims to explore the effectiveness of Edmentum Exact Path, an AI-based system, in improving 8th-grade students' mathematics achievement and affective and cognitive engagement in the Southern United States.

Methods

This study utilized a quasi-experimental design involving 78 students from socioeconomically disadvantaged backgrounds, comparing those receiving supplemental Edmentum Exact Path-led instruction and traditional teacher-led methods to a control group receiving only the teacher-led instructions. Mathematics achievement was measured through pre-tests and post-tests, while student engagement was assessed using the 5-point Student Engagement Instrument (SEI). Statistical analysis was performed using t-tests and ANOVA.

Results and Conclusions

The findings revealed statistically significant improvements in mathematics achievement for both the experimental and control groups. However, the group using teacher-led instruction showed a statistically significant improvement in affective engagement, while no statistically significant differences were observed in cognitive engagement between groups.

Implications

The findings suggest that integrating AI-based systems like Edmentum Exact Path may enhance affective engagement in mathematics among students from rural areas. However, the impact on cognitive engagement remains unclear, indicating a need for further research. These results support the potential for integrating AI-based systems to enhance mathematics achievement in rural settings.

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基于人工智能的系统对农村地区数学成绩的影响：定量研究

背景尽管教育取得了进步，但 K-12 学生的数学成绩仍然很差，尤其是在资源和师资有限的农村地区。基于人工智能（AI）的系统已被越来越多地采用，以支持学生的不同学习需求，并通过适应学生需求的个性化学习体验来提高数学成绩。现有研究记录了人工智能系统对数学成绩的积极影响。然而，目前还缺乏关于人工智能系统对农村学生数学成绩和参与度的研究。本研究关注基于人工智能的 Edmentum Exact Path，分析了它在提高美国南部农村学校八年级学生数学成绩和参与度方面的有效性，从而弥补了现有的文献空白。通过考察认知和情感参与以及学生的数学成绩，本研究探讨了基于人工智能的系统在学生学习中的作用。目的本研究旨在探索 Edmentum Exact Path（一种基于人工智能的系统）在提高美国南部八年级学生数学成绩以及情感和认知参与方面的有效性。方法本研究采用准实验设计，涉及 78 名来自社会经济弱势背景的学生，将接受以 Edmentum Exact Path 为主导的补充教学和传统教师指导方法的学生与仅接受教师指导的对照组学生进行比较。数学成绩通过前测和后测进行测量，而学生参与度则通过 5 点学生参与度问卷（SEI）进行评估。统计分析采用 t 检验和方差分析。结果与结论研究结果显示，实验组和对照组的数学成绩均有显著提高。然而，使用教师指导的实验组在情感参与度方面有明显的统计意义上的提高，而在认知参与度方面，实验组和对照组之间没有统计意义上的显著差异。启示研究结果表明，整合 Edmentum Exact Path 等人工智能系统可提高农村地区学生的数学情感参与度。然而，对认知参与的影响仍不清楚，这表明需要进一步研究。这些结果支持了整合人工智能系统以提高农村地区数学成绩的潜力。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope