绘制移动化转向：STEM 教育中移动学习创新的文献计量探索

摘要

移动技术的快速普及及其重塑科学、技术、工程与数学（STEM）教育的潜力，使得系统考察这一领域的研究演进成为迫切需要。本研究以一项全面的文献计量分析，勾勒了 STEM 教育中移动学习（m-learning）的知识结构与新兴趋势。研究以 Scopus 收录的 1462 篇期刊论文为语料，借助 VOSviewer 实施引文分析、共被引分析与共词分析，揭示了有影响力的作者、主导性主题与演进中的概念格局。结果显示出三大研究域：基础性学习理论、教学策略与用户接受模型；并识别出四个主题聚类：借助新兴技术的沉浸式与协作式学习、数字教学法中的人工智能整合、发展中地区的基础设施与可及性挑战，以及高等教育系统对移动学习的采纳。尽管研究产出显著增长，本研究仍指出若干长期存在的挑战，包括移动技术获取的不平等、教师培训不足，以及移动工具与 STEM 学习目标之间的弱对齐。这些局限凸显了以下需求：契合建构主义的教学设计、可规模化基础设施的投入与制度性政策支持，在教育资源匮乏的地区尤为如此。本研究是 STEM 教育中移动学习领域最早的大规模文献计量考察之一；通过对碎片化文献的综合，本研究揭示了关键的研究空白，并为未来探究提供了路线图，尤其关注人工智能驱动的学习设计、包容性教学法与 STEM 教育中可持续的技术整合。

Abstract

Given the rapid proliferation of mobile technologies and their potential to reshape science, technology, engineering, and mathematics (STEM) education, there is a pressing need to systematically examine how research in this domain has evolved over time. This study maps the intellectual structure and emerging trends of mobile learning (m-learning) in STEM education through a comprehensive bibliometric analysis. Drawing from a corpus of 1,462 journal articles indexed in Scopus, the study employed citation, co-citation, and co-word analyses using VOSviewer to uncover influential authors, dominant themes, and evolving conceptual patterns. The findings reveal three major research domains: foundational learning theories, pedagogical strategies, and user acceptance models. Four thematic clusters were identified: immersive and collaborative learning through emerging technologies, AI integration in digital pedagogy, infrastructural and accessibility challenges in developing contexts, and adoption of m-learning in higher education systems. Despite a marked growth in research output, the study highlights persistent challenges, including inequitable access to mobile technologies, insufficient teacher training, and weak alignment between mobile tools and STEM learning objectives. These limitations underscore the need for constructivist-aligned instructional designs, investment in scalable infrastructure, and institutional policy support, particularly in underserved educational settings. This study represents one of the first large-scale bibliometric investigations of m-learning in STEM education. By synthesizing a fragmented body of literature, it reveals critical research gaps and provides a roadmap for future inquiry, with particular attention to AI-driven learning design, inclusive pedagogies, and sustainable technology integration in STEM education.