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<p>Improving teaching effectiveness is a key goal in higher education. The ARCS motivational model (Attention, Relevance, Confidence, Satisfaction) has become widely adopted in educational environments. This systematic review examines 24 studies from 2015 to 2024, sourced from ProQuest, Web of Science, and Scopus, selected from an initial pool of 948 publications. The review reveals three areas where research on the ARCS model falls short: (1) inconsistent application of ARCS principles across diverse educational contexts, (2) absence of longitudinal studies assessing long-term academic impacts, and (3) geographical bias, with 75% of studies concentrated in China. This review contributes to existing knowledge by documenting the successful integration of emerging technologies (AI, VR) with the ARCS model, which significantly enhances student engagement and learning outcomes. Our analysis provides evidence-based approaches for adapting the model to different educational contexts and recommends the development of standardized assessment tools, suggesting that cross-cultural and longitudinal studies are essential for optimizing the model’s effectiveness across diverse educational settings.</p>

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Impact of attention, relevance, confidence, satisfaction (ARCS) model on teaching effectiveness in higher education: a systematic review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月29日

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  • Qiong Mei,
  • Nee Nee Chan,
  • Ranjit Singh Gill,
  • Neevaarthana Subramaniam,
  • Saeid Motevalli,
  • Chin-Siang Ang

摘要

Improving teaching effectiveness is a key goal in higher education. The ARCS motivational model (Attention, Relevance, Confidence, Satisfaction) has become widely adopted in educational environments. This systematic review examines 24 studies from 2015 to 2024, sourced from ProQuest, Web of Science, and Scopus, selected from an initial pool of 948 publications. The review reveals three areas where research on the ARCS model falls short: (1) inconsistent application of ARCS principles across diverse educational contexts, (2) absence of longitudinal studies assessing long-term academic impacts, and (3) geographical bias, with 75% of studies concentrated in China. This review contributes to existing knowledge by documenting the successful integration of emerging technologies (AI, VR) with the ARCS model, which significantly enhances student engagement and learning outcomes. Our analysis provides evidence-based approaches for adapting the model to different educational contexts and recommends the development of standardized assessment tools, suggesting that cross-cultural and longitudinal studies are essential for optimizing the model’s effectiveness across diverse educational settings.