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<p>Metacognitive competency is a key factor in the success of hybrid learning; however, few studies have focused on developing comprehensive tools to assess this concept in such contexts. The current paper aimed to develop and validate the Metacognitive Competency Scale for College Students in Hybrid Learning (MCS-HL). A sequential exploratory mixed-methods design was used to conduct the study in three phases: item development, content validation, and psychometric testing. The study also utilized Exploratory Graph Analysis (EGA) from a network perspective. The MCS-HL consists of 34 items across six dimensions: Metacognitive Awareness of Hybrid Learning, Strategic Goal Formulation in Dual Contexts, Reflexive Adaptation to Learning Challenges, Flexible Problem-Solving in Hybrid Contexts, Technology-Driven Self-Regulation, and Collaborative Metacognition in Learning Networks<b>.</b> The results indicated that the scale had suitable psychometric properties, including construct validity, divergent validity, suitable internal consistency (Cronbach's alpha &gt; 0.7), and test–retest reliability (ICC &gt; 0.85). Furthermore, the measurement invariance of the MCS-HL across gender was confirmed. Additionally, network analysis further supported the six-factor structure of the MCS-HL. In conclusion, the MCS-HL can help instructors identify students' strengths and weaknesses across both genders, thereby enhancing teaching strategies and improving learning outcomes in hybrid settings.</p>

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Development and validation of the metacognitive competency scale for college students in hybrid learning (MCS-HL): insights from the network analysis perspective

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Ayoub Hamdan Al-Rousan,
  • Mohammad Nayef Ayasrah,
  • Mohamad Ahmad Saleem Khasawneh,
  • Yusra jadallah abed Khasawneh

摘要

Metacognitive competency is a key factor in the success of hybrid learning; however, few studies have focused on developing comprehensive tools to assess this concept in such contexts. The current paper aimed to develop and validate the Metacognitive Competency Scale for College Students in Hybrid Learning (MCS-HL). A sequential exploratory mixed-methods design was used to conduct the study in three phases: item development, content validation, and psychometric testing. The study also utilized Exploratory Graph Analysis (EGA) from a network perspective. The MCS-HL consists of 34 items across six dimensions: Metacognitive Awareness of Hybrid Learning, Strategic Goal Formulation in Dual Contexts, Reflexive Adaptation to Learning Challenges, Flexible Problem-Solving in Hybrid Contexts, Technology-Driven Self-Regulation, and Collaborative Metacognition in Learning Networks. The results indicated that the scale had suitable psychometric properties, including construct validity, divergent validity, suitable internal consistency (Cronbach's alpha > 0.7), and test–retest reliability (ICC > 0.85). Furthermore, the measurement invariance of the MCS-HL across gender was confirmed. Additionally, network analysis further supported the six-factor structure of the MCS-HL. In conclusion, the MCS-HL can help instructors identify students' strengths and weaknesses across both genders, thereby enhancing teaching strategies and improving learning outcomes in hybrid settings.