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<p>The rapid development of artificial intelligence and the digital economy has intensified demand for big data talent, while higher education continues to face challenges in aligning curriculum provision with changing industry requirements. Drawing on Outcome-Based Education and a learning environment perspective, this study examines an AI-supported blended learning environment for big data education. The environment combines face to face instruction with a set of digital learning supports. Rain Classroom and Chaoxing are used to facilitate interaction and classroom participation, while Edu Coder supports practical training in a cloud-based setting. In this way, the learning environment is intended to support both students’ conceptual understanding and the development of observable practical competence. The study draws on multisource data from a Big Data Technology course, including 1358 records of in class learning activities, 168 records of experimental tasks, and 92 valid student questionnaires. The questionnaire demonstrated excellent internal consistency and strong sampling adequacy, with a Cronbach’s alpha of 0.977 and a KMO value of 0.936. The blended learning environment is examined in terms of four dimensions, including instructional support and feedback, student involvement and interaction, task organisation and practice support, as well as learner autonomy and temporal flexibility. Multiple regression was used to examine how students’ perceptions of these dimensions were associated with their endorsement of the blended learning environment. The results show that students’ endorsement was most strongly associated with the perceived capacity of the environment to compensate for limitations of traditional classroom teaching and with their positive acceptance of the digital learning arrangements. The findings further suggest that the environment was associated with higher engagement, stronger practical skills and problem-solving, and improvement in self-directed learning. Overall, the study shows how an AI-supported blended course in big data education can be examined as a coordinated learning environment through behavioural, task based, and perceptual evidence.</p>

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Design and exploratory evaluation of an AI-supported blended learning environment for big data education

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Mingyou Liu,
  • Rong Xiao,
  • Yitao Wang

摘要

The rapid development of artificial intelligence and the digital economy has intensified demand for big data talent, while higher education continues to face challenges in aligning curriculum provision with changing industry requirements. Drawing on Outcome-Based Education and a learning environment perspective, this study examines an AI-supported blended learning environment for big data education. The environment combines face to face instruction with a set of digital learning supports. Rain Classroom and Chaoxing are used to facilitate interaction and classroom participation, while Edu Coder supports practical training in a cloud-based setting. In this way, the learning environment is intended to support both students’ conceptual understanding and the development of observable practical competence. The study draws on multisource data from a Big Data Technology course, including 1358 records of in class learning activities, 168 records of experimental tasks, and 92 valid student questionnaires. The questionnaire demonstrated excellent internal consistency and strong sampling adequacy, with a Cronbach’s alpha of 0.977 and a KMO value of 0.936. The blended learning environment is examined in terms of four dimensions, including instructional support and feedback, student involvement and interaction, task organisation and practice support, as well as learner autonomy and temporal flexibility. Multiple regression was used to examine how students’ perceptions of these dimensions were associated with their endorsement of the blended learning environment. The results show that students’ endorsement was most strongly associated with the perceived capacity of the environment to compensate for limitations of traditional classroom teaching and with their positive acceptance of the digital learning arrangements. The findings further suggest that the environment was associated with higher engagement, stronger practical skills and problem-solving, and improvement in self-directed learning. Overall, the study shows how an AI-supported blended course in big data education can be examined as a coordinated learning environment through behavioural, task based, and perceptual evidence.