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Information literacy of college students from library education in smart classrooms: based on big data exploring data mining patterns using Apriori algorithm

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Abstract

The rapid advancement of IoT technology presents transformative opportunities across various sectors, with education being a prominent beneficiary. Smart classrooms, a product of IoT integration, are being widely adopted to create technology-enhanced, student-centric learning environments that cater to students' information literacy needs, particularly during events like pandemics. This widespread adoption generates substantial amounts of educational data, commonly known as big data, necessitating innovative solutions for analysis and utilization. To solve these challenges, this paper proposes utilizing the Apriori algorithm—a data mining technique renowned for uncovering valuable patterns and associations within extensive datasets. This paper evaluates the impact of various information resources with differing quality, considering individuals' information literacy skills. Utilizing data mining techniques, it delves into university students' information literacy data, integrating it with the university library resources to establish a data-driven information literacy education model. It then focuses on criteria, components, and effective methods for instructing college students in information literacy. Finally, a diverse group of students, from first-year undergraduates to doctoral candidates at a specific university, is studied for their engagement in information literacy instruction. Based on the experimental findings, sophomore students exhibited the highest level of participation at 75.9% accuracy, while postgraduate students received more information literacy training than undergraduates and Ph.D. students. When comparing this method to others, such as SVM, KNN, LR, RF, and DT, it achieved superior performance. Additionally, the quality of information literacy training in university libraries was assessed through three dimensions: student learning, behavior, and achievements. Only junior, senior, and first-year graduate students scored above 4, with scores of 4.18, 4.15, and 4.26, respectively.

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Authors and Affiliations

  1. College of International Cooperation, Xi’an International University, Xian, 710077, Shaanxi, China

    Si Chen & Ying Xue

  2. Normal College, Yanbian University, Yanbian, 133002, Jilin, China

    Xiangzhe Cui

  3. Department of Education, Sehan University, Jeollanam-do, 58447, Korea

    Xiangzhe Cui

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  1. Si Chen
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  2. Ying Xue
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  3. Xiangzhe Cui
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Correspondence to Xiangzhe Cui.

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Chen, S., Xue, Y. & Cui, X. Information literacy of college students from library education in smart classrooms: based on big data exploring data mining patterns using Apriori algorithm. Soft Comput 28, 3571–3589 (2024). https://doi.org/10.1007/s00500-023-09621-8

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