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.

物联网技术的快速发展为各个行业带来了变革机遇，教育是主要受益者。智能教室是物联网集成的产物，正在被广泛采用，以创建技术增强的、以学生为中心的学习环境，以满足学生的信息素养需求，特别是在流行病等事件期间。这种广泛的采用产生了大量的教育数据，通常称为大数据，需要创新的分析和利用解决方案。为了解决这些挑战，本文建议利用 Apriori 算法，这是一种数据挖掘技术，以在广泛的数据集中发现有价值的模式和关联而闻名。本文考虑个人的信息素养技能，评估不同质量的各种信息资源的影响。利用数据挖掘技术，深入挖掘大学生信息素养数据，与高校图书馆资源整合，建立数据驱动的信息素养教育模式。然后重点讨论指导大学生信息素养的标准、组成部分和有效方法。最后，研究了不同群体的学生（从一年级本科生到特定大学的博士生）参与信息素养教学的情况。实验结果显示，大二学生的参与水平最高，准确率为75.9%，而研究生比本科生和博士生接受了更多的信息素养培训。学生。将该方法与 SVM、KNN、LR、RF 和 DT 等其他方法进行比较时，它取得了优越的性能。此外，还通过学生学习、行为和成绩三个维度评估大学图书馆信息素养培训质量。只有大三、大四和研究生一年级得分在4以上，得分分别为4.18、4.15和4.26。