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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. [ABSTRACT FROM AUTHOR]

The mental health of college students not only affects their personal development but also impacts the environment of the campus. Moreover, it affects the harmony of the family and stability of the society. This phenomenon has attracted great attention from the society. New technological paradigms such as the Internet of Things (IoT) and data mining offer new opportunities to detect, assess, and care for the mental health of students early. This paper explains the psychological problems of college students and analyzes the application of IoT and data mining in the mental health of students. The contents of mental health in colleges are investigated, and big data techniques are employed to build an innovative model for students. First, an online psychological consulting platform is designed followed by online evaluation standards and online/offline teaching fusion courses to understand mental health. Next, a neural network algorithm is developed and trained on the datasets to accurately classify the contents of students' mental health, and the corresponding results are obtained. The model is evaluated in terms of accuracy. Results show that the proposed innovative model achieves better results with 88% accuracy. Through comparative analysis, it is demonstrated that the use of IoT and big data technology can provide technical support for the innovation of college student's mental health and education and can solve more problems efficiently. [ABSTRACT FROM AUTHOR]