The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.
Adaptive Learning System (ALS) is a supportive environment, which dynamically provides learners with services that can satisfy their demand for personalized learning in accordance with the differentiation of their individual traits. At present, study on ALS is still in the exploratory stage, and there are still many fields that deserve to be studied thoroughly. User characteristic model is the foundation and core of ALS and the key to the implementation of intelligent and personalized recommendation service. Based on this, this paper intends to analyze learners’ characteristics in ALS through several dimensions, such as basic information, interest, preference, cognitive level and learning style, through which learners’ user characteristic model is established. In the end, ALS, which supports the function of personalized recommendation, is implemented based on this model. It is suggested by the result of the simulation experiment that ALS, which is developed through this model, demonstrates a satisfying effect in recommendation, and it can dynamically recommend appropriate learning resources in accordance with learners’ personalized demands through which learners’ quality and efficiency of learning can be effectively enhanced to a certain extent.
In the digital intelligence era, users have higher requirements for machine aided learning environment experience. How to provide personalized learning support services based on users' different characteristics has become a hot topic for researchers. Intelligent learning system (ILS) is a learning support environment that can dynamically diagnose users' different learning needs and then provide personalized services. However, the current research on intelligent learning systems is still in the exploratory stage, and the research results need to be improved in the aspect of intelligent recommendation effect. Based on this, this paper will further explore the personalized recommendation technology solution of intelligent learning system on the basis of the analysis of relevant case results. In order to improve the recommendation accuracy of intelligent learning system, we will focus on the analysis of the system architecture, feature model construction method and recommendation process from the perspective of user feature model. The simulation experiment analysis shows that the research results have certain advantages in the personalized recommendation effect, which can dynamically provide the current user with a suitable personalized learning path to meet the user's learning needs.
With the rapid development of the mobile internet technology, m-learning is gradually becoming a key modern remote learning way. With the combination of mobile technology and digitalized technology, a learning mode based on mobile terminals is created to provide a learning environment unlimited by the time and space for learners, thus meeting the real-time learning needs of learners. However, the current mobile terminal oriented learning system is still in the exploratory stage, and how to build an intelligent learning system for mobile terminal access is a hot spot concerned by numerous researchers. Based on this, this paper explores the intelligent mobile learning system (IMLS) solution from the two technical dimensions of user terminal recognition and learning content adaptability, and describes its development environment and key technologies. Experimental results show that users' convenience and access can be enhanced to some extent by the system, which will further enhance their learning quality.
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