Research On Student Interest Modeling In Smart Learning Environment

With the advent of the "Internet+" era and the rapid development of emerging technologies such as the Internet of Things,cloud computing,big data,and artificial intelligence,the era of technological change education has arrived.Diversification of resources,scale of data and intelligence of calculation provides an opportunity for the research and practice of personalized support services.Personalized learning is the future learning method of smart education appeal,and the learner interest modeling is the core of personalized learning service.Although the research of smart classroom has achieved certain results,there are also deficiencies that cannot be ignored.How to use smart classroom to meet the "individualized needs" of learners and give student"personalized feedback" is still an urgent problem to be solved.Therefore,building a student interest modeling in a smart learning environment is beneficial for the learner to better capture the students’ interest in learning and individualized needs and provide personalized learning services.The construction method of the student interest modeling proposed in this paper is aimed at the quantitative analysis of students’ interest in classroom learning.The student interest modeling in the smart learning environment is constructed by combining the three-dimensional vector space mode.The model represents students’interest in classroom learning from three dimensions of explicit behavior of interest.That is to say,the students’ interest information is collected from the three dimensions of the students’ attention,classroom participation and emotional experience.Corresponding data collection rules and quantitative standards are designed for each of the three dimensions.In the aspect of data collection,this study indirectly collects students’ attention in the classroom based on the sitting posture features that are easier to collect in the classroom environment.A sitting-attention level mapping rule base is designed.By capturing the students’ sitting characteristics to match the corresponding level of attention in the classroom.Quantify students’ classroom participation by the frequency of class participation behaviors.A observation table for recording classroom participation behavior was designed to record the students’ classroom participation behavior and frequency.The student’s emotional experience is measured by the facial expression characteristics of the student.The facial activity unit coding system is designed.Based on this,the coding pairs are constructed with eyebrows and mouth features.And matching with the designed facial expression rule recognition library to obtain the corresponding seven types of expressions,and further dividing the seven expressions into interest and non-concern.In terms of quantitative technology,a combination of linear weighting and K-means clustering is chosen to quantify students’interest in classroom learning.Finally,combined with the recording of videos in the smart classroom environment to implement an example analysis of the constructed student interest modeling.Extracting the three dimensions of interest explicit behavior that include the students’ class attention,class participation,and learning emotions in the teaching video clips.The students’ classroom learning interest is calculated by the corresponding quantitative technique to verify the validity of the model.