The Research And Implementation Of Online Classroom Student Concentration Detection System

Under the historical background of the continuous development of artificial intelligence technology,the education industry in China has also gradually introduced artificial intelligence technology to assist teaching.At the beginning of 2020,Covid-19 hit the world,due to the impact of the epidemic,universities and institutions are vigorously developing online classes,which is a phenomenon that has made a huge impact on the existing traditional teaching structure,while also bringing the opportunity to digitize teaching and learning.However,under the current environment,a teaching and learning evaluation system for online classes has yet to be established.Online classes are evaluated by assessing the behaviour of students in the classroom,particularly in terms of their concentration.Quality assessment in the online classes can improve students’ learning as well as inform teachers’ teaching strategies.Therefore,the assessment of online classes has an important place in modern teaching and learning,not only as a basis for successful teaching and learning activities.Moreover,it is widely used in the implementation of educational decisions.Conversely,there are many problems that need to be solved in the evaluation system of online classes within the new teaching format.Firstly,in comparison to traditional face-to-face teaching,teachers in online classes can only observe the status of learners via cameras,which makes it difficult to precisely judge the learning status of students.Secondly,communication between teachers and students in online classes is not as timely as in traditional classrooms where face-to-face communication is not possible.Thirdly,the existing assessment methods are not able to consider the impact of objective factors,such as the environment,which is not conducive to a comprehensive assessment of the learner’s status.In summary,this paper proposes a student concentration detection system that is particularly suitable for online classes.Firstly,a deep learning algorithm is applied to identify the expressions of each student,classifying them into positive,negative and neutral expressions,and judging the concentration level of each student based on the specific circumstances in which they live.Secondly,the online class concentration detection system is analyzed in its entirety,with a comprehensive introduction of the system’s implementation idea and general logic,and a detailed description of the functions of every module.Lastly,the results are validated against the teacher’s subjective assessment.As a result,the system is simple to use and the results are highly accurate and precise,which provides online teachers with an intelligent and comprehensive feedback on student concentration.In addition,it provides a way for online students to observe their own learning status and improves the overall quality of teaching in the online classes.