In order to help primary and middle school students improve their self-efficacy and reduce their workload, a K12 exercise recommendation algorithm based on educational neuroscience is proposed in this paper. Experimental tests show that, under the same number of classroom exercises, the exercises recommended by the algorithm can help the target students to achieve greater ability improvement, and thus achieve the effect of reducing the workload of primary and secondary school students.

The recommendation method based on collaborative filtering has some shortcomings in the field of exercise recommendation, such as lack of interpretability and rationality. The existing methods for students' cognitive diagnosis are too rough to measure students' mastery of knowledge point, and the measurement of students' ability has the disadvantage of hysteresis. This paper proposes an exercise recommendation method aimed at improving students' mastery of the specified knowledge point faster. For the designated student and the designated knowledge point, this method can choose the exercise that can help student improve the level of mastery of the knowledge point as fast as possible in all the exercises including the knowledge point, and recommend to the student. This method is based on the improved cognitive diagnosis method and Long Short-term Memory Networks LSTM, and recommends exercises for the target students that can improve the knowledge level of students similar to the target students. According to the experimental test, the exercises recommended by this method can help target students to improve their mastery of the target knowledge point to a greater extent under the condition of doing the same number of exercises.

This paper presents a method of exercises recommendation based on machine learning. This method can recommend more suitable exercises to students according to the category they belong to. Firstly, we use linear regression and EM algorithm to accurately model the students' mastery of each knowledge point. For each knowledge point, students are divided into several categories according to their mastery of the knowledge point and their average mastery of all knowledge points. For each knowledge point, according to the student history answer record, find out the exercise that can make each kind of student get bigger promotion respectively. For the students who need to recommend the exercises that contain the specified knowledge points, we first use the k-nearest neighbor algorithm to classify the students, and then recommend the exercises suitable for the students. It has been proved by experiments that this method can help students to achieve greater improvement in the same number of exercises.

For each subject, the syllabus specifies what knowledge points students should master and how well they should master them. Whether students can acquire the ability required by the syllabus is an important evaluation criterion for a student's learning achievements. However, most of the exercises for k-12 students at present contain knowledge points in the examination syllabus, but the difficulty of the examination of knowledge points is inconsistent with the teaching syllabus. Solving and studying such exercises does not help students acquire the skills required by the syllabus quickly. Therefore, students should choose exercises that fit well with the syllabus when practicing. However, at present, there is no way to recommend exercises that fit well with the syllabus for students, so as to improve learning efficiency. In order to solve the above problems, this paper proposes an exercise recommendation method for k-12 students based on the syllabus. It is proved by experiment that this method can recommend exercises that fit well with the syllabus for students, so as to improve their learning efficiency. The use of this method can help students achieve better learning outcomes and achieve higher scores in the final examination than ordinary students under the condition of learning and mastering the same number of exercises with the same difficulty.

Students cheating in exams destroys the fair principle of evaluation and affects the normal teaching order of the school. Therefore, the examination cheating detection has the vital significance. The existing cheating detection methods have disadvantages such as insufficient modeling accuracy for students, lag in cognitive diagnosis, difficulty in detecting multi-source plagiarism and low accuracy. In order to solve the disadvantages of traditional methods, this paper proposes a method for detecting cheating in multi-index examinations based on feed-forward neural network. This paper first proposes RAE algorithm which combines linear regression and EM algorithm for students' cognitive diagnosis. We use RAE algorithm and LSTM neural network to obtain the knowledge point mastery degree of each student based on the history problem solving and the knowledge point mastery degree based on the exam problem solving. Then, according to the information of students' cognitive level, seat distribution in the examination room, students' habit of guessing answers at normal times, similarity of examination papers, etc., we get several indicators to judge whether students cheat. Finally, we take various indicators obtained through various methods as characteristics and use feed-forward neural network to classify whether students cheat or not. The experimental results show that the accuracy and recall of this method are significantly higher than those of several popular methods.