Design And Implementation Of Adaptive Learning System For Online Courses

The concept of big data and the continuous improvement of related technologies have brought a new model for the development of all walks of life.As a basic field of education,learners' learning information and curriculum update iterations provide the basis for the application of big data technology.Big data technology also brings valuable new opportunities for the development of the education industry.At the same time,the emergence of online education platform makes learning no longer limited by time and space.How to enable learners to find courses that can truly achieve learning goals in rich and complicated courses has become one of the focuses of education and education platforms.As a typical application of educational big data,adaptive learning systems are designed to provide learners with personalized learning services.The system can provide learning paths adaptively according to the different learning characteristics of the learners,helping the learners to complete their skill goals faster and better.However,at present,the research on adaptive learning system in China is still in the stage of theoretical research,and the number of systems for platform construction is limited.Most online education platforms simply "move" the Internet after simply changing the learning content of the original book,making it easy for learners to get lost.At the same time,as one of the important strategies of the country,the lack of talents needs to be resolved in the field of cyber security.Therefore,this paper takes the network security discipline as an example to design and implement an adaptive learning system for online courses,in order to provide new ideas for the follow-up research of the adaptive learning system in the special field.The specific content is as follows:1.Based on the general model of the adaptive learning system,the functions of each module in the overall structure of the domain-specific special subject system are improved,and the overall workflow of the system is designed.2.Introduce computerized evaluation in the student module,and design an adaptive evaluation system structure based on cognitive diagnosis theory,realize personalized selection model for different learners,and enrich the learner's results through more detailed and special evaluation results.The model lays the foundation for adaptive course path recommendations.Through the experimental results of a large number of data the correctness of the architecture is proved.The hybrid model used in the cognitive diagnosis module also has better performance indicators than the traditional model,which provides a new idea for the construction of the learner model;3.In the domain model,the knowledge structure of domain-based subject knowledge based on knowledge map technology is completed,and through the introduction of the actual learning path,the sequence mining algorithm is introduced to realize the dynamic update of knowledge structure.The PrefixSpan algorithm used is excellent in terms of computing speed and storage space occupancy.The application of knowledge maps and data mining techniques provides a new means for the construction of knowledge models;4.In the adaptive engine module,a support vector regression model is adopted to implement the recommendation of the personalized course path.In the state of extracting the knowledge structure and the learner's cognitive level information,the learner is provided with an adaptive learning path through the prediction of the skill completion index.And the training and testing of user data,it is determined that the support vector regression algorithm has a good effect in the prediction,and the parameters and algorithms in the algorithm are optimally combined.