"Big Data Management" Experimental Teaching Auxiliary System Design And Implementation

In order to make the "Big Data Management" course experiment platform better serve daily experiment teaching and make full use of the teaching advantages brought by the cloud experiment platform,it is necessary to develop an auxiliary experiment teaching system based on the experiment platform.On the one hand,it provides students with an experimental environment quickly and provides excellent experimental examples for students to learn;on the other hand,it provides teachers with hot issues and behavior evaluation information that occur in students' computer experiments to assist teachers in scoring experiments.Based on the analysis of the experimental characteristics of the "Big Data Management" course,a "Big Data Management" experimental teaching auxiliary system was designed in accordance with the needs of supporting a large number of students online experiments at the same time and assisting teachers in scoring experiments.The system mainly includes teaching auxiliary authority control module,experimental resource configuration module,automated environment deployment module,on-site reproduction module and student behavior evaluation module.The teaching auxiliary authority control module provides different authority control measures for different entities in the system to prevent unauthorized behavior.On the one hand,the experimental resource configuration module realizes the on-demand acquisition of resources and solves the problem of insufficient experimental resources for students.On the other hand,it ensures the utilization of platform resources through two methods of manual recycling and automatic recycling.The automatic experimental environment deployment module realizes the automatic deployment of complex experimental environments,and can quickly build a corresponding heterogeneous database experimental environment for students according to experimental requirements.At the same time,in view of the lack of experimental resources,the module also provides two solutions for students to choose from: resource application and environmental degradation.The on-site reproduction module can provide students with excellent learning examples.Teachers can use this module to show experimental results with learning value to students in the designated class,and students can learn by themselves.The student behavior evaluation module combines the big data processing capabilities of Hadoop-Spark and the accuracy of the Light GBM data mining algorithm.It collects students' computer experiment behavior data for processing and analysis,and obtains graphic information such as student experiment completion,Linux environment familiarity,experiment attitude.So as to help teachers evaluate experimental results more objectively.Finally,the typical Mongo experiment of big data management is used to verify the system.The test shows that the system is easy to deploy,all functions are complete and available,and the actual performance of the system is in line with the original design intention,which can provide good assistance for "teaching" and "learning".