A big data learning analysis system and method

Abstract

The invention relates to a big data study analysis system and method. The system comprises a collecting layer, a platform layer and an application layer. The collecting layer is used for acquiring study experience data, converting the study experience data into activity stream flow data compiled according to a preset format and transmitting the activity stream data to the platform layer. The platform layer is used for setting up a study record warehousing system to store the activity stream data, receiving an authentication request of the application layer and allowing the application layer to acquire the activity stream data when the authentication request is passed. The application layer is used for acquiring the activity stream data from the platform layer to analyze the activity stream data and showing the analysis result. According to the big data study analysis system, the study experience data can be analyzed according to behaviors of a user.

Description

technical field [0001] This application relates to the technical field of big data analysis, in particular to a big data learning analysis system and method. Background technique [0002] The specific applications of big data in the field of education mainly include "Learning Analytics" (Learning Analytics, LA) and "Educational Data Mining" (Educational Data Mining, EDM). Since learning is a complex interactive process between students and learning content, learning environment, learning companions and teachers, the basic data of the two aspects of "educational data mining" and "learning analysis" should be data based on the learning process. It includes not only the data of students, learning content, learning environment, learning companions and teachers, but also the operation data of various objective resources such as student scores and the relationship data between these factors. These data contain huge analytical value. [0003] With the rapid development of related ...

AI Technical Summary

Problems solved by technology

Fragmentation, diversity, data heterogeneity and other complex characteristics of learning experience make learning experience tracking difficult to achieve at the technical level

[0004] Although the current learning management system (LMS) can support the technical implementation of the SCORM (Shareable Content Object Reference Model, shared content object reference model) courseware resource package, it provides compatibility and reuse of courseware resources between different teaching platforms, but SCORM The educational measurement methods in the study are relatively simple in concept and operation, and are limited to tracking the learning process of specific learning materials (such as: course content, evaluation results, etc.), ignoring the learning situation and personalized information (such as: learning time, background, etc.)

Images