Video learning behavior analysis method in SPOC environment based on time dimension

Abstract

The invention discloses a video learning behavior analysis method in an SPOC environment based on time dimension. The method comprises the steps: 1), obtaining basic data which comprises student dataand video learning data; 2) classifying students according to the student data; 3) classifying the video learning data according to student classification; 4) performing statistical analysis and visual presentation on the video learning data of different types of students; and 5) analyzing the learning behaviors of different types of students in different time dimensions according to the statistical result. According to the invention, classification statistics is carried out on the video learning data based on the time dimension, differences of different types of students on the video learningdata under different time factors are analyzed, time factors influencing the learning effect of the students are found out, feedback is provided for teachers and the students, the teachers are helpedto make personalized teaching schemes, and the students are assisted to improve the learning effect.

Description

technical field [0001] The present invention relates to the technical field of video learning data analysis, in particular to a method for analyzing video learning behavior in a time-dimension-based SPOC environment. Background technique [0002] The application of Small Private Online Course (SPOC) has innovated the teaching mode and promoted the reform and development of education. In the SPOC learning environment, video resources are one of the most important learning resources, and learning videos are the most basic and important activities for learners. Through the analysis of students' video learning data, the learning characteristics of different types of students can be obtained, potential factors that affect students' learning effects can be found, and auxiliary support can be provided for students' self-regulated learning and teachers' personalized teaching. [0003] Most of the current research on learning behavior analysis analyzes various learning data in onlin...

AI Technical Summary

Problems solved by technology

Most of the current work studies the relationship between different learning behaviors and learning effects, but there is no fine-grained analysis of the similarities and differences of students' learning behaviors under the influence of different time factors, and there is no comparison of learning behaviors of different types of students

At the same time, the mining of learners' video learning data is not deep enough, there is no detailed discussion of each video learning data, and there is no distinction between different types of students in terms of learning behavior characteristics.

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