Online open course video quality quantitative evaluation method

Abstract

The invention belongs to the field of education informatization, and particularly relates to an online open course video quality quantitative evaluation method. The method comprises the following steps: S1, calculating a matrix of a video watching duration proportion; S2, calculating a total knowledge output quantity and a video attraction index of the course video; S3, calculating a knowledge conversion rate; S4, calculating the effective total output of the video knowledge. Compared with the prior art, the invention has the following beneficial effects: (1) based on an education big data analysis and statistics method, quantitative and objective evaluation of video quality is realized, and subjectivity of traditional evaluation is broken through; (2) the method has universality and can be used for evaluating the quality of online video resources of any platform, and the problems caused by traditional subjective scoring and different evaluation standards are solved; (3) the video quality evaluation method established by the invention solves the problems of how to quantize knowledge and convert knowledge into mathematical mechanisms, and has a certain scientific theoretical value.

Description

technical field [0001] The invention belongs to the field of educational informatization, and in particular relates to a quantitative evaluation method for online open course video quality. Background technique [0002] The rapid development of online open courses has encountered problems such as low video resource utilization and high student dropout rates. The reason for this phenomenon is that some course videos do not conform to the rules of online learning or the video quality is low. Therefore, it is very necessary to quantitatively evaluate the quality of online open course videos to improve the effect of online learning and the utilization of online course resources. [0003] The traditional course evaluation method adopts the way of scoring by experts. Experts watch some videos of each course and then score each video according to the standards set by the scoring. This evaluation mode is based on the subjective judgment of experts, and partial viewing cannot fully ...

AI Technical Summary

Problems solved by technology

This evaluation mode is based on the subjective judgment of experts, and part of the viewing can not fully understand the overall situation of the course, and the evaluation standards of each expert are not consistent, the evaluation has a certain degree of randomness and one-sidedness

On the other hand, there are also limitations in judging the quality of videos solely based on the academic performance of online learners

Moreover, the degree of difficulty of the course assessment is inconsistent, and it is impossible to form a unified evaluation standard

This means that academic performance cannot be used as a criterion to evaluate the quality of videos or even courses

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