Research on Behavior and Evaluation System of Online Learning

DOI

10.2991/978-94-6463-192-0_91How to use a DOI?

Keywords

e-learning; learning model; content reconstruction; evaluation system

Abstract

In the context of digital educational means, especially the rise of online teaching and mobile learning, how to use online teaching resources well, understand learners’ learning behavior, encourage and guide learners to participate in learning, and obtain the maximum learning efficiency in a limited time is a common problem between teachers and students. In this paper, under the assumption that positive behavioral incentive helps to enhance the learning effect, we construct a learning model based on the Internet + learning platform, reconstruct the learning module and evaluation of the learning APP, and guide the learning behavior, carry out goal incentive and guide participation in learning to enhance the learning effect through big data analysis. Over the experimental test, the Internet learning platform based on the model of ability-improvement can play a role in promoting the study habits and comprehensive performance, which based on the proposal of model construction and evaluation optimization of mobile learning platform.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Lihong Wang
AU  - Weijie Gou
PY  - 2023
DA  - 2023/07/04
TI  - Research on Behavior and Evaluation System of Online Learning
BT  - Proceedings of the 2023 2nd International Conference on Educational Innovation and Multimedia Technology (EIMT 2023)
PB  - Atlantis Press
SP  - 704
EP  - 715
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-192-0_91
DO  - 10.2991/978-94-6463-192-0_91
ID  - Wang2023
ER  -