Research on Model Design about Learning Result Prediction and Intervention Based on Dynamic Data Mining

DOI

10.2991/mmetss-19.2019.191How to use a DOI?

Keywords

Data mining, Result prediction, Learning intervention, Model design

Abstract

Predicting the academic performance of online learners and promptly intervening and guiding them is an effective way to improve the effectiveness of online learning. How to predict the academic performance and behavior of online learners, implement academic early warning based on prediction results, and provide evidence for teaching decision-making is one of the problems that network education needs to solve, and also an important research issue in educational big data research. This study uses the decision tree method in data mining technology to predict the learning behavior and performance of online learners, and constructs adaptive learning outcome prediction and intervention models. The application scenarios of the model are analyzed from the perspective of curriculum developers, teachers and students.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - chengling Zhao
AU  - Min Li
PY  - 2019/10
DA  - 2019/10
TI  - Research on Model Design about Learning Result Prediction and Intervention Based on Dynamic Data Mining
BT  - Proceedings of the 2019 4th International Conference on Modern Management, Education Technology and Social Science (MMETSS 2019)
PB  - Atlantis Press
SP  - 932
EP  - 935
SN  - 2352-5398
UR  - https://doi.org/10.2991/mmetss-19.2019.191
DO  - 10.2991/mmetss-19.2019.191
ID  - Zhao2019/10
ER  -