Automatic generation of concept maps from students' answers (2)
Recommendations of student activities (in our case questions ) can be generated as a complete sequence of actions as a single recommendation element. Several methods have already been developed, also based on deep learning approaches (e.g. LSTM ), but they all rely on a large amount of data with metadata. Our iMath dataset does not currently meet the minimum requirements, but maybe we will get there.
Example:
Parse our course questions and student responses to them (how they answered questions ). Construct concept map as advised by Zhou.
Reference:
[1] Y. Zhou, C. Huang, Q. Hu, J. Zhu, and Y. Tang, “Personalized learning full-path recommendation model based on LSTM neural networks,” Information Sciences, vol. 444, pp. 135–152, May 2018.
Author of the tip:
Andrej Košir
University of Ljubljana