@inproceedings{0f2fb1b47bda422d944838009c71439c,
title = "Student performance estimation based on topic models considering a range of lessons",
abstract = "This paper proposes a prediction framework for student performance based on comment data mining. Given the comments containing multiple topics, we seek to discover the topics that help to predict final student grades as their performance. To this end, the paper proposes methods that analyze students{\textquoteright} comments by two topic models: Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA). The methods employ Support Vector Machine (SVM) to generate prediction models of final student grades. In addition, Considering the student grades predicted in a range of lessons can deal with prediction error occurred in each lesson, and achieve further improvement of the student grade prediction.",
author = "Sorour, {Shaymaa E.} and Kazumasa Goda and Tsunenori Mine",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-19773-9_117",
language = "English",
isbn = "9783319197722",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "790--793",
editor = "Cristina Conati and Neil Heffernan and Antonija Mitrovic and {Felisa Verdejo}, M.",
booktitle = "Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings",
address = "Germany",
note = "17th International Conference on Artificial Intelligence in Education, AIED 2015 ; Conference date: 22-06-2015 Through 26-06-2015",
}