Using Strongly Typed Genetic Programming for knowledge discovery of course quality from e-learning's web log

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Yudistira:2013:KST,

• author = "Novanto Yudistira and Sabriansyah Rizqika Akbar and Achmad Arwan",
• booktitle = "5th International Conference on Knowledge and Smart Technology (KST, 2013)",
• title = "Using Strongly Typed Genetic Programming for knowledge discovery of course quality from e-learning's web log",
• year = "2013",
• pages = "11--15",
• month = jan # " 31 2013-" # feb,
• address = "Chonburi, Thailand",
• isbn13 = "978-1-4673-4850-8",
• keywords = "genetic algorithms, genetic programming, LMS, e-learning, knowledge",
• DOI = "doi:10.1109/KST.2013.6512779",
• abstract = "Learning Management System (LMS) has become the popular instrument in academic institutions by providing feasible pedagogical interaction. In the abundance of registered users take some activities inside LMS, the result of analysing the quality of courses becomes remarkable feedback for teachers to enhance their teaching program via e-learning. Unexceptionally, mining web server log has been fascinating area in e-education environment. Our objective is to find interrelationships knowledge among e-learning web log's metrics. Strongly Typed Genetic Programming (STGP) as the cutting the edge technique for finding accurate rule inductions is used to achieve the goal. Revealed knowledge may useful for teachers or academicians to rearrange strategies in the purpose of improving e-learning usage quality based on the course activities.",
• notes = "Also known as \cite{6512779}",

}

Genetic Programming entries for Novanto Yudistira Sabriansyah Rizqika Akbar Achmad Arwan

Citations