In this paper, we outline the importance of discussion fora for e-learning applications. Due to a weak structure or size of the discussion forum, recommendations are required in order to help learners finding relevant information within a forum. We present a generic personalization framework and evaluate the framework based on a recommender architecture for the e-learning focused discussion forum Comtella-D. In the evaluation, we examine different sources of user feedback and the required amount of user interaction to provide recommendations. The outcomes of the evaluation serve as source for a personalization rule, which selects the most appropriate recommendation strategy based on available user input data. We furthermore conclude that collaborative filtering techniques can be utilize successfully in small data sets, like e-learning related discussion fora.
Описание
Recommendations in Online Discussion Forums for E-Learning Systems | IEEE Journals & Magazine | IEEE Xplore
%0 Journal Article
%1 5288522
%A Abel, Fabian
%A Bittencourt, Ig Ibert
%A Costa, Evandro
%A Henze, Nicola
%A Krause, Daniel
%A Vassileva, Julita
%D 2010
%J IEEE Transactions on Learning Technologies
%K discussion-forum recommender
%N 2
%P 165-176
%R 10.1109/TLT.2009.40
%T Recommendations in Online Discussion Forums for E-Learning Systems
%U https://ieeexplore.ieee.org/document/5288522
%V 3
%X In this paper, we outline the importance of discussion fora for e-learning applications. Due to a weak structure or size of the discussion forum, recommendations are required in order to help learners finding relevant information within a forum. We present a generic personalization framework and evaluate the framework based on a recommender architecture for the e-learning focused discussion forum Comtella-D. In the evaluation, we examine different sources of user feedback and the required amount of user interaction to provide recommendations. The outcomes of the evaluation serve as source for a personalization rule, which selects the most appropriate recommendation strategy based on available user input data. We furthermore conclude that collaborative filtering techniques can be utilize successfully in small data sets, like e-learning related discussion fora.
@article{5288522,
abstract = {In this paper, we outline the importance of discussion fora for e-learning applications. Due to a weak structure or size of the discussion forum, recommendations are required in order to help learners finding relevant information within a forum. We present a generic personalization framework and evaluate the framework based on a recommender architecture for the e-learning focused discussion forum Comtella-D. In the evaluation, we examine different sources of user feedback and the required amount of user interaction to provide recommendations. The outcomes of the evaluation serve as source for a personalization rule, which selects the most appropriate recommendation strategy based on available user input data. We furthermore conclude that collaborative filtering techniques can be utilize successfully in small data sets, like e-learning related discussion fora.},
added-at = {2023-06-12T15:11:33.000+0200},
author = {Abel, Fabian and Bittencourt, Ig Ibert and Costa, Evandro and Henze, Nicola and Krause, Daniel and Vassileva, Julita},
biburl = {https://www.bibsonomy.org/bibtex/20cb0200f703bc90eeea7a09e6a475c41/brusilovsky},
description = {Recommendations in Online Discussion Forums for E-Learning Systems | IEEE Journals & Magazine | IEEE Xplore},
doi = {10.1109/TLT.2009.40},
interhash = {1b181abf14b87de7e7f5a6e54dd47c5f},
intrahash = {0cb0200f703bc90eeea7a09e6a475c41},
issn = {1939-1382},
journal = {IEEE Transactions on Learning Technologies},
keywords = {discussion-forum recommender},
month = {April},
number = 2,
pages = {165-176},
timestamp = {2023-06-12T15:11:33.000+0200},
title = {Recommendations in Online Discussion Forums for E-Learning Systems},
url = {https://ieeexplore.ieee.org/document/5288522},
volume = 3,
year = 2010
}