A standard form of citation of this article is:
Nadolski, Rob J., van den Berg, Bert, Berlanga, Adriana J., Drachsler, Hendrik, Hummel, Hans G.K., Koper, Rob and Sloep, Peter B. (2009). 'Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies'. Journal of Artificial Societies and Social Simulation 12(1)4 <https://www.jasss.org/12/1/4.html>.
The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:
@article{nadolski2009,
title = {Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies},
author = {Nadolski, Rob J. and van den Berg, Bert and Berlanga, Adriana J. and Drachsler, Hendrik and Hummel, Hans G.K. and Koper, Rob and Sloep, Peter B.},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {12},
number = {1},
pages = {4},
year = {2009},
URL = {https://www.jasss.org/12/1/4.html},
keywords = {Recommendation Strategy; Simulation Study; Way-Finding; Collaborative Filtering; Rating},
abstract = {Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. Such systems should also be practically feasible and be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis and optimisation of PRS requirements prior to starting the costly process of their development, and practical implementation (including testing and revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).},
}The following can be copied and pasted into a text file, which can then be imported into a reference database that supports imports using the RIS format, such as Reference Manager and EndNote.
TY - JOUR
TI - Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies
AU - Nadolski, Rob J.
AU - van den Berg, Bert
AU - Berlanga, Adriana J.
AU - Drachsler, Hendrik
AU - Hummel, Hans G.K.
AU - Koper, Rob
AU - Sloep, Peter B.
Y1 - 2009/01/31
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 12
IS - 1
SP - 4
UR - https://www.jasss.org/12/1/4.html
KW - Recommendation Strategy; Simulation Study; Way-Finding; Collaborative Filtering; Rating
N2 - Recommender systems for e-learning demand specific pedagogy-oriented and hybrid recommendation strategies. Current systems are often based on time-consuming, top down information provisioning combined with intensive data-mining collaborative filtering approaches. However, such systems do not seem appropriate for Learning Networks where distributed information can often not be identified beforehand. Providing sound way-finding support for lifelong learners in Learning Networks requires dedicated personalised recommender systems (PRS), that offer the learners customised advise on which learning actions or programs to study next. Such systems should also be practically feasible and be developed with minimized effort. Currently, such so called light-weight PRS systems are scarcely available. This study shows that simulation studies can support the analysis and optimisation of PRS requirements prior to starting the costly process of their development, and practical implementation (including testing and revision) during field experiments in real-life learning situations. This simulation study confirms that providing recommendations leads towards more effective, more satisfied, and faster goal achievement. Furthermore, this study reveals that a light-weight hybrid PRS-system based on ratings is a good alternative for an ontology-based system, in particular for low-level goal achievement. Finally, it is found that rating-based light-weight hybrid PRS-systems enable more effective, more satisfied, and faster goal attainment than peer-based light-weight hybrid PRS-systems (incorporating collaborative techniques without rating).
ER -