@article {10.3844/jcssp.2014.906.924,
article_type = {journal},
title = {HYBRID SEARCH AND DELIVERY OF LEARNING OBJECTS SYSTEM},
author = {Ilukwe, Anthony N. and Biletsky, Yevgen},
volume = {10},
number = {6},
year = {2014},
month = {Jan},
pages = {906-924},
doi = {10.3844/jcssp.2014.906.924},
url = {https://thescipub.com/abstract/jcssp.2014.906.924},
abstract = {Retrieving learning material from the internet is a tedious process that has begged for a solution to filter out of the cluster of data and irrelevant material on the internet and deliver material that is relevant to a specific user. The Hybrid Search and Delivery of Learning Objects (HSDLO) system, put forward in this study, facilitates the personalized search and delivery of such learning material from the internet. The system combines a number of mechanisms to perform this: Keyword‐based search, concept‐based search and personalization. The keyword-and concept-based search methods are responsible for establishing the relevance of each learning material retrieved from the web. The system presented in this study builds upon work done in the previous iteration by additional functionality; further decoupling the subsystems to improve modularity; perfection of the personalization subsystem; and a redesign of the user interface to a simpler form with Web2.0 sensibilities. Additionally, the personalization subsystem is substantially extended, allowing for a learner to have a profile active within the system during a session in which he or she is logged in and following a search, for the profile to be adapted and stored in memory for subsequent sessions. This functionality has been tested and successfully evaluated.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}