@article {787,
	title = {Visualizing the educational data mining literature},
	journal = {European Journal of Engineering Research and Science},
	volume = {TBD},
	year = {2020},
	month = {TBD},
	abstract = {<p>This article provides a visualization of a literature review in students{\textquoteright} performance prediction using educational data mining (EDM) techniques for the period 2015-2019. The results of the review are presented concisely and simply with the use of diagrams. Various aspects of the literature are examined, such as the algorithms adopted, the type of results drawn, the educational setting of the application and the actual exploitation of the outcomes. Findings indicate that tertiary education dominates the EDM field; in contrast, the focus given to secondary and primary education is minimal.</p>
},
	keywords = {Educational Data Mining, Literature review, Student Performance, Visualization},
	author = {I. Papadogiannis and N. Platis and V. Poulopoulos and Costas Vassilakis and G. Lepouras and M. Wallace and G. Karountzou}
}
@conference {Katifori2008133,
	title = {Selected results of a comparative study of four ontology visualization methods for information retrieval tasks},
	booktitle = {Proceedings of the 2nd International Conference on Research Challenges in Information Science, RCIS 2008},
	year = {2008},
	note = {cited By (since 1996) 0},
	month = {6},
	pages = {133-140},
	abstract = {The need for effective ontology visualization for design, management and browsing has arisen as a result of the progress in the areas of Semantic Web and Personal Information Management. There are several ontology visualizations available through existing ontology management tools, but not as many evaluations to determine their advantages and disadvantages and their suitability for various ontologies and user groups. This work presents selected results of an evaluation of four visualization methods in ProtΓ{\textcopyright}gΓ{\textcopyright}.},
	keywords = {Communication, Comparative studies, Information management, Information retrieval, Information science, Information services, Information theory, Management, Management information systems, Ontology, Ontology managements, Personal Information managements, Semantic Web, Telecommunication services, User groups, User interfaces, Visualization, visualization methods},
	doi = {10.1109/RCIS.2008.4632101},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-57349172797\&partnerID=40\&md5=1f36a9a0febecb929cd7904c84004143},
	author = {Akrivi Katifori and Elena Torou and Costas Vassilakis and Giorgos Lepouras and C. Halatsis}
}
@conference {Golemati200662,
	title = {A context-based adaptive visualization environment},
	booktitle = {Proceedings of the International Conference on Information Visualisation},
	year = {2006},
	note = {cited By (since 1996) 0},
	month = {7},
	pages = {62-67},
	abstract = {Digital libraries and historical archives are increasingly employing visualization systems to facilitate the information retrieval and knowledge extraction tasks of their users. Typically, each organization employs a single visualization system, which may not suit best the needs of certain user groups, specific tasks, or properties of document collections to be visualized. In this paper we present a context-based adaptive visualization environment, which embeds a set of visualization methods into a visualization library, from which the most appropriate one is selected for presenting information to the user. Methods are selected by examining parameters related to the user profile, system configuration and the set of data to be visualized, and employing a set of rules to assess the suitability of each method. The presented environment additionally monitors user behavior and preferences to adapt the visualization method selection criteria. Β{\textcopyright} 2006 IEEE.},
	keywords = {(PL) properties, Adaptive systems, Adaptive visualization, Behavioral research, Digital libraries, document collections, Extraction, Information analysis, Information science, Information services, Information systems, Information visualization, Knowledge extraction, Libraries, Retrieval (MIR), Search engines, Selection criterion, Set of rules, Solvent extraction, Specific tasks, System configurations, User behaviors, User groups, user profiling, Visualization, visualization methods, Visualization systems},
	doi = {10.1109/IV.2006.5},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-45149121827\&partnerID=40\&md5=ec45ad555b132faaf0bd2ca53e3292dc},
	author = {M. Golemati and C. Halatsis and Costas Vassilakis and Akrivi Katifori and Giorgos Lepouras}
}
