Applied Technologies and Innovations

  Previous Article | Back to Volume | Next Article
  Abstract | References | Citation | Download | Preview | Statistics
Volume 11
Issue 1
Online publication date 2015-06-25
Title Personalized recommendation strategies for eLearning: An AHP approach
Author Ramo Sendelj, Ivana Ognjanovic
Abstract
eLearning has become a key adjunct to both, education in general and the business world; it isbecoming an important tool to allow the flexibility and quality requested by such a kind of learningprocess. One of recent challenges in eLearning industry is personalized learning (PL), aimed onmeeting the needs and aspirations of each individual learner. A PL can be considered as a facility foran individual to access, combine, configure and manage digital resources (knowledge assets andservices) related to their present learning needs and interests. The role of teachers in PL is also enhanced, since they should monitor learners’ progress, make dynamic coherence between educational goals and students’ achievements, and provide all needed recourses accordingly. 
The variety of PL systems are already developed, the most attempts of learner personalization are focused on the level of knowledge, background and hyperspace experience, preferences and interests, or even learning styles and achievements. It still does not fully address the issue of inteligent personalized recommendations stimulated by the huge wealth of opportunities for collaboration and communication offered by semantic technologies and intelligent reasoning techniques. In this paper, focusing on the well-known Analytical Hierarchical Process (AHP) method, we propose a framework for addressing different kinds of learners’ preferences in PL, integration with historical data and experiences, and making recommendations and personalization accordingly. Firstly, we are focused on making analyses of relevant kinds of preferences defined by both, learners and teachers over learning process in general (including indicators of progress, learning styles, pedagogice approach, etc), learning resources and learners’ interests and goals. Also, relevant historical data should be recognized with appropriate retrieving methods and potential web resources if applicable. Finally, semantic structure should be proposed as conceptual framework enabling integration of all gathered data and application of AHP algorithm for processing. The final output of this paper is integrated approach for representing and reasoning over preferences in PL, with effective order decision outcomes in a way that it makes personalized recommendations over available resources,services and dynamic actions.
Citation
References
Attwell G., 2007. The personal learning environments - the future of eLearning? eLearning Paper
 
Babad E., Darley J.M., and Kaplowitz H., 1999. "Developmental aspects in students' course selection", Journal of Educational Psychology, Vol.91, pp.157-168
 
Berners-Lee T., 2007. Berners-Lee warns of changes ahead, Computing Magazine
 
Blaikie N., 1993. Analysis of quantitative data: From description to explanation. SAGE Publications Ltd
 
Boutilier C. et al., 2003. "CP-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements", Journal of Artificial Intelligence Research, Vol.21, pp.135-191
 
Boutilier C., Bacchus F., Brafman R.I., 2001. "UCP-Networks: A directed graphical representation of conditional utilities", Proc. 17th Conf. in Uncertainty in AI, pp.56-64
 
Brafman R.I. and Domshlak C., 2002. Introducing variable importance tradeoffs into CP-nets, Workshop on Planning and Scheduling with Multiple Criteria, pp.69-76
 
Brafman R., Domshlak C., Kogan T., 2004. "Compact value-function representations for qualitative preferences", The 20th Annual Conference on Uncertainty in Artificial Intelligence. Canada, pp.51-58
 
Büyüközkan G., Çifçi G., Güleryüz S., 2011. "Strategic analysis of healthcare service quality using fuzzy AHP methodology", Expert Systems with Applications, Vol.38(8), pp.9407-9424
 
Chatti M.A., Agustiawan M.R., Jarke M., Specht M., 2010. "Toward a personal learning environment framework", International Journal of Virtual and Personal Learning Environments, Vol.1(4), pp.66-85
 
Chen M.K. and Wang S., 2010. "The critical factors of success for information service industry in developing international market: Using analytic hierarchy process (AHP) approach", Expert Systems with Applications, Vol.37, No.1, pp.694-704
 
Cobo A., Rocha R., and Rodríguez-Hoyosc C., 2013. "Evaluation of the interactivity of students in virtual learning environments using a multicriteria approach and data mining", Behaviour and Information Technology
 
Dillenbourg P., Järvelä S., and Fischer, F., 2009. "The evolution of research on computer-supported collaborative learning: From design to orchestration", In: Balacheff N., Ludvigsen S., de Jong T. et al. (Eds.), Technology-Enhanced Learning. Principles and products, pp.3-19
 
Domshlak C., Hüllermeier У., Kaci S., 2011. "Preferences in AI: An overview, Artificial Intelligence, Vol.175(7-8), pp.1037-52
 
Figueira J., Mousseau V., Roy B., 2005. "Electre methods, Multiple criteria decision analysis: State of the art surveys", International Series in Operations Research and Management Science, Vol.78(3), pp.133-153
 
Forman E.H., Gass S.I., 2001. "The analytical hierarchy process - An exposition", Operations Research, Vol.49(4), pp.469-487
 
Hwang C.L., Yoon K., 1981. "Multiple Attribute Decision Making, Methods and Applications, Springer-Verlag, Berlin Heidelberg
 
Jafari A., McGee P., Carmean C., 2006. "Managing courses, defining learning: What faculty, students, and administrators want", EDUCAUSE Review, Vol.41(4), pp.50-71
 
Jeremic Z., Jovanovic J., Gasevic D., 2009. "Semantic Web Technologies for the Integration of Learning Tools and Context-aware Educational Services", Proceedings of the 8th International Semantic Web Conference (ISWC 2009)
 
Jovanovic J., Knight C., Gasevic D., Richards, G., 2007. "Ontologies for Effective Use of Contextin e-Learning Settings", Educational Technology and Society, Vol.10(3), pp.47-59
 
Jovanovic J., Casevic D., Stanković M., Jeremić Z., Siadaty M., 2009. "Online Presence in Adaptive Learning on the Social Semantic Web", Proceedings, Workshop on Social Computing in Education, Canada, pp.891-896
 
Karlsson J., Olsson S., Ryan K., 1997. "Improving Practical Support for Large-scale Preference Prioritising", J. Requirements Eng., Vol.2(1), pp.51-67
 
Keeny R.L., Raiffa H., 1976. "Decisions with Multiple Objectives: Preferences and Value Tradeoffs", Wiley series in probability and mathematical statistics (John Wiley and Sons Inc.)
 
Liberatore M.J., Nydick R.L., 1997. "Group Decision Making in Higher Education using AHP", Research in Higher Education, Vol.38(5), pp.593-614
 
Liu'an K., Xiaomei W., Lin Y., 2012. "The Research of Teaching Quality Appraisal Model Based on AHP", International Journal of Education and Management Engineering, Vol.9, pp.29-34
 
Ljucović J., Ognjanovic I., Šendelj R., 2014. "Integration of historical data in AHP algorithm, IT Conference. Žabljak, Montenegro
 
Mukhtar H., Belaïd D., Bernard G., 2009. "A quantitative model for user preferences based on qualitative specifications, International Conference on Pervasive services, pp.179-188
 
Ognjanovic I., Sendelj R., 2011. "Making judgments and decisions about relevant learning resources, Proc.of the 20th International Electrotechnical and Computer Science Conference, (ERK 2011). Portoroz, Slovenia, pp.409-412
 
Ognjanovic I., Sendelj R., 2012. "Teachers' requirements in dynamically adaptive e-learning systems", 4th International Conference on Education and new Learning Technologies (EDULEARN12), Barcelona
 
Ognjanovic I., Gasevic D. and Bagheri E., 2013. "A Stratified Framework for Handling Conditional Preferences: An Extension of the Analytic Hierarchy Process", Expert Systems with Applications, Vol. 40, pp.1094-1115
 
Roper-Low G.C., 1990. "The Analytic Hierarchy Process and its application to an information technology", The Journal of Operational Research, Vol.41(1), pp.49-59
 
Saaty T.L., 1980. The Analytic Hierarchy Process. New York: McGraw-Hill
 
Smulders D., 2002. Designing for Learners, Designing for Users, eLearn Magazine
 
Stankevic M., 2008. "Modeling Online Presence", Proceedings of the First Social Data on the Web Workshop, Karlsruhe, Germany
 
Stirling W.C., Frost R.L., Nokleby R.L., and Luo Y., 2007. "Multicriterion Decision Making with Dependent Preferences", IEEE Symposium on Computational Inteligence in Multicriteria Decision Making
 
Tobin D.R., 2000. All Learning is Self Directed, ASTD
 
Turski Z., 2008. "Multi-attribute contractors ranking method by applying ordering of feasible alter-natives of solutions in terms of preferability technique, Technological and Economic Development of Economy, Vol.14(2), pp.224-239
 
Wilson N., 2011. "Computational techniques for a simple theory of conditional preferences", Artificial Intelligence, Vol.175, No.7-8, pp.1053-1091
 
Yu Z., Yu, Z., Zhou X., Nakamura Yu., 2010. "Toward an Understanding of User-Defined Conditional Preferences", Proceedings of the 8th IEEE International Conference on Dependable, Autonomic and Secure Computing, pp.203-208
 
Yüksel M., 2012. "Evaluating the Effectiveness of the Chemistry Education by Using the Analytic Hierarchy Process", International Education Studies, Vol.5, No.5, pp.79-91
 
Zimmerman B.J., 2002. "Becoming a self-regulated learner: An overview", Theory into practice, Vol.41(2), pp.64-70
Keywords AHP algorithm, learning environment, personalized learning
DOI http://dx.doi.org/10.15208/ati.2015.03
Pages 16-27
Download Full PDF Download
  Previous Article | Back to Volume | Next Article
Share
Search in articles
Statistics
Journal Published articles
ATI 263
Journal Hits
ATI 706093
Journal Downloads
ATI 7527
Total users online -