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dc.contributor.authorGaneshan, Kathiravelu
dc.contributor.authorLi, Xiaosong
dc.date.accessioned2016-05-09T19:55:58Z
dc.date.available2016-05-09T19:55:58Z
dc.date.issued2015-10
dc.identifier.urihttps://hdl.handle.net/10652/3357
dc.description.abstractWe propose a web based intelligent student advising system using collaborative filtering, a technique commonly used in recommendation systems assuming that users with similar characteristics and behaviors will have similar preferences. With our advising system, students are sorted into groups and given advice based on their similarities to the groups. If a student is determined to be similar to a group students, a course preferred by that group might be recommended to the student. K-means algorithm has been used to determine the similarity of the students. This is an extremely efficient and simple algorithm for clustering analysis and widely used in data mining. Given a value of K, the algorithm partitions a data set into K clusters. Seven experiments on the whole data set and ten experiments on the training data set and testing data set were conducted. A descriptive analysis was performed on the experiment results. Based on these results, K=7 was identified as the most informative and effective value for the K-means algorithm used in this system. The high performance, merit performance and low performing student groups were identified with the help of the clusters generated by the K-means algorithm. Future work will make use of a two-phase approach using Cobweb to produce a balanced tree with sub-clusters at the leaves as in [11], and then applying K-means to the resulting sub-clusters. Possible improvements for the student model were identified. Limitation of this research is discussed.en_NZ
dc.language.isoenen_NZ
dc.publisherFrontiers in Education Conference (FIE)en_NZ
dc.relation.urihttp://fie2015.org/en_NZ
dc.rightsAll rights reserveden_NZ
dc.subjectk-meansen_NZ
dc.subjectclusteringen_NZ
dc.subjectcollaborative filteringen_NZ
dc.subjectrulesen_NZ
dc.subjectintelligent academic advising systemsen_NZ
dc.subjectcoursesen_NZ
dc.titleAn intelligent student advising system using collaborative filteringen_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.rights.holderFrontiers in Education Conference (FIE)en_NZ
dc.subject.marsden080109 Pattern Recognition and Data Miningen_NZ
dc.subject.marsden080105 Expert Systemsen_NZ
dc.subject.marsden130305 Educational Counsellingen_NZ
dc.identifier.bibliographicCitationGaneshan, K., & Li, X. (2015, October). An Intelligent Student Advising System Using Collaborative Filtering. In M. DeAntonio (Ed.), Proceedings of the Frontiers in Education Conference 2015 (pp.2194-2201)en_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.publication.spage2194en_NZ
unitec.publication.lpage2201en_NZ
unitec.publication.titleProceedings of the Frontiers in Education Conference 2015en_NZ
unitec.conference.titleFrontiers in Education Conference 2015en_NZ
unitec.conference.orgIEEE Education Societyen_NZ
unitec.conference.orgIEEE Computer Societyen_NZ
unitec.conference.orgASEE Educational Research and Methods Divisoionen_NZ
unitec.conference.orgFrontiers in Education Conference (FIE)en_NZ
unitec.conference.orgUniversity of Texas at El Paso (UTEP)en_NZ
unitec.conference.orgNew Mexico State Universityen_NZ
unitec.conference.locationCamino Real Hotel and Conference Center (El Paso, Texas)en_NZ
unitec.conference.sdate2015-10-21
unitec.conference.edate2015-10-24
unitec.peerreviewedyesen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.identifier.roms58487en_NZ
unitec.institution.studyareaComputing


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