Educational Data Mining (EDM) is a recent area yet with many fields to be researched. Applying Data Mining techniques to education data help us dealing with issues that would be hard without them. With its techniques and methods we try to discover behaviours and strategies, both for students and teachers. This information will take us towards the discovery of which strategies must be avoided, which teaching strategies can be adapted to each kind of student or to anticipate which students will fail so they can be helped since an early stage. In This Paper we have conducted a Questionnaire Survey on 500 Software Engineers to understand the present scenario of EDM

Nandakumar, Dr. A .N.

V, Mrs. Manjula

International Journal of Innovative Technology and Research (IJITR)

Manjula V* et al.(IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCHVolume No.3, Issue No.2, February – March 2015, 1938 – 1941.2320 –5547 @ 2013 http://www.ijitr.com All rights Reserved. Page | 1938A Study on Educational Data MiningThrough Questionnaire SurveyMrs. MANJULA VAssistant Professor,Department of Information Science and Engineering,School of Engineering and Technology,Jain University Bangalore & Research Scholar,Jain University Bangalore.Dr. A .N. NANDAKUMARProfessor, Principal,R.L.Jalappa institute of Technology,Bangalore & Research Supervisor,Jain University Bangalore.Abstract: Educational Data Mining (EDM) is a recent area yet with many fields to be researched. ApplyingData Mining techniques to education data help us dealing with issues that would be hard without them. Withits techniques and methods we try to discover behaviours and strategies, both for students and teachers. Thisinformation will take us towards the discovery of which strategies must be avoided, which teaching strategiescan be adapted to each kind of student or to anticipate which students will fail so they can be helped since anearly stage. In This Paper we have conducted a Questionnaire Survey on 500 Software Engineers tounderstand the present scenario of EDM.Keywords: EDM, strategies & Questionnaire.INTRODUCTIONEducational Data Mining is an emerging discipline,concerned with developing methods for exploring theunique types of data that come from educationalsettings, and using those methods to betterunderstand students, and the settings which they learnin.Whether educational data is taken from students' useof interactive learning environments, computer-supported collaborative learning, or administrativedata from schools and universities, it often hasmultiple levels of meaningful hierarchy, which oftenneed to be determined by properties in the data itself,rather than in advance. Issues of time, sequence, andcontext also play important roles in the study ofeducational data.Educational Data Mining focuses on developing newtools and algorithms for discovering data patterns.EDM develops methods and applies techniques fromstatistics, machine learning, and data mining toanalyze data collected during teaching and learning.EDM tests learning theories and informs educationalpractice.Educational data mining is emerging as a researcharea with a suite of computational and psychologicalmethods and research approaches for understandinghow students learn. New computer-supportedinteractive learning methods and tools—intelligenttutoring systems, simulations, games—have openedup opportunities to collect and analyze student data,to discover patterns and trends in those data, and tomake new discoveries and test hypotheses about howstudents learn. Data collected from online learningsystems can be aggregated over large numbers ofstudents and can contain many variables that datamining algorithms can explore for model building.GOALS OF EDM1. Predicting students’ future learning behaviorby creating student models that incorporatesuch detailed information as students’knowledge, motivation, metacognition, andattitudes2. Discovering or improving domain modelsthat characterize the content to be learnedand optimal instructional sequences3. Studying the effects of different kinds ofpedagogical support that can be provided bylearning software4. Advancing scientific knowledge aboutlearning and learners through buildingcomputational models that incorporatemodels of the student, the domain, and thesoftware’s pedagogy.RESULTS & ANALYSISEducational Data Mining is a promising disciplinewhich has an imperative impact on predictingstudents’ academic performance.Strongly Agree 367Agree 91Disagree 29Strongly Disagree 13Manjula V* et al.(IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCHVolume No.3, Issue No.2, February – March 2015, 1938 – 1941.2320 –5547 @ 2013 http://www.ijitr.com All rights Reserved. Page | 1939In the survey carried out for 500 random Engineers,73 % of the population agrees that Educational DataMining is a promising discipline which has animperative impact on predicting students’ academicperformance,18 % of the population agrees to it and 6% Disagrees whereas 3% of the population stronglydisagrees.Data mining techniques can support educationsystem for decision making.Strongly Agree 261Agree 178Disagree 47Strongly Disagree 14In the survey carried out for 500 randomEngineers,52 % of the population strongly agree toData mining techniques can support education systemfor decision making,36 % agree to it whereas 9 %disagree and 3 % of the population strongly disagreeto it.As the education sector blooms every year,graduates are facing stiff competitions to ensuretheir employability in the industry.Strongly Agree 307Agree 144Disagree 41Strongly Disagree 10In the survey carried out for 500 random Engineers,61 % strongly agree to  As the education sectorblooms every year, graduates are facing stiffcompetitions to ensure their employability in theindustry, 29 % of the population agree to it ,whereas8 % of the population disagree to it and 2 % stronglydisagree.Educational data mining (EDM) is an area full ofexciting opportunities for researchers andpractitioners.Strongly Agree 318Agree 141Disagree 27Strongly Disagree 14In the survey carried out for 500 random Engineers,62 % of the population strongly agree thatEducational data mining (EDM) is an area full ofexciting opportunities for researchers andpractitioners , 28 % of the population agree to it ,5 %of the population disagree to it and  3% stronglydisagree to it.Database conversion and schema integration ismost difficult task in building and maintaining adata warehouse for an Educational instituteManjula V* et al.(IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCHVolume No.3, Issue No.2, February – March 2015, 1938 – 1941.2320 –5547 @ 2013 http://www.ijitr.com All rights Reserved. Page | 1940Strongly Agree 260Agree 119Disagree 56Strongly Disagree 65In the survey carried out for 500 randomEngineers,52 % of the population strongly agree thatDatabase conversion and schema integration is mostdifficult task in building and maintaining a datawarehouse for an Educational institute , 24 % of thepopulation agree to it,11 % of the population disagreeto it and 13% of the population strongly disagree toit.Do you feel a Model is required which studiesstudent behavior?Strongly Agree 250Agree 148Disagree 67Strongly Disagree 35In the survey carried out for 500 random Engineers,for the question, do you feel a Model is requiredwhich studies student behavior? 50 % of thepopulation strongly agrees that a model is required ,30 % of the population agree to it ,13 % of thepopulation disagree to it and 7% of the populationstrongly disagree to it.Do you feel a Model is required for betterment ofstudent’s behavior?Strongly Agree 248Agree 132Disagree 91Strongly Disagree 29For the question asked, do you feel a Model isrequired for betterment of student’s behavior?50 % of the population strongly agrees that a modelis required and 26 % agree to it, whereas 24 % ofpopulation feels that a model is not required.Do you feel a Model Database of student’sbehavior should be there in University?Strongly Agree 237Agree 158Disagree 60Strongly Disagree 45For the question posed as Do you feel a ModelDatabase of student’s behavior should be there inUniversity? 79 % of the population agree that adatabase is required to trace behavior whereas 21 %of the population disagree to it.Manjula V* et al.(IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCHVolume No.3, Issue No.2, February – March 2015, 1938 – 1941.2320 –5547 @ 2013 http://www.ijitr.com All rights Reserved. Page | 1941The architecture of the Data warehouse can bestudied and documented to become   standard andknown to everyone developing data warehouse.Strongly Agree 313Agree 108Disagree 43Strongly Disagree 36The architecture of the Data warehouse can bestudied and documented to become standard andknown to everyone developing data warehouse,63 %of the population strongly agree to it,22 % ofpopulation agrees to it whereas 15 % of thepopulation disagree to it.There are key issues playing roles in the designand implementation of data warehouse that needto be determined.Strongly Agree 304Agree 109Disagree 56Strongly Disagree 31The architecture of the Data warehouse can bestudied and documented to become   standard andknown to everyone developing data warehouse.83 %of the population agree to it and 17 % of thepopulation disagree to it.REFERENCES[1]. Witten, I.H. and Frank, E., Data MiningPractical Machine Learning Tools andTechniques with Java Implementations.Morgan Kaufmann. 2000.[2]. Adriaans, P. and Zantinge, D., Data Mining.Addison-Wesley. 1996.[3]. R. Kohavi and F. Provost, Glossary of Terms,in Spec. Issue on Apps of Machine Learningand the KDD Process, Machine LearningJournal, 30, pp. 271-274. Kluwer. 1998.[4]. .H. Witten and E. Frank, Data MiningPractical Machine Learning Tools andTechniques with Java Implementations.Morgan Kaufmann. 2000.[5]. Antunes, C., Oliveira, A.: Generalization ofPattern-Growth Methods for SequentialPattern Mining with Gap Constraints, p. 239-251 in MLDM 2003[6]. Dr. T.N Manjunath  and Ravindra S [2012]Realistic Analysis of Data ware housing andDatamining Application in Education Domain.International Journal of Machine learning andcomputing Vol.2  No.4 August 2012[7]. P. Ramasubramanian , Iyakuti andP.Thangavelu [2009]  Enhanced data mininganalysis in Higher educational System.African Journal of Mathematics and computerscience Research Vol.(9). PP.184-188,October 2009[8]. Vladimir Ivancevic,Milan  celilcovic[2011]An application of Educational data miningtechniques at Faculty of Technical sciences inNovi sad. ICIT. The international conferenceon Information Technology. 2011.

A STUDY ON EDUCATIONAL DATA MINING THROUGH QUESTIONNAIRE SURVEY

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A STUDY ON EDUCATIONAL DATA MINING THROUGH QUESTIONNAIRE SURVEY

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