Open Access Open Access  Restricted Access Subscription or Fee Access

An Impact of Emotional Happiness on Students Personality in Learning Environment

L. Arockiam, S. Charles, M. Amala Jayanthi, S. Mercy

Abstract


Students are the heart of the Educational Environment. The Objective of the education focuses on students. The Motto of the education is to refine the students’ knowledge, skills and attitudes under efficient supervision in the educational environment. As per Bloom’s theory on learning activity, a person’s learning activity not only improves his/her knowledge and mental skills (i.e. Cognitive) but also his/her emotional areas (i.e. Affective). Educational Data Mining is one of the blooming applications in educational sector. It helps in better understanding of students’ learning process and their overall involvement in the process, focused on the improvement of the quality and the profitability of the educational system. Personality is defined as the combination of emotional, Attitudinal, Behavioural Responses of an Individual. Personality is an intrinsic factor of affectivity. Emotional Happiness is an affective category. This research is to study the influence of the Emotional Happiness of students on their personality. Emotional Happiness of the students is evaluated using Oxford Happiness inventory. The Personality of the student is determined by the Eysenck Personality Inventory and Criterion Reference Model. This paper finds out the impact of Emotional Happiness on the personality of the students in their learning process based on the supervised and unsupervised techniques for analysing the students’ dataset. Multi-Layer Perceptron and EM clustering Technique is employed to classify the students based on the Emotional Happiness and Personality. The study determines the association between the students’ Emotional Happiness and Personality through descriptive and predictive modelling using mapping or function. It reveals that there exists a positive correlation between student Emotional Happiness and Personality. This investigation allows the teaching community to understand students’ Emotional Happiness and Behavioural, Attitudinal, Emotional Growth during their learning process as Personality and provide them appropriate counselling to develop them as good human persons in the society.


Keywords


Multi-Layer Perceptron, Expectation Maximization (EM) Clustering, Criterion Reference Model, Bloom’s Taxonomy, Affective Domain, Eysenck Personality Inventory, Personality Types, Oxford Happiness Inventory, Emotions, Emotional Happiness

Full Text:

PDF

References


Hans Jürgen Eysenck&Sybil B. G. Eysenck (1975). Manual of the Eysenck Personality Questionnaire. London: Hodder and Stoughton.

Sybil B. G. Eysenck, Hans Jürgen Eysenck& Paul Barrett (1985). "A revised version of the psychoticism scale". Personality and Individual Differences6 (1): 21–29. DOI:10.1016/0191-8869(85)90026-1.

Brown, D. H. (2000). Principles of language learning & teaching. (4th ed.). New York: Longman. (pp. 142-152)

Relations Between Affect and Personality: Support for the Affect-Level and Affective-Reactivity Views. JamesJ. Gross, Steven K. Sutton and Timothy Ketelaar

A study on the relationship between extroversion-introversion and risk-taking in the context of second language acquisition, Zafar Shahila, Meenakshi, K.International Journal of Research Studies in Language Learning 2012 January, Volume 1 Number 1, 33-40

Khalian, M.,Borounjeni, F.Z.,Mustapa,N.,Sulaiman,M.N. : k-Means Divide and Conquer Clustering . In : International Conference on Computer and Automation Engineering, PP.306-309.IEEE Computer Society , Los Alamitos (2009).

Choi, S.C., Hart, P.E., Stork, D.G,: Pattern Classification, 2nd edn.John . Wiley& sons Inc., Chichester (2000).

Wu.X. Kumar, V., Quinlan. J.R., Ghosh. J., Yang. Q., Motoda.H. McLachalan, G.J., Ng, A., Liu B., Yu P.S., Yu, P.S., Zhou, Z,-h., Steinbach, M., Hand, D.J., Steinberg. D.: Top 10 Algorithms in Data mining Knowl.laf.Syst.14:!-37(2008)

Ayers, E., Nurgent,R., Dean , N.: Skill Set Profile Clustering Based on Student Capability Vectors Compute from Online Tutoring Data .In : Baker,R.S.J.D.,Barnes.T., Beck,J.E.(eds) Proceedings of 1st International Conference on Educational Data Mining ,Montreal,Qubec,Canada,June 20-21,pp210-217(2008)

Pavik Jr., P.I., Cen , H., Wu, L., Koedinger, K.R.: Using Item-type Performance Covariance to Improve the skill Model of an Existing Tutor . In: Proceedings 1 st International Conference on Educational Data mining, Canada, June 20-21.pp.77-86(2008)

Green, T.M., Jeong, D.H., Fisher. B.: Using Personality Factors to Predict Interface Learning Performance. In: 43 rd Hawaii International Conference on System Sciences. IEEE Computer Society, Honolulu, HI, January 5-8, pp. 1-10. IEEE Computer Society, Los Alamitos (2010)

Chiu, C .: Cluster Analysis for Cognitive Diagnosis : Theory and Applications . Ph.D.Dissertation, Educational Psychology, University of Illinois at Urbana Champaign (2008)

Averse., Nugent , R., Dean ,N: A Comparison of student skill Knowledge Estimates Educational Data mining In: 2nd International Conference on Educational Data mining, Cordoba ,Spain, July 1-3 ,pp.1-10(2009)

Nghe,N.T., Janecek,P., Haddawy.P: A Comparative Analysis of Techniques for predicting Academic Performance. Paper Presented at 37 th ASEE/IEEE Frontiers in Education Conference, Milwaukee,WI, October 10-13(2007)

Marshall.L, Austin, M.: The relationship between software skills and Subject specific Knowledge, Theory and Practice .Learning and Teaching Projects.

L. Arockiam., S.Charles, V.Arul Kumar., P.Cijo. A Recommender System for Rural and urban Learners.Trends in Computer Science, Engineering and Information TechnologyCommunications in Computer and Information Science, 2011, Volume 204, Part 1, 619-627, DOI:10.1007/978-3-642-24043-0_63

Han.J., Kamber, M.: Data mining Concepts and Techniques, 2nd edn. Morgan Kaufmann Publishers. San Francisco (2006)

Gabriela-Alina Sauciuc , Categorization in the Affective Domain , In: Kokinov, B., Karmiloff-Smith, A., Nersessian, N. J. (eds.) European Perspectives on Cognitive Scienc, New Bulgarian University Press, 2011 ISBN 978-954-535-660-5

Frijda, N. H. (1986). The emotions. New York: Cambridge University Press.

Weiner, B., & Graham, S. (1984). An attributional approach to emotional development. In C. E. Izard, J. Kagan, & R. B. Zajonc (Eds.), Emotions, cognition,and behavior. New York: Cambridge University Press.

1norazlina Khamis ,Sufian Idris “Issues and Solutions in Assessing Object-oriented programming Skills in the Core Education of Computer Science and Information Technology”, 12th WSEAS International Conference on COMPUTERS, Heraklion, Greece, July 23-25, 2008.

Xindong Wu · Vipin Kumar · J. Ross Quinlan · Joydeep Ghosh Qiang Yang · Hiroshi Motoda · Geoffrey J. McLachlan · Angus Ng Bing Liu Philip S. Yu · Zhi-Hua Zhou · Michael Steinbach · David J. Hand · Dan Steinberg, “Top 10 algorithms in data mining”, Springer-Verlag London Limited, Knowl Inf Syst 14:1–37, 2008.

Ayers, E, Nugent, R, Dean, N. .Skill Set Profile Clustering Based on Student Capability Vectors Computed from Online Tutoring Data..Educational Data Mining 2008: 1st International Conference on Educational Data Mining,Proceedings ,R.S.J.d. Baker, T. Barnes, and J.E. Beck (Eds), Montreal, Quebec, Canada, June 20-21, pp.210-217, 2008

M. Ramaswami and R. Bhaskaran, “A Study on Feature Selection Techniques in Educational Data Mining, Journal of Computing, Volume 1, Issue 1, December 2009.

Madjid Khalilian, Farsad Zamani Boroujeni, Norwati Mustapha, Md.Nasir Sulaiman, "K-Means Divide and Conquer Clustering,", International Conference on Computer and Automation Engineering ,IEEE Computer Society, pp. 306-309, 2009.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.