Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.

Students' performances in higher education institution are evaluated based on their academic achievement. Despite of having technical skills obtained by the graduates during their study in university, it is crucial for them to have other additional skills that help them in decision making and...

Full description

Saved in:

Bibliographic Details
Main Author:	Jiwa Noris, Hamid
Format:	Thesis
Language:	eng eng
Published:	2008
Subjects:	QA76 Computer software
Online Access:	https://etd.uum.edu.my/175/1/Jiwa_Noris_Hamid.pdf https://etd.uum.edu.my/175/2/Jiwa_Noris_Hamid.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.175
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA76 Computer software
spellingShingle	QA76 Computer software Jiwa Noris, Hamid Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.
description	Students' performances in higher education institution are evaluated based on their academic achievement. Despite of having technical skills obtained by the graduates during their study in university, it is crucial for them to have other additional skills that help them in decision making and problem solving. This study has presented a model of academic achievement with respect to self-readiness of the public universities graduates. Two data mining methods were used in this study such as logistic regression and neural network to obtain the model with the highest accuracy. The selection of data mining approaches was based on the ability of data mining as a powerful tool for academic analysis purposes. In higher educational institution, data mining can be used for the process of uncovering hidden trends and patterns that help them forecast the students' achievement. In this study, a dataset comprises of public higher educational institution graduates demographics and self- readiness information was analyzed. The results show that descriptive and self readiness produce higher accurate percentage compared to the self readiness alone. The result also find that neural network is the best model to be developed compared to logistic regression while field of study, citizenship, ready to face working world and challenges, problem solving, decision making and group working are the best predictor for academic achievement.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Jiwa Noris, Hamid
author_facet	Jiwa Noris, Hamid
author_sort	Jiwa Noris, Hamid
title	Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.
title_short	Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.
title_full	Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.
title_fullStr	Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.
title_full_unstemmed	Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.
title_sort	modeling academic achievement with respect to self-readiness of public universities graduates.
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2008
url	https://etd.uum.edu.my/175/1/Jiwa_Noris_Hamid.pdf https://etd.uum.edu.my/175/2/Jiwa_Noris_Hamid.pdf
_version_	1747826853361483776
spelling	my-uum-etd.1752013-07-24T12:05:56Z Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates. 2008-06-01 Jiwa Noris, Hamid College of Arts and Sciences (CAS) Faculty of Information Technology QA76 Computer software Students' performances in higher education institution are evaluated based on their academic achievement. Despite of having technical skills obtained by the graduates during their study in university, it is crucial for them to have other additional skills that help them in decision making and problem solving. This study has presented a model of academic achievement with respect to self-readiness of the public universities graduates. Two data mining methods were used in this study such as logistic regression and neural network to obtain the model with the highest accuracy. The selection of data mining approaches was based on the ability of data mining as a powerful tool for academic analysis purposes. In higher educational institution, data mining can be used for the process of uncovering hidden trends and patterns that help them forecast the students' achievement. In this study, a dataset comprises of public higher educational institution graduates demographics and self- readiness information was analyzed. The results show that descriptive and self readiness produce higher accurate percentage compared to the self readiness alone. The result also find that neural network is the best model to be developed compared to logistic regression while field of study, citizenship, ready to face working world and challenges, problem solving, decision making and group working are the best predictor for academic achievement. 2008-06 Thesis https://etd.uum.edu.my/175/ https://etd.uum.edu.my/175/1/Jiwa_Noris_Hamid.pdf application/pdf eng validuser https://etd.uum.edu.my/175/2/Jiwa_Noris_Hamid.pdf application/pdf eng public masters masters Universiti Utara Malaysia Abdullah concerned about varsity rankings. (2008, January 23). NST Online. Retrieve on January 15,2008 from http://www.nst.com.my. Abu, B., Johan, 0.M., Mansor, S.M.S.S. & Jaafar, H.(2007). Kepelbagaian Gaya Pembelajaran Dan Kemahiran Belajar Pelajar Universiti Di Fakulti Pendidikan, UTM Johor. Research Vote No: 71881. Jabatan Asas Pendidikan, UTM. Ahmad, A., Idrus, S. M., Malik, N. N. N.A., Murad, N. A., Ngajikin, N. H. & Esa, R. M. (2006 ). Pengaruh Gaya Hidup Pelajar Kejuruteraan di Kolej Kediaman UTM Terhadap Pencapaian Akademik National Student Development Conference (NASDEC) 2006, 8-9 August 2006, Kuala Lumpur, Malaysia. Adams, J. (2000). Work Experience as a Predictor of MBA Performance. College Student Journal. June, 2000. A step in the right direction. (2007, November 7). The Star Online. Retrieved on January 15, 2008 from http://www.thestar.com. Behrouz, M., Kashy, D. A., Kortemeyer, G. & Punch, W. F. (2003). Predicting Student Performance: An Application of Data Mining with the Educational Web-based System Lon- Capa. 33rd ASEE/IEEE Frontiers in Education Conference. November 5-8, Boulder. Belanger, F., Lewis, T., Kasper G. M., Smith, W. J., & Harrington,K.V.(2007). Are Computing Student Different? An Analysis of Coping Strategies and Emotional Intelligence. IEEE Transactions on Education, 50 (3), August 2007. Be wary of corruption.(2007, December 7). The Star Online. Retrieved on January 15, 2008 from http://www.thestar.com Byrne, P. & Lyons, G. (2001). The effects of students attributes on success in programming. ACMSIGCSE Bulletin, Vol(33)- 3, September 2001, pg 49 - 52. Camara, W. J. & Echternacht, G. (2000). The SAT I and High School Grades: Utility in Predicting Success in College. College Boarg Research Report No. RN-10. New York: Research and Development. Carrol, B. R. (2006). The effects of school violence and crime on academic achievement. Master dissertation. Davidson College. Close, W. & Solberg, S. (2007). Predicting Achievement, distress, and retention among lower-income Latino youth. Journal of Vocational Bahavior. Retreived January 20, 2008 from Science Direct database. Chamorro-Premuzic, T. & Furnham, A. (2003). Personality Predicts Academic Performance: Evidence from two longitudinal university samples. Research in Personality, 37,319 - 338. Cripps, A. (1996). Using Artificial Neural Nets to Predict Academic Performance. Darmayanti, T. (1994). Readines for Self-Directed Learning and Achievement of the Students of Universitas Terbuka (The Indonesian Open Learning University). Published Master Thesis. Michigan: UMI Dissertation Services. DeBerard, M. S, Spielmans, G. I. & Julka, D. L. (2004). Predictus of academic achievement and retention among college freshmen: A longitudinal study. College Student Journal, 38 (1), 66 - 80. Ditcher, A. & Tetley, J (1999). Factors influencing university students' academic success: what do students and academics think?. HERDSA Annual International Conference, Melbourne, 12-15 July. Early checks on student options. (2008, January 14). The Star Online. Retrieved on January 15, 2008 from http://www.thestar.com Exploit Your Potential, Grads told. (2007, December 4). The Star Online. Retrieved on January 15,2008 from http://www.thestar.com Facey-Shaw, L. & Golding, P. (2005). Effects of peer tutoring and attitude on academic performance of first year introductory programming students. 35th ASEE/IEEE Frontiers in Education Conference, October 19 - 22, Indianapolis, IN. Golding, P. & Donaldson, 0. (2006). Predicting Academic Performance. 36th ASEE/IEEE Frontiers n Education Conference, October 28-31,2006, San Diego, CA. Golding, P. & McNamarah, S. (2005). Predicting Academic Performance in the School of Computing & Information Technology (SCIT). 3th ASEE/IEEE Frontiers in Education Conference. October 19-22, Indianapolis. Graduate Careers Council of Australia Retrieved on March 3, 2008 from http://www.graduatecareers.com.au/content/view/full/3137 Han, J. & Chang, K. C. (2002). Data Mining for web Intelligence. Computer, vol. 35 (11), 64-70. Harding, T. S., Passow, H. J., Carpenter, D. D. & Finelli, C. J. (2003). An examination of the relationship between academic dishonesty and professional behavior. 33rd ASEE/IEEE Frontiers in Education Conference, November 5-8, Boulder, CO. Hendrich, U. G. (2007). The prediction of first-year academic achievement at a South African University of Technology: The importance of English Lunguage Proficiency. Unpublished Paper. Herzog, S. (2006). Estimating Student Retention and Degree-Completion Time: Decision Tree and Neural Networks Vis-his Regression. New Direction of institutional Research, Fall 2006.San Francisco, CA: Jossey: Bass. Hii, C. (2007, December 23). Distance Learning a boon for busy working adults. The Star Online. Ho, D. Y. F. & Spinks, J.A.(2004). Multivariate prediction of academic performance by Hong Kong University students. Contemporary Educational Psychology,10(3),249-259. Hoefer, P. & Gould, J. (2000). Assessment of admission criteria for predicting students' academic performance. Journal of Education for Business, 75(4), 225. Johnson, M. D. (2005). Academic Performance of Transfer versus "Native " students in natural resources and sciences. College Student Journal, 39 (3), 570 - 579. Junemann, M. A. P., Lagos, P. A. S., & Arriagada, R. C. (2007). Neural Networks to predict Schooling Failure/ Success. In Junemann, M. A. P., Lagos, P. A. S., & Arriagada, R. C., Nature Inspired problem- Solving Methods n Knowledge Engineering (pp. 571 - 579). Berlin/ Heidelberg: Springer. Kalles, D. & Pierrakeas, C. (2006). Analyzing Student Performance in Distance Learning with Genetic Algorithms and Decision Trees. Applied Artificial Intelligence, 20: 655 - 674. Kamini, R. S. (2008, January 23). Universities: New Courses to Boost Student Intake. NST Online. Retrieved on January 15,2008 from http://www.nst.com.my. Leslie, K. (2007). The impact of emotional intelligence on the academic performance of at-risk high school students. Ph.D. Dissertation. University of the Incarnate Word, Texas, US. Linacre, J. M. (2001). Data Mining and Rasch Measurement. Rasch Measurement Transactions, Fall 2001, 15 (2), 826-7. Ling, K. S. (2008, January 23). Schools: An Alternative Path. NST Online. Retrieved on January 15,2008 from http://www.nst.com.my. Luan, J. (2002). Data Mining and Knowledge Management in Higher Education - Potential Applications. Preceeding at AIR 2002, Toronto, Canada. Luan, J. (2004). Data Mining Applications in higher education. SPSS Inc. Luan, J. (2006). Data Mining Applications in Higher Education. SPSS Inc. Lufi, D., Parish-Plass, & Cohen, A. (2003). Persistence in Higher Education and its relationship to other personality variables. College Student Journal, 37 (1), 50 - 59. Mesir, B.,Yatim, J. M., Aziz, A. A., Jamal, M. H. & Abideen, M. Z.(2006). Gaya Hidup Pelajar Cemerlang dalam Persekitaran Kampus.Nationa1 Student Development Conference (NASDEC) 2006,8-9 August 2006, Kuala Lumpur, Malaysia. Mohamed, M. (2007, November 14). Plan to shape varsities of world class. The Star Online. Retrieved on January 15,2008 from http://www.thestar.com Mustapha not happy with English Programmes at varsities. (2008, January 23). The Star Online. Retrieved on January 15,2008 from http://www.thestar.com Naik, B. (2004). Using Neural Network to Predict MBA student success. College Student Journal. March, 2004. Nguyen, T. N., Jacenek, P. & Haddawy, P. (2007). A Comparative Analysis of Techniques for Predicting Academic Performance. 37th ASEE/IEEE Frontiers in Education Conference. October 10-13, Milwaukee. Nik Anis, M. (2007, November 27). PM: Acquire Knowledge. The Star Online. Retrieved on January 15,2008 from http://www.thestar.com Nunamaker, J. F. & Chen, M. (1990).System Development in Information System Research. Proceedings of the 23rd Annual Hawaii International Conference on System Science. Ogor, E. N. (2007). Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques. Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007). 354-359. Peters, K. A. (2007). The academic success of students at an HBCU in Maryland Ph.D thesis. Morgan State University. Sam, S. (2007). Academic Success through group work: Linking social skills, achievement, learning behaviors. Ph.D. Dissertation. George Mason University, Virginia, US. Sattar, M. A., Lee, C. J. & Caso, R. (2004). Work in progress: The Relationship between Objective and /or Subjective Admission Criteria and success Outcome in Undergraduate Engineering. 34th ASEE/IEEE Frontiers in Education Conference, October 20-23, 2004, Savannah, GA. Stacy, K. (2007). Academic success: Are virtual high schools working in Georgia? Ph. D. Dissertation, Capella University, Minnessota, US. Superby, J. F. & Vandamme, J. P. (2006 ). Determination of Factors influencing the achievement of the first-year university students using data mining methods. Proceedings of the Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems (ITS 2006). Jhongli, Taiwan. Suziena, U. N. (2008, January 23). Higher Education: Incentives for postgraduates. NST Online. Retrieved on January 15,2008 fiom http://www.nst.com.my. The Manage Mentor . Demand Forecating using Qualitative & Quantitative methods. Retreived January 26,2008, from http://www.themanagementor.com/EnlightenMentorAreas/finance/mgr/DemandForecastQualitativeQuantitative.htm The Spur of Poor Grades. (2008, January 23). NST Online. Retrieved on January 15,2008 from http://www.nst.com.my. Thomas, E. H. & Galambos, N. (2004). What Satisfies Students? Mining Student-Opinion Data with Regression and Decision Tree Analysis. Research in Higher Education., 45 (3), 251-269. Ullah, H. & Wilson, M. A. (2007). Students ' Academic Success and Its Association to Student involvement with Learning and Relationships with Faculty and Peers. College Student Journal; Dec 2007 Part B, 41(4), 1192 - 1202. Yahaya, A., Boon, Y., Hashim, S. & Hamid, W. Z. W. (2002). Hubungan Di Antara Gaya Pembelajaran Dengan Pencapaian Akademik Pelajar. International Conference on The challenge on Learning and Teaching in Brave New World". Hatyai City College Thailand and School of Cognitive and Education, UUM, 14-16 October 2002. Yue, Y. & Scutter, M. (2000). Quantitative decision making for cued land reconnaissance: A defense case study. International Transactions in Operational Research, 7 (2000), 449 - 463.

Modeling Academic Achievement With Respect to Self-Readiness of Public Universities Graduates.

Similar Items