International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12
line decor
  
line decor

 Volume 5, Issue 4 (April 2018), Pages: 1-5

----------------------------------------------

 Original Research Paper

 Title: Filtering the course outcomes for engineering mathematics lab via Rasch model

 Author(s): N. Lohgheswary 1, *, Z. M. Nopiah 2, A. A. Aziz 3, E. Zakaria 4

 Affiliation(s):

 1Faculty of Engineering and Built Environment, SEGi University, Kota Damansara, Malaysia
 2Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia
 3Faculty of Computing and IT, King Abdulaziz University, Jeddah, Saudi Arabia
 4Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, Malaysia

 https://doi.org/10.21833/ijaas.2018.04.001

 Full Text - PDF          XML

 Abstract:

The aim of this study is to use Rasch model as a method to filter the suitable course outcomes for Engineering Mathematics lab. A pre-test was conducted for each of the Vector Calculus, Linear Algebra and Differential Equation subjects. The analysis of each subject was run against the Rasch model. The person item distribution map managed to divide the course outcomes into different categories. The categories are very difficult, difficult, moderate and easy. From a total of 16-course outcomes from 3 subjects, only 8-course outcomes were filtered and chosen for the suggested Engineering Mathematics lab session. Partial derivatives, line integrals, Greens’ theorem, vector space, power series, first and second order of differential equations, second order non-homogeneous differential equations and Fourier series are identified as the course outcomes for the lab sessions. A two-hour lab session is suggested for each of the course outcomes. Conducting lab sessions for Engineering Mathematics subjects parallel with traditional lectures will help the students widen their knowledge in Engineering Mathematics and to perform better in the subjects. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Vector calculus, Linear algebra, Differential equation, Course outcome, Rasch model, Lab session

 Article History: Received 10 October 2017, Received in revised form 17 January 2018, Accepted 18 January 2018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.04.001

 Citation:

 Lohgheswary N, Nopiah ZM, Aziz AA, and Zakaria E (2018). Filtering the course outcomes for engineering mathematics lab via Rasch model. International Journal of Advanced and Applied Sciences, 5(4): 1-5

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I4/Lohgheswary.html

----------------------------------------------

 References (16)

  1. Adair D and Jaeger M (2014). Making engineering mathematics more relevant using a computer algebra system. International Journal of Engineering Education, 30(1): 199-209.   [Google Scholar]
  2. Barry SI and Chapman J (2007). Predicting university performance. ANZIAM Journal, 49: 36-50. https://doi.org/10.21914/anziamj.v49i0.304   [Google Scholar]
  3. Botana F, Abanades MA, and Escribano I (2014). Using free open source software to teach mathematics. Computer Applications in Engineering Education, 22(4):728-735. https://doi.org/10.1002/cae.21565   [Google Scholar]
  4. Chen FX (2013). Research of scientific computing in the engineering linear algebra teaching. Applied Mechanics and Materials, 333: 2218-2221. https://doi.org/10.4028/www.scientific.net/AMM.333-335.2218   [Google Scholar]
  5. Ghulman HA and Masodi MS (2009). Modern measurement paradigm in Engineering Education: Easier to read and better analysis using Rasch-based approach. In the International Conference on Engineering Education, IEEE, Kuala Lumpur, Malaysia: 1-6. https://doi.org/10.1109/ICEED.2009.5490624   [Google Scholar]
  6. Gynnild V, Tyssedal J, and Lorentzen L (2005). Approaches to study and the quality of learning. Some empirical evidence from engineering education. International Journal of Science and Mathematics Education, 3(4): 587-607. https://doi.org/10.1007/s10763-005-5178-4   [Google Scholar]
  7. Lee S, Harrison MC, Pell G, and Robinson CL (2008). Predicting performance of first year engineering students and the importance of assessment tools therein. Engineering Education, 3(1): 44-51. https://doi.org/10.11120/ened.2008.03010044   [Google Scholar] 
  8. Linacre JM (2008). WINSTEPS, computer program [version 3.68]. Chicago, USA. Available online at: http://www.winsteps.com   [Google Scholar]  
  9. Maat SM and Zakaria E (2011). Exploring students' understanding of ordinary differential equations using computer algebra system. Turkish Online Journal of Educational Technology, 10(3):123-128.   [Google Scholar]
  10. Noinang S, Wiwatanapataphee B, and Wu TH (2008). Teaching-learning Tool for Integral Calculus. In the 13th Asian Technology Conference in Mathematics, Suan Sunandha Rajabhat University, Bangkok, Thailand.   [Google Scholar]     
  11. Nopiah ZM, Fuaad NFA, Tawil NM, Hamzah FM, and Othman H (2015). Student achievement at pre-university level: Does it influence the achievement at the University?. Journal of Engineering Science and Technology: Special Issue on UKM Teaching and Learning Congress, Taylor's University, Selangor, Malaysia, 68-76.   [Google Scholar]     
  12. Schmidt K, Rattleft P, and Hussmann PM (2008). The impact of CAS use in introducing engineering mathematics. In: Fitt AD, Norbury J, Ockendon H, and Wilson E (Eds.), Progress in industrial mathematics at ECMI 2008: 635-659. Springer Science & Business Media, Berlin, Germany.   [Google Scholar]  
  13. Shacham M, Braunes N, Ashurst WR, and Cutlip MB (2008). Can i trust this software package? an exercise in validation of computational results. Chemical Engineering Education, 42(1): 53-59.   [Google Scholar]
  14. Tang HE, Li VL, and Julaihi NH (2010). The relationships between students' underachievement in mathematics courses and influencing factors. Procedia-Social and Behavioral Sciences, 8: 134-141. https://doi.org/10.1016/j.sbspro.2010.12.019   [Google Scholar] 
  15. Tang HE, Voon LL, and Julaihi NH (2008). The impact of 'high-failure rate'mathematics courses on UiTM sarawak full-time diploma students' academic performance. Research Management Institute, Universiti Teknologi MARA, Malaysia.   [Google Scholar]     
  16. Zeynivannezhad F (2014). Mathematical thinking in differential equations through a computer algebra system. Ph.D. Dissertation, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.   [Google Scholar]