Home   >   CSC-OpenAccess Library   >    Manuscript Information
Self-motivation and Academic Performance In Computer Programming Language Using a Hybridised Machine Learning Technique
Isaac Kofi Nti, Juanita Ahia Quarcoo
Pages - 12 - 30     |    Revised - 30-04-2019     |    Published - 01-06-2019
Volume - 8   Issue - 2    |    Publication Date - June 2019  Table of Contents
Self-Confidence, Self-Efficacy, Individual-Interest, Positive-Thinking, Focus, Personal Goals, Motivating Environment, Academic Performance, Random Forest, Super Vector Machine.
The advancement in artificial intelligence (AI) and Machine learning (ML) have made it easier to foreknown feature happens from current and past trends. Once Self-efficacy and self-confidence are believed to be, an individual trait associated with academic brilliance. Using a hybridised Random Forest and Support Vector Machine (RFSVM) ML model we predicted students' academic performance in computer programming courses, based on their self-confidence, self-efficacy, positive thinking, focus, big goals, a motivating environment and demographic data. Benchmarking our RFSVM model against Decision Tree (DT) and K-Nearest Neighbour (K-NN) model, the RFSVM recorded and accuracy of 98% as against 95.45% for DT and 36.36% for K-NN. The error between actual values and predicted values of the RFSVM model was better (RMSE = 0.326401, MAE = 0.050909) and compared with the K-NN (RMSE = 2.671397, MAE = 1.954545) and DT models (RMSE = 0.426401, MAE = 0.090909). The results further revealed that students with a high level of self-confidence, self-efficacy and positive thinking performed well in computer programming courses.
1 Google Scholar 
2 refSeek 
3 ResearchGate 
4 Doc Player 
5 Scribd 
6 SlideShare 
Adejo, O. W., & Connolly, T. (2018). Predicting student academic performance using multi-model heterogeneous ensemble approach. Journal of Applied Research in Higher Education, 10(1), 61-75. https://doi.org/10.1108/JARHE-09-2017-0113.
Affum-osei, E., Adom, E. A., Barnie, J., & Forkuoh, S. K. (2014). Achievement motivation, academic self-concept and academic achievement among high school students. European Journal of Research and Reflection in Educational Sciences, 2(2), 24-37. https://doi.org/10.1016/B978-0-08-097086-8.92153-6.
Agrawal, H., & Mavani, H. (2015). 10 Student Performance Prediction using Machine Learning, 4(03), 111-113.
Agustiani, H., Cahyad, S., & Musa, M. (2016). Self-efficacy and Self-Regulated Learning as Predictors of Students Academic Performance. The Open Psychology Journal, 9(1), 1-6. https://doi.org/10.2174/1874350101609010001.
AL-Baddareen, G., Ghaith, S., & Akour, M. (2015). Self-Efficacy, Achievement Goals, and Metacognition as Predicators of Academic Motivation. Procedia - Social and Behavioral Sciences, 191, 2068-2073. https://doi.org/10.1016/j.sbspro.2015.04.345.
Al-rahmi, W. M. (2013). The Impact of Social Media use on Academic Performance among university students : A Pilot Study. Journal Of Information Systems Research and Innovation, 1-10. https://doi.org/http://seminar.utmspace.edu.my/jisri/.
Al-rahmi, W. M., Zeki, A. M., Alias, N., & Saged, A. A. (2017). Social Media and its Impact on Academic Performance among University Students. The Anthropologist, 28(1-2), 52-68. https://doi.org/10.1080/09720073.2017.1317962.
Alkis, N., & Temizel, T. T. (2018). The Impact of Motivation and Personality on Academic Performance in Online and Blended Learning Environments. Journal of Educational Technology & Society, 21(3), 35-47.
Alwagait, E., Shahzad, B., & Alim, S. (2015). Impact of social media usage on students academic performance in Saudi Arabia. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2014.09.028.
Attuquayefio, NiiBoi, S., & Addo, H. (2014). Using the UTAUT model to analyze students' ICT adoption. International Journal of Education & Development Using Information & Communication Technology, 10(3), 75-86. Retrieved from http://ezproxy.usq.edu.au/login? url=http://search.ebscohost.com/login.aspx?direct=true&db=ehh&AN=97923459&site=ehost-live.
Belgiu, M., & Dragu, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
Bernard, K. J., & Dzandza, P. E. (2018). Effect of social media on academic performance of students in Ghanaian Universities: A case study of University of Ghana, Legon. Library Philosophy and Practice, 2018(February).
Bhardwaj, B. K., & Pal, S. (2011). Data Mining : A prediction for performance improvement using classification. International Journal of Computer Science and Information Security, 9(4), 136-140.
Bingham, T., & Conner, M. (2010). The New Social Learning: A Guide to Transforming Organizations through Social Media. Berrett-Koehler Store, San Francisco, CA.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
Cetin, B. (2015). Academic Motivation And Self-Regulated Learning In Predicting Academic Achievement in College. Journal of International Education Research (JIER), 11(2), 95-106. https://doi.org/10.19030/jier.v11i2.9190.
Chang, C. S., Liu, E. Z. F., Sung, H. Y., Lin, C. H., Chen, N. S., & Cheng, S. S. (2014). Effects of online college student's Internet self-efficacy on learning motivation and performance. Innovations in Education and Teaching International, 51(4), 366-377. https://doi.org/10.1080/14703297.2013.771429.
Chen, J. F., Hsieh, H. N., & Do, Q. H. (2014). Predicting student academic performance: A comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks. Algorithms, 7(4), 538-553. https://doi.org/10.3390/a7040538.
Cigan, V. (2014). Relationship between students' motivation and their socio-demographic characteristics. Linguistica, 54(1), 11-30. https://doi.org/10.4312/linguistica.54.1.11-30.
Cortez, P., & Silva, A. (2008). Using Data Mining To Predict Secondary School Student Performance. 5th Annual Future Business Technology Conference, 2003(2000), 5-12. https://doi.org/10.13140/RG.2.1.1465.8328.
Devasia, T., Vinushree, T. P., & Hegde, V. (2016). Prediction of Students Performance using Educational Data Mining. 16.
Dogan, U. (2015). Student engagement, academic self-efficacy, and academic motivation as predictors of academic performance. Anthropologist, 20(3), 553-561. https://doi.org/10.1080/09720073.2015.11891759.
Dweck, S. carol, Walton, M. G., & Cohen, L. G. (2014). Academic Tenacity. United States and. Retrieved from www.gatesfoundation.org.
Faggella, D. (2018). What is Machine Learning? Retrieved October 8, 2018, from https://www.techemergence.com/what-is-machine-learning/.
Ferguson, L. H. (2017). Mindset, Academic Motivation, And Academic Self-Efficacy As Correlates Of Academic Achievement Among Undergraduate Students in Communication Sciences and. Andrews University. Retrieved from https://digitalcommons.andrews.edu/dissertations/1648/.
Fleischer, J. E. (2015). Information Communication Technology Usage Patterns in Second Cycle Schools: A Study of Two Selected Senior High Schools in Ghana.
Goga, M., Kuyoro, S., & Goga, N. (2015). A recommender for improving the student academic performance. Procedia - Social and Behavioral Sciences, 180(November 2014), 1481-1488. https://doi.org/10.1016/j.sbspro.2015.02.296.
Grunschel, C., Schwinger, M., Steinmayr, R., & Fries, S. (2016). Effects of using motivational regulation strategies on students' academic procrastination, academic performance, and well-being. Learning and Individual Differences, 49, 162-170. https://doi.org/10.1016/j.lindif.2016.06.008.
Holland, A. A., Hughes, C. W., Harder, L., Silver, C., Bowers, D. C., & Stavinoha, P. L. (2016). Effect of motivation on academic fluency performance in survivors of pediatric medulloblastoma. Child Neuropsychology, 22(5), 570-586. https://doi.org/10.1080/09297049.2015.1023272.
Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63-84. https://doi.org/10.1016/j.edurev.2015.11.002.
Javed, K., Gouriveau, R., & Zerhouni, N. (2014). SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization. Neurocomputing, 123, 299-307. https://doi.org/10.1016/j.neucom.2013.07.021.
Jung, K. R., Zhou, A. Q., & Lee, R. M. (2017). Self-efficacy, self-discipline and academic performance: Testing a context-specific mediation model. Learning and Individual Differences, 60(October), 33-39. https://doi.org/10.1016/j.lindif.2017.10.004.
Khasanah, A. U., & Harwati. (2017). A Comparative Study to Predict Student's Performance Using Educational Data Mining Techniques. IOP Conference Series: Materials Science and Engineering, 215(1). https://doi.org/10.1088/1757-899X/215/1/012036.
Kieti, J. M. (2017). An Investigation into Factors Influencing Students' Academic Performance in Public Secondary Schools in Matungulu Sub-County, Machakos County. South Eastern Kenya University. https://doi.org/10.1111/j.1469-7610.2010.02280.x.
Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs, goals, and effort regulation matter? Learning and Individual Differences, 25, 67-72. https://doi.org/10.1016/j.lindif.2013.01.005.
Lee, W., Lee, M. J., & Bong, M. (2014). Testing interest and self-efficacy as predictors of academic self-regulation and achievement. Contemporary Educational Psychology, 39(2), 86-99. https://doi.org/10.1016/j.cedpsych.2014.02.002.
Mammadov, S., Cross, T. L., & Ward, T. J. (2018). The Big Five personality predictors of academic achievement in gifted students: Mediation by self-regulatory efficacy and academic motivation. High Ability Studies, 00(00), 1-23. https://doi.org/10.1080/13598139.2018.1489222.
Maya, k. G. (2015). Achievement scripts, media influences on Blacks students' academic performance, self-perceptions and carrier interests. Journal of Black Psychology, 42(3), 195-220. https://doi.org/10.1177/0095798414566510.
Mind-Tools. (2018). How Self-Motivated Are You: Taking Charge of Your Goals and Achievements. Retrieved from https://www.mindtools.com/pages/article/newLDR_57.htm.
Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1(1), 42-71. https://doi.org/10.14786/flr.v1i1.13.
Muwonge, C. M., Schiefele, U., Ssenyonga, J., & Kibedi, H. (2017). Self-regulated learning among teacher education students: Motivational beliefs influence on the use of metacognition. Journal of Psychology in Africa, 27(6), 515-521. https://doi.org/10.1080/14330237.2017.1399973.
Okyeadie Mensah, S., Nizam, D. I., Mensah, O. S., & Nizam, I. (2016). the Impact of Social Media on Students' Academic. International Journal Of Education, Learning & Training (IJELT), 1(1), 14-21. https://doi.org/10.24924/ijelt/2016.11/v1.iss1/14.21.
Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting students' academic performance using artificial neural network: A case study of an engineering course. The Pacific Journal of Science and Technology, 9(1), 72-79.
Osmanbegovic, E., & Suljic, M. (2012). Data mining approach for predicting student performance. Journal of Economics and Business, X(1), 3-12.
Osmanbegovic, E., Suljic, M., & Agic, H. (2014). Determining Dominant Factor for Students Performance Prediction by Using Data Mining. Vitez-Tuzla-Zagreb-Beograd-Bucharest, XVII(34), 147-158.
Owusu-Acheaw, M., & Larson, A. G. (2015). Use of Social Media and its Impact on Academic Performance of Tertiary Institution Students: A Study of Students of Koforidua Polytechnic, Ghana. Journal of Education and Practice, 6(6), 94-101.
Perera, H. N., & DiGiacomo, M. (2015). The role of trait emotional intelligence in academic performance during the university transition: An integrative model of mediation via social support, coping, and adjustment. Personality and Individual Differences, 83, 208-213. https://doi.org/10.1016/j.paid.2015.04.001.
Rajashree, D., Dash, P. K., & Bisoi, R. (2014). A self adaptive differential harmony search based optimized extreme learning machine for financial time series prediction. Swarm and Evolutionary Computation, 19, 25-42. https://doi.org/10.1016/j.swevo.2014.07.003.
Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63, 604-612. https://doi.org/https://doi.org/10.1016/j.chb.2016.05.084.
Rimfeld, K., Kovas, Y., Dale, P. S., & Plomin, R. (2016). True grit and genetics: Predicting academic achievement from personality. Journal of Personality and Social Psychology, 111(5), 780-789. https://doi.org/10.1037/pspp0000089.
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804-818. https://doi.org/10.1016/j.oregeorev.2015.01.001.
Ross, M., Perkins, H., & Bodey, K. (2016). Academic motivation and information literacy self-efficacy: The importance of a simple desire to know. Library and Information Science Research, 38(1), 2-9. https://doi.org/10.1016/j.lisr.2016.01.002.
Salomon, A., & Ben-David, K. Y. (2016). High-school students' perceptions of the effects of non-academic usage of ICT on their academic achievements. Computers in Human Behavior, 64, 143-151. https://doi.org/https://doi.org/10.1016/j.chb.2016.06.024.
Schmitt, N., Keeney, J., Oswald, F. L., Pleskac, T. J., Billington, A. Q., Sinha, R., & Zorzie, M. (2009). Prediction of 4-Year College Student Performance Using Cognitive and Noncognitive Predictors and the Impact on Demographic Status of Admitted Students. Journal of Applied Psychology, 94(6), 1479-1497. https://doi.org/10.1037/a0016810.
Schwartz, T. D. B. (2017). The Effect of Student-Led Conferencing at School and at Home on Goal-Setting , Goal-Fulfillment , Effort , Achievement , Intrinsic Motivation , and Satisfaction for Montessori Lower Elementary 3rd Year Students. The St. Catherine University. Retrieved from https://sophia.stkate.edu/maed/224.
Stajkovic, A. D., Bandura, A., Locke, E. A., Lee, D., & Sergent, K. (2018). Test of three conceptual models of influence of the big five personality traits and self-efficacy on academic performance: A meta-analytic path-analysis. Personality and Individual Differences, 120(February 2017), 238-245. https://doi.org/10.1016/j.paid.2017.08.014.
Sudha, S., & Kavitha, E. S. (2016). The Effect of Social Networking on Students ' Academic Performance : the Perspective of Faculty Members of Periyar. Library Philophy and Practice, 14(55). Retrieved from http://digitalcommons.unl.edu/libphilprac%0Ahttp://digitalcommons.unl.edu/libphilprac/1455.
Suprayogi, M. N., & Valcke, M. (2014). Academic Efficacy, Goal Orientation, and Academic Achievement; A Comparative Study between Belgium and Indonesian Students.
Talsma, K., Schüz, B., Schwarzer, R., & Norris, K. (2018). I believe, therefore I achieve (and vice versa): A meta-analytic cross-lagged panel analysis of self-efficacy and academic performance. Learning and Individual Differences, 61(October 2017), 136-150. https://doi.org/10.1016/j.lindif.2017.11.015.
Trolian, T. L., Jach, E. A., Hanson, J. M., & Pascarella, E. T. (2017). Influencing Academic Motivation : The Effects of Student - Faculty Interaction Influencing Academic Motivation : The Effects of Student - Faculty Interaction. Journal of College Student Development, 57(7), 810-826.
Tuen, W., Leung, V., Yee, T., Pan, W., Wu, C., Lung, S. C., & Spengler, J. D. (2019). Landscape and Urban Planning How is environmental greenness related to students ' academic performance in English and Mathematics ? Landscape and Urban Planning, 181(1), 118-124. https://doi.org/10.1016/j.landurbplan.2018.09.021.
Vaghela, C., Bhatt, N., & Patel, P. U. (2015). A Survey on Various Classification Techniques for Clinical Decision Support System. International Journal of Computer Applications, 116(23), 975-8887.
Wakefield, K. (2013). A guide to machine learning algorithms and their applications. Retrieved August 1, 2018, from https://www.sas.com/en_gb/insights/articles/analytics/machine-learning-algorithms.html.
Wentworth, D. K., & Middleton, J. H. (2014). Technology use and academic performance. Computers and Education, 78(September 2014), 306-311. https://doi.org/10.1016/j.compedu.2014.06.012.
Wilson, K., & Narayan, A. (2016). Relationships among individual task self-efficacy, self-regulated learning strategy use and academic performance in a computer-supported collaborative learning environment. Educational Psychology, 36(2), 236-253. https://doi.org/10.1080/01443410.2014.926312.
Yadav, S. K., & Pal, S. (2012). Data Mining : A Prediction for Performance Improvement of Engineering Students using Classification. World of Computer Science and Information Technology Journal WCSIT, 2(2), 51-56. https://doi.org/10.1142/9789812771728_0012.
Yeboah, Y. K. (2014). Investigating the Low Performance of Students' English in the Basic Education Certificate Examination in the Sunyani Municipality. UNIVERSITY OF GHANA, LEGON. https://doi.org/10.1038/253004b0.
Yusif, H. M., Yussof, I., & Noor, A. H. S. M. (2011). Determinants of Students Academic Perform- Ance in Senior High Schools : a Binary Logit Approach, 31(3), 107-117. https://doi.org/10.4314/just.v31i3.12.
Zimmerman, B. J., & Kitsantas, A. (2014). Comparing students' self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology, 39(2), 145-155. https://doi.org/10.1016/j.cedpsych.2014.03.004.
Mr. Isaac Kofi Nti
Department of Computer Science Sunyani Technical University - Ghana
Mrs. Juanita Ahia Quarcoo
Department of Computer Science Sunyani Technical University - Ghana

View all special issues >>