Entry Behaviour, Learner Motivation, Self-Regulation and Academic Performance of First Year ICT Students: Evidence from Kibi Technical Institute

Authors

  • Augustine Weyage University of Cape Coast
  • Richard Adade University of Cape Coast

DOI:

https://doi.org/10.55927/ijcs.v2i4.8934

Keywords:

Student Motivation, Academic Performance, Kibi Technical Institute

Abstract

The study investigated the impact of entry behaviour on academic performance among first-year ICT students at Kibi Technical Institute, while also exploring learner motivation and self-regulation as predictive factors. The study, rooted in motivation and self-regulation theory, utilized a positivist approach and an explanatory design. Data, gathered from 100 first-year ICT students using a structured questionnaire and academic records, were analysed using IBM SPSS Statistics v26. Results showed that self-regulation significantly predicted academic performance, whereas entry behaviour and learner motivation had insignificant effects. It recommends integrating self-regulation training programs into the curriculum to empower students with effective learning strategies and management techniques

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References

Abutabenjeh, S., & Jaradat, R. (2018). Clarification of research design, research methods, and research methodology: A guide for public administration researchers and practitioners. Teaching Public Administration, 36(3), 237-258.

Aciro, R., Onen, D., Malinga, G. M., Ezati, B. A., & Openjuru, G. L. (2021). Entry grades and the academic performance of university students: a review of literature. Education Quarterly Reviews, 4(1).

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.

Al Shehry, A., & Youssif, S. M. A. (2017). Factors affecting academic performance of undergraduate students at Najran Preparatory Year for Girls-Najran University 2015-2016. International Journal of Asian social science, 7(1), 1-18.

Alamer, A., & Alrabai, F. (2022). The causal relationship between learner motivation and language achievement: New dynamic perspective. Applied Linguistics.

Alivernini, F., & Lucidi, F. (2008). The Academic Motivation Scale (AMS): Factorial structure, invariance and validity in the Italian context. Testing, Psychometrics, Methodology in Applied Psychology, 15(4), 211-220.

Al-Kumaim, N. H., Alhazmi, A. K., Mohammed, F., Gazem, N. A., Shabbir, M. S., & Fazea, Y. (2021). Exploring the impact of the COVID-19 pandemic on university students’ learning life: An integrated conceptual motivational model for sustainable and healthy online learning. Sustainability, 13(5), 2546.

Anderson, L. W., & Bourke, S. F. (2013). Assessing affective characteristics in the schools. Routledge.

Anwer, K., & Saleem, Q. (2018). Determining the relationship between motivation towards learning and academic performance among medical students. Annals of Abbasi Shaheed Hospital And Karachi Medical & Dental College, 23(4), 191-198.

Ashaeryanto, A., Kristina, T. N., & Hadianto, T. (2017). The Relationships of The types of Entry Selection of Students with their Learning Motivation, Learning Strategies, and Learning Achievement. Journal Pendidikan Kedokteran Indonesia: The Indonesian Journal of Medical Education, 6(1), 1-10.

Behfar, K., & Okhuysen, G. A. (2018). Perspective—Discovery within validation logic: Deliberately surfacing, complementing, and substituting abductive reasoning in hypothetico-deductive inquiry. Organization Science, 29(2), 323-340.

Bergin, T. (2018). An introduction to data analysis: Quantitative, qualitative and mixed methods. Sage.

Berndt, A. E. (2020). Sampling methods. Journal of Human Lactation, 36(2), 224-226.

Binder, T., Sandmann, A., Sures, B., Friege, G., Theyssen, H., & Schmiemann, P. (2019). Assessing prior knowledge types as predictors of academic achievement in the introductory phase of biology and physics study programmes using logistic regression. International Journal of STEM Education, 6(1), 1-14.

Brieger, E., Arghode, V., & McLean, G. (2020). Connecting theory and practice: reviewing six learning theories to inform online instruction. European Journal of Training and Development.

Brown, C., Spiro, J., & Quinton, S. (2020). The role of research ethics committees: Friend or foe in educational research? An exploratory study. British Educational Research Journal, 46(4), 747-769.

Budiman, R. (2016). Developing learning media based on augmented reality (AR) to improve learning motivation. Journal of Education, Teaching and Learning, 1(2), 89-94.

Çaliskan, M. (2014). Effect of Cognitive Entry Behaviors and Affective Entry Characteristics on Learning Level. Educational Sciences: Theory and Practice, 14(5), 1816-1821.

Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and extrinsic incentives jointly predict performance: a 40-year meta-analysis. Psychological bulletin, 140(4), 980.

Creswell, J. W. (2014). A concise introduction to mixed methods research. SAGE publications.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Dalati, S., & Marx Gómez, J. (2018). Surveys and questionnaires. In Modernizing the Academic Teaching and Research Environment (pp. 175-186). Springer, Cham.

Darling-Hammond, L., & Cook-Harvey, C. M. (2018). Educating the Whole Child: Improving School Climate to Support Student Success. Learning Policy Institute.

Deci, E. L., & Ryan, R. M. (1985). Cognitive evaluation theory. In Intrinsic motivation and self-determination in human behavior (pp. 43-85). Springer, Boston, MA.

Deeks, J. J., Higgins, J. P., & Altman, D. G. (2019). on behalf of the Cochrane Statistical Methods Group. Analysing data and undertaking meta-analyses.

Denhardt, K. G., & Aristigueta, M. P. (2008). Performance management systems: Providing accountability and challenging collaboration. In Performance Information in the Public Sector (pp. 106-122). Palgrave Macmillan, London.

Downes, S. (2022). Connectivism. Asian Journal of Distance Education.

Dźwigoł, H. (2018). Scientific research methodology in management sciences. Фінансово-кредитна діяльність: проблеми теорії та практики, (2), 424-437.

El-Adl, A., & Alkharusi, H. (2020). Relationships between self-regulated learning strategies, learning motivation and mathematics achievement. Cypriot Journal of Educational Sciences, 15(1), 104-111.

Erdogan, T., & Senemoglu, N. (2016). Development and validation of a scale on self-regulation in learning (SSRL). SpringerPlus, 5(1), 1-13.

Francis, B. K., & Babu, S. S. (2019). Predicting academic performance of students using a hybrid data mining approach. Journal of medical systems, 43, 1-15.

George, D., & Mallery, P. (2018). Descriptive statistics. In IBM SPSS Statistics 25 Step by Step (pp. 126-134). Routledge.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.

Harding, S. M., English, N., Nibali, N., Griffin, P., Graham, L., Alom, B., & Zhang, Z. (2019). Self-regulated learning as a predictor of mathematics and reading performance: A picture of students in Grades 5 to 8. Australian journal of education, 63(1), 74-97.

Hendricks, G. P. (2019). Connectivism as a learning theory and Its relation to open distance education. Progressio, 41(1), 1-13.

Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In New challenges to international marketing. Emerald Group Publishing Limited.

Hetherington, E., McDonald, S., Racine, N., & Tough, S. (2020). Longitudinal predictors of self-regulation at school entry: Findings from the all our families cohort. Children, 7(10), 186.

Johansson, J., Contero, M., Company, P., & Elgh, F. (2018). Supporting connectivism in knowledge based engineering with graph theory, filtering techniques and model quality assurance. Advanced Engineering Informatics, 38, 252-263.

Johnson, D. B., Bordeaux, J., Kim, J. Y., Vaupel, C., Rimm, D. L., Ho, T. H., ... & Dakappagari, N. (2018). Quantitative Spatial Profiling of PD-1/PD-L1 Interaction and HLA-DR/IDO-1 Predicts Improved Outcomes of Anti–PD-1 Therapies in Metastatic MelanomaPD-1/PD-L1 Interaction and HLADR/IDO-1 Predict Anti–PD-1 Outcome. Clinical Cancer Research, 24(21), 5250-5260.

Johnson, R. B., & Christensen, L. (2019). Educational research: Quantitative, qualitative, and mixed approaches. Sage publications.

Kaur, P., Saini, S., & Vig, D. (2018). Metacognition, self-regulation and learning environment as determinant of academic achievement. Indian Journal of Health & Wellbeing, 9(5).

Kickert, R., Meeuwisse, M., M. Stegers-Jager, K., V. Koppenol-Gonzalez, G., R. Arends, L., & Prinzie, P. (2019). Assessment policies and academic performance within a single course: The role of motivation and self-regulation. Assessment & Evaluation in Higher Education, 44(8), 1177-1190.

Kokkinos, C. M., & Voulgaridou, I. (2018). Motivational beliefs as mediators in the association between perceived scholastic competence, self-esteem and learning strategies among Greek secondary school students. Educational Psychology, 38(6), 753-771.

Koomson, A. K., Brown, P., Anyagre, P., Ahiatrogah, P., & Dawson-Brew, F. (2017). Educational psychology. Cape Coast: College of Distance Education, University of Cape Coast.

Kwaah, C. Y., & Palojoki, P. (2018). Entry characteristics, academic achievement and teaching practices: A comparative study of two categories of newly qualified teachers in basic schools in Ghana. Cogent Education, 5(1), 1561144.

Lakens, D. (2022). Sample size justification. Collabra: Psychology, 8(1), 33267.

Lee, D., Watson, S. L., & Watson, W. R. (2019). Systematic literature review on self-regulated learning in massive open online courses. Australasian Journal of Educational Technology, 35(1).

Li, J., Ye, H., Tang, Y., Zhou, Z., & Hu, X. (2018). What are the effects of self-regulation phases and strategies for Chinese students? A meta-analysis of two decades research of the association between self-regulation and academic performance. Frontiers in Psychology, 9, 2434.

Liu, X., & Li, H. (2021). A Preliminary Study on Connectivism—Constructivism Learning Theory Based on Developmental Cognitive Neuroscience and Spiking Neural Network. Open Journal of Applied Sciences, 11(8), 874-884.

Majeed, I. (2019). Understanding positivism in social research: A research paradigm of inductive logic of inquiry. International Journal of Research in Social Sciences, 9(11), 118-125.

Matcha, W., Gašević, D., & Pardo, A. (2019). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226-245.

Melesse, S., & Molla, S. (2018). The contribution of school culture to students' academic achievement: The case of secondary and preparatory schools ofAssosa zone, Benshangul Gumuz regional state, Ethiopia. Research in Pedagogy, 8(2), 190-203.

Mohajan, H. K. (2018). Qualitative research methodology in social sciences and related subjects. Journal of economic development, environment and people, 7(1), 23-48.

Muhammad, A. S., Bakar, N. A., Mijinyawa, S. I., & Halabi, K. A. (2015). Impact of motivation on students’ academic performance: A case study of University Sultan Zainal Abidin students. The American Journal of Innovative Research and Applied Sciences, 1(6), 221-226.

Muller, C. (2018). Parent involvement and academic achievement: An analysis of family resources available to the child. In Parents, their children, and schools (pp. 77-114). Routledge.

Njoroge, E., Mulwa, D. M., & Kiweu, J. M. (2023). Students’ entry behaviour and learning environment as determinants of students’academic achievement: Case of Public Secondary Schools in Machakos County, Kenya. European Journal of Education Studies, 10(1).

Omeihe, K. O. (2021, July 13). Non-Probability Sampling. British Academy of Management.

Pandey, P., & Pandey, M. M. (2021). Research methodology tools and techniques. Bridge Center.

Park, Y. S., Konge, L., & Artino, A. R. (2020). The positivism paradigm of research. Academic Medicine, 95(5), 690-694.

Peiffer, H., Ellwart, T., & Preckel, F. (2020). Ability self-concept and self-efficacy in higher education: An empirical differentiation based on their factorial structure. PLoS One, 15(7), e0234604.

Peng, P., & Kievit, R. A. (2020). The development of academic achievement and cognitive abilities: A bidirectional perspective. Child Development Perspectives, 14(1), 15-20.

Popovici, A. A. (2022). Relation of Carl Menger's philosophy of economics to Auguste Comte's positivism.

Puspitarini, Y. D., & Hanif, M. (2019). Using Learning Media to Increase Learning Motivation in Elementary School. Anatolian Journal of Education, 4(2), 53-60.

Rafiola, R., Setyosari, P., Radjah, C., & Ramli, M. (2020). The effect of learning motivation, self-efficacy, and blended learning on students’ achievement in the industrial revolution 4.0. International Journal of Emerging Technologies in Learning (iJET), 15(8), 71-82.

Rahawarin, Y., Hakim, R., Sari, W. W., Ramdani, N. S., Kasmar, I. F., Wulandari, S., ... & Arifin, Z. (2020). Seven Motivations of Students Selecting Department of Islamic Teaching Education in Public University. Asian Social Science and Humanities Research Journal (ASHREJ), 2(1), 45-55.

Rahi, S. (2017). Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. International Journal of Economics & Management Sciences, 6(2), 1-5.

Reparaz, C., Aznárez-Sanado, M., & Mendoza, G. (2020). Self-regulation of learning and MOOC retention. Computers in Human Behavior, 111, 106423.

Riley, G. (2020). Theoretical perspectives. In Unschooling (pp. 21-36). Palgrave Macmillan, Cham.

Robson, K., Plangger, K., Kietzmann, J. H., McCarthy, I., & Pitt, L. (2015). Is it all a game? Understanding the principles of gamification. Business horizons, 58(4), 411-420.

Rosalba, A., David, O., Geoffrey M, M., Betty A, E., & George L, O. (2021). Entry Grades and the Academic Performance of University Students.

Roşeanu, G., & Drugaş, M. (2011). The Admission Criteria to the University as Predictors for Academic Performance: A Pilot Study. Journal of Psychological & Educational Research, 19(2).

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American psychologist, 55(1), 68.

Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.

Sahranavard, S., Miri, M. R., & Salehiniya, H. (2018). The relationship between self-regulation and educational performance in students. Journal of education and health promotion, 7.

Schwinger, M., Wirthwein, L., Lemmer, G., & Steinmayr, R. (2014). Academic self-handicapping and achievement: A meta-analysis. Journal of educational psychology, 106(3), 744.

Shah, S. S., Shah, A. A., & Khaskhelly, N. (2018). Service quality, customer satisfaction and customer loyalty: some evidences from Pakistani banking sector. Grassroots, 51(2).

Sharma, V. N., Chakraborty, M., & Roy, P. (2022). Explanatory Research Analysis Between Work Loyalty And Employee Engagement In The Select Hotels & Restaurants Of The North East. Journal of Positive School Psychology, 6(7), 699-707.

Siemens, G. (2017). Connectivism. Foundations of learning and instructional design technology.

Song, H. S., Kalet, A. L., & Plass, J. L. (2016). Interplay of prior knowledge, self‐regulation and motivation in complex multimedia learning environments. Journal of Computer Assisted Learning, 32(1), 31-50.

Stockemer, D., Stockemer, G., & Glaeser. (2019). Quantitative methods for the social sciences (Vol. 50, p. 185). Quantitative methods for the social sciences: Springer International Publishing.

Stratton, S. J. (2021). Population research: convenience sampling strategies. Prehospital and disaster Medicine, 36(4), 373-374.

Sukor, R., Mohd Ayub, A. F., Norhasnida, Z., & Nor Khaizura, A. R. (2017). Influence of students’ motivation on academic performance among non-food science students taking food science course. International Journal of Academic Research in Progressive Education and Development, 6(4), 104-112.

Weiten, W. (2006). A Very Critical Look at the Self-Help Movement.

Werner, K. M., & Milyavskaya, M. (2019). Motivation and self‐regulation: The role of want‐to motivation in the processes underlying self‐regulation and self‐control. Social and Personality Psychology Compass, 13(1), e12425.

Wildemuth, B. M. (Ed.). (2016). Applications of social research methods to questions in information and library science. Abc-Clio.

Williams, K., & Williams, C. (2011). Five key ingredients for improving motivation. Research in Higher Education Journal,11.http://aabri.com/manuscripts/11834.pdf.

Zainuddin, Z. (2018). Students' learning performance and perceived motivation in gamified flipped-class instruction. Computers & education, 126, 75-88.

Zarouk, M., Olivera, E., Peres, P., & Khaldi, M. (2020). The impact of flipped project-based learning on self-regulation in higher education. International Journal of Emerging Technologies in Learning (iJET), 15(17), 127-147.

Zimmerman, B. J., & Tsikalas, K. E. (2018). Can computer-based learning environments (CBLEs) be used as self-regulatory tools to enhance learning? In Educational Psychologist (pp. 267-271). Routledge.

Zimmerman, B. J. (1994). Dimensions of academic self-regulation: A conceptual framework for education. Self-regulation of learning and performance: Issues and educational applications, 1, 33-21.

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Published

2024-04-30

How to Cite

Weyage, A., & Richard Adade. (2024). Entry Behaviour, Learner Motivation, Self-Regulation and Academic Performance of First Year ICT Students: Evidence from Kibi Technical Institute. International Journal of Contemporary Sciences (IJCS), 1(6), 243–264. https://doi.org/10.55927/ijcs.v2i4.8934

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