IJBTT

Student Employment Context and Learning Achievement Cluster Forecasting Model for Educational Technologists

Student Employment Context and Learning Achievement Cluster Forecasting Model for Educational Technologists
	© 2022 by IJETT Journal
	Volume-70 Issue-11
	Year of Publication : 2022
	Authors : Tipparat Sittiwong, Wongpanya S. Nuankaew, Patchara Nasa-Ngium, Pratya Nuankaew
	DOI : 10.14445/22315381/IJETT-V70I11P235

How to Cite?

Tipparat Sittiwong, Wongpanya S. Nuankaew, Patchara Nasa-Ngium, Pratya Nuankaew, "Student Employment Context and Learning Achievement Cluster Forecasting Model for Educational Technologists," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 324-337, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P235

Abstract
This research investigated the insight analysis of learning patterns that affect graduates' employment prospects. There are three objectives: 1) to study the context and learning patterns that encourage learners' achievement through educational data mining techniques, 2) to develop the forecasting models and construct an application for forecasting appropriate academic achievement clusters, and 3) to assess the application for forecasting the appropriate academic achievement clusters. Samples were collected from 227 students at the Department of Educational Technology and Communication, the Faculty of Education, Naresuan University, during the academic year 2015 – 2020. Research instruments were divided into two categories: statistical and data mining. The results showed that the context of the students in the curriculum offered a high level of academic performance distributed at high levels of academic achievement, which was 3.58 of an average overall student GPA. In addition, the developed forecasting model provided a high level of accuracy, which was 71.11 percent accuracy. At the same time, the level of satisfaction with the application was the highest level of satisfaction, which was 4.21 of the overall satisfaction with 0.66 of S.D. It can be concluded that this research is valuable and beneficial to educational technologists, who are driving the adoption of technology in education.

Keywords
Insight Analysis, Learning Patterns, Educational Data Mining, Forecasting Model.

Reference
[1] N. Bunwirat and S. Boonsathorn,"Inquiry-Based Learning: an Effective Pedagogical Approach for Empowering 21st Century Learners and Education 4.0 in Thailand," Political Science and Public Administration Journal, vol. 9, no. 1, 2018.
[2] J. Siririn, "Higher Education 4.0: The Adaptation of Global University," National Interest, vol. 2, no. 6, pp. 49–55, 2021.
[3] T. Suksai, P. Suanpang, and R. Thangchitcharoenkhul, "A Digital Leadership Development Model for School Administrators in Basic Education to Fulfill the Thailand 4.0 Policy," PSAKU International Journal of Interdisciplinary Research, vol. 10, no. 2, 2021.
[4] S. Thonthong and S. Paengthai, "The Model of Early Childhood Education Management, Education era 4.0 of Educational Institutions under the Office of the Basic Education Commission," Journal of Graduate MCU KhonKaen Campus, vol. 6, no. 3, pp. 490–502, 2019.
[5] S. Wittayasin, "Education Challenges to Thailand 4.0," International Journal on Integrated Education Development, vol. 2, no. 2, pp. 29–35, 2018.
[6] K.B. Aviso, F.P.A. Demeterio, J.I.B. Janairo, R.I.G. Lucas, M.A.B. Promentilla, R.R. Tan and D.E.C. Yu, "What University Attributes Predict for Graduate Employability?," Cleaner Engineering and Technology, vol. 2, pp. 100069, 2021. Crossref, https://doi.org/10.1016/j.clet.2021.100069
[7] E. Courtial and C. Garrouste, "Model Predictive Control Strategy to Forecast Employability in Earth Sciences," IFAC Proceedings Volumes, vol. 47, no. 3, pp. 10731–10736, 2014. Crossref, https://doi.org/10.3182/20140824-6-ZA-1003.02563
[8] K. K. C. Seah, J. Pan, and P. L. Tan, "Breadth of University Curriculum and Labor Market Outcomes," Labour Economics, vol. 65, pp. 101873, 2020. Crossref, https://doi.org/10.1016/j.labeco.2020.101873
[9] Ş. S. Nartgün and R. Özen, "Investigating Pedagogical Formation Students' Opinions about Ideal Teacher, Teaching Profession, Curriculum, Responsibility, Public Personnel Selection Examination (ppse) and Employment: A Metaphor Study," Procedia - Social and Behavioral Sciences, vol. 174, pp. 2674–2683, 2015. Crossref, https://doi.org/10.1016/j.sbspro.2015.01.951
[10] L. Gao, Z. Zhao, C. Li, J. Zhao, and Q. Zeng, "Deep Cognitive Diagnosis Model for Predicting Students' Performance," Future Generation Computer Systems, vol. 126, pp. 252–262, 2022. Crossref, https://doi.org/10.1016/j.future.2021.08.019
[11] P. Nuankaew, “Dropout Situation of Business Computer Students, University of Phayao,” International Journal of Emerging Technologies in Learning (iJET), vol. 14, no. 19, 2019. Crossref, https://doi.org/10.3991/ijet.v14i19.11177
[12] A. Konstanti and A. Moropoulou, "Hybrid Educational Methodology for the Cognitive Domain of Built Heritage Protection Interconnecting Secondary with Tertiary Level Education," International Journal of Engineering Pedagogy (iJEP), vol. 3, no. 4, 2013. Crossref, https://doi.org/10.3991/ijep.v3i4.2761
[13] E. Araka, E. Maina, R. Gitonga, and R. Oboko, "A Conceptual Model for Measuring and Supporting Self-Regulated Learning using Educational Data Mining on Learning Management Systems," in 2019 IST-Africa Week Conference (IST-Africa), pp. 1–11, 2019. Crossref, https://doi.org/10.23919/ISTAFRICA.2019.8764852
[14] W. S. Nuankaew, P. Nuankaew, D. Teeraputon, K. Phanniphong, and S. Bussaman, “Perception and Attitude Toward Self-Regulated Learning of Thailand’s Students in Educational Data Mining Perspective,” International Journal of Emerging Technologies in Learning (iJET), vol. 14, no. 9, pp. 34–49, 2019. Crossref, https://doi.org/10.3991/ijet.v14i09.10048
[15] C. Romero and S. Ventura, "Educational Data Mining and Learning Analytics: An Updated Survey," WIREs Data Mining and Knowledge Discovery, 2020. Crossref, https://doi.org/10.1002/widm.1355
[16] C. Romero and S. Ventura, "Educational Data Mining: A Review of the State of the Art," IEEE Transactions on Systems, Man, and Cybernetics, Part C Applications and Reviews, vol. 40, no. 6, pp. 601–618, 2010. Crossref, https://doi.org/10.1109/TSMCC.2010.2053532
[17] P. Nuankaew and W. S. Nuankaew, "Student Performance Prediction Model for Predicting Academic Achievement of High School Students," European Journal of Educational Research, vol. 11, no. 2, pp. 949–963, 2022. Crossref, https://doi.org/10.12973/eu-jer.11.2.949
[18] M. Sarfraz, K. F. Khawaja, and L. Ivascu, "Factors Affecting Business School Students' Performance During the COVID-19 Pandemic: A Moderated and Mediated Model," The International Journal of Management Education, vol. 20, no. 2, pp. 100630, 2022. Crossref, https://doi.org/10.1016/j.ijme.2022.100630
[19] R. Mitola, E. Rinto, and E. Pattni, “Student Employment as a High-Impact Practice in Academic Libraries: A Systematic Review,” The Journal of Academic Librarianship, vol. 44, no. 3, pp. 352–373, 2018. Crossref, https://doi.org/10.1016/j.acalib.2018.03.005
[20] A. Kenny, V. Dickson-Swift, N. DeVecchi, C. Phillips, B. Hodge, and Y. Masood, "Evaluation of a Rural Undergraduate Nursing Student Employment Model," Collegian, vol. 28, no. 2, pp. 197–205, 2021. Crossref, https://doi.org/10.1016/j.colegn.2020.07.003
[21] B. S. Baert, B. Neyt, T. Siedler, I. Tobback, and D. Verhaest, "Student Internships and Employment Opportunities After Graduation: A Field Experiment," Economics of Education Review, vol. 83, pp. 102141, 2021. Crossref, https://doi.org/10.1016/j.econedurev.2021.102141
[22] J. Mitchell, "Juggling Employment and Studies: Nursing Students' Perceptions of the Influence of Paid Employment on their Success," Nurse Education Today, vol. 92, pp. 104429, 2020. Crossref, https://doi.org/10.1016/j.nedt.2020.104429
[23] K. R. B. Calma, A. Williams, S. McInnes, and E. Halcomb, "New Graduate Employment in General Practice: Perceptions of Final-Year Nursing Students," Nurse Education in Practice, vol. 54, pp. 103115, 2021. Crossref, https://doi.org/10.1016/j.nepr.2021.103115
[24] P. Bryła, "The Impact of International Student Mobility on Subsequent Employment and Professional Career: A Large-scale Survey Among Polish Former Erasmus Students," Procedia - Social and Behavioral Sciences, vol. 176, pp. 633–641, 2015. Crossref, https://doi.org/10.1016/j.sbspro.2015.01.521
[25] E. Hartikainen, J. Suhonen, and E. Sutinen, "IMPDET - an Online PhD Study Program in Educational Technology," in Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), pp. 578–580, 2006. Crossref, https://doi.org/10.1109/ICALT.2006.1652508
[26] P. Bakker, D. Carrington, A. Goodchild, I. Hayes, H. Purchase, and P. Strooper, "The Communicating Technologist: an Educational Challenge," in Proceedings Frontiers in Education 1995 25th Annual Conference. Engineering Education for the 21st Century, vol. 2, pp. 4a4.1-4a4.4, 1995. Crossref, https://doi.org/10.1109/FIE.1995.483173
[27] R. Hartley, Kinshuk, and R. Koper, "Preparing Coming Generations of Educational Technologists: Technology, Pedagogy and Curricula–Changes and Challenges," in Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), pp. 890–892, 2007. Crossref, https://doi.org/10.1109/ICALT.2007.208
[28] Haryono, Y. Utanto, Budiyono, E. Subkhan, and S. Zulfikasari, “The Implementation of Educational Technologists’ Competencies in Improving Learning Quality,” in 2019 5th International Conference on Education and Technology (ICET), pp. 76–80, 2019. Crossref, https://doi.org/10.1109/ICET48172.2019.8987215
[29] C. Schröer, F. Kruse, and J. M. Gómez, “A Systematic Literature Review on Applying CRISP-DM Process Model,” Procedia Computer Science, vol. 181, pp. 526–534, 2021. Crossref, https://doi.org/10.1016/j.procs.2021.01.199
[30] M. Cazacu and E. Titan, "Adapting CRISP-DM for Social Sciences," BRAIN. Broad Research in Artificial Intelligence and Neuroscience, vol. 11, no. 2Sup1, 2021. Crossref, https://doi.org/10.18662/brain/11.2Sup1/97
[31] P. Nuankaew and P. Temdee, "Matching of Compatible Different Attributes for Compatibility of Members and Groups," International Journal of Mobile Learning and Organisation, vol. 13, no. 1, pp. 4–29, 2019. Crossref, https://doi.org/10.1504/IJMLO.2019.096