Synthesis of Data Science Competency for Higher Education Students

dba5f299-7607-4a54-96d6-7c2b5588d90b20220131083722386naun:naunmdt@crossref.orgMDT DepositInternational Journal of Education and Information Technologies2074-131610.46300/9109http://www.naun.org/cms.action?id=3037111202211120221610.46300/9109.2022.16https://npublications.com/journals/educationinformation/2022.phpSynthesis of Data Science Competency for Higher Education StudentsSajeewanPratsriDivision of Information and Communication Technology for Education, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, 10800 ThailandPrachyanunNilsookDivision of Information and Communication Technology for Education, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, 10800 ThailandPanitaWannapiroonDivision of Information and Communication Technology for Education, Faculty of Technical Education, King Mongkut’s University of Technology North Bangkok, 10800 ThailandThe research aims to Data Science Performance Synthesis for Higher Education Students and Data Science Performance Suitability Assessment for Higher Education Students. The research instruments include 1) data science performance synthesis tables, 2) expert interviews in data science performance assessments, 3) expert questionnaires to assess the consistency of data science performance. Analytical methods include 1) analyzing the frequency obtained from the content analysis table, 2) synthesis of content from interviews, 3) analyzing performance consistency, and components of data science performance, from data science synthesis for higher education students, finding that data performance for higher education students consists of five performances: 1) programming skills, 2)elementary statistics, 3) fundamentals of data science, 4) data preparation, and 5) Big data analytics.1312022131202210110911https://npublications.com/journals/educationinformation/2022/a222008-011(2022).pdf10.46300/9109.2022.16.11https://npublications.com/journals/educationinformation/2022/a222008-011(2022).pdf10.1145/3481056.3481104S. Pratsri, P. Nilsook, and P. Wannapiroon, “Developing a Conceptual Framework for Remote Practice Learning,” pp. 140–144, 2021, doi: 10.1145/3481056.3481104. 10.1080/10691898.2019.1647768J. E. Broatch, S. Dietrich, and D. Goelman, “Introducing Data Science Techniques by Connecting Database Concepts and dplyr,” J. Stat. Educ., vol. 27, no. 3, pp. 147–153, 2019, doi: 10.1080/10691898.2019.1647768. IBM, “The Data Science Skills Competency Model,” 2020. 10.15700/saje.v38n1a1431U. Ramnarain and M. Hlatswayo, “Teacher beliefs and attitudes about inquiry-based learning in a rural school district in South Africa,” South African Journal of Education, vol. 38, no. 1. 2018, doi: 10.15700/saje.v38n1a1431. 10.1109/bigdata.2017.8258190J. S. Saltz and N. W. Grady, “The ambiguity of data science team roles and the need for a data science workforce framework,” Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017, vol. 2018-Janua, pp. 2355– 2361, 2017, doi: 10.1109/BigData.2017.8258190. 10.5539/hes.v11n3p70T. Meepung, S. Pratsri, and P. Nilsook, “Interactive Tool in Digital Learning Ecosystem for Adaptive Online Learning Performance,” High. Educ. Stud., vol. 11, no. 3, p. 70, 2021, doi: 10.5539/hes.v11n3p70. Y. Demchenko et al., “EDISON Data Science Framework ( EDSF ): Customising Education and Training for Career Development and Capacity Building,” vol. 675419, no. 675419, p. 675419, 2015. 10.1109/vl/hcc50065.2020.9127198P. Pereira, “Towards Helping Data Scientists,” Proc. IEEE Symp. Vis. Lang. Human-Centric Comput. VL/HCC, vol. 2020-Augus, pp. 16–17, 2020, doi: 10.1109/VL/HCC50065.2020.9127198. 10.1007/s11121-016-0633-8S. R. Johnson, E. T. Pas, and C. P. Bradshaw, “Understanding and measuring coach-teacher alliance: A glimpse inside the ‘black box,’” Prev. Sci., vol. 17, no. 4, pp. 439–449, 2016, doi: 10.1007/s11121-016- 0633-8. 10.1109/temscon.2019.8813604G. J. Miller, “The influence of big data competencies, team structures, and data scientists on project success,” 2019 IEEE Technol. Eng. Manag. Conf. TEMSCON 2019, 2019, doi: 10.1109/TEMSCON.2019.8813604. G. U. Interface, “Data Science for,” no. July, pp. 1–10, 2018. K. A. Dill-McFarland et al., “An integrated, modular approach to data science education in the life sciences,” bioRxiv, pp. 1–20, 2020, doi: 10.1101/2020.07.25.218453. L. Cao, “Data Science: Profession and Education,” IEEE Intell. Syst., vol. 34, no. 5, pp. 35–44, 2019, doi: 10.1109/MIS.2019.2936705. L. Cao, “Data science: A comprehensive overview,” ACM Comput. Surv., vol. 50, no. 3, 2017, doi: 10.1145/3076253. 10.1089/big.2019.0100K. T. Rodolfa, A. de Unanue, M. Gee, and R. Ghani, “A Clinical Approach to Training Effective Data Scientists,” arXiv, 2019. 10.1016/j.compedu.2018.06.008M. Al-Emran, V. Mezhuyev, and A. Kamaludin, “Technology Acceptance Model in M-learning context: A systematic review,” Comput. Educ., vol. 125, pp. 389–412, Oct. 2018, doi: 10.1016/j.compedu.2018.06.008. 10.1109/dsw.2018.8439915A. Farahi and J. C. Stroud, “The Michigan Data Science Team: A Data Science Education Program with Significant Social Impact,” 2018 IEEE Data Sci. Work. DSW 2018 - Proc., pp. 120–124, 2018, doi: 10.1109/DSW.2018.8439915. 10.46300/9109.2021.15.33N. Rafique, “Education, Political Awareness, and Political Participation: a Case of Rahim Yar Khan District of Pakistan,” Int. J. Educ. Inf. Technol., vol. 15, pp. 372–384, 2021, doi: 10.46300/9109.2021.15.39. 10.1109/vl/hcc50065.2020.9127269P. Pereira, J. Cunha, and J. P. Fernandes, “On Understanding Data Scientists,” Proc. IEEE Symp. Vis. Lang. Human-Centric Comput. VL/HCC, vol. 2020-Augus, 2020, doi: 10.1109/VL/HCC50065.2020.9127269. 10.5539/hes.v10n4p36S. Pratsri and P. Nilsook, “Design on Big data Platform-based in Higher Education Institute,” High. Educ. Stud., vol. 10, no. 4, p. 36, 2020, doi: 10.5539/hes.v10n4p36. 10.31838/jcr.07.01.88K. S. Praharshita, S. S. Aravabhumi, S. Attaluri, S. Mandava, S. Raghavendran, and S. K. Hasane Ahammad, “Bigdata and machine learning models for dimentionality reduction platform,” Journal of Critical Reviews, vol. 7, no. 1. pp. 449–452, 2020, doi: 10.31838/jcr.07.01.88. U. Fayyad and H. Hamutcu, “Analytics and Data Science Standardization and Assessment Framework,” Harvard Data Sci. Rev., pp. 1–33, 2020, doi: 10.1162/99608f92.1a99e67a. L. C. Tencies, “Data Science Competency Framework,” Australia, 2017. 10.1016/j.procs.2017.05.240C. Dichev and D. Dicheva, “Towards Data Science Literacy,” Procedia Comput. Sci., vol. 108, no. December, pp. 2151–2160, 2017, doi: 10.1016/j.procs.2017.05.240. D. Donoho, “50 Years of Data Science,” J. Comput. Graph. Stat., vol. 26, no. 4, pp. 745–766, 2017, doi: 10.1080/10618600.2017.1384734. M. Soni, H. Singh, and N. Sethi, “A state of the art survey of data mining techniques for software engineering data,” Int. J. Appl. Eng. Res., vol. 10, no. 55, pp. 1512–1522, 2015. G. Strawn, “Data Scientist,” IT Prof., vol. 18, no. 3, pp. 55–57, 2016, doi: 10.1109/MITP.2016.41. L. Cao, “Data Science: Profession and Education,” IEEE Intell. Syst., vol. 34, no. 5, pp. 35–44, 2019, doi: 10.1109/MIS.2019.2936705. 10.1109/access.2018.2870535H. Hu, Y. Luo, Y. Wen, Y. S. Ong, and X. Zhang, “How to Find a Perfect Data Scientist: A DistanceMetric Learning Approach,” IEEE Access, vol. 6, pp. 60380–60395, 2018, doi: 10.1109/ACCESS.2018.2870535. D. Basic, I. Advanced, and E. Participates, “Learning and Information Technology Standard Competency Matrix.”