Abstract: The purpose of this research is to propose how we can encourage non-computing major first-generation-college-bound students to be actively involved in learning data analytics. Non-computing major students have limited opportunities to take a data analytics related course. The computing major programs have the resource limit for offering none-major electives. The first-generation-college-bound students need more mentoring for their future directions. For this purpose, we challenge the following three goals: 1) What practices can we use with underpinning scientific evidences? 2) How can we enhance engagement of students with very limited interventions from an instructor? And, 3) how can we motivate the participation of the non-computing major students? To accomplish these goals, we start with developing a series of summer workshops that is Evidence-Based Practices (EBP)-guided, student-driven, and applied. Based upon the EBPs, we develop a series of student-driven summer workshops, not a regular elective course, with an emphasis on application of data analytics to the main areas in which the students are interested. We conclude that the EBP first helped us to develop these workshop series for applied data analytics with underpinning scientific evidences. Second, these workshops using active learning methods allowed the students to have their strong engagement with very limited interventions from an instructor. Third, these workshops motivated the participation of the non-computing major students by influencing the students to seek how data analytics can be applied to their domain. Last, the outcome of their team project allowed them to experience undergraduate research.
Keywords: Active Learning Methods, Data analytics, Evidence-Based Practice, Experiential learning, First-Generation-College-Bound, Non-Computing Major
Download this article: ISEDJ - V16 N5 Page 37.pdf
Recommended Citation: Chung, S. (2018). Data Analytics Workshop Series for Non-Computing Major First-Generation-College-Bound Students. Information Systems Education Journal, 16(5) pp 37-44. http://isedj.org/2018-16/ ISSN: 1545-679X. (A preliminary version appears in The Proceedings of EDSIGCON 2017)