Threshold Crossings in Data Science and Cybersecurity Professional Development: A Six Week Research Experience for STEM High School Teachers
- July 28, 2020
- Posted by: RSIS
- Categories: Computer Science and Engineering, IJRSI
International Journal of Research and Scientific Innovation (IJRSI) | Volume VII, Issue VII, July 2020 | ISSN 2321–2705
Joseph Mbogo Wairungu1, Faith Maina2, Abdul Serwadda3
1 ,2Curriculum and Instruction, Texas Tech University
3Computer Science, Texas Tech University
Abstract:- This paper explores the experiences of 11 high school STEM teachers learning cybersecurity concepts during a six-week summer Research Experience for Teachers (RET) program. We applied the threshold concept lens to make meaning of the troublesome knowledge that teachers found difficult and how they overcame those difficulties during the data science and cybersecurity professional development. Thresholds are core concepts considered troublesome in learning that, once understood, have a significant impact on the learner’s mastery of a discipline. The threshold concept theory offers a potential lens on teachers’ learning, focusing on concepts that are troublesome and transformative. The study involved 11 high school teachers—six men and five women—teaching mathematics, chemistry, and physics in grades 9-12. We used naturalistic observation to record teachers’ behavior during their liminal and transformative stages. We found evidence that after the RET program the teachers’ level of interest, perception, confidence, and motivation improved. This was an indicator of a quality transformation, a key concept in threshold crossing.
Keywords: cybersecurity, data science, high school teachers, threshold concept.
I. INTRODUCTION
Data democratization and the age of Big data has transformed the world together with the tools and skills that employees use to perform their functions (Markow, Braganza, Taska, Miller,& Hughes, 2017). The digital world sees massive daily data generation adding to the volume and variety of Big Data (Joglekar & Pise, 2016). There is a need for institutions to make insights from the voluminous data for competitive advantage (Provost & Fawcett, 2013). Consequently, data science prominence is growing in industries, academia, among other circles (Kross & Guo, 2019). Markow et al. (2017) opined that the demand for data scientists has put pressure on the supply of data science talent. PwC and Business-Higher Education Forum-BHEF (2017) reported that the use of data science skills can improve many sectors including cybersecurity.
