Integrating big data and cloud computing topics into the computing curricula: A modular approach

Abstract Big data and cloud computing collectively offer a paradigm shift in the way businesses are now acquiring, using, and managing information technology. This creates the need for every CS student to be equipped with foundational knowledge in this collective paradigm and possess some hands-on experience in deploying and managing big data applications in the cloud. This study argues that, for substantial coverage of big data and cloud computing concepts and skills, the relevant topics need to be integrated into multiple core courses across the CS curriculum rather than creating additional courses and performing a major overhaul of the curriculum. Our approach to including these topics is to develop autonomous competency-based learning modules for specific core courses in which their coverage might find an appropriate context. In this paper, four such modules are discussed, and our classroom experiences during these interventions are documented. Student performance data and survey results show reasonable success in attaining student learning outcomes, enhanced engagement, and interests.

[1]  John P. Dougherty,et al.  NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing: Status Report , 2018, SIGCSE.

[2]  Debzani Deb,et al.  Teaching Big Data and Cloud Computing: A Modular Approach , 2018, 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

[3]  John Biggs,et al.  What the student does: teaching for enhanced learning , 1999 .

[4]  Lillian N. Cassel,et al.  ACM Task Force on Data Science Education: Draft Report and Opportunity for Feedback , 2019, SIGCSE.

[5]  R. Voorhees Competency‐Based Learning Models: A Necessary Future , 2001 .

[6]  Julie E. Mills,et al.  Engineering Education, Is Problem-Based or Project-Based Learning the Answer , 2003 .

[7]  Randy H. Katz,et al.  Experiences teaching MapReduce in the cloud , 2012, SIGCSE '12.

[8]  Bina Ramamurthy A Practical and Sustainable Model for Learning and Teaching Data Science , 2016, SIGCSE.

[9]  Brian R. Belland,et al.  Instructional Scaffolding in STEM Education: Strategies and Efficacy Evidence , 2016 .

[10]  Suzanne J. Matthews Using Phoenix++ MapReduce to introduce undergraduate students to parallel computing , 2017 .

[11]  Benjamin S. Bloom,et al.  A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives , 2000 .

[12]  Annemarie S. Palincsar,et al.  Motivating Project-Based Learning: Sustaining the Doing, Supporting the Learning , 1991 .

[13]  Mohammad Hammoud,et al.  A Cloud Computing Course: From Systems to Services , 2015, SIGCSE.

[14]  Keith Irwin,et al.  A Module-based Approach to Teaching Big data and Cloud Computing Topics at CS Undergraduate Level , 2019, SIGCSE.

[15]  Reynold Xin,et al.  Apache Spark , 2016 .

[16]  Joshua Eckroth,et al.  Teaching Future Big Data Analysts: Curriculum and Experience Report , 2017, 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW).

[17]  Susan A. Brown,et al.  MSIS 2016 Global Competency Model for Graduate Degree Programs in Information Systems , 2017, Commun. Assoc. Inf. Syst..