We are experiencing an intensive period of innovation, we need to keep our students in mind and prepare them with competencies needed for their future job market. In recent years, we have heard many buzz words such as Big Data, Data Science, and Cloud Computing in academia. The common denominator of all of them is the great enthusiasm and the need for data analytics skills in the next generation of college graduates. The pervasive nature of big data and cloud technologies is not limited to computer science or informatics, it touches upon many disciplines. The McKinsey Global Institute (Manyika et al., 2011) has predicted that by 2018 the U.S. could face a shortage of between 140,000 to 190,000 people with deep analytical skills, and a shortage of 1.5 million managers and analysts who know how to leverage data analysis to make effective decisions. The demand for such skills has been on a steady rise and in most predications about the job market, such skills are expected to be the most valuable and well-paid in the future. Therefor, this is a promising area for expanding the LIS universe.
Effective teaching of both data analytics and cloud-computing requires intensive hands-on lab experience. “Research has shown that hands-on experiences in the science laboratory play a central role (arguably the central role) in scientific education” (Brinson, 2015, p. 218). In a data analytics hands-on lab, students learn how to methodically deploy data collection tools to collect large data sets and how to use computational tools to extract meaningful patterns from collected data.
By 2011, nearly 3 million students were enrolled in fully online programs (Enduventures, 2012). More than 70% of academic leaders now see online learning as the critical strategic component of higher education (Allen & Seaman, 2015). LIS programs are also increasingly moving to the online teaching and learning environments. For example, the School of Information at Kent State University, now offers almost all its courses in an online format. Moving to a dominantly online learning environment makes it challenging to equip our students with data analysis and cloud-computing skills. In particular, the methods for providing in-lab experience requires rethinking, because, as Brinson (2015) observes, “Computer-based and remote data acquisition, virtual simulations, and automated processes have all challenged and altered the methods and practices of what have traditionally been considered ‘hands-on’ labs” (p. 219). Recently, systematic reviews of data from more than 120 studies in the past ten years find equal or greater outcome achievements in virtual/remote labs as in traditional hands-on labs (Brinson, 2015). However, the success of such labs requires novel and creative approaches in teaching. For example, one of the challenges of hands-on lab is how to assess the learning outcomes beyond using quizzes as the major assessment method, or how we can design and assess proper assignment for deploying cloud technologies?
To summarize, to prepare competitive LIS graduates for the job market, we face a challenge in educating our students in the areas of data analytics and cloud technologies. Addressing this challenge depends on how we can integrate hands-on lab experiences required for cloud computing and data analytics into the curricula of online education in LIS programs. To address this question, this study conducted a feasibility study of a Virtual Computing Lab (VCL). VCL is an integrated environment for distance experimenting, learning and testing, without the fear of breaking the system. In other words, it is a place for fearless experimentation in data analytics and cloud technologies. It makes it possible for the students in the online courses to remotely connect to the lab and work with different environments crafted for them to learn a variety of skills and to experiment with a wide range of computational tools. VCL can be conceptualized as a Lab-as-a-Service (LaaS) platform that can be integrated in many courses. It is a new form of lab which replaces the brick and mortar lab in the era of cloud-computing and allows our students to walk into a virtual lab in a distance learning context and directly interact with the cloud-computing environment and work with tools required for learning data analytics skills.
Currently, there are different technologies available to create a VCL for distance education. This study compared three of the main existing options including VMware remote desktop, Amazon Workspaces, and Apache VCL. For this purpose, the author designed a teaching scenario for the Social Media Analytic workshop to use a prototype VCL. The findings showed the pros and cons of each solutions for integrating VCL in online LIS education.
The Virtual Computation Lab (VCL) Prototype project illustrated that VCL has great potential in improving both the teaching and learning experience of LIS distance education, particularly for courses in the broad area of data analytics. VCL can considerably reduce the frustration and barriers for students to access analytical tools and reduce the amount of time instructors spend on troubleshooting trivial problems, allowing them to focus on interacting with the students to improve their learning experience. However, the project revealed a major gap in the existing infrastructure both in terms of hardware and expertise. Considering the increasing importance of distance education, it seems of paramount importance that LIS programs invest in these areas, particularly by improving the in-house expertise on cloud-computing infrastructure.
References:
Allen, I. E., & Seaman, J. (2015). Grade level: Tracking online education in the United States . Babson Survey Research Group and Quahog Research Group, LLC. Retrieved May 7, 2015, from http://onlinelearningconsortium.org/read/survey-reports-2014/
Brinson, J. R. (2015). Learning outcome achievement in non-traditional (virtual and remote) versus traditional (hands-on) laboratories: A review of the empirical research. Computers & Education , 87 , 218–237.
Eduventures, I. (2012). Online higher education market update 2012/13: Executive summary . Retrieved from http://www.eduventures.com/insights/online-higher-education-market-update/download/
Manyika, J., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Retrieved May 7, 2015, from http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation
