The education in the 21st century is moving in a direction where almost all learning activities are dominated by computational technologies. While people are discussing the ramifications of the digitalized trend in the next decade, I will say that the prospect of education can be considered from a positive perspective with the introduction of big data analytics in both academic and management aspects. This essay will explore the future of education in 2030 in terms of the use of dataveillance of student and its impacts on the improvement of academic activities and learning management.
This essay is consisted of three parts. It first gives a brief background of the current situation of education in 2020s, highlighting the increasingly growing role of e-learning and the importance of digital technology in traditional campus-based learning environment. The background then leads to the key part of the article, which illustrates the employment of big data concept in improving educational practice, divided in two aspects – academic content and learning management. The academic improvement explores the benefits of dataveillance on learning efficacy, and on early identification of students’ problems and designing solutions. At the learning management level, the use of students’ data can be used by technology providers to optimize the design of learning tools and platforms.
Background of The Status Que of Education in 2020s
To predict the scenario of education in 2030, I need to first provide a brief introduction of the status que of education in 2020s – it is the premise of my projection of the prospect of what education will be in the next decade. One of the prominent characteristics featuring the education in 2020s is the combination of brick and mortar schooling and online learning trend. Thanks to the growth of smart devices penetration rate and the Internet, as well as the demand for closing the education gap both in developed and developing countries, e-learning over the past decades has been greater than ever in 2020s. As a result, digital learning is playing the predominant role of the 21st century in both education and adult training fields due to its advantages of low cost, high convenience, and accessibility (Pappas, 2019). This trend can be seen in the Online Education Statistics made by Bastrikin (2020), among a total of 19.7 million students enrolled in degree-granting courses, 6.6 million have chosen distance education/online options, with the majority of distance education students are undergraduates (5.5 million). As for learning behaviors of students, 87% of students reported using smart devices to access online study programs, and 67% complete course work via the Internet. From the perspective of faculty experience, in 2019, 46% of faculty members reported to have taught online courses for credit, in compared to 39% in 2016. The same trend be seen in adult training, with 77% of US companies using online learning In 2017 Elearning,Market Analysis, Trends And Forecasts (2020).
Adding to the point is the COVID-19 pandemic in 2020, which highlights the importance of distance learning across all sectors of education, from pre-K 12 to tertiary education. The pandemic has necessitated 93% of institutions to constitute policies on remote work for staff, while 43% of schools launch online learning courses.
Given the context, it is safe to predict that the next decade will witness the further employment of digital technology in teaching and management of education. This remainder of the essay aims to illustrate the relationship between new technology and digital learning in terms of several indicators, namely, dataveillance, the use of big data, and relationships between learning and other businesses.
Dataveillance in Studying Students’ Learning Behaviors
Among these factors mentioned above, the first character featuring 2030 will be the dataveillance using big data to track students’ activities, whether it be academic performance or biometric measurements. In order to establish the relationship between big data and education in 2030, I will first clarify the definition of dataveillance and big data. According to Wikipedia, the term is a portmanteau of data and surveillance, which means the practice of monitoring and collecting online data as well as metadata (“Dataveillance,” 2020), whereas “Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software” (“Big Data,” 2020). When it comes to education, it means the data produced by students during the learning process, including the data generated as they are taking courses (Dahdouh et al., 2018). For centuries, to discipline students has been one of the prominent tasks of educators. After the introduction of computational techniques, the onus is shifted from the hands of teachers to the intelligent behavior management system in schools (Selwyn et al., 2020), which is defined as ‘code/space’ – a setting where brick-and-mortar spaces are intertwined with software code (Kitchin & Dodge, 2011). This mechanism tracks students’ academic statistics and biometric measurements, translating the student behavior information into digital data in favor of further improvement of educational activity in school and supportive intervention schemes.
At the academic level, firstly, the dataveillance mechanism help detect and solve education problems during the process of learning. Given the data analytics of all facets of students’ progress, whether these be their attendance frequency in specific course, exam marks, the comparison of the time spend on different courses online, teachers can find the areas that need to be fine-tuned, whether these be the setup of curriculum content and period or pedagogical methods. For instance, if statistics indicate that students spend an excessive amount of time to finish a particular course or homework, this probably means that the module needs to be improved in order to make it more suitable for the learners (Dahdouh et al., 2018).
Furthermore, dataveillance can predict learners’ future performances and therefore extent academic support before problems occurs. Thanks to big data analytics of student academic performance, educators can anticipate what difficulties are most frequently encountered by students, and then establish early intervention plan rather than react after difficulties occur. For example, this support can extent as early as to recruitment stage by identifying students who are most likely to struggle academically in their first semesters. Taking this point one step further, of the recruitment per se also benefit from dataveillance, when students behavioral information analytics help recruiters to better understand and predict the preferences of applicants, providing answers to questions as to what prospective applicants concerns more during their application process, what is the main reason they apply to certain schools (Dennis, 2019). As well as fostering academic performance, the analysis of data also contribute to the development of adaptive and customized learning systems (Dahdouh et al., 2018). It will indicate preferences of students and teachers behaviors in their educational activities, such as the most used browsers, the software or apps they use to access resources, etc. Such information will then be feedbacked to companies which design educational software and Learning Management Systems (LMSs), so these educational technology providers can design new or improve existing educational products in a more personalized and customized way that caters to discrete needs of individual users.
Adding to the point is the dataveillance contribute to the student recruitment of schools. According to the report Hudzik (2020), the COVID-19 disruption has resulted in overall shrinkage in the international student enrollments. In the US. For example, in America, available data indicate a 10% revenue shrinkage in 2020 and about 25% by 2021, which means $550–650 billion loss in the following years (Huang et al., 2020; McNichol & Leachman, 2020). This begs a question – what behavioral information can be applied to better recruit and enroll international students? – Again, it is the big data analytics that help recruitment teams understand the preference of prospective applicants during their application. Given the information, recruiters can better understand the key motivator for prospective applicants to apply to a school, or the main reason for their decisions, are used the data as indicators for schools’ branding strategies that evoke resonance with prospective international applicants (Dennis, 2019).
This article illustrates the future of education in 2030 with the employment of dataveillance and big data. The discussion of the importance data analytics is focused mainly on both academic and recruitment aspects. From the academic level, the collect and analytics of students learning behaviors and biometric information will play an increasingly important role in LMSs, for example by detecting students’ struggle with courses and offering early intervention, and by customizing curriculum design and pedagogy in a more personalized way. Furthermore, data analytics will be shared with educational platforms or product providers, as the basis to fine tune their design of content and service. While the prospect of digital education in 2030 remains lots of uncertainties to debate, I hope this essay can provide some insights into the potential importance of dataveillance and big data in this field.
Bastrikin, A. (2020). Online Education Statistics. Education Data. https://educationdata.org/online-education-statistics
Big Data. In Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Big_data
Dahdouh, K., Dakkak, A., Oughdir, L., & Messaoudi, F. (2018). Big data for online learning systems. Education and Information Technologies : The Official Journal of the IFIP Technical Committee on Education TA – TT –, 23(6), 2783–2800. https://doi.org/10.1007/s10639-018-9741-3 LK – https://royalroads.on.worldcat.org/oclc/7858340077
Dataveillance. In Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Dataveillance
Dennis, M. J. (2019). How to recruit international students in the future. Enrollment Management Report, 23(6), 8–9. https://doi.org/https://doi.org/10.1002/emt.30574
Elearning,Market Analysis, Trends And Forecasts. (2019). Global Industry Analysts, Inc. https://www.strategyr.com/market-report-e-learning-forecasts-global-industry-analysts-inc.asp
Huang, C.-C., Stone, C., Windham, K., & Beltrán, J. (2020). Putting the Size of the Needed COVID-19 Fiscal Response in Perspective. https://www.cbpp.org/research/federal-budget/putting-the-size-of-the-needed-covid-19-fiscal-response-in-perspective
Hudzik, J. K. (2020). Post-COVID Higher Education Internationalization. https://www.nafsa.org/sites/default/files/media/document/trends-insights-september-2020.pdf
Kitchin, R., & Dodge, M. (2011). Code/Space: Software and Everyday Life. MIT Press.
McNichol, E., & Leachman, L. (2020). States Continue to Face Large Shortfalls Due to COVID-19 Effects. https://www.cbpp.org/research/state-budget-and-tax/states-continue-to-face-large-shortfalls-due-to-covid-19-effects
Pappas, C. (2019). Top 20 eLearning Statistics For 2019 You Need To Know. Elearning Industry. https://elearningindustry.com/top-elearning-statistics-2019
Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45(1). https://doi.org/10.1080/17439884.2020.1694944