Emerging technologies are responsible for revolutionizing how people communicate and interact. The technological changes have taken up various structural forms. Among them is one that allows for the reduction of physical proximity in education, thus negating the need for classrooms, textbooks, and the physical presence of teachers. Regardless, similar to all other applications in smart devices, emerging technologies in education attempt to make their objective more efficient, involving, and pleasurable. The possibilities of the emerging technologies are limitless for students, employees, and teachers among the various other users. Higher educational institutions such as universities have adopted some of the emerging technologies such as mobile and distance learning and the use of learning management systems (Strecker, Kundisch, Lehner, Leimeister & Schubert, 2018). However, it is vital to appreciate the notion that gaps will persist between technologies that interest school administrators and the technologies of their choice.
Technological Devices
Through research, emerging technologies applicable in education were identified. They include adaptive learning technologies, mobile learning, the internet of things, LMS (learning management system), and A.I. (artificial intelligence), among others. The number of emerging technologies is not limited to the previously mentioned list but represents the most utilized technologies in the education as well as labor sectors.
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Adaptive Learning Technologies
The first technology to be assessed is adaptive learning technology; which relies on machine-driven data to quantify the educational progress of students. The technology also dynamically adapts to the level or type of class content, individual capability, or specific skill realization. According to Garcia-Cabot, de-Marcos & Garcia-Lopez (2015), the supporters of adaptive learning technologies assume that it can provide critical solutions to the challenges limiting the education system. Among the solutions are cost-effectiveness, increased access, and higher quality. The technology is already adopted in various institutions globally, such as the University of Wollongong, Australia, The New York Institute of Technology, USA, and Nottingham Trent University, U.K., among others (Garcia-Cabot, de-Marcos & Garcia-Lopez, 2015). In the mentioned institutions, the technology is adopted to the institution’s needs to track the progression of students in their education. By being able to identify students with below-average performances, the technology can allow for the faculty to take action before they drop out (Garcia-Cabot, de-Marcos & Garcia-Lopez, 2015). The active utilization and positive outcome of the technology in the stated schools have resulted in its extensive adoption by both students and faculty. The technology has made learning institutions and organizations accept a data-driven culture, allowing them to be proactive in their actions.
M-Learning (Mobile Learning)
Portable devices have made it possible for students to access learning material on the go. According to a 2016 report cited by Crompton & Burke (2018), over 51% of global web browsing took place on smartphones and tablets. The usage surpassed that of desktop browsing for the first time. Mobile devices are appreciated as devices that allow for in-depth and connected learning. The overall outcome of the combination of the two strategies is greater access to education. According to Garcia-Cabot, de-Marcos & Garcia-Lopez (2015), m-learning provides the needed gateway to personalized learning and working environments. Their design allows new students to attain the freedom of exploring each unit at their respective speed. The technology has been modifications in various ways to meet the needs of institutions such as Purdue University, USA, and Kent State University, USA. Through their “SpedApps” and “Hotseat” mobile applications, they have allowed learners to post and respond to questions using whatever technology available. Students can attain necessary documents and other relevant necessities using the application. M-learning has become among the most dynamic approaches to modern education.
The Internet of Things (IoT)
The Internet of Things (IoT) consists of objects bestowed with computing powers. It involves the embedding of processors or sensors for transmitting information across a network. In the learning realm, technology is valid in notifying the course of the content being taught (Abbasy & Quesada, 2017). The learning process takes place when the faculty collaborates with industry professionals to offer students the needed experience of using IoTs. This collaboration also falls under education as it prepares the students to tackle the challenges of the workforce. By providing centers for university-industrial collaborations, learning institutions can research, coach, and attain practical experience, thereby nurturing better understanding. The learning outcomes are essential for enhanced improvements in technology and its application in education (Abbasy & Quesada, 2017). For example, the University of Texas at Arlington, USA, involved their students in a research study where they investigated the effects of emotions and education. The research involved the monitoring of biological factors that paralleled their emotional state.
Learning management systems (LMS)
Today, the role of LMS (Learning management systems) is recognized by various learning institutions as they offer numerous advantages while exploiting various emerging technologies (Janson, Söllner, & Leimeister, 2017). The objectives of the technology vary widely depending on its use and the desired outcomes. Learning institutions use the technology for the training purposes of students; on the other hand, it is also applicable in tracking the performance of the students (Janson, Söllner, & Leimeister, 2017). The tool is also useful for the teaching staff as some have integral e-learning tools used to develop learning material. In other words, the technology takes advantage of more efficient resources as they are launch to improve the overall performance of students. For example, at the University of Mary Washington, USA, the facility applied LMS, to allow students and faculty members to register and access necessary learning material. In other institutions, they incorporate the technology into applications that can run on smart devices, thereby increasing the usability of the technology.
Artificial Intelligence (A.I.)
Integration of artificial intelligence in education would result in the automation of basic activities such as grading of students and tracking of their performances. Though researchers are yet to develop self-aware computers and other machinery, A.I. is present in some current learning systems (Barnes, Boyer, Hsiao, Le, & Sosnovsky, 2016). The roles may be minor, but with advancing research it is expected to take up more complex duties. Its initial application was in grading. The grading process is time-consuming, and time is a vital resource needed to help students better comprehends the educational material. Though A.I. may not adequately replace humans in the grading process, it reduces the load on teachers as they attempt to grade and statistically analyze the performance of the students. Another potential use of A.I. is the identification of gaps in the educational materials. Such gaps are the primary reasons for students not performing as expected when all other factors remain constant (Barnes, Boyer, Hsiao, Le & Sosnovsky, 2016). The help that A.I. will offer has the potential of revolutionizing education by eliminating unnecessary information.
Implications, Challenges, and Barriers
Technology is considered the sturdiest factor that has actively shaped the education sector in the past two decades. Numerous learning institutions are welcomingly integrating technology into the process by providing the needed hardware and web-based platform for their benefit (Strecker et al., 2018). However, technology still experiences some challenges in its application that is not limited to the education sector only. The most salient challenge is the need for training. Users of the technology, such as teachers, are expected to be conversant with its application for maximum effectiveness and optimum results. However, lack of adequate training and resources limits the use of the various technologies, hence its low utilization in learning institutions (Strecker et al., 2018). The explicit form of training also requires the availability of the teaching staff to accurately present the technology and information.
Another challenge to the implementation of technology in various organizations is the user perception and preferences. Regardless of the elimination of all external barriers to the utilization of technology and its pedagogy, it is the priority of the user to exploit the resources (Strecker et al., 2018). The attitudes and beliefs of technology users play a critical factor in the employment of technology within an organization. It has a direct correlation with the confidence, skills, and awareness of the technology and its outcomes. For example, in the education sector, many teachers were trained without the use of modern technologies. Therefore, they are less likely to be proactive in the adoption and implementation of these technologies in the learning processes (Strecker et al., 2018). Reducing such resistance to the implementation of technology in various organizational functions will have the positive results of increased and better performance among the user.
References
Abbasy, M., & Quesada, E. (2017). Predictable Influence of IoT (Internet of Things) in the Higher Education. International Journal Of Information And Education Technology , 7 (12), 914-920. doi: 10.18178/ijiet.2017.7.12.995
Barnes, T., Boyer, K., Hsiao, S., Le, N., & Sosnovsky, S. (2016). Preface for the Special Issue on AI-Supported Education in Computer Science. International Journal Of Artificial Intelligence In Education , 27 (1). doi: 10.1007/s40593-016-0123-y
Dyer, J., Godfrey, P., Jensen, R., & Bryce, D. (2016). Strategic management (1st ed.). Hoboken, NJ: Wiley.
Janson, A., Söllner, M., & Leimeister, J. (2017). Individual Appropriation of Learning Management Systems—Antecedents and Consequences. AIS Transactions On Human-Computer Interaction , 9 (3), 173-201. doi: 10.17705/1thci.00094
Strecker, S., Kundisch, D., Lehner, F., Leimeister, J., & Schubert, P. (2018). Higher Education and the Opportunities and Challenges of Educational Technology. Business & Information Systems Engineering , 60 (2), 181-189. doi: 10.1007/s12599-018-0522-8
