Emerging EdTechs Amidst the COVID-19 Pandemic: Cases in Higher Education Institutions

Introduction

From early 2010s the research on how Information Technology (IT) transforms higher education (HE) institutions is flourishing. A rapid increase in online education across secondary and tertiary institutions all over the world is explained by the advent of the World Wide Web (www) (Allen and Seaman, 2017) and its penetration into conservative Higher Education Institutions (HEIs) (Stone, 2017; de Wit, 2018). In terms of adding apps into HEI learning processes, there are number of new technologies that could play a positive role. For instance, over the last years, Big Data has attracted the interest of academia. This technology provides higher education institutions to make more adjusted decision making on allocation of IT resources that is expected “to improve the quality of educational services, guide students to provide student experience, deliver higher completion rates, and improve student persistence”. HEIs are adopting cloud architectures and, new digital devices, entering digital platforms (Coursera), create ecosystems between students and lecturers. This often results in more data generation and new opportunities for Big Data-driven analytics to find patterns in the data, predictive maintenance, and prescriptive maintenance to play out several scenarios of future performance of the HEI (Ali, 2019a; Ali, 2019b; Ali, 2020).

Big data has provided new opportunities for more insight about student expectations, their intentions, and their challenges. This all contributes to better online learning process. Through new analytics, HEIs can improve “understanding of their learners’ challenges and apply the resultant insight to emphatically enhance the improvement” (Slade and Prinsloo, 2013). Rio Salado University, which enrols over 41,000 students in both online and in-campus courses provides a good case of developing a new analytics software for tracking student performance, which could be used for more efficient decision-making. Such apps enable student-oriented learning environments, which helps non-traditional students to achieve high academic goals “through personalised intermediations “(Crush, 2019). In such settings, tutors get new insight into the individual progression of their students, and they are well informed to act if things are going in the wrong direction (Ali et al., 2019). Such, technology-based learning provides necessary data for new generation of apps which can illuminate what works and what does not work so that learning outcomes can be enhanced using informed mediation. In this case, the intuition and own perception of a lecturer gets supported with AI enabled systems, providing feedback. Mobile apps developed by HE institutions differ in two main types, including tools for enhancing learning, which solidify the content of lectures, self-test students’ knowledge of the subject, and allow collaboration with peers and tools for organising studies, which easy student transition into university life and assist with various aspects of university experience (Pechenkina, 2017). Such systems rely on data growth and require access to this data across other courses, departments (looking for similarities and average student profiles) and across university itself. Additional challenge is to integrate such distributed data, as it is typically non structured and distributed in thousands of desktops.