Insights on Implications of Cognitive Computing in Leveraging Online Education Systems

Introduction

There has been a significant improvement in the method of delivering knowledge since the last decade using e-learning (Weller, 2003). It offers a vast landscape of knowledge about elementary to professional courses to global students sitting at the comfort of their private location. There are various benefits of online learning i.e., i) it offers a higher degree of flexibility for both instructors and students concerning pace, schedule, and location, ii) there are higher ranges of programs of various streams and disciplines with the accessibility of enriched course materials, iii) online education offers higher accessibility towards studying irrespective of the location of participants, iv) it can cater to the demands of students owing to highly customized learning, and v) it is cost-effective compared to conventional classrooms (Means et al., 2009; Muilenburg et al., 2005; Wallace, 2003). The existing structures of online learning are of varied types, i.e., generalized learning management systems, virtual classrooms, and gamification learning management systems (Kantipudia et al., 2021). The commonly used tools for online learning are Zoom, Skype, Google Meet, online whiteboards, social media channels, document management tools (e.g., Evernote, Dropbox, OneDrive, MS Office, G-Suite). However, irrespective of available beneficial factors, there are various challenges in online learning (Anderson, 2004; Salmon, 2013; Rudestam & Schoenholtz, 2009; Bottou & LeCun, 2004; Dabbagh & Bannan-Ritland, 2005). The primary challenge is associated with achieving adaptability of the tools and methods of using online learning. The secondary challenge is associated with the technical issues due to the dependencies of the potential bandwidth of the internet as well as other supporting tools. In recent years, there have been various technologies that have been used over online learning systems to deal with the problems associated with it as well as to enhance the teaching-learning experience (Rudestam & Schoenholtz-Read, 2009; Li et al., 2016; Raymond et al., 2016; Green & McNeese, 2007; Salmon, 2013). It was observed that cognitive computing plays a contributory role in improving the online learning system. With a primary adoption of signal processing and artificial intelligence, cognitive computing offers a wider set of technological platforms for improving the system performance of online education delivery systems. There are various platforms in this regard, viz., speech recognition, computer vision, natural language processing, logical reasoning, and machine learning. An instructor can customize assistance for specific students using cognitive computing which reduces the workload for the teachers while it balances the demands of the query handling of the students at the same time. The online education system has a massive number of students who cannot be personally attended to by all the instructors at the same time; hence cognitive computing plays the role of taking care of these demands (Dessì et al., 2019; Vonderwell & Zachariah, 2005; Lehmann et al., 2014; Leong, 2011; West et al., 2013). The significant contribution of cognitive computing in the education system is its capability to formulate decision-making to address the demands of the students. However, it is not a simplified process to directly apply cognitive computing as it is a highly computation-intensive process that involves complexities in understanding the dynamic demands of the students.