Analytics has been viewed as a messiah that has the potential to transform every aspect of our functioning, be it industry or society. As is the case with most technologies, as compared to most other domains, industry has been in the forefront of adopting analytics tools and approaches to businesses.
What is noteworthy is that analytics and artificial intelligence (AI) in particular are receiving a lot of attention in the areas of research and innovation in academic institutions around the world. However, most of this work is yet to be put to use for the education processes within the academic system. Analytics and AI have the potential to deliver superior learning experience and targetted problem solving capabilities which need to be explored by the academics practitioners.
The key concerns of the industry in terms of employability of the talent pool equipped with the right skills and their retention are centred around the input profiles of candidates into the workforce. Universities are saddled with their own set of challenges with respect to students and learning management processes; namely, selection of right candidates for different programmes, control of dropout ratio, performance in assessments, etc.
Many of these challenges could be addressed by smart analytics solutions. Sentiment analysis based on surveys could help administrators understand the trends of students’ preferences to subjects, infrastructure quality, teaching methodology and disposition towards subjects, thus enabling prompt actions to address the issues at hand and thus improve the overall student experience. Trends analysis would help the institutions to study competition, the volume, velocity and variety of enquiries, their financial capability, conversions, spend on critical items, and capacity planning for various programme offerings.
Data mining could provide pointers to several questions that often remain a puzzle—high or low performance of students, original native locations of students, language barriers, economic and social status, other options made available to students mid way through the programme. AI could contribute in developing personalised coaching and counselling services, predict student performance or outcomes for various initaitives and bots that could become buddies to the students to navigate through the university system.
While the science of analytics could be used to come up with the right recommendations or suggest directions, the starting point for a successful framework is the availability of required data. Universities do collect huge variety of data and generate a massive amount of data related to students that could prove to be the goldmine for making decisions, but most of it may not be readily available in the digital formats. Hence the imperative to get the education administrators to become data focused has to be underscored.
The critical success factor for making analytics intrinsic to the functioning of the academic system is to create the awareness of importance of capturing and handling the myriad data with care and building pilot systems to win over the naysayers in the initial stage.
Uma Ganesh is chairperson, Global Talent Track, a corporate training solutions company