Beyond elementary, middle and senior school level education, if we consider higher education, the situation is not much better either. In professional courses like engineering, medical and graduation/post graduate studies, the cream of cream students are able to get admissions in reputed institutions but the majority enrol in institutions (private and government) that neither have the resources and infrastructure nor the teaching –learning practices that make a great learning environment. The students graduating mostly lack critical skills and therefore they struggle in job markets. Such institutions obtain mandatory approvals from AICTE, UGC, etc. but deliver sub-standard teaching. The prescribed detailing of ‘Learning Outcomes’ are neither understood nor tested accurately in these institutions.
Whether the students completing professional course have the ability to succeed in securing jobs, is often referred to as ‘Employability’. It is the real test of learning happening in the Higher Educational Institutions. In case the students struggle to get jobs, it points towards a lack of employability meaning that they do not possess the attributes required to get and maintain the job. According to the India Skills Report 2019, the employability of technical graduates stood at 63.11 % (a significant increase from 42.08% in 2018), and for MBA and polytechnic graduates, it stood at 47.18 % and 45.90 % respectively. The Report also highlighted the fact that around 70% of youth face problems due to lack of professional guidance in finding jobs as per their skills.
Various issues and interventions have been suggested and discussed over several years; to improve learning at various each levels of education. Integration of technology in learning process is often hailed as ‘the solution’ to solve the ‘learning crisis’. However, it is only the incorporation of technology in a way which clearly defines the role of interaction of teachers/faculty with learners as ‘Mentors’; can accelerate learning.
The draft New Education Policy has clearly defined objectives for inclusion of technology in education – It aims to involve technology “to support teacher preparation and development, improve teaching, learning and evaluation processes, and enhance educational access to disadvantaged groups and in education planning and administration”. It is thus clear government is aiming to leverage technology in order to improve all aspects of education delivery. However, only technology may not suffice, unless we remodel and seamlessly integrate the ‘human’ aspect in the learning process.
When experts worry that technology will replace humans in most jobs in near future, it is not likely to happen with teaching. An education model popularly known as ‘Blended Learning’ which mixes or blends both online (digital) and face to face (classroom setting) learning, works the best when it comes to enhancing learning. Since this mode of instruction delivery combines the benefits of both the modes of delivery, it is going to be the predominant mode of instruction in near future.
This fact has been reiterated by many researchers in research papers and reports. In 2016, McKinsey reported the findings of a study done by Bill and Melinda Gates Foundation, stating that students who attend schools implementing ‘Blended Learning’ reported better levels of understanding in reading as well as Maths.
Blended Learning mode of teaching also helps in development of critical ‘soft’ skills that are critical for enhancing employability in students enrolled in higher and professional education courses.
Recognising the importance of incorporating digital mode of teaching delivery, the Indian government’s mission ‘Digital India’ took several innovative steps like promoting e-learning, indigenously designed MOOCS (Massive Open Online Course), Swayam Prabha (a project for transmitting high quality educational content), National Digital Library etc. These measures were aimed at increasing the Gross Enrolment Ratio (GER) in higher education from 24.5 (2016-17) to 30 by 2020. Also, these measures served to improve flexibility and access of education, thereby strengthening the education ecosystem of the country.
Thus, in order to improve ‘learning’ there is a need to have a framework to impart education in a way that seamlessly blends the ‘Physical’ (by deep involvement of teacher in learning process) and ‘Digital’ (use of technology in pedagogical process) elements. Such a framework may be called a ‘Phygital’ model or framework.
A framework, that can incorporate and integrate elements of –
- Knowledge – the concepts of the subject,
- Interaction – face to face interaction with teachers,
- Personalization- delivery keeping in mind speed, interests etc. of the learners, Competency or skill development and
- Career Choice
is desirable. Besides, it should be able to provide the flexibility of carrying out the learning process at a time and speed suited to the learners. Such a framework with could be used for an enhanced learning experience and improved learning outcomes in students. In the proposed system, the broad-content topics of the syllabus (of any subject) may be fragmented into modules (k-modules) to be delivered to the students digitally along with short assessments on each topic.
Lots of data will be generated when the machine stores each learners data on ‘learning paths’ (a sequencing of the k-modules) including their assessment scores. Technologies like ‘Artificial Intelligence’ (AI) can be used to analyse this ‘big data’.
AI uses things like Predictive Analytics algorithms to identify learning gaps (which the assessment scores can reveal) and suggest timely remedial measures like counselling etc.
The assessments delivered to the learners should be innovative designed to test conceptual and analytical knowledge, different from the traditional assessments that test only the conceptual or superficial knowledge of learners.
The rich data generated by the machine can be then analysed and inputs can be given to teachers to prepare for their interactive class with learners. The teachers would also be alerted on timely interventions for students who consistently score low on the competency scale. Thus, there will be an ‘enhanced role’ for the teacher, meaning they would be required to focus more on the ability to influence and motivate the learner.
This framework of delivering education could be basically viewed as having the potential of serving as a personalized tutor accompanying the student. In such a scenario, it can be safely predicted that students/ learners would require fewer classroom interactions as compared to the traditional system, thereby freeing up valuable time of the teachers.
The system would have the objective of ‘personalizing’ the learning experience, based on the voluminous interaction data between the machine, the teacher and the peer groups.
Usually, the students after higher secondary are confused about the available career options for them. The framework that is being suggested here serves an additional purpose of giving insights on the same. The proposed model will generate rich data on learner’s assessment scores, learning paths followed, the other disciplines on which the learner showed interest, the learner’s cognitive abilities, social skills, teamwork, competencies developed during the program, the competencies on basic foundational skills like literary and mathematical abilities, etc. This data could be analysed to generate insights which would ultimately help students make smarter career choices for themselves. Also, the machine using AI could preemptively suggest a career-development path for each learner.
Such a model of delivering education with a unique blend of technology and human intervention, is; in fact essential to expand the outreach of quality education to the vast majority of people. Also, it can help in making a significant contribution towards improving the learning outcomes at all levels of education.
Thus, technology or digital intervention can in no way replace the essential role of a teacher as facilitator and mentor, but it definitely helps in smarter utilization of human resources.
