Leveraging Technology for Learning

The Right of Children to Free and Compulsory Education (RTE) Act, 2009, which was enacted in 2010, binds the government to ensure that all children in the age group of 6-14 years have access to admission, attendance and completion of elementary education. But, only attending school does not ensure learning. If children fail to learn at elementary levels, then they and their parents lack the motivation to continue further. Though India has made some progress in achieving quantitative indicators like enrolment levels, completion rates and development of physical infrastructure; the quality of education in schools leaves a lot to be desired. The World Bank Report that focused on ‘learning’, released in 2018, reported that three quarters of kids in rural India are unable to perform simple 2-digit calculations till Grade 5.

The quality of education being delivered in schools is measured through ‘Learning Outcomes’, which are assessment standards indicating the level of understanding a learner is expected to achieve in a particular grade. It was only in 2012 that the then Planning Commission acknowledged that there as a problem with the learning outcomes, thus admitting that learning is not happening in schools. Realising the need to collect data on quality of learning happening at elementary levels and to ensure consistent improvements in learning ability; the RTE Act was subsequently amended in 2017 to include class-wise and subject-wise learning outcomes.

The Annual Status of Education Report (ASER) 2018 reports mixed results when it comes to a state-wise analysis of reading and arithmetic ability in kids. The report also notes that while some states show considerable improvement in reading and arithmetic abilities over last four years, the change at the national level is small. It eventually goes on to say that “…only are we not creating a sufficiently literate population but that most of our population is functionally illiterate”. Also, the quality of learning in government schools is worse than private schools. According to the ASER 2018, the Std. II level text readers in government schools were 44.2% whereas 65.1% in private schools. Similarly, the arithmetic ability gauged by the ability to perform simple division by children was 22.7% and 39.8% in government and private schools respectively (See Table 1).

Clearly, trends point towards a ‘learning crisis’—the education system that we have been following has become dysfunctional and learning is not happening as desired.

Beyond elementary, middle and senior school level education, when we talk of higher education, the situation is more or less similar. In professional courses like engineering, medical and graduation/post graduate studies, the cream students are able to secure berth in reputed institutions but majority have to enrol in institutions that neither have the resources and infrastructure nor the teaching–learning practices that can create a great learning environment. Hence, the pass-out students lack critical skills and fail the test of employability. Though the accreditation and ranking agencies in India like the NAAC, AICTE, UGC etc. prescribe detailing of ‘Learning Outcomes’—these are neither understood nor are tested accurately in these institutions.

‘Employability’ is the real test of learning happening in the Higher Educational Institutions (HEI) and lack of employability only indicates that the pass-outs do not possess the attributes required to get and maintain the job. According to 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 youths face problems due to lack of professional guidance in finding jobs as per their skills.

Hence, it is clear that learning is not taking place at desired pace and levels throughout. Issues and interventions that have been suggested are many and are also different for each category of education. Here, we talk of exploring the possibility of incorporating use of technology in right way to accelerate learning and role of interaction of teachers/faculty with learners as ‘Mentors’.

One aspect which is common in recommendations is the scope for increased use of technology to deliver education–whether it is at school level or higher education. 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”. Thus, it is clear that technology will be leveraged to improve all aspects of education delivery. However, only technology is not the answer. The ‘human’ aspect cannot be ignored.

It has been reiterated by many technology experts that ‘teaching’ is one of the few jobs that will not be completely eliminated by the disruptive technologies like Robotics and Artificial Intelligence. Thus, an education model with a blend of teacher-directed instruction (often referred to as Blended Learning) in most classes, and inquiry-based learning in some, works best when it comes to increasing the learning outcomes. This fact has been brought out in several research papers and reports. A McKinsey Report in 2016 reported findings of a study done by Bill and Melinda Gates Foundation, that students who attend schools implementing ‘Blended Learning’ reported better levels of understanding in reading as well as Maths. This method of education delivery works well at higher education levels too as it enables ‘soft’ skill development, which are critical for enhancing employability.

A framework, that can incorporate and integrate elements of Knowledge—the concepts of the subject, Interaction—face to face interaction with teachers, Personalisation-delivery keeping in mind speed, interests etc. of the learners, competency or skill development and career choice besides providing the flexibility of time to learners. Framework with above features could be used for enhanced learning experience and improved outcomes in students. In the proposed system, the broad-content topics of the syllabus will be fragmented into modules (may be called k-modules) to be delivered to the students digitally along with short assessments on each topic.

Technologies like ‘Artificial Intelligence’ (AI) can be used to analyse ‘big data’ generated during the course of the learner’s interaction with the machine on ‘learning paths’ (a sequencing of the k-modules) that would include the assessment scores. AI may use Predictive Analytics algorithms to identify learning gaps (which the assessment scores can reveal) and suggest timely remedial measures like counselling etc. The assessments delivered here should be different from the traditional assessments that test only the conceptual knowledge of learners.

The teacher will be guided by the machine to prepare for her interactive class on the basis of the level of understanding gained by the students on specific topics and would also be alerted on timely interventions in case the students who could score low on the competency scale. Thus, there will be an ‘enhanced role’ for the teacher—to focus more on the ability to influence, motivate the learner.

This framework would have the potential of serving as a personalised tutor accompanying the student in which the student would need fewer classroom interactions. The system would ‘personalise’ the learning experience, based on the voluminous interaction data between the machine, the teacher and the peer groups.

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 programme, the competencies on basic foundational skills like literary and mathematical abilities, etc. This data would help students make smarter career choices for themselves which would ultimately result in enhanced employability. An analysis of this data continuously processed by the machine would pre-emptively suggest a career-development path for each learner.

This model of delivering the service of education will be a unique blend of technology with teacher intervention, which is required for expanding the outreach of quality education to the masses as well as making a significant contribution towards improving the learning outcomes at all levels of education. It can also aid in delivering quality education to far–flung areas.

Though technology cannot replace the need and importance of human interaction and intervention, it can definitely help in smarter utilisation of human resources.