Scheme For Financial Assistance For Setting Up Of Electronics And ICT Academies (2014)

Policy Update
Purbaa Jagannath

Background

In this climate of rapid technological change, the knowledge gained in the classroom was not taking long to become outdated, and higher education policy was inadequate to address the issue because it was difficult to revise course offerings, train teachers, and spread new expertise to thousands of colleges. The policy problem wasn’t just a lack of graduates, however. It was inadequate teaching capacity which could be updated on a regular basis.

In the field of electronics and information and communication technology (ICT), which is subject to continuous technological transformation, cloud computing, high-performance computing, language technologies, embedded systems and more recently artificial intelligence, machine learning and applications related to Industry 4.0, the quality of the faculty emerged as a strategic constraint between technological change and human capital employability.

It has led to the creation of the Scheme for Financial Assistance for Setting Up of Electronics and ICT Academies. Though the concept was introduced in the government planning years ago prior to 2014, the scheme of seven academies was approved by the Ministry of Electronics and Information Technology (then the Ministry of Department of Electronics and Information Technology) in November 2014. It was designed to be different from the institutional logic of establishing a new group of teaching universities.

The government would have to open dedicated academies within top institutes – five Category-A academies at IIT Kanpur, IIT Guwahati, NIT Patna, NIT Warangal and IIITDM Jabalpur and two Category-B academies at IIT Roorkee and MNIT Jaipur – and leverage the faculty, labs, reputation and networks of these institutes to improve teachers in a far broader higher-education system.

The original design led to faculty development being viewed as a multiplier intervention. A trained engineer impacts one project, but a trained teacher has the potential to impact subsequent generations of students, change the course of a department, oversee research, and pass knowledge on to other teachers. Finally the scheme targeted faculty and mentors not only in engineering institutions but also the science, arts and commerce colleges and polytechnics. The proposed causal chain was ambitious but well defined: faculty specialization training would lead to enhanced quality and technological relevance of faculty teaching; improved quality and technological relevance of faculty teaching would lead to enhanced graduate capabilities; enhanced graduate capabilities would reduce the industry–academy gap and enhance their employability.

The design also took account of a special feature of the E&ICT sector: that the policy needed to be flexible enough to not be based on a set list of technologies. At first, the programme was oriented toward the emerging fields of automotive and medical electronics, cloud computing, mobile technologies, high-performance computing, GIS and language technologies. At the end of the scheme, the academy had on-boarding topics such as: Artificial Intelligence, Machine Learning, Robotics, Internet of Things, Cybersecurity, Industry 4.0 and other modern fields of study. One of the most significant institutional characteristics of the model was the flexibility of changing the content of the scheme without redesigning the whole scheme.

The financial allocation for the scheme was a one-time investment of the sum of ₹147.48 crore, which included grant-in-aid from the government and the scheme’s own internal revenue, and was originally conceived to be implemented over a five-year timeframe, ending in November 2019. The scheme was then rationalised and the total outlay revised as ₹87.69 crore (including ₹77.26 crore for grant-in-aid and ₹10.43 crore for internal revenue) with extension of implementation period as per annual reporting of MeitY.

This operational end date was later changed to 30 November 2022. It is analytically significant that this extension is not.It is not this extension, which is analytically significant. It enabled the academies to develop, but it is important not to interpret performance as the outcome of a brief five-year intervention: the system has grown over an eight-year period, including the dramatic upheaval, and digital acceleration, of COVID.

The set target for the collectivity was training 92,800 faculty members.The set target for the collectivity was to train 92,800 faculty members. The original plans called for an annual training of 4,000 faculty members at Category-A academies and 1,600 faculty members at Category-B academies. Such targets have been rationalised between institutions. While a numerical target gave an accountability metric, it also influenced the scheme to be measured and countable in terms of training throughput. Whether enough people were trained at the academies is a central policy question. It was whether the scheme established a sustainable framework to enable faculty to continue to refresh their skills as technology evolved.

Functioning

The 7 academies were more of semi-specialised capacity building units within institutions already equipped with technical faculty, laboratories, classrooms and research facilities. This embedding brought down the cost of a whole new national training system. The IIPA impact assessment subsequently revealed that newly developed resources were able to be used in partnership with the resources of the parent institution, enabling the sharing of laboratories, equipment and expert faculty. The scheme is public policy in the sense that it is not a duplication of public funding of IITs, NITs and IIITs.

The training was mainly conducted through Faculty Development Programmes (FDPs) which extended beyond a traditional refresher course. Academies had to develop special modules and offer practical training, create curriculum based on industry input, facilitate continuous training, provide research-oriented courses and industry links. They might also train students and professionals as well on a fee basis. It was not a revenue-generating that was an after-thought: the scheme was intended to give the academies a chance to become more economically viable over time.

To deliver, three main approaches were followed – conventional classrooms, online programmes and National Knowledge Network (NKN) enabled virtual classrooms. With these co-existing modes, the academies were able to test out a hub and spoke structure. Physical training continued to hold significance for laboratories and practical work, and virtual delivery enabled specialists to be available to those unable to attend from their host institutions. The model gained a special significance during the pandemic as the ability to move training online kept the faculty-development system from going into dormancy.

The scheme also evolved several modes of institutional outreach, and did not obligate all participants to make trips to the seven host institutions. The assessment documents for each of the activity centres at IIT Roorkee and memorandum of understanding (MoU) models from NIT Warangal, IIT Kanpur, and NIT Patna were prepared. Remote-centre approaches from MNIT Jaipur and IIITDM Jabalpur were also prepared. Leave arrangements are important because leave and travel costs, institutional budgets, are significant issues for faculty at smaller institutions. Over time the network was therefore gradually phased into a distributed training architecture, swapping out the seven-node model.

Technical depth and pedagogical breadth were both reflected in the course design. The academies also provided programs related to outcome-based education, curriculum development, intellectual property rights, research writing, MOOCs, Moodle, classroom delivery, blended learning and the application of ICT tools in the classroom. This combination covered two aspects of faculty capacity, that is, knowledge of a new technology and faculty’s knowledge of how to learn, research and teach with it.

The logic for the scheme was thus more advanced than the number of FDPs suggests. The ideal academy would be able to link four assets that are typically separate in Indian higher education—frontier technical knowledge, teaching practice, industry exposure, and institutional infrastructure. The problem with it was that its level of integration differed between academies. The decentralised model facilitated experimentation, but also resulted in varied partner arrangements, revenue models, course mixes and outreach strategies. Later, IIPA found that a lack of a clearly defined, centrally controlled definition of partners and their roles was an issue that needed to be addressed.

Performance

The scheme expanded considerably on the most readily available output measures. According to the report by the MeitY, academies had held 347 FDPs till 28 February 2018, which covered a total of 13,220 participants. At the end of 2021-22, the system has achieved 1,603 FDPs and 3,18,743 beneficiaries, including 93,926 faculty and 2,24,817 students and others. As of 31 October 2022, just days before the end of the scheme, 1,950 FDPs and 3,41,669 beneficiaries (of which 1,09,972 were faculty and 2,31,697 were students/others) had been reported by MeitY. As per the later IIPA impact assessment (till January 2023), the total number of programmes is over 2000 and the total number of faculty beneficiaries is about 1.125 lakh.

Figure 1: Scaling Up the E&ICT Academy Training System. Source: Author’s compilation based on MeitY Annual Reports 2017–18, 2021–22 and 2022–23. The 2018 figure reports total participants, while the later reports separately identify faculty beneficiaries.

The target comparison is significant, but must be precise. As of October 2022, the 1,09,972 faculty reported by MeitY is approximately 118.5 per cent of the target of 92,800 for the collective faculty. The IIPA assessment found all seven academies to be in excess of their rationalised institutional targets. Output wise the scheme was not only able to achieve its targeted volume but also established demand to train beyond the planned volume.

The number of students and ‘other’ beneficiaries is also an indicator of growth. As of October 2022, the number of non-faculty beneficiaries exceeded the number of faculty beneficiaries by more than 200%. The situation could be considered mission drift if the scheme is seen as a faculty scheme. A better understanding is that the academies evolved into enablers for a broader training environment. The same content, instructors and infrastructure could be leveraged by students, professionals, and institutional partners to maximise asset use and potentially create revenue. The downside of the policy is that a large base of beneficiaries should not water down the original aim of improving faculty.

Institution-level evidence demonstrates the reasons for the lack of uniformity in operational trends at the level of aggregates. For instance, the academy of IIT Guwahati claimed to have registered 307 FDPs and 17,678 participants over a period of seven years and had reached its new faculty target by March 2022. It has been implemented through its programmes in institutions all over the North-East and beyond, thus showcasing a regional-hub approach to the scheme. Other academies formed other mixes of remote centres, partnerships and income-generating activities. So decentralisation generated institutional innovation and at the same time less meaning to be gained from simple league-table comparison between academies.

Financial performance is also more favourable if expenditure is related to the size of the activity, but not just a simple cost per trainee figure. It said that the collective expenditure done by the 31st January 2023 on the rationalised grant envelope was ₹69.33 crore and the academies had also developed physical infrastructure, virtual platforms and reusable content. These are investments: not just ongoing training expenses. Meanwhile, there were challenges with the planned amount of self-financing. Among the various institutes, NIT Warangal’s revenue was approximately ₹4.62 crore, which amounted to approximately one-quarter of the expenditure made by the institute. IIT Guwahati had revenue of approximately ₹2.02 crore. Achieving complete financial independence in all academies was still an aspiration.

This is a good lesson in the design of a programme. While self-generated revenue can be a corrective to course design and spurs responsiveness to demand, a private fee system has externalities that cannot be fully captured by self-generated revenue, and a public system of faculty development exists. A teacher trained in a less well-resourced college can bring a significant social return on investment in the future for those students, but produce little income for the academy in the short-term. Sustainability should instead refer to an efficient model of mixed financing, rather than withdrawal of public financing.

Impact

Perhaps the best evidence of the scheme is that training was being used once it had been finished, far stronger than the sheer number of FDPs. The results of the beneficiary survey showed that approximately 80 per cent of the respondents used their newly gained knowledge to various extents to influence or help others. Over one-third said they helped 1-20 people; smaller groups helped to a much larger extent. This is in line with the faculty development theory that states that the initial trainee is only the ‘first wave’ of the intervention.

Another important indicator is research use. Sixty-four.73 per cent of the respondents in the IIPA survey reported that they have applied the knowledge acquired from the academies to research and development. That does not qualify for an increase in publications or patents, or quality of research, but it is more substantial than a satisfaction rating, since it identifies a post-training application. It also implies that not only were the academies refresher workshops for teachers, but they were also entry points into new technical disciplines for faculty.

Figure 2: Selected Indicators from the IIPA Impact Assessment. Source: Indian Institute of Public Administration, Impact Assessment of the Scheme of Financial Assistance for Setting up of Electronics and ICT Academies. Indicators refer to different survey/beneficiary measures and should not be read as a single common denominator.

The content profile also helps to argue technological adaptation by the scheme. The academy portfolio was categorised into core areas, emerging and contemporary technologies and Industry 4.0. It identified significant correlation with newly developed technologies and concluded that in sample, around 40% have taken courses relevant to Industry 4.0. The assessment also revealed gaps, notably in terms of comparatively lower focus on immersive technologies (AR/VR/MR), on drones, on smart applications. What is important to note from a policy perspective is that the institutional arrangement was able to include additional fields as they emerged.

There was also an infrastructural effect that was not captured by traditional measures of beneficiaries.There was an infrastructural impact from the academies as well that traditional measures of beneficiaries underestimate. Various institutions purchased physical laboratories, digital learning systems, virtual laboratories, conferencing equipment, content libraries and learning-management systems. The scheme resulted in an existing infrastructure and training capacity, which was distributed between the high quality institutions and can continue beyond the life of a single course. The collective outcome was an ecosystem of labs, courses, pedagogy, platforms, partnerships and institutional knowledge, IIPA wrote.

The geographic scope was significant but not even. The seven academies were situated in various geographical areas and other remote centres and online modes expanded access. The IIT Guwahati case is particularly notable since the academy showed a definite preference to institutions in the North-East, where participation can be significantly expensive due to distance from other technology hubs. However, national is not synonymous with universal. Seven host institutions would not have been able to resolve Internet connectivity problems, laboratory availability, college funding and faculty release time throughout the higher-education system.

The level of gender participation serves as another factor of increased access. In the assessment, IIPA reported that women accounted for approximately 38 per cent of the total beneficiaries. This is not gender parity and the report fails to determine if there was a systematic difference in participation in the different technology areas and/or academies. However, flexible and online learning formats can mitigate certain travel and scheduling constraints that can limit participation in professional development. Further monitoring, in future, should therefore be based on disaggregating enrolment, completion and post-training outcomes on the basis of gender, rather than relying on aggregate participation.

The link between the scheme and employability is less obvious. While employability was a stated overarching goal, the causal pathway is from faculty training to the transformation of teaching and curriculum to student ability to the labour market outcomes. There is no evidence in administrative reporting of training outputs, and no evidence in the IIPA survey of knowledge use, but neither alone means that the academies had made a meaningful difference to graduate employment. This distinction is crucial to an analytical evaluation. Without longitudinal student or institutional data, the scheme cannot be said to have directly led to better employability but it can be said that it has set the stage for better employability.

The same precaution ought to be taken with sweeping statements on GDP, innovation and India’s technological competitiveness. Faculty capacity is clearly one of those inputs and the academies linked faculty to the core areas of the digital economy. However, numerous other factors act as intermediaries between the macroeconomic effects and research funding, firm demand, infrastructure, curricula, student quality and labour-market conditions. Narrow and strong is the defensible conclusion: the scheme established a national public system for bridging the gap between the “new” and the “old” orders in terms of faculty knowledge, and there is survey evidence that a significant percentage of the beneficiaries did use that knowledge outside of the classroom.

Emerging Issues

The first issue that arises is the question of measurement. The monitoring architecture of the scheme, in terms of measuring the ‘delta’ generated by the training, was much weaker, but the scheme was otherwise well governed and managed to be counted. A cumulative beneficiary number does not indicate whether a teacher restructured a class or course, created a new lab, mentored students on their projects in new technology, written research, or how a teacher helped students’ outcomes. After the course, IIPA has suggested a system wherein beneficiaries can be tracked. If there were no such longitudinal measurement, policy makers are in danger of rewarding volume and not ability.
The difference between enrolments and unique individuals is the second issue. It is desirable that faculty can attend more than one FDP each year as it is required in a subject area where ongoing updates are required. However, cumulative training events are not the same as the number of different faculty reached when the events are repeated. The latter should be reported using a mature monitoring system, but also unique beneficiaries. The former measures breadth, the latter depth and continuing professional development.
Third, there is a ceiling effect with short duration training. While faculty exposure to a new discipline can be an efficient way to introduce faculty to a new discipline, it is not possible to create expertise in semiconductor design, cyber security, AI, robotics or quantum technologies by exposure alone. Sequenced learning and laboratory practice, datasets or computing resources, mentoring and opportunities to apply knowledge through research or teaching are required for advanced capacity. The next generation of the scheme will need to clarify the distinction between courses for awareness and competency-based courses and advanced trainer certification.
Fourth, the industry–academic relationship continued to be skewed. The industry experts were present, and though there were opportunities for partnership and corporate training, IIPA identified a need for greater engagement opportunities, including internships, live projects, industry visits, case studies, consulting and co-operative research. This is because the notion of ‘emerging technology’ is not a fixed academic term, as relevance depends on varying production processes and firm demand. A weak faculty-development system that fails to remain relevant to the frontier can only create technologies that are out of step with the industry.
Fifth, decentralisation resulted in innovation and governance ambiguity. The definition, responsibilities and accountability of partners were not always uniform across individual academies, who had developed their own partner networks and delivery models. A centralised partnership approach does not have to be the end of local autonomy. It may instead include minimum requirements for the quality of training, learning, assessment, data reporting, IP, fees and conflict-of-interest protections, and allow academies to select a partner that is suitable in the local region.
A more realistic concept is needed for financial sustainability. The expectation of self-sustaining academies helped to develop revenue generation, but it was observed that there is a variance in revenue and this must not be the only indicator of success. If a large group of private sector students can earn a higher monetary return from a course than a small group of public sector students can earn from a subsidised course, then the large group of public sector students may earn a higher social return. It is worth highlighting that public funding needs to be tied to public-value goals, such as geographical inclusion, priority technologies, as well as weaker institutions and measurable post-training outcomes, and that part of the system can be funded through commercial and professional programmes.
Last, the 2014 – 2022 period showed that delivery infrastructure is a strategic asset. Online training has been a key aspect of the pandemic; at IIPA, it was discovered that online training could also keep higher numbers more accessible, be more cost effective, and allow for ongoing communication after training. However, in many hardware intensive and laboratory intensive technologies, a fully online/remote setup would not suffice. The right architecture is a mix of national digital content, and regional physical nodes to support laboratories, projects and advanced hands-on work.

Way Forward

The change from a programme counting system to a faculty capability system should be the first step. Each Academy should have a common longitudinal identifier for the beneficiaries and record the number of faculty reached, repeat participation, level of course, assessment performance, and post training application. At 6 months and 12 months, follow-up should include questions concerning the introduction or modification of a course, research into the use of technology, supervision of student projects, development of laboratory exercises, industry connections, and training of colleagues. This type of data would make the scheme into a learning policy, rather than an output-monitoring system.
Second, training programmes should be structured in a stackable manner. A three tiered structure (foundation, applied and advanced trainer/research level) would enable a faculty member to move up the levels without having to do numerous disjointed FDPs. At higher levels, outputs must be demonstrable: working prototype, curriculum module, lab exercise, research proposal, open educational resource or evaluated teaching intervention. This would ensure the availability of short FDPs and provide a pathway to advanced capability.
Thirdly, the totality of the seven academies’ content and infrastructure needs to be regarded as a common national public good. The recommendation of IIPA, which is to have a centralised system but with decentralised control, is useful in particular. A shared web platform might be used to record courses, trainers, virtual labs, recordings, assessment and learning materials while individual academies are responsible for specific fields and regional delivery. This would eliminate duplication and enable that faculty to obtain the expertise from the best of the various academies in the concerned technology.
Fourth, the hub-and-spoke model needs to be extended via state university accredited nodes in localities.Fourth, the hub-and-spoke system needs to be expanded by the nodes in state universities in localities. The seven leading institutions must not be knowledge bottlenecks. Regional nodes can deliver facilitation and/or laboratory support, while the teaching skills and content can be delivered at a national level. This architecture would directly solve travel and access barriers and provide a way for course portfolios to be aligned with the industrial ecosystems in the region, say electronics manufacturing, automotive systems, agritech, language technologies and cybersecurity according to the area’s needs.
Fifthly, there is a need to move from occasional involvement to structured co-production. Industry advisory should be included in priority courses and advanced courses should feature live data, projects, internships or problems. Academies should have the flexibility to link with industry partners related to their specialisms, with transparency via national standards. Advanced pathways, which lead to faculty qualification, should explicitly have joint research and incubation as a second-stage goal.
Sixth, equity should be a clearly- articulated performance measure. The monitoring should break down participation and completion across gender, geographic location, institution type and career stage. Public subsidy can then be directed towards faculty in tier-2, tier-3 and tier-4 locations, polytechnics or institutions with limited resources among the public institutions. Whilst online access can be helpful, it does not replace access to the lab, and inclusion policy needs to provide for both online and hands-on access.
Seventh, differentiated funding should be the basis of financial sustainability. Grant support for public interest training in national priority areas should be continued. Programmes for the industry, professional certificates and Continuing Education courses can provide internal revenues. Delivering shared digital content can help to lower marginal delivery costs, and common procurement and pooled infrastructure can help avoid duplication. The aim should be to be financially resilient and to make best use of public resources as well as not aiming to make every academy commercially self-financing.
Ultimately, the experience of 2014-2022 backs the idea of continuation through redesign as opposed to replication. The scheme showed that a few high capacity institutions can develop a much larger faculty-development network in India, that the physical and digital infrastructure can be established and reused, and that evidence of the use of gained knowledge after the training can be created. The major problem with it was that it lacked a measure of what is changed as a result of the activity. The second phase should then focus on maintaining scale, but on building capacity, application, and institutional change as the building blocks for success. While the policy relevance of the first phase has been confirmed with the subsequent decision by MeitY to continue and scale-up the E&ICT Academy model, the more critical challenge is to ensure that scaling it up brings deeper, measurable improvements in the technological capability of the faculties of higher education in India.

Selected References and Important Links

Ministry of Electronics and Information Technology (MeitY) (2017–18), Annual Report 2017–18, Government of India. Section: Scheme of Financial Assistance for setting up of Electronics and ICT Academies. Available at: https://www.meity.gov.in/static/uploads/2024/02/35-1.pdf
Ministry of Electronics and Information Technology (MeitY) (2020–21), Annual Report 2020–21, Government of India. Available at: https://www.meity.gov.in/writereaddata/files/MeitY_AR_English_2020-21.pdf
Ministry of Electronics and Information Technology (MeitY) (2021–22), Annual Report 2021–22, Government of India. Section: Graduate-level Scheme of Financial Assistance for setting up of Electronics and ICT Academies.
Ministry of Electronics and Information Technology (MeitY) (2022–23), Annual Report 2022–23, Government of India. Section: Graduate-Level Scheme of Financial Assistance for setting up of Electronics and ICT Academies.
Malhotra, Charru (2023), Impact Assessment of the Scheme of Financial Assistance for Setting up of Electronics and ICT Academies, Indian Institute of Public Administration, New Delhi. Available at: https://www.iipa.org.in/publication/public/uploads/article/22161684476570.pdf
Indian Institute of Technology Guwahati (2021–22), Annual Report 2021–22, E&ICT Academy section. Available at: https://www.iitg.ac.in/report/docs/AR/ANNUAL_REPORT_2021-22.pdf
Malaviya National Institute of Technology Jaipur, E&ICT Academy: Objectives and Activities. Available at: https://icsm2014.mnit.ac.in/eict/objective_activities
Ministry of Electronics and Information Technology (2024–25), Annual Report 2024–25, Government of India. Section: Electronics & ICT Academy Scheme – Phase-II. Available at: https://www.meity.gov.in/static/uploads/2024/12/10fcadec462c330211502fed3d24ea83.pdf

About the Contributor

Purbaa Jagannath is an intern with IMPRI, currently pursuing her master’s in Social work at Tata Institute of Social science, Mumbai. Her research interests lie in social science, governance, and social issues.

Acknowledgements

The author extends sincere gratitude to the IMPRI team for their guidance and support along with the reviewers Pragya and Apurva Jha for their valuable feedback and insights.

Disclaimer: All views expressed in the article belong solely to the author and not necessarily to the organization