‘Indian engineering students gain in first two years, high-order thinking is poor’: Study

According to preliminary results seen by The Indian Express, students from disadvantaged sections in India make either comparable or greater skill gains than their advantaged cohorts.

The World Bank and Stanford University surveyed roughly 5,000 first-year and third-year B.Tech students from 200 randomly-selected public and private engineering institutes last year.

A learning outcome assessment of undergraduate engineering students in the country, conducted by Stanford University and the World Bank, suggests that Indian students make substantial gains in Mathematics and critical thinking skills in the first two years of their education compared to their counterparts in China and Russia. But their overall higher order thinking skills are “substantially lower” than the Chinese and Russians.

The World Bank and Stanford University surveyed roughly 5,000 first-year and third-year B.Tech students from 200 randomly-selected public and private engineering institutes last year. These 200 institutes did not include the Indian Institutes of Technology or the IITs. Similar learning assessments were also conducted for engineering students in China and Russia.

According to preliminary results seen by The Indian Express, students from disadvantaged sections in India make either comparable or greater skill gains than their advantaged cohorts. For example, the study shows that a disadvantaged student in India scores 0.228 points more from the median gain of advantaged cohorts in Mathematics. The finding is significant against the backdrop that an engineering degree is one of the aspirational educational qualifications for financially and socially backward students.

As per the study, disadvantaged students include those from socially backward communities, rural areas and poor families. Advantaged candidates are those from urban areas, wealthier families and socially advantaged communities.

According to the study, active teaching practices such as less lectures and more group activities are, predictably, found more prevalent in private engineering colleges than public institutions within the country. Incidentally, if one were to compare elite government engineering institutions in India — defined by the study as colleges that admit students through highly competitive entrance tests like JEE (Advanced) and JEE (Main) — to their non-elite counterparts, then active teaching practices are more common in the latter.

The inter-country comparisons throw up some interesting results. For instance, the study shows that Indian engineering aspirants start college with similar academic skill levels as Russian students, but less skills than Chinese students. However, once the Indian students join college, they make make significant skill gains in comparison to China and sometimes to Russia.

The inter-country comparisons are important as the majority of the world’s new engineering graduates come from China, India and Russia. About three decades ago, developed countries such as the US and Japan used to supply the largest chunk of world’s engineers. Detailed findings of this survey, which is part of the Technical Education Quality Improvement Programme (TEQIP) supported by the World Bank, will be presented formally to the HRD Ministry this week.

Source: The Indian Express dated 4 April, 2018
Link: http://indianexpress.com/article/education/indian-engineering-students-gain-in-first-two-years-high-order-thinking-is-poor-study-5122475/

How machine learning can be a catalyst for transforming education

Futurist Arthur C Clarke wrote, “Any sufficiently advanced technology is indistinguishable from magic.” The magic of software (giving data and rules to get answers) is often confused with the magic of machine learning (giving data and answers to get rules) but it is machine learning not software that is transforming the world of computer chess.

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We’d like to make the case that machine learning is transforming online education, but Indian online education is held back by regulatory cholesterol. Before diving into online education, let’s reflect on challenges in education.

Knowing must shift to learning because Google knows everything. Metrics need shifting from inputs to outcomes because only money is not working. Differentiation and personalization are not about making things easier for children but making learning accessible by tapping into motivations and abilities. Assessment needs to shift from annual exams to regular feedback. Teachers knowing content is not the same as their ability to create learning.

Lifelong learning needs a continuum between prepare, repair and upgrade. Employability is an objective. Timetables are an industrial-era model of one size fits all that blunt choices and learner agency. Most importantly, if you think formal education is everything, then just look at the president of the US.

Many educators agree online learning can transform education, but they don’t know how. Textbook and PowerPoint repackaged e-learning—the digital equivalent of paving the cow path rather than building a highway—mean that, so far, online offerings have not been able to blunt the obvious downsides of physical classrooms (one size fits all, huge costs, uneven teacher and quality) despite obvious advantages (teaching with different speeds to people with different backgrounds and different starting points, class of one, cost, on-the-go, on-demand, crowdsourced and gamified).

We believe that the massification of machine learning could be the missing ingredient—enabling personalisation, flip classrooms, rethinking assessments, and enabling non-conventional credentialing.

Personalisation via intelligent tutor systems that track “mental steps” and modify feedback, exercises, explanations and intervention to promote self-regulation, self-monitoring and self-explanation would revolutionise engagement. A recursive and real-time meta-analysis of learning outcomes across students, cohorts, schools would considerably improve the efficacy of flip classrooms.

Natural language, computer vision, and deep learning could answer student questions.

These systems are infrastructure to improve the signalling value of non-conventional or micro-credentialing, which in turn would discover the cognitive, behavioural and affective preferences for each learner. The biggest impact would be in assessment by moving it from an event to a process and reducing its labour intensity; for instance, tools like Sochobots, Lingolens and Gradescope use computer vision and machine learning to grade students’ work (even stuff like essays).

However, Indian online education is held back by regulatory cholesterol that distinguishes between distance and online education.

E-commerce would never have happened if financial regulators had insisted on separating the offline and online. UPI/ BHIM have gone from 0.1 million transactions in the month before demonetization to 140 million last month; they will reach a billion in a year.

Payments for Indian consumers are almost free (marginal cost), while in the US regulations have protected margins for private platforms.

India’s regulatory issues include hubris (the ability of regulators to anticipate all situations), micromanaging (including defining the type of web links on your website) and continuous lobbying because of poor state capacity to effectively regulate, supervise and enforce.

It is too late for evolution; we need a revolution under which universities do not require permission to launch any online courses.

Regulators can prescribe broad guidelines with a policy objective of creating biodiversity and innovation in business and operating models that would tackle the difficult trade-off between cost, quality and scale. Like with most treatment of regulatory cholesterol, this revamped regulation would be accompanied by improved supervision and strengthened consumer protection. But drunk-driving is not an argument against cars and regulations that ban or make online education difficult are silly.

Einstein once said that if you judge a fish by its ability to climb a tree, it will live its life believing it is stupid.

Physical classrooms— because of the limitations of time and space—often make this error. India needs a massification and vocation aliza ti on of higher education at a cost that only online learning can do. This needs machine learning.

But before that we need changes to our regulatory cholesterol. The writers are with TeamLease Services and Schoolguru Eduserve, respectively

Source: Hindustan Times dated April 4, 2018