Skip to main content

See and Learn

By: Diptiman Dewan

children understand better when they see than when they read.If technology can be made an enabler to let children study subjects with a visual dimension added to books. learning is faster and consequent and dropouts rates of students also decline.

"computers bring about a dramatic change from the mundane process of learning at school and reduce dropout rates, as seen in pilot projects we have conducted in Karnataka. using networking technology, teachers and students can see each other real -time.while kids from multiple villages can see and hear one other on screen, they also take the same class together. every child is audible and visible in the different classrooms," explains Aravind Sitaraman, President, Inclusive Growth, CISCO.

Cisco is using networking technology to educate children in rural areas. "one teacher- many classrooms" is a method followed whereby students can see the teachers and what he or she teaches; the teacher can point out at students individually to answer questions using audio-video connectivity, and teach using applications online, which are all visible to attendees. the focus, says Sitaraman, is to make technology a knowledge- enabler as as disparity of teachers in rural v/s urban India makes it imperative to have one teacher for a much large number of students across geographic spreads.

so, what does it take a model like this? according to Sitaraman, it requires a number of stakeholders and hence, cohesion with the ecosystem and service providers is critical. BSNL is the largest network provider which has a national presence and helps in network connectivity.; companies, big and small, with specific strength in particular domains, content providers who provide content in local languages; partners in education space, etc. are the main stakeholders in this service.

finally government itself is a major stakeholder, which spends a huge amount of money and has made considerable infrastructure investments. according to Sitaraman, the model can be replicated by the industry. technology can enable education to be interactive and fun as well as reach the masses. skill training and other services including healthcare are the other initiatives where technology can play a major role in India.

Comments

Popular posts from this blog

Advantages and Disadvantages of EIS Advantages of EIS Easy for upper-level executives to use, extensive computer experience is not required in operations Provides timely delivery of company summary information Information that is provided is better understood Filters data for management Improves to tracking information Offers efficiency to decision makers Disadvantages of EIS System dependent Limited functionality, by design Information overload for some managers Benefits hard to quantify High implementation costs System may become slow, large, and hard to manage Need good internal processes for data management May lead to less reliable and less secure data

Inter-Organizational Value Chain

The value chain of   a company is part of over all value chain. The over all competitive advantage of an organization is not just dependent on the quality and efficiency of the company and quality of products but also upon the that of its suppliers and wholesalers and retailers it may use. The analysis of overall supply chain is called the value system. Different parts of the value chain 1.  Supplier     2.  Firm       3.   Channel 4 .   Buyer

Big-M Method and Two-Phase Method

Big-M Method The Big-M method of handling instances with artificial  variables is the “commonsense approach”. Essentially, the notion is to make the artificial variables, through their coefficients in the objective function, so costly or unprofitable that any feasible solution to the real problem would be preferred, unless the original instance possessed no feasible solutions at all. But this means that we need to assign, in the objective function, coefficients to the artificial variables that are either very small (maximization problem) or very large (minimization problem); whatever this value,let us call it Big M . In fact, this notion is an old trick in optimization in general; we  simply associate a penalty value with variables that we do not want to be part of an ultimate solution(unless such an outcome is unavoidable). Indeed, the penalty is so costly that unless any of the  respective variables' inclusion is warranted algorithmically, such variables will never be p