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According to a Pakistani physicist, Pervez Hoodbhoy, the Islamist revisionism of Pakistan's education system initially began in 1976, when an act of parliament required all government, along with private schools (except those teaching the British O levels from grade 9) were mandated to follow a curriculum that includes learning outcomes for the ...
The education system in Pakistan [4] is generally divided into six levels: preschool (for the age from 3 to 5 years), primary (years one to five), middle (years six to eight), secondary (years nine and ten, leading to the Secondary School Certificate or SSC), intermediate (years eleven and twelve, leading to a Higher Secondary School ...
While the analysis of educational data is not itself a new practice, recent advances in educational technology, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings.
The National Education Assessment System (NEAS), (Urdu: قومی ماموریہَ برائے نظامِ تشخیصِ تعلیم) was an initiative of the Government of Pakistan aimed at identifying gaps, challenges, and diagnosing the strengths and weaknesses of the education system by measuring students' learning achievements. It sought to ...
In the 2010, TI Pakistan reported that about 23.7% of those surveyed received admission in educational institutions through non-normal and alternate procedures. [54] One of the biggest problems identified in the country is the presence of a non-uniform educational system. [55]
The data reveals that the literacy rate of Indonesia is 90%, Malaysia is 89% and Pakistan is 62.8%, which is significantly lower in compared to the other two countries. In comparison to these other two countries, Pakistan has the more poverty and inequality within its country.
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make was formalized by providing an economic perspective on the value of “extracted knowledge” in terms of its payoff to the organization [8] This decision-theoretic classification framework [8] was applied to a real ...