Islamization under General Zia Al-Haq (1977-1988) : An Analysis

As a result of the political crisis in Pakistan, the Martial Law regime of General Zia Al-Haq came into power on July 5, 1977. The process of Islamization was given a new boost during the period of Zia Al-Haq 1977- 1988. He launched a comprehensive scheme to eradicate non-Islamic practices in Pakistani state and society. His Islamization program contemplated significant reforms in the legal-constitutional, socioeconomic and educational institutions of Pakistan. The principles of Zakāt -‘Ushr ordinance, Islamic Ḥudūd and Penal code were introduced in the country. To Islamizing the economy Ribā abandoning and Profit and Loss sharing accounts in banks were initiated. Besides, he renamed parliament as Majlis Al-Shūrā; the Federal Sharī‘at Court, Sharī‘at Appellate Benches and Sharī‘ah Council were established in the country. Under the umbrella of Nizām-e-Muṣṭafā, social reforms were introduced, through the stressing of sanctity of the Holy month of Ramaḍān, enforcement of the bans on gambling and encouragement of chadar for women. Un-Islamic programs were banned on television and radio and news in Arabic was made compulsory. The stated objectives of President Zia’s Islamization policies were to lead Pakistan in the direction of truly Islamic state. However, the critics of his polices considered it a tool for legitimizing and enhancing his political powers in the country.

A Framework to Predict the Student S Performance in Programing Courses

Academic grades prediction is considered as one of the hot research areas since last decade, which comes under the domain of educational data mining. It has been observed that in undergraduate computer science programs, programming courses are considered challenging. This results in higher tendency of earning lower grades, failures or drop-outs than other computer science subjects. An early prediction of the students who have high probability of failure (known as at-risk students) will enable the instructors to intervene and provide extra guidance to learners. An accurate prediction of student?s grades can directly influence the overall quality of any degree program and the retention rate of the institution. This research presents a machine learning based classification model for undergraduate students grades prediction, enrolled in any programming course(s) in traditional education system. The proposed model is built after careful collection and pre-processing of data, appropriate feature selection, and model evaluation based on four metrics namely accuracy, precision, recall and F1-score. Six widely used supervised machine learning techniques including Random Forest, Artificial Neural Network, K-Nearest Neighbors, Na?ve Bayes, Ordinal Regression, and Support Vector Machine are used after tuning and optimization. The data used for this research is collected from a private sector university in Lahore. The collected data covers two major domains: student?s academic record and demographic data. The results show that Support Vector Machine and K-Nearest Neighbors give highest scores (ranging from 81% to 94%) for all the evaluation metrics and for all the seven programming courses considered for this study.