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4. E-learning Services for Rural Communities

Author

Hussain, Shariq, Wang, Zhaoshun, and Rahim, Sabit

Subjects
Computer Science - Computers and Society
Abstract

Information and communication technologies brought-in tools and techniques in the field of education that introduced new concepts of teaching and learning. Learning management system is one of the key tools used in educational institutes to facilitate e-learning. There is remarkable digital divide among urban and rural areas. In this paper, we present a model for providing e-learning services to remote/rural areas in order to promote and facilitate modern education. A dedicated resource center, hosting the learning management system, facilitates e-learning centers through Internet. The overall goal of this model is to have a cost-effective learning environment equipped with latest technologies to provide learners an opportunity to get insight into new information and communication technologies and e-learning environment. The model offers new teaching methodology with enhance utilization of learning management system in teaching and learning. Basic characteristics and technical aspects will be considered as well. The study will also promote development and usage of open-source technologies., Comment: 6 pages, 6 figures, 3 tables

Published
2013
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10. Children’s Exposure with Technological Gadgets and its Relationship with Gender, Age, and Family Structure: A study on COVID -19 Pandemic Lockdown at Home.

Author

Qutoshi, Sadruddin Bahadur, Jabeen, Zahra, and Rahim, Sabit

Subjects
COVID-19 pandemic, STAY-at-home orders, CHILD behavior, TECHNOLOGY
Abstract

In the context of Gilgit-Baltistan (GB), Pakistan, this study examines the children’s exposure with technological gadgets and its relationship with gender, age/grade level, and family structure during Covid-19 pandemic lockdown. Through a survey questionnaire, the data were collected from 150 parents. By using Statistical Package for Social Sciences (SPSS 21.0), the data were analyzed. Both the Descriptive (percentages and frequencies) and Inferential (independent t-test) statistics were applied. The findings of the study revealed that 18% children at informed parents’ program level (i.e., 0-3years), 40% at ECD level (3-8years) and 42% at upper primary level were expose with technological gadgets. However, boys were found to be more exposed with technological gadgets (i.e., 70.7%) as compared to girls (i.e., 28%) during Covid-19 Pandemic Lockdown period in GB. Findings showed that 38% children were found to be in nuclear families, 55% children were in joint and only 6% were in extended families. Mostly children use mobile and laptop/ computers (39.3%) whereas only 16.7% children use TV in Gilgit. It was also revealed that 58.7% parents supervise while 41.3% parents do not supervise their children while using technological gadgets. However, there was no significant difference between male and female children across age group. The study recommended to carry out a research study in the future to investigate the family structure and use of technological devices by increasing the sample size and research sites to get more insights. [ABSTRACT FROM AUTHOR]

Published
2023
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33. Teaching Chinese Language using open source software: A moodle based application.

Author

Rahim, Sabit, Tie Sun, Begum, Afsana, Hussain, Shariq, and Jabeen, Gul

Abstract

Moodle has changed one of the traditional roles of Teachers and students with all are learner pedagogy and also provide collaborative learning activities. The activities such as chatting, wiki, blogs, forums, audio, and video recording in online provide environment for very shy and reserved students, who can build their confidence and prepare themselves for face-to-face communication with native speakers. The authors propose course content template, a best learning prototype using moodle and propose environment to the communities for interactive learning process. [ABSTRACT FROM PUBLISHER]

Published
2011
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34. Machine learning techniques for software vulnerability prediction : a comparative study

Author

Jabeen, Gul, Rahim, Sabit, Afzal, Wasif, Khan, Dawar, Khan, Aftab Ahmed, Hussain, Zahid, Bibi, Tehmina, Jabeen, Gul, Rahim, Sabit, Afzal, Wasif, Khan, Dawar, Khan, Aftab Ahmed, Hussain, Zahid, and Bibi, Tehmina

Abstract

Software vulnerabilities represent a major cause of security problems. Various vulnerability discovery models (VDMs) attempt to model the rate at which the vulnerabilities are discovered in a software. Although several VDMs have been proposed, not all of them are universally applicable. Also most of them seldom give accurate predictive results for every type of vulnerability dataset. The use of machine learning (ML) techniques has generally found success in a wide range of predictive tasks. Thus, in this paper, we conducted an empirical study on applying some well-known machine learning (ML) techniques as well as statistical techniques to predict the software vulnerabilities on a variety of datasets. The following ML techniques have been evaluated: cascade-forward back propagation neural network, feed-forward back propagation neural network, adaptive-neuro fuzzy inference system, multi-layer perceptron, support vector machine, bagging, M5Rrule, M5P and reduced error pruning tree. The following statistical techniques have been evaluated: Alhazmi-Malaiya model, linear regression and logistic regression model. The applicability of the techniques is examined using two separate approaches: goodness-of-fit to see how well the model tracks the data, and prediction capability using different criteria. It is observed that ML techniques show remarkable improvement in predicting the software vulnerabilities than the statistical vulnerability prediction models.

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