No abstract

Internet and Communication Technology (ICT) is being applied extensively in education to allow students to obtain information at anytime from anywhere. Social media is a new approach that is used to enhance and improve the delivery of education. Facebook groups and YouTube channels are the most operated networks that manage to deliver information. In this paper, we will study the impact of applying Facebook groups and YouTube videos on students' academic achievements in computer programming labs especially in object-oriented programming 2 (OOP2) lab at the Hashemite University. The practical programming lab plays an important role in understanding the theoretical programming concepts, for this reason programming lab is chosen as a case study. The proposed methodology embeds the social media networks as new major dimension in the teaching process of OOP2 Lab side by side with traditional lectures and e-learning tool such as Moodle. In this research: three surveys are utilized respectively to inspect the reasons of the weakness in OOP2 lab, evaluate the course learning outcomes (CLOs) by students before and after applying the proposed methodology and to investigate the opinions of students toward using social media networks within learning process. The results showed that operating the social media sites used by students on a daily basis creates a friendly and close educational environment as well as enhancing the academic results of students.

The trend of accessing the internet by different types of devices increases the challenges for organisations who are embracing e-assessment sessions as part of the e-learning process. These organisations face the unethical behaviour of unauthorised students as well as the costly expenses to provide secure exam systems. Different user authentication methods are briefly discussed in this paper for the e-assessment environment. As we pursue the optimistic version of the full meaning of e-learning and e-assessment, a theoretical model for smartphone devices is proposed to authenticate the identity of students before and during the e-assessment session.

Recently, audio data has increasingly becomes one of the prevalent source of information, especially after the exponential growth of using Internet, digital libraries systems and digital mobile devices. The currently massive amount of audio data stimulates working on developing custom audio retrieval tools to facilitate the audio retrieval tasks. The most familiar audio retrieval systems are based on searching using keyword, title or authors. This study presents the feasibility of using MEL Frequency Cepstral Coefficients (MFCCs) to extract features and Dynamic Time Warping (DTW) to compare the test patterns for Arabic audio news. The study proposes and implements architecture for content based audio retrieval system that is dedicated for the Arabic Audio News. The proposed architecture (ARANEWS) utilizes automatic speech recognition for isolated Arabic keyword speech mode; template based automatic speech recognition approach, MFCCs and DTW. ARANEWS presents a style of retrieval system that based on modeling signal waves and measuring the similarity between features that are extracted from spoken queries and spoken keywords. One of the major components that compose ARANEWS system is feature Database (ARANEWSDB). ARANEWSDB stores the extracted features (MFCCs) from the spoken keywords that are prepared to retrieve Arabic audio news. ARANEWS supports using Query by Humming (QBH) and Query by Example (QBE) instead of using query by text.

Teaching and exam proctoring represent key pillars of the education system. Human proctoring, which involves visually monitoring examinees throughout exams, is an important part of assessing the academic process. The capacity to proctor examinations is a critical component of educational scalability. However, such approaches are time-consuming and expensive. In this paper, we present a new framework for the learning and classification of cheating video sequences. This kind of study aids in the early detection of students’ cheating. Furthermore, we introduce a new dataset, “actions of student cheating in paper-based exams”. The dataset consists of suspicious actions in an exam environment. Five classes of cheating were performed by eight different actors. Each pair of subjects conducted five distinct cheating activities. To evaluate the performance of the proposed framework, we conducted experiments on action recognition tasks at the frame level using five types of well-known features. The findings from the experiments on the framework were impressive and substantial.