Peer assessment has attracted more attention an effective assessment tool in recent years. Peer assessment refers to the arrangement for peers to consider the quality of learning outcomes of others of similar status, it received attention of various studies due to the growing interest in the student centered approach as learners need to be involved in the learning process even in the assessment. This paper is a critical review of previous studies on peer assessment in English as Second/Foreign Language (ESL/EFL) context. Fifteen peer assessment studies from 2004 to 2017 were extensively reviewed and systematically analyzed. Peer assessment of the reviewed studies were on the quality of the writing outcomes of students in the EFL/ESL contexts, as writing skill occupies an important role in teaching English language. As a critical review paper of these studies, this paper highlights practical use of peer assessment and the important challenges or issues that need to be considered when utilizing peer assessment in the classroom. This paper hopes that practical measures of peer assessment will be utilized effectively by educators in the ESL and EFL classrooms in the near future.

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles.

Age prediction is an active study field that can substantially affect many computer vision problems like object recognition. In this paper, an accurate system with extensive experiments is proposed to provide an efficient and accurate approach for age range prediction of people from their facial images. Histogram Equalization technique is used to reduce the illumination effects on all facial images taken from FG-NET and UTD aging databases, and image resizing is used to unify all image sizes. Moreover, Histogram of Oriented Gradient (HOG) and Local Binary Pattern (LBP) are used to extract the features of these images. Then, Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) are used for the classification processes. To evaluate the performance of the proposed system, both Leave-One-Out (LOO) and Confusion Matrix (CM) are used. The extensive and intensified experiments improved the age range predicting performance up to 98.6%.

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