A review of instructional delivery in social work education using ICT tools

Education has been aptly referred to as a veritable tool for meaningful development. The quality and effectiveness of teaching determine to a great extent the overall quality of education and the attainment of educational goals and objectives. Information and Communication Technology ICT) has been globally acclaimed as a tool that can accelerate and promote teaching and learning (National Policy for Information Technology, 2001). As opined by Kirschmer and Waperies, (2003), Information and Communication Technology can make the school more efficient and productive, there by engendering a variety of tools to enhance and facilitate teachers’ professional activities. This theoretical paper reiterates the vital role of ICT in effective teaching and learning generally and essentially in Social Work Education. It kicked off by examining the concepts of ICT and social work; establishing the need for ICT tools in instructional delivery in social work. The paper thereafter examined some applications of ICT tools in Social Work Education delivery and emphasizes the need for social work educators to judiciously utilize the opportunities and benefits inherent in ICT oriented instructional delivery. The paper concludes with a caution on the potential impact of ICT on professional identity of confidentiality

Strategies for graceful ageing in Nigeria

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Toward Supporting CS1 Instructors and Learners With Fine-Grained Topic Detection in Online Judges

Online judges (OJ) are a popular tool to support programming learning. However, one major issue with OJs is that problems are often put together without any associated meta-information that could, for example, be used to help classify problems. This meta-information could be extremely valuable to help users quickly find what types of problems they need most. To face this problem, several OJ administrators have recently begun manually annotating the topics of problems based on computer science-related subjects, such as dynamic programming, graphs, and data structures. Initially, these topics were used to support programming competitions and experienced learners. However, with OJs being increasingly used to support CS1 classes, such topic annotation needs to be extended to suit CS1 learners and instructors. In this work, for the first time, to the best of our knowledge, we propose and validate a predictive model that can automatically detect fine-grained topics of problems based on the CS1 syllabus. After experimenting with many shallow and deep learning models with different word representations based on cutting-edge NLP techniques, our best model is a CNN, achieving an F1-score of 88.9%. We then present how our model can be used for various applications, including (i) facilitating the search process of problems for CS1 learners and instructors and (ii) how it can be integrated into a system to recommend problems in OJs