International Journal of Social Media and Interactive Learning Environments
- Editor in Chief
- Dr. Qiyun Wang
- ISSN online
- ISSN print
- 4 issues per year
The use of social media has drawn significant attention from educators in recent years. An increasing number of practitioners have started using social media in their teaching. IJSMILE proposes and fosters discussion on the affordances of social media (social networking sites) for teaching and learning, with emphasis on the potential ways and concerns of using social media in the educational context and implications for designing interactive and collaborative learning environments.
Topics covered include
- Social networking sites and web 2.0 tools for learning
- Technological affordances of social media
- Issues and concerns with using social media for learning
- New technologies, e.g. iPad, iPhone, netbooks, e-books, cloud computing
- Web-based collaborative and interactive learning
- Computer-supported collaborative learning
- Design and development of web-based learning environments
- Informal learning with social technologies
- Socio-cultural learning
- Ubiquitous and mobile learning
- Online course design and development
- Online learning and life-long learning
- Learning support and management systems
The objectives of IJSMILE are to provide an avenue for practitioners and researchers to share their practices and concerns, and their empirical research studies on using social media and other web 2.0 tools to support formal and informal learning. It also aims to promote effective design and use of web-based learning environments for social interaction and collaboration. The international dimension is emphasised in order to get a full picture of social media usage in different cultural and educational contexts.
IJSMILE provides a vehicle to help professionals, academics, researchers and teachers working in the field of educational technology, information technology, curriculum design and subject teaching to share their experiences and also to learn from each other's work.
IJSMILE publishes original research papers, case studies and conceptual papers. Special issues devoted to important topics in using social media for teaching and learning are also published.
IJSMILE is indexed in:
Editor in Chief
- Wang, Qiyun, Nanyang Technological University, Singapore
- Zhu, Zhiting, East China Normal University, China
- Barker, Philip, University of Teesside, UK
- Chen, Nian-Shing, National Sun Yat-sen University, Taiwan, Province of China
- Collis, Betty, University of Twente, Netherlands
- Ho, Kwok Keung, HK Teachers' Association, Hong Kong SAR, China
- Kawachi, Paul, Open University of China , China
- Kinshuk, , Athabasca University, Canada
- Plomp, Tjeerd, University of Twente, Netherlands
- Van Den Akker, Jan, Institute for Curriculum Development (SLO), Netherlands
Editorial Board Members
- Abedin, Babak, University of Technology, Sydney, Australia
- Baijnath, Narend, University of South Africa, South Africa
- Chen, Li, Beijing Normal University, China
- Dabbagh, Nada, George Mason University, USA
- Gao, Ping, University of Northern Iowa , USA
- Jegede, Olugbemiro, Association of African Universities, Ghana
- Kang, Haijun, Kansas State University, USA
- Khine, Myint Swe, Emirates College for Advanced Education, United Arab Emirates
- Lacasa, Pilar, Universidad de Alcalá, Spain
- Li, Wenhao, Central China Normal University, China
- Looi, Chee-Kit, National Institute of Education, Singapore
- Lu, Hong, Shandong Normal University, China
- Mazer, Joseph, Clemson University, USA
- Ni, Xiaopeng, Cleveland State University , USA
- Nieveen, Nienke, Institute for Curriculum Development (SLO), Netherlands
- Ozuem, Wilson, University of Gloucestershire, UK
- Pardo, Abelardo, University of Sydney, Australia
- Ren, Youqun, East China Normal University , China
- Sharma, Ramesh, Ambedkar University Delhi, India
- Singh, Mohini, RMIT University, Australia
- Wang, Lu, Capital Normal University, China
- Woo, Huay Lit, National Institute of Education, Singapore
- Yan, Hanbing, East China Normal University, China
- Yang, Harrison Hao, State University of New York at Oswego, USA
- Zhao, Jianhua, South China Normal University, China
- Zulkardi, H., Universitas Sriwijaya, Indonesia
A few essentials for publishing in this journal
- Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
- Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
- Briefs and research notes are not published in this journal.
- All our articles go through a double-blind review process.
- All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
- There are no charges for publishing with Inderscience, unless you require your article to be Open Access (OA). You can find more information on OA here.
- All articles for this journal must be submitted using our online submissions system.
- View Author guidelines.
Finding at-risk students
9 August, 2022
Traditionally, attendance and exam results have been the main way in which educators can show whether or not a student is struggling with the course. This is done retrospectively. With the advent of cloud-based learning technology and online courses, especially during the COVID-19 pandemic, these metrics are not necessarily the best way to catch at-risk students so that they can be helped. The converse of that is that this technology can be used to provide and analyse useful data about the students, which can itself highlight those that might be struggling more quickly than can conventional assessment. Moreover, it can do this in a much more timely manner than a retrospective look at attendance and infrequent exam results. Owen P. Hall Jr. of the Graziadio Business School at Pepperdine University in Malibu, California, USA, describes a machine-learning approach to detecting at-risk students in the International Journal of Social Media and Interactive Learning Environments. "At-risk" is a three-pronged definition alluding to whether a student is considering leaving a course, whether the institution is planning to end the student's place on the course, or whether they are in a probationary period because of problems they have faced or concerns their teachers have about their course work, attendance, and results. [...]More details...