AbstractOnline collaborative learning is implemented extensively in higher education. Nevertheless, it remains challenging to help learners achieve high-level group performance, knowledge elaboration, and socially shared regulation in online collaborative learning. To cope with these challenges, this study proposes and evaluates a novel automated assessment and feedback approach that is based on knowledge graph and artificial intelligence technologies. Following a quasi-experimental design, we assigned a total of 108 college students into two conditions: an experimental group that participated in online collaborative learning and received automated assessment and feedback from the tool, and a control group that participated in the same collaborative learning activities without automated assessment and feedback. Analyses of quantitative and qualitative data indicated that the introduced automated assessment and feedback significantly promoted group performance, knowledge elaboration, and socially shared regulation of collaborative learning. The proposed knowledge graph-based automated assessment and feedback approach shows promise in providing a valuable tool for researchers and practitioners to support online collaborative learning.
Massive Open Online Courses (MOOCs) have developed into a significant international movement, showing great promise in addressing equity, quality, and efficiency issues in global education. To date, many MOOCs have been developed specifically for teacher professional development (TPD). In this regard, an important empirical question remains to be addressed: How and to what extent can MOOCs support equity, quality, and efficiency in teacher professional development? To help fill this knowledge gap, this study, conducted from 2014 to 2016, focused on persistent teacher-learners in a TPD MOOC that was offered for seven consecutive rounds by the X-Learning Center of Peking University. The study found that more than 15% of the 105,383 teachers who enrolled in this MOOC were persistent teacher-learners, defined as learners who enrolled in multiple rounds. Data analysis showed that these persistent Keywords: MOOC, teacher professional development, persistent teacher-learners, self-regulated learning teacher-learners had diverse motivations for re-enrollment, including refreshing conceptual understanding, achieving higher scores, earning course certification, and discussing practical problems. The study also found that the persistent teacher learners developed self-regulated learning skills in the course of multiple rounds of the MOOC and showed significantly higher learning achievement than one-time enrollees. Qualitative and quantitative analysis of both clicklog data and interview data revealed additional insights into the persistent teacherlearners' learning within the MOOC and their real-world teaching practice beyond the MOOC. Overall, this study contributes to an improved understanding of the potential of MOOCs as an alternative TPD delivery mode in developing countries and sheds light on the future design of effective TPD through MOOCs. ; This work was created with financial support from the UK Government's Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government's Department for International Development; the International Development Research Centre, Canada or its Board of Governors; or the Foundation for Information Technology Education and Development
Learning Analytics for the Global South is a compilation of papers commissioned for the Digital Learning for Development (DL4D) project. DL4D is part of the Information Networks in Asia and Sub-Saharan Africa (INASSA) program funded jointly by the International Development Research Centre (IDRC) of Canada and the Department for International Development (DFID) of the United Kingdom, and administered by the Foundation for Information Technology Education and Development (FIT-ED) of the Philippines. DL4D aims to examine how digital learning could be used to address issues of equity, quality, and efficiency at all educational levels in developing countries. Over the past two years, DL4D has brought together leading international and regional scholars and practitioners to critically assess the potentials, prospects, challenges, and future directions for the Global South in key areas of interest around digital learning. It commissioned discussion papers for each of these areas from leading experts in the field: Diana Laurillard of the University College London Knowledge Lab, for learning at scale; Chris Dede of Harvard University, for digital game-based learning; Charalambos Vrasidas of the Centre for the Advancement of Research and Development in Educational Technology, for cost-effective digital learning innovations; and for learning analytics, the subject of this compilation, Dragan Gašević of the University of Edinburgh Moray House School of Education and School of Informatics. Each discussion paper is complemented by responses from a developing country-perspective by regional experts in Asia, Latin America, Africa, and the Middle East. Learning Analytics for the Global South considers how the collection, analysis, and use of data about learners and their contexts have the potential to broaden access to quality education and improve the efficiency of educational processes and systems in developing countries around the world. In his discussion paper, Prof. Gašević articulates these potentials and suggests how learning analytics could support critical digital learning and education imperatives such as quality learning at scale and the acquisition of 21st century skills. Experts from Africa (Paul Prinsloo of the University of South Africa), Mainland China (Bodong Chen of the University of Minnesota, USA and Yizhou Fan of Peking University, People's Republic of China), Southeast Asia (Ma. Mercedes T. Rodrigo of the Ateneo de Manila University, Philippines), and Latin America (Cristóbal Cobo and Cecilia Aguerrebere, both of the Ceibal Foundation, Uruguay) situate Prof. Gašević's proposals in their respective regional contexts, framing their responses around six key questions: 1. What are the main trends and challenges in education in your region? 2. How can learning analytics address these challenges? 3. What models of learning analytics adoption would be most effective in your region? 4. What are the barriers in adoption of learning analytics in your region and how could these be mitigated? 5. How do you envision ethical use and privacy protection in connection with learning analytics being addressed in your region? 6. How can the operationalization of learning analytics be futureproofed in your region? We hope that this compilation will serve as a springboard for deeper conversations about the adoption and sustained use of learning analytics in developing countries – its potential benefits and risks for learners, educators, and educations systems, as well as the ways to move forward that are rigorous, context-appropriate, ethical, and accountable. ; This work was created with financial support from the UK Government's Department for International Development and the International Development Research Centre, Canada. The views expressed in this work are those of the authors and do not necessarily represent those of the UK Government's Department for International Development; the International Development Research Centre, Canada or its Board of Governors; the Foundation for Information Technology Education and Development; or the editors.
