Speaker

Prof. Xiangyang Zhang

School of Foreign Studies, Suqian University, China

Research Area: ODeL, Open Pedagogy, Online Learning, Learning Culture

Title：Access and Success: Open Educational Resources for ODeL in Pandemic Crisis

Abstract：The sudden outbreak of the Covid-19 has made schools and universities close down and people has to be staying at home to keep social/physical distancing. However, the teaching and learning has not stopped but switched to online. Then remote teaching prevails with the surge of demand for Open Educational Resources. The term “Open Educational Resources (OER)” was coined during the forum held by UNESCO with the definition as “the open provision of education resources, enabled by information and communication technologies, for consultation, use and adaptation by a community of users for non-commercial purposes (UNESCO, 2002).” The OER movement came into being with the participation of diverse international organisations and relative stakeholders and counterparts. However, OER is a generic term, an umbrella which covers diverse ideas and practices. This OER movement has resonated widely in the western world, but in China, the Open Educational Practice has been advocated by the central Educational authority and participated and implemented by Elite and commonplace universities in China with the form of free and open online courses, that is MOOCs.

In this speech, the speaker first details the backdrop of the OER definition and its global and national impact in 5 Rs with reference especially to higher education in micro, macro perspectives. The speaker will make some comments on the current practices of creation, adoption and adaptation of OER in America, Europe, Africa and Asia, showcasing some models of online learning and teaching. Finally, the speaker will demonstrate some online learning platforms to argue that discreet educational technology cannot be sustainable in the post COVID-19 era.

Dr.Manjula Sanjay Koti

Professor & Head, Department of MCA, Sir M. Visvesvaraya Institute of Technology, Bangalore, India

Research Area: Data Mining, Artificial Intelligence, Internet of Things, Data Analytics, Data  Warehousing,  Analysis and design of Algorithms, Software Testing, Pattern Recognition, Operating Systems, Database Management System, Software Testing and Practices

Title: Technological Advancements in Indian Education System

Abstract: India is known as one of the world's top education destination in the global education industry. With some of the best universities and colleges India, has been successful in attracting bright talents all over the world. Indian education system is robust and built on strong foundations. According to Indian Brand Equity Foundation Indian education market is currently pegged at $100 billion with higher education contributing to 59.7%, school education at 38.1%, pre-school at 1.6% and the remainder 0.6% consists of technology and multi-media based education. According, to a report released by All India Survey on Higher Education 2018-2019 by Ministry of Human Resource Development the total enrolment in higher education has been estimated to be 37.4 million with 19.2 million male and 18.2 million female. Female constitute 48.6% of the total enrolment. Distance enrolment constitutes about 10.62% of the total enrolment in higher education, of which 44.15% are female students. Availability of broadband and hi-speed internet along with low cost computers to tier 2 and tier 3 cities has made learning seamless without any glitches. There is a need for improvement in higher education so that the knowledge of students, faculties and staff can be improved.  By adopting the latest technologies, we will be able to achieve this. Machine learning brings out the significant progressive changes. Machine Learning is revolutionizing the entire education system and the nation. This is achieved by Adaptive Learning, Predictive analytics, Increasing efficiency, personalized learning, Learning Analytics and accurately grading assignments. The author focus on how the early warning system can be developed for a student who is at risk of graduation by considering the behavioural and other data of students. This work also presents how machine learning helps the faculties to concentrate on each and every student when the class size is very huge, thereby discussing on the data management in the education scenario.