Learning, Teaching, and Assessment Methods for Contemporary Learners

Dr Srinivasa K G is a Professor in the Department of Data Science and Artificial Intelligence at the International Institute of Information Technology, Naya Raipur, India. He is the recipient of a number of awards for his scholarly work, has published more than 150 research papers at international conferences and journals, and has authored eight textbooks. Dr Srinivasa has been a visiting researcher at several universities abroad, is the Principal Investigator for a number of funded projects, and is the senior member of the Institute of Electrical and Electronics Engineers (IEEE) and Association for Computing Machinery (ACM). His research areas include data mining, machine learning, and cloud computing, innovative teaching practices in engineering education, pedagogy, outcomes-based education, and teaching philosophy. Dr Muralidhar Kurni is Associate Professor in the department of CSE at Anantha Lakshmi Institute of Technology & Sciences, Ananthapuramu,India. He received his Ph.D. in Computer Science and Engineering from JNTUA, Ananthapuram, in 2021. He is a senior member of IEEE and a professional member of ACM. His research interests include Learning Analytics, Learning Strategies, Digital Pedagogy, Design Thinking, Pedagogy refinement & Engineering Education Research. Dr Saritha K is Associate Professor, Department of Computer Science Engineering, Presidency University, Bangalore, India. Previously she worked as Principal, S.V. Degree & P.G. College, Anantapur. She has conducted several skill development activities for students at Madanapalle Institute of Technology & Science, and two of the student applications developed during the period were selected as 'Best Applications' by the Andhra Pradesh State Government. Dr Saritha K has more than twenty years of teaching experience and has presented more than 15 papers at various national & international conferences and in journals. Her research interests aredigital pedagogy, design thinking, pedagogy refinement, data mining, and machine learning.

• Introduction.- Crossover Learning.- Learning Through Collaborative Argumentation.- Incidental Learning.- Context-Based Learning.- Computational Thinking.- Learning by Doing.- Embodied Learning.- Adaptive Teaching/Learning.- Analytics of Emotions.- Stealth Assessment.- Pedagogy for E-learning.- Harnessing the Power of AI to Education.