BIS Seminar: Dr Ami Peiris

14 April, 2018 - 10:30 to 12:00
General Purpose North (39A), Room 201

A Design Science-based Program of Research into Recommender Systems for Education

Advances in technology and the increasing need for more effective and efficient delivery of knowledge to learners have driven the development and use of Recommender-based systems to enhance training and education. This is important because educational institutions and organisations of all types spend millions of dollars providing training and education to their students and employees. Developing ways to improve the effectiveness and efficiency of these educational efforts offer the potential to save organizations and society in general a great deal of money. The purpose of this paper is to describe a program of research conducted at the University of Auckland into recommender-driven online learning systems. The program used a design science approach as the basis for all the studies. A series of six empirical research studies were conducted with the overall objective of examining what works and what does not work in the use of these systems. We discuss the lessons learned in the process. The evolution in design, challenges in building, and different needs that had to be met with formative and summative evaluations for each different type of system are discussed. Implications for practitioners and researchers and future research opportunities are noted.

Dr Ami Peiris

Dr K. Dharini Amitha (Ami) Peiris is a lecturer at the University of Auckland. Her current research interests are in decision support systems, recommender systems, evaluation of IS, design science research in IS, online learning systems. data analytics and exponential technologies. She received her PhD and MPhil from the University of Auckland, New Zealand and her BSc (Hons) from Kingston University, Surrey, United Kingdom. She has nearly 20 years’ experience in designing courses and lecturing in database systems, big data and internet of things, business intelligence, and data mining at all levels from undergraduate to executive programs.

For more information about this speaker, please see The University of Auckland staff page.