Working Towards a Comprehensive Instructional Framework for CSCL Support
Nikol Rummel, Ruhr-University Bochum, Germany
It Takes Two to Tango...Artificial Intelligence in Education and Learning Analytics for Scaling Up Exploratory Learning
Manolis Mavrikis, University College London, United Kingdom
Better Ways to Do Multiple-choice Testing
Martin Bush, London South Bank University, United Kingdom
Using Technology to Create Meaningful, Relevant Learning Experiences
David Guralnick, Kaleidoscope Learning, United States
Working Towards a Comprehensive Instructional Framework for CSCL Support
Nikol Rummel
Ruhr-University Bochum
Germany
Brief Bio
Dr. Nikol Rummel is a Full Professor and head of the Educational Psychology Lab in the Institute of Educational Research at Ruhr-University Bochum, Germany. She is also an Adjunct Professor in the Human–Computer Interaction Institute at Carnegie Mellon University, Pittsburgh,USA. Dr. Rummel was (2016–2017) president of the International Society of the Learning Sciences (ISLS). She is Associate Editor of the International Journal of Computer-Supported Collaborative Learning, and Editorial Board member of the Journal of the Learning Sciences, of the International Journal of Artificial Intelligence in Education, and of Learning and Instruction. Her research published in numerous journal articles and book chapters focuses on developing and evaluating instructional support for learning in computer- supported settings, with an emphasis on CSCL and on adaptive learning support, in particular.
Abstract
Building on a recent position paper (Rummel, Walker & Aleven, 2016), I will first contrast different Dystopian and Utopian visions of the future of computer-supported collaborative learning (CSCL). Against this background, I will argue that in order to avoid proving support to collaborative learners in an overly simplistic manner, we ought to work towards a comprehensive instructional framework for CSCL support. I will introduce an initial set of support dimensions of such a framework. By presenting sample research that focusses on one support dimension, and contrasting it with research that takes into account more than one dimension, I will illustrate what we can gain by cutting across different support dimensions. The point I want to make is that we need research directed at turning the set of so far mostly unconnected support dimensions into an instructional framework that allows orchestrating support across multiple dimensions, in order to be able to provide nuanced and flexible support to collaborative learners in computer-based settings.
It Takes Two to Tango...Artificial Intelligence in Education and Learning Analytics for Scaling Up Exploratory Learning
Manolis Mavrikis
University College London
United Kingdom
Brief Bio
Manolis Mavrikis is an Associate Professor in Learning Technologies at UCL Knowledge Lab. He holds a BSc in Mathematics from University of Athens, Greece with an emphasis in education, MSc with distinction in Informatics and PhD in Artificial Intelligence in Education from the University of Edinburgh. His research centers on designing evidence-based intelligent technologies that provide direct feedback to learners, and in employing learning analytics to help teachers, schools, education ministries or researchers develop an awareness and understanding of the processes involved in learning. Manolis has been principal investigator on several interdisciplinary EU projects, most recently iTalk2learn.
Abstract
Drawing on examples from a series of funded projects on artificial intelligence in education (AIED) and learning analytics (LA), I will argue that working in tandem these technologies can bootstrap their own adoption in the classroom and help scaling up beyond it. Using intelligent exploratory (or open) learning environments as a use case, I will present how the data these systems generate for the purpose of supporting the learner (e.g. learner modelling, feedback provision, additivity) open up opportunities for supporting teachers, educators and learning designers. I will demonstrate that although both areas of research have had significant advances, light integration between the two is problematic and more work is needed to close the gap. This should challenge us all, as designers and developers, to seize the opportunities afforded by the rich technological context but also take into careful account the requirements of our users and the challenges they face.
Better Ways to Do Multiple-choice Testing
Martin Bush
London South Bank University
United Kingdom
Brief Bio
Martin Bush is an Associate Professor within the Division of Computer Science and Informatics in the School of Engineering at London South Bank University (LSBU). He has a BSc in Electronic and Electrical Engineering from London University King's College (1986, 1st class hons), and a PhD ‘by publication’ in software quality assurance from LSBU (1994). He has also undertaken research on asynchronous digital circuit design whilst at LSBU. Having spent many years teaching mostly technical computing-related subjects at LSBU, with multiple-choice tests being the standard method employed for both formative and summative assessment, Martin became fascinated with novel variants of the traditional multiple-choice test. This led him to undertake both empirical and theoretical work in this area, which has been documented in a series of publications since 1999. In 2010 Martin dropped down from full-time to part-time at LSBU in order to establish a startup software development company; the company's main product is the online multiple-choice test platform QuizSlides.com.
Abstract
Guesswork harms the reliability of traditional multiple-choice tests, and this is one reason why many educators feel uneasy about using multiple-choice tests for summative assessment. The reliability of a multiple-choice test can be improved by including more questions, but longer tests are more time consuming to take, let alone to create. Negative marking is often used to discourage pure guesswork, and therefore to enhance test reliability, however some “educated” guesswork is to be expected.
A more effective way of reducing guesswork is to give test takers greater freedom to express their preferences in relation to the answer options associated with each question. For example, test takers can be asked to assign an order of preference to the answer options, or allowed to select two or more answer options whenever they have no clear first preference.
Dr Martin Bush will explain the research he has undertaken within this area, and present consequent recommendations to educators who are using – or thinking of using – multiple-choice tests for formative and/or summative assessment.
Using Technology to Create Meaningful, Relevant Learning Experiences
David Guralnick
Kaleidoscope Learning
United States
Brief Bio
David Guralnick holds a Ph.D. from Northwestern University, where his work synthesized concepts from the fields of computer science, instructional design, and cognitive psychology. Over the past 25 years, he has designed simulation-based training applications, performance-support systems, a great variety of online courses for business and university audiences, mobile applications, and specialized authoring tools which allow non-technical people, such as writers and trainers, to build e-learning courses and scenarios. Dr. Guralnick is president of New York-based Kaleidoscope Learning; president of the International E-Learning Association (IELA) and founding chair of the International E-Learning Awards program; a regular keynote speaker at international conferences; chair of the International Conference on E-Learning in the Workplace (ICELW); Editor-in-Chief of the International Journal on Advanced Corporate Learning (iJAC); founding chair of the American Society for Training & Development (ASTD)'s New York E-learning Special Interest Group; and an Adjunct Professor at Columbia University. His work has been featured in Wired magazine, Training magazine (as an Editor’s Choice), and the Wall Street Journal, and he is the recipient of numerous e-learning design awards.
Abstract
When designing online learning, we often lead with considerations such as the following: Does the course include the right content? Is it sequenced well? Are learning events spaced appropriately? These are all worthwhile questions, but I'd argue that they're only part of the story of successful learning initiatives. Perhaps as significant is whether a learner connects emotionally with the overall learning experience.
I suggest that we can make use of technological advances to help us create learning experiences that learners appreciate and connect with. In this session, I'll discuss ways in which we can make use of new (and not-so-new) technologies to better design experiences that resonate with learners. I'll explore user experience design, situated learning, and potential uses of new technologies, among other things, all in the context of the design of effective educational experiences.