Background and activities
Michail (Michalis) Giannakos is a professor of interaction design and learning technologies at the Department of Computer Science of NTNU, and Head of the Learner-Computer Interaction lab (https://lci.idi.ntnu.no/). His research focuses on the design and study of emerging technologies in online and hybrid education settings, and their connections to student and instructor experiences and practices. Giannakos has co-authored more than 150 manuscripts published in peer-reviewed journals and conferences (including Computers & Education, Computers in Human Behavior, IEEE TLT, Behaviour & Information Technology, BJET, ACM TOCE, CSCL, Interact, C&C, IDC to mention few) and has served as an evaluator for the EC and the US-NSF. He has served/serves in various organization committees (e.g., general chair, associate chair), program committees as well as editor and guest editor on highly recognized journals (e.g., BJET, Computers in Human Behavior, IEEE TOE, IEEE TLT, ACM TOCE). He has worked at several research projects funded by diverse sources like the EC, Microsoft Research, The Research Council of Norway (RCN), US-NSF, the German agency for international academic cooperation (DAAD) and Cheng Endowment; Giannakos is also a recipient of a Marie Curie/ERCIM fellowship, the Norwegian Young Research Talent award and he is one of the outstanding academic fellows of NTNU (2017-2021).
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2021) Supporting Learners in a Crisis Context with Smart Self-Assessment. Lecture Notes in Educational Technology.
- (2020) Mapping child–computer interaction research through co-word analysis. International Journal of Child-Computer Interaction. vol. 23-24.
- (2020) Utilizing multimodal data through fsQCA to explain engagement in adaptive learning. IEEE Transactions on Learning Technologies. vol. 13 (4).
- (2020) An Introduction to Non-formal and Informal Science Learning in the ICT Era. Lecture Notes in Educational Technology.
- (2020) Science Learning in the ICT Era: Toward an Ecosystem Model and Research Agenda. Lecture Notes in Educational Technology.
- (2020) Monitoring Children’s Learning Through Wearable Eye-Tracking: The Case of a Making-Based Coding Activity. IEEE pervasive computing. vol. 19 (1).
- (2020) Fitbit for learning: Towards capturing the learning experience using wearable sensing. International Journal of Human-Computer Studies. vol. 136.
- (2020) Games for Artificial Intelligence and Machine Learning Education: Review and Perspectives. Lecture Notes in Educational Technology.
- (2020) Embodied Interaction and Spatial Skills: A Systematic Review of Empirical Studies. Interacting with computers.
- (2020) Using Multimodal Learning Analytics to Explore how Children Experience Educational Motion-Based Touchless Games. CEUR Workshop Proceedings. vol. 2610.
- (2020) Motion-Based Educational Games: Using Multi-Modal Data to Predict Player’s Performance. IEEE Conference on Computatonal Intelligence and Games, CIG.
- (2020) Multimodal learning analytics to inform learning design: Lessons learned from computing education. Journal of Learning Analytics. vol. 7 (3).
- (2020) Seeking Information on Social Commerce: An Examination of the Impact of User- and Marketer-generated Content Through an Eye-tracking Study. Information Systems Frontiers.
- (2020) Endowing Head-Mounted Displays with Physiological Sensing for Augmenting Human Learning and Cognition. CEUR Workshop Proceedings. vol. 2610.
- (2020) Looking at the Design of Making-Based Coding Activities Through the Lens of the ADDIE Model. Lecture Notes in Educational Technology.
- (2020) Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach. International Journal of Information Management. vol. 53.
- (2020) How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data. Lecture Notes in Computer Science (LNCS). vol. 12067.
- (2020) ITiCSE 2019 working groups report. SIGCSE Bulletin inroads. vol. 52 (1).
- (2020) Multimodal data capabilities for learning: What can multimodal data tell us about learning?. British Journal of Educational Technology (BJET). vol. 51 (5).
- (2020) Eye-tracking and artificial intelligence to enhance motivation and learning. Smart Learning Environments.