Background and activities
My background is in the area of Human Computer Interaction and Collaborative/cooperative learning. In particular my doctoral work was in the area of using eye-tracking data to explain the differences between, and predict, experts and novice groups; good and poor students; functional and non-functional groups. The main context for the application of my research has been education. My research interests are primarily in the area of Applied Machine Learning and Human-Computer Interaction (HCI) with heavy emphases on groups’ behavior and physiological data such as eye-tracking, EEG, facial expressions (theoretical and practical methods in digital interaction). I seek to understand relations between users’ data (eye-tracking, system log data, users’ actions) and the profile of the user (expertise, motivation, strategy, performance) based on empirical experimentation (controlled experiments) and mixed methods analysis (utilizing a multitude of digital technologies). The knowledge gained from these studies is then used to provide feedback to the group or adapt for the needs of the group in a proactive manner. For this effort, in my studies I have combined eye-tracking and users’ actions to provide more comprehensive results through data science, statistics and machine learning practices.
After finishing my doctoral studies in 2015, I started working on developing methods based on Extreme Values Theory (EVT), a methodological space to compute features from abnormalities in data emerging out of collaborative work. EVT is well suited for big data time series. The results show an improvement over contemporary feature extraction methods in terms of their prediction capabilities. During the same period, I have expanded my application area beyond collaborative and educational technologies and have conducted studies in the context of ecommerce, information systems and Entertainment Computing.
Selected publications (for a full list please visit my Scholar page)
Sharma, K., Chavez-Demoulin, V., & Dillenbourg, P. (2018). Nonstationary modelling of tail dependence of two subjects’ concentration. The Annals of Applied Statistics, 12(2), 1293-1311.
Papavlasopoulou, S., Sharma, K., & Giannakos, M. N. (2018). How do you feel about learning to code? Investigating the effect of children’s attitudes towards coding using eye-tracking. International Journal of Child-Computer Interaction.
Prieto, L. P., Sharma, K., Kidzinski, Ł., & Dillenbourg, P. (2018). Orchestration load indicators and patterns: In-the-wild studies using mobile eye-tracking. IEEE Transactions on Learning Technologies, 11(2), 216-229.
Sharma, K., Chavez-Demoulin, V., & Dillenbourg, P. (2017). An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-tracking Data. Journal of Learning Analytics, 4(3), 140- 164.
Schneider, B., Sharma, K., Cuendet, S., Zufferey, G., Dillenbourg, P., & Pea, R. (2016). Using mobile eye-trackers to unpack the perceptual benefits of a tangible user interface for collaborative learning. ACM Transactions on Computer-Human Interaction (TOCHI), 23(6), 39.
Sharma, K., Olsen, J. K., Aleven, V., & Rummel, N. (2018, September). Exploring Causality Within Collaborative Problem Solving Using Eye-Tracking. In European Conference on Technology Enhanced Learning (pp. 412-426). Springer, Cham.
Ozgur, A. G., Wessel, M. J., Johal, W., Sharma, K., Özgür, A., Vuadens, P., ... & Dillenbourg, P. (2018). Iterative Design of an Upper Limb Rehabilitation Game with Tangible Robots. In ACM/IEEE International Conference on Human-Robot Interaction (HRI) (p. 187).
Sharma, K., Papavlasopoulou, S., Giannakos, M., & Jaccheri, L. (2018, June). Kid Coding Games and Artistic Robots: Attitudes and Gaze Behavior. In Proceedings of the Conference on Creativity and Making in Education (pp. 64-71). ACM.
Håklev, S., Sharma, K., Slotta, J., & Dillenbourg, P. (2017, September). Contextualizing the co-creation of artefacts within the nested social structure of a collaborative MOOC. In European Conference on Technology Enhanced Learning (pp. 67-81). Springer, Cham
Papavlasopoulou, S., Sharma, K., Giannakos, M., & Jaccheri, L. (2017, June). Using Eye-Tracking to Unveil Differences Between Kids and Teens in Coding Activities. In Proceedings of the 2017 Conference on Interaction Design and Children(pp. 171-181). ACM.
Sharma, K., Alavi, H. S., Jermann, P., & Dillenbourg, P. (2016). A gaze-based learning analytics model: in-video visual feedback to improve learner's attention in MOOCs. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 417-421). ACM
Prieto, L. P., Sharma, K., Dillenbourg, P., & Jesús, M. (2016). Teaching analytics: towards automatic extraction of orchestration graphs using wearable sensors. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 148-157). ACM.
Sharma, K., Jermann, P., Nüssli, M. A., & Dillenbourg, P. (2013). Understanding collaborative program comprehension: Interlacing gaze and dialogues. In Proceedings of Computer Supported Collaborative Learning (CSCL 2013) (Vol. 1, No. EPFL-CONF-184007, pp. 430-437).
Sharma, K., Jermann, P., Nüssli, M. A., & Dillenbourg, P. (2012). Gaze Evidence for different activities in program understanding. In Proceedings of 24th Annual conference of Psychology of Programming Interest Group (No. EPFL-CONF-184006).
Scientific, academic and artistic work
- (2019) Multimodal data as a means to understand the learning experience. International Journal of Information Management. vol. 48.
- (2019) Digital storytelling for good with Tappetina game. Entertainment Computing. vol. 30.
- (2019) Coding activities for children: Coupling eye-tracking with qualitative data to investigate gender differences. Computers in Human Behavior.
- (2019) Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach. British Journal of Educational Technology.
- (2019) Coding games and robots to enhance computational thinking: How collaboration and engagement moderate children’s attitudes?. International Journal of Child-Computer Interaction. vol. 21.
- (2018) Iterative Design of an Upper Limb Rehabilitation Game with Tangible Robots. ACM/IEEE International Conference on Human-Robot Interaction (HRI).
- (2018) Semantically Meaningful Cohorts Enable Specialized Knowledge Sharing in a Collaborative MOOC. Lecture Notes in Computer Science. vol. 11082 LNCS.
- (2018) Gaze-Driven Design Insights to Amplify Debugging Skills: A Learner-Centered Analysis Approach. Journal of Learning Analytics. vol. 8 (3).
- (2018) Combining gaze, dialogue, and action from a collaborative intelligent tutoring system to inform student learning processes. Proceedings (International Conference of the Learning Sciences). vol. 2 (2018-).
- (2018) How do you feel about learning to code? Investigating the effect of children’s attitudes towards coding using eye-tracking. International Journal of Child-Computer Interaction. vol. 17.
- (2018) Evidence for Programming Strategies in University Coding Exercises. Lecture Notes in Computer Science. vol. 11082.
- (2018) Exploring Causality Within Collaborative Problem Solving Using Eye-Tracking. Lecture Notes in Computer Science. vol. 11082 LNCS.
Part of book/report
- (2018) Learner-computer interaction. NordiCHI '18 - Proceedings of the 10th Nordic Conference on Human-Computer Interaction, Oslo, Norway — September 29 - October 03, 2018.
- (2018) Adult Perception of Gender-Based Toys and Their Influence on Girls’ Careers in STEM. Entertainment Computing – ICEC 2018.
- (2018) Interlacing Gaze and Actions to Explain the Debugging Process. 13th International Conference of the Learning Sciences (ICLS) 2018.
- (2018) Kid Coding Games and Artistic Robots: Attitudes and Gaze Behavior. Proceedings of the Conference on Creativity and Making in Education.