Alisa Lincke

Senior lecturer

Department of Computer Science and Media Technology Faculty of Technology

alisa.lincke@lnu.se

+46 470-70 84 15

PhD in Computer and Information Science, Linnaeus University, Sweden, 2020
MSc in Computer and Information Science, Linnaeus University, Sweden, 2013
MSc in Computer Sceince, Odessa National Polytechnical University, Ukraine 2012

Alisa Lincke is a researcher in the field of Data Sceince and Recommender Systems. Her main work is to provide the data-driven solutions for effective decision making or recommendation. It includes understading the problem domain, data collection, data processing, using machine learning tools, statistical techniques, contextualization tool to provide the solutions to a given problem, and reporting results.

Research

decision-support systems;
data-driven applications;
contextualized data-driven applications;
recommender systems

My research groups

My ongoing research projects

My completed research projects

Publications

Article in journal (Refereed)

Hammar, T., Jonsén, E., Björneld, O., Askfors, Y., Andersson, M.L., et al. (2024). Potential Adverse Drug Events Identified with Decision Support Algorithms from Janusmed Risk Profile : A Retrospective Population-Based Study in a Swedish Region. Pharmacy. 12 (6).
Status: Published
Lincke, A., Fagerström, C., Ekstedt, M., Löwe, W., Backåberg, S. (2023). A comparative study of the 2D- and 3D-based skeleton avatar technology for assessing physical activity and functioning among healthy older adults. Health Informatics Journal. 29 (4).
Status: Published
Lincke, A., Roth, J., Macedo, A.F., Bergman, P., Löwe, W., et al. (2023). AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images. Translational Vision Science & Technology. 12 (11).
Status: Published
Lincke, A., Fagerström, C., Ekstedt, M., Löwe, W., Backåberg, S. (2021). Skeleton avatar technology as a way to measure physical activity in healthy older adults. Informatics in Medicine Unlocked. 24.
Status: Published
Lincke, A., Jansen, M., Milrad, M., Berge, E. (2021). The performance of some machine learning approaches and a rich context model in student answer prediction. Research and Practice of Technology Enhanced Learning. 16.
Status: Published
Fellman, D., Lincke, A., Berge, E., Jonsson, B. (2020). Predicting Visuospatial and Verbal Working Memory by Individual Differences in E-Learning Activities. Frontiers in Education. 5.
Status: Published
Fellman, D., Lincke, A., Jonsson, B. (2020). Do Individual Differences in Cognition and Personality Predict Retrieval Practice Activities on MOOCs?. Frontiers in Psychology. 11. 1-10.
Status: Published
Backåberg, S., Hellström, A., Fagerström, C., Halling, A., Lincke, A., et al. (2020). Evaluation of the Skeleton Avatar Technique for Assessment of Mobility and Balance Among Older Adults. Frontiers of Computer Science. 2.
Status: Published
Herault, R.C., Lincke, A., Milrad, M., Forsgärde, E., Elmqvist, C. (2018). Using 360-degrees interactive videos inpatient trauma treatment education : design, development and evaluationaspects. Smart Learning Environments. 5.
Status: Published

Conference paper (Refereed)

Hanscam, E., Lincke, A., Mohammed, A.T., Van Oeveren, D., Witcher, R. (2024). Digital Excavations : Text Mining Approaches for a Better Archaeology. .
Sokratis, K., Lincke, A., Marianthi, G. (2024). Learning Analytics for Open Learning Environments : Connection to 21st Century Skills. Proceedings of the 2nd ETELT 2024 Workshop. 23-36.
Milrad, M., Herodotou, C., Grizioti, M., Lincke, A., Girvan, C., et al. (2023). Combining Design Thinking with Emerging Technologies in K-12 Education. Methodologies and Intelligent Systems for Technology Enhanced Learning, 13th International Conference. MIS4TEL 2023. 15-27.
Hagelbäck, J., Lincke, A., Löwe, W., Rall, E. (2019). On the Agreement of Commodity 3D Cameras. Proceedings of the 2019 International Conference on Image Processing, Computer Vision, & Pattern Recognition. 36-42.
Hagelbäck, J., Liapota, P., Lincke, A., Löwe, W. (2019). Variants of Dynamic Time Warping and their Performance in Human Movement Assessment. 21st International Conference on Artificial Intelligence (ICAI'19: July 29 - August 1, 2019, Las Vegas, USA). 9-15.
Lincke, A., Fellman, D., Jansen, M., Milrad, M., Berge, E., et al. (2019). Correlating Working Memory Capacity with Learners´ Study Behavior in a Web-Based Learning Platform. Proceedings of the 27th International Conference on Computers in Education Conference Proceedings. 90-92.
Lincke, A., Jansen, M., Milrad, M., Berge, E. (2019). Using Data Mining Techniques to Assess Students’ Answer Predictions. ICCE 2019 - 27th International Conference on Computers in Education, Proceedings : Volume 1. 42-50.
Lincke, A., Lozano Prieto, D., Herault, R.C., Forsgärde, E., Milrad, M. (2019). Visualizing learners’ navigation behaviour using 360 degrees interactive videos. Proceedings of the 15th International Conference on Web Information Systems and Technologies. 358-364.
Hagelbäck, J., Liapota, P., Lincke, A., Löwe, W. (2019). The performance of some machine learning approaches in human movement assessment. 13th Multi Conference on Computer Science and Information Systems (MCCSIS). 35-42.
Lincke, A., Lundberg, J., Thunander, M., Milrad, M., Lundberg, J., et al. (2018). Diabetes Information in Social Media. Proceedings of the 11th International Symposium on Visual Information Communication and Interaction (VINCI '18). 104-105.
Herault, R.C., Lincke, A., Milrad, M., Forsgärde, E., Elmqvist, C., et al. (2018). Design and Evaluation of a 360 Degrees Interactive Video System to Support Collaborative Training for Nursing Students in Patient Trauma Treatment. 26TH INTERNATIONAL CONFERENCE ON COMPUTERS IN EDUCATION (ICCE 2018). 298-303.
Sotsenko, A., Jansen, M., Milrad, M., Rana, J. (2016). Using a Rich Context Model for Real-Time Big Data Analytics in Twitter. 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD WORKSHOPS (FICLOUDW). 228-233.
Sotsenko, A. (2015). Exploring Different Use Cases for a Rich Context Model for Mobile Applications. Proceedings of Doctoral Symposium of the 9th International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT 2015). 23-31.
Sotsenko, A., Jansen, M., Milrad, M. (2015). Using a Rich Context Model for People-to-People Recommendation. 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), 24-26 Aug. 2015, Rome. 703-708.
Sotsenko, A., Jansen, M., Milrad, M. (2014). Implementing and Validating a Mobile Learning Scenario Using Contextualized Learning Objects. Proceedings of the 22^nd International Conference on Computers in Education ICCE 2014 : November 30, 2014 - December 4, 2014, Nara, Japan. 522-527.
Sotsenko, A., Jansen, M., Milrad, M. (2014). Using a Rich Context Model for a News Recommender System for Mobile Users. UMAP 2014 Extended Proceedings : Posters, Demos, Late-breaking Results and Workshop Proceedings of the 22nd Conference on User Modeling, Adaptation, and Personalization co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization (UMAP2014) Aalborg, Denmark, July 7-11, 2014.. 13-16.
Sotsenko, A., Jansen, M., Milrad, M. (2013). About the Contextualization of Learning Objects in Mobile Learning Settings. QScience Proceedings : Vol. 2013, 12th World Conference on Mobile and Contextual Learning (mLearn 2013). 67-70.
Sotsenko, A., Jansen, M., Milrad, M. (2013). Supporting Content Contextualization in Web Based Applications on Mobile Devices. Proceedings of the 9th International Conference on Web Information Systems and Technologies : Aachen, Germany, 8-10 May, 2013. 501-504.

Chapter in book (Refereed)

Hammar, T., Nilsson, D., Björneld, O., Sving, C., Lincke, A. (2025). Machine Learning to Improve Decision Support for Preventing Adverse Drug Events. Intelligent Health Systems – From Technology to Data and Knowledge. IOS Press. 245-246.
Kopacheva, E., Lincke, A., Björneld, O., Hammar, T. (2025). Detecting Adverse Drug Events in Clinical Notes Using Large Language Models. Intelligent Health Systems – From Technology to Data and Knowledge. IOS Press. 892-893.
Sotsenko, A., Zbick, J., Jansen, M., Milrad, M. (2016). Flexible and Contextualized Cloud Applications for Mobile Learning Scenarios. Mobile, Ubiquitous, and Pervasive Learning : Fundaments, Applications, and Trends. Springer. 167-192.
Sotsenko, A., Zbick, J., Jansen, M., Milrad, M. (2015). Contextualization of Mobile Learners. Mobile Learning : Trends, Attitudes and Effectiveness. Nova Science Publishers, Inc.. 39-54.

Other (Other academic)

Hanscam, E., Hanscam, R., Lincke, A., Milrad, M., Mohammed, A.T., et al. (2024). Exploring Antiquity Geographic Regions.

Doctoral thesis, comprehensive summary (Other academic)

Lincke, A. (2020). A Computational Approach for Modelling Context across Different Application Domains. Doctoral Thesis. Växjö, Linnaeus University Press. 106.

Licentiate thesis, comprehensive summary (Other academic)

Sotsenko, A. (2017). A Rich Context Model : Design and Implementation. Licentiate Thesis. Växjö, Faculty of Technology, Linnaeus University. 103.