Kamila Misiejuk is a researcher at the Centre for the Science of Learning & Technology (SLATE), University of Bergen. 

Her Ph.D. research focused on peer assessment and learning analytics.

She is leading the International Society for Quantitative Ethnography Resources Committee (ISQE-RC) and is a student member of the International Society for Quantitative Ethnography (ISQE) board.

PRESENTATIONS:

10.02.23 How has feedback from learning analytics dashboards helped students’ learning? (PhD trial lecture), University of Bergen.

05.12.22 Peer assessment and learning analytics: Potential and challenges, ENVISION Project Multiplier Event.

01.02.21 Incorporating sentiment analysis with epistemic network analysis to enhance discourse analysis of Twitter data, 2nd International Conference on Quantitative Ethnography.

02.12.20 DigiTrans: Learning design in online courses, Læringsmiljøutvalget, Bergen, Norway.

26.03.19 Measuring learning in the age of big data, Bergen Artificial Intelligence and Machine Learning Symposium, Bergen, Norway.

13.03.19 Exploring peer assessment with learning analytics, PeergradeCon in Copenhagen, Denmark. 

11.09.18 Challenges in using learning analytics to explore peer assessment data, 4th International Association for Educational Assessment Annual Conference, Oxford, UK.

21.06.16 Presentation of the state-of-the-field on Learning Analytics and Knowledge (SoF LAK) Project, Opening of the Centre for the Science of Learning & Technology (SLATE) in Bergen, Norway.

12.04.16 Web analytics (guest lecture), Undergraduate Course INFO262 Interaction Design at the Bergen University, Norway.

PANELS / SYMPOSIUMS:

02.02.21 Symposium: Challenges and Solutions to Examining Twitter Data: Reflections from QE-COVID19 Data Challenge with Woodson Hobbs, Rogers Kaliisa, Kamila Misiejuk, Melanie Pfeffer, David Sanchez, Jennifer Scianna, Mamta Shah, Hamideh Talafian, Karl Vachuska, Yeyu Wang, Susie Wnag, Monique Woodard and Szilvia Zorgo at the 2nd International Conference on Quantitative Ethnography 2020.

02.02.21 Women in QE with Golnaz Arastoopour Irgens, Mamta Shah, Danielle Espino and Kamila Misiejuk at the 2nd International Conference on Quantitative Ethnography 2020.

21.10.19 Teaching Quantitative Ethnography with Brendan Eagan, Eric Hamilton and Kamila Misiejuk at the International Conference on Quantitative Ethnography 2019, Madison, Wisconsin, USA.

24.10.22 Misiejuk, K. & Misfeldt, M. LA research methods 3: Quantitative Ethnography. PhD course, INFO900: Introduction to Learning Analytics. Online

29.03.21 Misiejuk, K. Introduction to Epistemic Network Analysis. University of Eastern Finland

31.01.21 Misiejuk, K., Porter, C. & Eagan, B. Introduction to Epistemic Network Analysis. 2nd International Conference on Quantitative Ethnography. Online

24.03.20 Misfeldt, M., Spikol, D., Bruun, J., Saqr, M., Kaliisa, R., Ruis, A., Eagan., B. and Misiejuk, K. Quantitative ethnography as a framework for network analysis - A discussion of the foundations for network approaches to learning analysis. 10th International Learning Analytics and Knowledge Conference in Frankfurt, Germany

20.10.19 Misiejuk, K. & Eagan, B. Introduction to Epistemic Network Analysis. 1st International Conference on Quantitative Ethnography in Madison, Wisconsin, USA

18.10.19 Misiejuk, K. Introduction to Epistemic Network Analysis. Carnegie Mellon University in Pittsburgh, Pennsylvania, USA

11.07.19 Rose, C., Liat, Y., Misiejuk, K., Hu, X., Mu, S., and Jingjing., Z. Approaches and Tools for Learning Analytics. Learning Design, Technology and the Learning Sciences: A Research Colloquium, Beijing Normal University in Beijing, China

07.06.19 Misiejuk, K. Introduction to Epistemic Network Analysis. 15th EATEL Summer School on Technology Enhanced Learning 2019, Bari, Italy

29.08.18 Misfeldt, M. & Misiejuk, K. Epistemic Network Analysis - Quantitative Ethnography, An Open Source Workshop. 2nd LASI-NORDIC Learning Analytics Summer Institute, Copenhagen, Denmark

Academic article

• Kaliisa, Rogers; Misiejuk, Kamila; López-Pernas, Sonsoles et al. (2025). How does artificial intelligence compare to human feedback? A meta-analysis of performance, feedback perception, and learning dispositions. (external link)
• Misiejuk, Kamila; López-Pernas, Sonsoles; Kaliisa, Rogers et al. (2025). Mapping the Landscape of Generative Artificial Intelligence in Learning Analytics: A Systematic Literature Review. (external link)
• Misiejuk, Kamila; Bastesen, Jarle; Ershova, Tatiana (2025). How does using generative AI for essay writing impact peer assessment patterns? Insights from early adopters. (external link)
• Kaliisa, Rogers; López-Pernas, Sonsoles; Misiejuk, Kamila et al. (2025). A Topical Review of Research in Computer-Supported Collaborative Learning: Questions and Possibilities. (external link)
• Misiejuk, Kamila; Samuelsen, Jeanette; Kaliisa, Rogers et al. (2025). Idiographic learning analytics: Mapping of the ethical issues. (external link)
• Misiejuk, Kamila; Kaliisa, Rogers; Scianna, Jennifer (2024). Augmenting assessment with AI coding of online student discourse: A question of reliability. (external link)
• Misiejuk, Kamila; Ness, Ingunn Johanne; Gray, Robert et al. (2023). Changes in online course designs: Before, during, and after the pandemic. (external link)
• Misiejuk, Kamila; Wasson, Barbara (2021). Backward evaluation in peer assessment: A scoping review. (external link)
• Misiejuk, Kamila; Wasson, Barbara; Egelandsdal, Kjetil (2021). Using learning analytics to understand student perceptions of peer feedback. (external link)

Doctoral dissertation

• Misiejuk, Kamila (2023). Using learning analytics to understand peer assessment: The importance of context data. (external link)

Academic lecture

• Misiejuk, Kamila (2023). How has feedback from learning analytics dashboards helped students’ learning?. (external link)
• Misiejuk, Kamila (2022). Peer assessment and learning analytics: Potential and challenges. (external link)
• Misiejuk, Kamila; Wasson, Barbara (2018). Challenges in Using Learning Analytics to Explore Peer Assessment Data. (external link)
• Misiejuk, Kamila (2018). Challenges in using learning analytics to explore peer assessment data. (external link)

Academic chapter/article/Conference paper

• Misiejuk, Kamila; Scianna, Jennifer; Kaliisa, Rogers et al. (2021). Incorporating Sentiment Analysis with Epistemic Network Analysis to Enhance Discourse Analysis of Twitter Data. (external link)
• Kaliisa, Rogers; Misiejuk, Kamila; Irgens, Golnaz Arastoopour et al. (2021). Scoping the Emerging Field of Quantitative Ethnography: Opportunities, Challenges and Future Directions. (external link)

Lecture

• Misiejuk, Kamila (2021). Incorporating sentiment analysis with epistemic network analysis to enhance discourse analysis of Twitter data. (external link)
• Misiejuk, Kamila (2020). DigiTrans: Learning design in online courses. (external link)
• Misiejuk, Kamila (2019). Exploring peer assessment with learning analytics. (external link)
• Misiejuk, Kamila (2019). Measuring learning in the age of big data. (external link)

Report

• Misiejuk, Kamila; Wasson, Barbara (2020). DigiTrans: Trends in Learning Design in Canvas Courses after Covid-19. SLATE Research Report 2020-3. (external link)
• Misiejuk, Kamila; Wasson, Barbara (2017). State of the Field Report on Learning Analytics. (external link)

Popular scientific book

• Ferguson, Rebecca; Coughlan, Tim; Egelandsdal, Kjetil et al. (2019). Innovating Pedagogy 2019. Open University Innovation Report 7.. (external link)

Poster

• Misiejuk, Kamila (2018). The Potential of Learning Analytics to Support Peer Assessment. (external link)

See a complete overview of publications in Cristin.

The ENVISION 2027 project addresses both the current need of transitioning from conventional classroom teaching of Bioscience studies into digital remote teaching, as well as improving the current status of online teaching and e-learning course modules across Europe for the future needs.

The Remote Intelligent Access to Labs in Higher Education (RIALHE) project aims to improve the students’ understanding of scientific procedures starting from remote access to real lab experiments followed by collaborative activities using tech platforms.