| Comparison of feature importance measures as explanations for classification models M Saarela, S Jauhiainen SN Applied Sciences 3 (2), 272, 2021 | 212 | 2021 |
| Learning Analytics: Fundaments, Applications, and Trends A Peña-Ayala, P Leitner, M Saarela, S Sergis, JJ Vie, DYT Liu, L Vigentini, ... Springer International Publishing, 2017 | 53 | 2017 |
| Analysing student performance using sparse data of core bachelor courses M Saarela, T Kärkkäinen JEDM-Journal of Educational Data Mining 7 (1), 3-32, 2015 | 53 | 2015 |
| Expert-based versus citation-based ranking of scholarly and scientific publication channels M Saarela, T Kärkkäinen, T Lahtonen, T Rossi Journal of Informetrics 10 (3), 693-718, 2016 | 42 | 2016 |
| Predicting hospital associated disability from imbalanced data using supervised learning M Saarela, OP Ryynänen, S Äyrämö Artificial Intelligence in Medicine 95, 88-95, 2019 | 28 | 2019 |
| Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator M Saarela, T Kärkkäinen Journal of Informetrics 14 (2), 101008, 2020 | 25 | 2020 |
| Discovering gender-specific knowledge from Finnish basic education using PISA scale indices M Saarela, T Kärkkäinen International Conference on Educational Data Mining, 60-67, 2014 | 25 | 2014 |
| Analysing the Nigerian Teacher's Readiness for Technology Integration. E Ifinedo, M Saarela, T Hämälänen International Journal of Education and Development using Information and …, 2019 | 23 | 2019 |
| Knowledge discovery from the programme for international student assessment M Saarela, T Kärkkäinen Learning Analytics: Fundaments, Applications, and Trends: A View of the …, 2017 | 22 | 2017 |
| Predicting math performance from raw large-scale educational assessments data: a machine learning approach M Saarela, B Yener, MJ Zaki, T Kärkkäinen JMLR workshop and conference proceedings, 2016 | 22 | 2016 |
| Explainable student agency analytics M Saarela, V Heilala, P Jääskelä, A Rantakaulio, T Kärkkäinen IEEE Access 9, 137444-137459, 2021 | 20 | 2021 |
| Robustness, stability, and fidelity of explanations for a deep skin cancer classification model M Saarela, L Geogieva Applied Sciences 12 (19), 9545, 2022 | 17 | 2022 |
| Robust principal component analysis of data with missing values T Kärkkäinen, M Saarela International Conference on Machine Learning and Data Mining in Pattern …, 2015 | 17 | 2015 |
| Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data. M Saarela, T Kärkkäinen International Conference on Educational Data Mining, 156--163, 2015 | 14 | 2015 |
| Course satisfaction in engineering education through the lens of student agency analytics V Heilala, M Saarela, P Jääskelä, T Kärkkäinen 2020 IEEE Frontiers in Education Conference (FIE), 1-9, 2020 | 12 | 2020 |
| Feature Ranking of Large, Robust, and Weighted Clustering Result M Saarela, J Hämäläinen, T Kärkkäinen 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data …, 2017 | 12 | 2017 |
| Understanding the study experiences of students in low agency profile: Towards a smart education approach V Heilala, P Jääskelä, T Kärkkäinen, M Saarela International conference on smart Information & communication Technologies …, 2019 | 11 | 2019 |
| Weighted clustering of sparse educational data M Saarela, T Kärkkäinen Proceedings of the European Symposium on Artificial Neural Networks …, 2015 | 11 | 2015 |
| The Finnish version of the affinity for technology interaction (ATI) scale: psychometric properties and an examination of gender differences V Heilala, R Kelly, M Saarela, P Jääskelä, T Kärkkäinen International Journal of Human–Computer Interaction 39 (4), 874-892, 2023 | 8 | 2023 |
| Automatic knowledge discovery from sparse and large-scale educational data: case Finland M Saarela Jyväskylä studies in computing, 2017 | 6 | 2017 |
Tommi KärkkäinenProfessor of Mathematical Information Technology, University of JyvaskylaE-mail megerősítve itt: jyu.fi
