Mirka Saarela

1.0k total citations · 1 hit paper
34 papers, 546 citations indexed

About

Mirka Saarela is a scholar working on Artificial Intelligence, Computer Science Applications and Education. According to data from OpenAlex, Mirka Saarela has authored 34 papers receiving a total of 546 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 10 papers in Computer Science Applications and 7 papers in Education. Recurrent topics in Mirka Saarela's work include Online Learning and Analytics (10 papers), Explainable Artificial Intelligence (XAI) (8 papers) and Online and Blended Learning (4 papers). Mirka Saarela is often cited by papers focused on Online Learning and Analytics (10 papers), Explainable Artificial Intelligence (XAI) (8 papers) and Online and Blended Learning (4 papers). Mirka Saarela collaborates with scholars based in Finland, Pakistan and United Kingdom. Mirka Saarela's co-authors include Susanne Jauhiainen, Tommi Kärkkäinen, Vili Podgorelec, Päivikki Jääskelä, Tuomo Rossi, Olli‐Pekka Ryynänen, Sami Äyrämö, Bülent Yener, Mohammed J. Zaki and Timo Hämäläinen and has published in prestigious journals such as IEEE Access, Applied Sciences and Information and Software Technology.

In The Last Decade

Mirka Saarela

26 papers receiving 526 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Comparison of feature importance measures as explanations for classification models

2021 256 citations Mirka Saarela et al. profile →

Peers

Mirka Saarela

AI

CSA

SPU

EDUCA

IS

Juanjuan Fan United States

Brij Mohan Gupta India

Weixin Liang United States

Jiangping Chen United States

Enes Eryarsoy Türkiye

Eduardo Mosqueira-Rey Spain

David Alonso-Ríos Spain

Anil Jadhav India

Shoko Wakamiya Japan

Juanjuan Fan United States

Mirka Saarela

129 ×0.7

AI

45 ×0.6

CSA

32 ×0.7

SPU

18 ×0.4

EDUCA

33 ×0.8

IS

Citations per year, relative to Mirka Saarela Mirka Saarela (= 1×) peers Juanjuan Fan

Countries citing papers authored by Mirka Saarela

Since Specialization

Citations

This map shows the geographic impact of Mirka Saarela's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mirka Saarela with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mirka Saarela more than expected).

Fields of papers citing papers by Mirka Saarela

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mirka Saarela. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mirka Saarela. The network helps show where Mirka Saarela may publish in the future.

Co-authorship network of co-authors of Mirka Saarela

This figure shows the co-authorship network connecting the top 25 collaborators of Mirka Saarela. A scholar is included among the top collaborators of Mirka Saarela based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mirka Saarela. Mirka Saarela is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Saarela, Mirka, et al.. (2026). Explainable Generative AI: A Two-Stage Review of Existing Techniques and Future Research Directions. AI. 7(1). 31–31.

2.

Saarela, Mirka, et al.. (2025). Quantitative Assessment of Explainability in Machine Learning Models : A Study on the OULA Dataset. Jyväskylä University Digital Archive (University of Jyväskylä). 101–103. 1 indexed citations

3.

Saarela, Mirka, et al.. (2025). Explainable AI in Education: Techniques and Qualitative Assessment. Applied Sciences. 15(3). 1239–1239. 8 indexed citations

4.

Zhao, Lei, et al.. (2025). Assessing and developing college students’ digital learning power: an empirical study based on questionnaire survey in a Chinese university. International Journal of Educational Technology in Higher Education. 22(1).

5.

Saarela, Mirka, et al.. (2025). Trade-offs between fairness and performance in educational AI: Analyzing post-processing bias mitigation on the OULAD. Information and Software Technology. 189. 107933–107933. 1 indexed citations

6.

Saarela, Mirka, et al.. (2024). Serious games in Science and Mathematics Education: a scoping umbrella review. International Journal of Serious Games. 11(4). 3–20.

7.

Saarela, Mirka. (2024). On the relation of causality- versus correlation-based feature selection on model fairness. Jyväskylä University Digital Archive (University of Jyväskylä). 56–64. 5 indexed citations

8.

Saarela, Mirka & Vili Podgorelec. (2024). Recent Applications of Explainable AI (XAI): A Systematic Literature Review. Applied Sciences. 14(19). 8884–8884. 28 indexed citations

9.

Durst, Susanne, et al.. (2024). A two-phase systematic literature review on the use of serious games for sustainable environmental education. Interactive Learning Environments. 33(3). 1945–1966. 7 indexed citations

10.

Hautala, Jarkko, et al.. (2024). A design for neural network model of continuous reading. Cognitive Systems Research. 88. 101284–101284.

11.

Saarela, Mirka, et al.. (2023). Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials. Journal of Computer Assisted Learning. 39(6). 1834–1851. 7 indexed citations

12.

Saarela, Mirka, et al.. (2022). Robustness, Stability, and Fidelity of Explanations for a Deep Skin Cancer Classification Model. Applied Sciences. 12(19). 9545–9545. 30 indexed citations

13.

Saarela, Mirka, et al.. (2022). The Finnish Version of the Affinity for Technology Interaction (ATI) Scale: Psychometric Properties and an Examination of Gender Differences. International Journal of Human-Computer Interaction. 39(4). 874–892. 18 indexed citations

14.

Saarela, Mirka, et al.. (2021). Explainable Student Agency Analytics. IEEE Access. 9. 137444–137459. 21 indexed citations

15.

Saarela, Mirka, Olli‐Pekka Ryynänen, & Sami Äyrämö. (2018). Predicting hospital associated disability from imbalanced data using supervised learning. Artificial Intelligence in Medicine. 95. 88–95. 23 indexed citations

16.

Saarela, Mirka, Bülent Yener, Mohammed J. Zaki, & Tommi Kärkkäinen. (2016). Predicting Math Performance from Raw Large-Scale Educational Assessments Data : A Machine Learning Approach. Jyväskylä University Digital Archive (University of Jyväskylä). 14 indexed citations

17.

Saarela, Mirka & Tommi Kärkkäinen. (2015). Do Country Stereotypes Exist in Educational Data? A Clustering Approach for Large, Sparse, and Weighted Data.. Educational Data Mining. 2 indexed citations

18.

Saarela, Mirka & Tommi Kärkkäinen. (2015). Weighted Clustering of Sparse Educational Data. Jyväskylä University Digital Archive (University of Jyväskylä). 5 indexed citations

19.

Saarela, Mirka & Tommi Kärkkäinen. (2015). Do Country Stereotypes Exist in PISA? A Clustering Approach for Large, Sparse, and Weighted Data. Jyväskylä University Digital Archive (University of Jyväskylä). 5 indexed citations

20.

Saarela, Mirka & Tommi Kärkkäinen. (2014). Discovering Gender-Specific Knowledge from Finnish Basic Education using PISA Scale Indices. Jyväskylä University Digital Archive (University of Jyväskylä). 60–67. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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