Relevance of learning analytics to measure and support students' learning in adaptive educational technologies

Let’s not forget: Learning analytics are about learning

Gašević, D., Dawson, S., & Siemens, G. (2015) · TechTrends · doi:10.1007/s11528-014-0822-x · Cited by 1,049 · see citing works

Process mining techniques for analysing patterns and strategies in students’ self-regulated learning

Bannert, M., Reimann, P., & Sonnenberg, C. (2013) · Metacognition and Learning · doi:10.1007/s11409-013-9107-6 · Cited by 322 · see citing works

Dissecting sequences of regulation and cognition: statistical discourse analysis of primary school children’s collaborative learning

Molenaar, I., & Chiu, M. M. (2013) · Metacognition and Learning · doi:10.1007/s11409-013-9105-8 · Cited by 33 · see citing works

Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance

Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025) · British Journal of Educational Technology · doi:10.1111/bjet.13544 · Cited by 805 · see citing works

Towards hybrid <scp>human‐AI</scp> learning technologies

Molenaar, I. (2022) · European Journal of Education · doi:10.1111/ejed.12527 · Cited by 348 · see citing works

The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning

Molenaar, I. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100070 · Cited by 217 · see citing works

Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data

Molenaar, I., Mooij, S. D., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107540 · Cited by 217 · see citing works

Multimodal Data Fusion in Learning Analytics: A Systematic Review

Mu, S., Cui, M., & Huang, X. (2020) · Sensors · doi:10.3390/s20236856 · Cited by 157 · see citing works

An integrated framework for course adapted student learning analytics dashboard

Aljohani, N. R., Daud, A. Y., Abbasi, R. A., Alowibdi, J. S., Basheri, M., & Aslam, M. A. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.03.035 · Cited by 100 · see citing works

Analysing adaptive scaffolds that help students develop <scp>self‐regulated</scp> learning behaviours

Munshi, A., Biswas, G., Baker, R., Ocumpaugh, J., Hutt, S., & Paquette, L. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12761 · Cited by 88 · see citing works

Unpacking perceived risks and AI trust influences pre-service teachers’ AI acceptance: A structural equation modeling-based multi-group analysis

Zhang, C., Hu, M., Wu, W., Kamran, F., & Wang, X. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12905-7 · Cited by 95 · see citing works

What can moment-by-moment learning curves tell about students’ self-regulated learning?

Molenaar, I., Horvers, A., & Baker, R. S. (2019) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.05.003 · Cited by 78 · see citing works

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Sharma, K., Papamitsiou, Z. K., Olsen, J. K., & Giannakos, M. N. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375498 · Cited by 66 · see citing works

Interplay of metacognitive experiences and performance in collaborative problem solving

Dindar, M., Järvelä, S., & Järvenoja, H. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103922 · Cited by 65 · see citing works

Personalized visualizations to promote young learners' SRL

Molenaar, I., Horvers, A., Dijkstra, R., & Baker, R. S. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375465 · Cited by 54 · see citing works

A Systematic Review of Self-Regulated Learning through Integration of Multimodal Data and Artificial Intelligence

de Mooij, S. M. M., Lämsä, J., Lim, L., Aksela, O., Athavale, S., Bistolfi, I., Jin, F. J.-Y., Li, T., Azevedo, R., Bannert, M., et al. (2025) · Educational Psychology Review · doi:10.1007/s10648-025-10028-0 · Cited by 48 · see citing works

Using sensing technologies to explain children's self-representation in motion-based educational games

Lee‐Cultura, S., Sharma, K., Papavlasopoulou, S., Retalis, S., & Giannakos, M. N. (2020) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3392063.3394419 · Cited by 43 · see citing works

The effect of cues for calibration on learners' self-regulated learning through changes in learners’ learning behaviour and outcomes

Laer, S. V., & Elen, J. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.02.016 · Cited by 42 · see citing works

Identifying individual differences using log-file analysis: Distributed learning as mediator between conscientiousness and exam grades

Theobald, M., Bellhäuser, H., & Imhof, M. (2018) · Learning and Individual Differences · doi:10.1016/j.lindif.2018.05.019 · Cited by 40 · see citing works

Metacognitive regulation contributes to digital text comprehension in E-learning

Burín, D. I., González, F., Barreyro, J. P., & Injoque‐Ricle, I. (2020) · Metacognition and Learning · doi:10.1007/s11409-020-09226-8 · Cited by 46 · see citing works

Affective states in digital game-based learning: Thematic evolution and social network analysis

Chen, X., Zou, D., Kohnke, L., Xie, H., & Cheng, G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0255184 · Cited by 38 · see citing works

Predictive learning analytics in online education: A deeper understanding through explaining algorithmic errors

Hlosta, M., Herodotou, C., Papathoma, T., Gillespie, A., & Bergamin, P. B. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100108 · Cited by 32 · see citing works

Measuring the Impact of COVID-19 Induced Campus Closure on Student Self-Regulated Learning in Physics Online Learning Modules

Zhang, T., Taub, M., & Chen, Z. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448150 · Cited by 28 · see citing works

How individual metacognitive awareness relates to situation-specific metacognitive interpretations of collaborative learning tasks

Çini, A., Malmberg, J., & Järvelä, S. (2020) · Educational Studies · doi:10.1080/03055698.2020.1834359 · Cited by 26 · see citing works

Clustering children's learning behaviour to identify self-regulated learning support needs

Dijkstra, R., Hinne, M., Segers, E., & Molenaar, I. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107754 · Cited by 23 · see citing works

Who’s in Charge?—Dealing with the Self-regulation Dilemma in Digital Learning Environments

Bergamin, P. B., & Hirt, F. S. (2018) · Progress in IS · doi:10.1007/978-3-319-73546-7_14 · Cited by 20 · see citing works

Young Learners’ Regulation of Practice Behavior in Adaptive Learning Technologies

Molenaar, I., Horvers, A., & Dijkstra, R. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02792 · Cited by 17 · see citing works

Towards a Methodological Framework for Sequence Analysis in the Field of Self-Regulated Learning

Laer, S. V., & Elen, J. (2018) · Frontline Learning Research · doi:10.14786/flr.v6i3.367 · Cited by 14 · see citing works

Dispositional Learning Analytics Structure Integrated with Recurrent Neural Networks in Predicting Students Performance

Thomas, J. J., & Ali, A. M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-33585-4_44 · Cited by 13 · see citing works

Tracing Undergraduate Science Learners’ Digital Cognitive Strategy Use and Relation to Performance

Mefferd, K., & Bernacki, M. L. (2023) · Journal of Science Education and Technology · doi:10.1007/s10956-022-10018-9 · Cited by 11 · see citing works

Perspectives de l'OCDE sur l'éducation numérique 2021

OCDE (2022) · Digital education outlook · doi:10.1787/d5fe6bd0-fr · Cited by 19 · see citing works

Level of consistency between students’ self-reported and observed study approaches in flipped classroom courses: How does it influence students’ academic learning outcomes?

Han, F. (2023) · PLoS ONE · doi:10.1371/journal.pone.0286549 · Cited by 6 · see citing works

Self-regulated learning, self-efficacy and life-long learning: the mediating role of future orientation

Stănescu, D. F., Frunzaru, V., & Ștefăniță, O. (2024) · Kybernetes · doi:10.1108/k-03-2024-0585 · Cited by 8 · see citing works

Emotional Learning Analytics in Education

Tychola, K. A., Vrochidou, Ε., & Papakostas, G. A. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2663-3.ch004 · Cited by 2 · see citing works

In Conversation: Bannert, Molenaar, & Winne – Multiple Perspectives on Researching and Supporting Self-Regulated Learning via Analytics

Raković, M., Bannert, M., Molenaar, I., Winne, P. H., & Gašević, D. (2024) · Springer eBooks · doi:10.1007/978-3-031-60571-0_4 · Cited by 2 · see citing works

Transfer of Theoretical Knowledge into Work Practice: A Reflective Quiz for Stroke Nurses

Fessl, A., Wesiak, G., & Pammer‐Schindler, V. (2018) · Progress in IS · doi:10.1007/978-3-319-73546-7_18 · Cited by 2 · see citing works

UYARLANABİLİR ÖĞRENME SİSTEMLERİNDE KULLANICI VERİLERİ

Ademi, N. (2019) · Turkish Studies - Information Technologies and Applied Sciences · doi:10.29228/turkishstudies.24822

ChatGPT-Assisted Codebook Design for Learning Analytics Datasets in Multiple Languages: A Case Study

Karimov, A., Saarela, M., Liu, X., Wei, Z., Zambrano, A. F., Barany, A., Baker, R. S., Ocumpaugh, J. L., Mammadova, S., & Kärkkäinen, T. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-12229-2_12

Capturing motivation and affect using multimodal data: toward theory-driven learning analytics

Cloude, E., Gabriel, F., & Azevedo, R. (2021) · Adelaide Research & Scholarship (AR&S) (University of Adelaide)

Using Learning Analytics to Measure Motivational and Affective Processes During Self-Regulated Learning with Advanced Learning Technologies

Gabriel, F. C., Cloude, E. B., & Azevedo, R. (2022) · Advances in analytics for learning and teaching · doi:10.1007/978-3-031-06333-6_6 · Cited by 9 · see citing works

Insights on the Interplay between Adaptive Learning and Learning Analytics

Mavroudi, A., Giannakos, M. N., & Krogstie, J. (2016) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2016.84 · Cited by 2 · see citing works

Supporting adaptive learning pathways through the use of learning analytics: developments, challenges and future opportunities

Mavroudi, A., Giannakos, M. N., & Krogstie, J. (2017) · Interactive Learning Environments · doi:10.1080/10494820.2017.1292531 · Cited by 104 · see citing works

Cognitive, metacognitive and motivational perspectives on learning analytics : Synthesizing self-regulated learning, assessment, and feedback with Learning Analytics

Schumacher, C. (2019) · MADOC (University of Mannheim)