Can crowdsourcing platforms be useful for educational research?

Prolific.ac—A subject pool for online experiments

Palan, S., & Schitter, C. (2018) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2017.12.004 · Cited by 3,450 · see citing works

Beyond the Turk: Alternative platforms for crowdsourcing behavioral research

Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2017.01.006 · Cited by 2,957 · see citing works

How video production affects student engagement: an empirical study of MOOC videos

Guo, P. J., Kim, J., & Rubin, R. (2014) · Proceedings of the first ACM conference on Learning @ scale conference · doi:10.1145/2556325.2566239 · Cited by 1,850 · see citing works

The Challenges of Defining and Measuring Student Engagement in Science

Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015) · Educational Psychologist · doi:10.1080/00461520.2014.1002924 · Cited by 1,044 · see citing works

An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results

Chmielewski, M., & Kucker, S. C. (2020) · Social Psychological and Personality Science · doi:10.1177/1948550619875149 · Cited by 1,027 · see citing works

In search for the most informative data for feedback generation: Learning analytics in a data-rich context

Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.05.038 · Cited by 449 · see citing works

Understanding in-video dropouts and interaction peaks inonline lecture videos

Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2556325.2566237 · Cited by 375 · see citing works

Data Collection via Online Platforms: Challenges and Recommendations for Future Research

Newman, A., Bavik, Y. L., Mount, M. P., & Shao, B. (2020) · Applied Psychology · doi:10.1111/apps.12302 · Cited by 307 · see citing works

Identifying cognitive engagement in the mathematics classroom

Helme, S., & Clarke, D. (2001) · Mathematics Education Research Journal · doi:10.1007/bf03217103 · Cited by 242 · see citing works

Is There a Cost to Convenience? An Experimental Comparison of Data Quality in Laboratory and Online Studies

Clifford, S., & Jerit, J. (2014) · Journal of Experimental Political Science · doi:10.1017/xps.2014.5 · Cited by 206 · see citing works

The Role of MTurk in Education Research: Advantages, Issues, and Future Directions

Follmer, D. J., Sperling, R. A., & Suen, H. K. (2017) · Educational Researcher · doi:10.3102/0013189x17725519 · Cited by 184 · see citing works

Student Engagement in Online Learning: A Review

Hu, M. C., & Li, H. (2017) · International Symposium on Educational Technology · doi:10.1109/iset.2017.17 · Cited by 143 · see citing works

Penetrating the black box of time-on-task estimation

Kovanović, V., Gašević, D., Dawson, S., Joksimovíc, S., Baker, R. S., & Hatala, M. (2015) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2723576.2723623 · Cited by 124 · see citing works

LAK21: 11th International Learning Analytics and Knowledge Conference

(2021) · doi:10.1145/3448139 · Cited by 122 · see citing works

Operationalizing and Detecting Disengagement Within Online Science Microworlds

Gobert, J. D., Baker, R. S., & Wixon, M. (2015) · Educational Psychologist · doi:10.1080/00461520.2014.999919 · Cited by 114 · see citing works

Combining Unsupervised and Supervised Classification to Build User Models for Exploratory Learning Environments

Amershi, S., & Conati, C. (2009) · Journal of Educational Data Mining · doi:10.5281/zenodo.3554659 · Cited by 110 · see citing works

Patterns, correlates, and reduction of homework copying

Palazzo, D. J., Lee, Y.-J., Warnakulasooriya, R., & Pritchard, D. E. (2010) · Physical Review Special Topics - Physics Education Research · doi:10.1103/physrevstper.6.010104 · Cited by 102 · see citing works

Identifying Productive Inquiry in Virtual Labs Using Sequence Mining

Perez, S., Massey-Allard, J., Butler, D., Ives, J., Bonn, D., Yee, N., & Roll, I. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-61425-0_24 · Cited by 79 · see citing works

Linking students' timing of engagement to learning design and academic performance

Nguyen, Q., Huptych, M., & Rienties, B. (2018) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3170358.3170398 · Cited by 71 · see citing works

Template for teaching and assessment of problem solving in introductory physics

Burkholder, E. W., Miles, J. K., Layden, T. J., Wang, K. D., Fritz, A., & Wieman, C. (2020) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.16.010123 · Cited by 55 · see citing works

Attention Please: Your Attention Check Questions in Survey Studies Can Be Automatically Answered

Pei, W., Mayer, A., Tu, K., & Yue, C. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380195 · Cited by 48 · see citing works

The validity and utility of activity logs as a measure of student engagement

Motz, B., Quick, J., Schroeder, N., Zook, J., & Gunkel, M. (2019) · Proceedings of the 9th International Conference on Learning Analytics & Knowledge · doi:10.1145/3303772.3303789 · Cited by 47 · see citing works

Untitled work

(2022) · doi:10.1145/3491102.3501957 · Cited by 47 · see citing works

The importance of learning to make assumptions

Fortus, D. (2008) · Science Education · doi:10.1002/sce.20295 · Cited by 45 · see citing works

Nowhere to Go: Parents' Descriptions of Children's Physical Activity During a Global Pandemic

Perez, D., Thalken, J., Ughelu, N. E., Knight, C. J., & Massey, W. V. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.642932 · Cited by 43 · see citing works

Copying@Scale: Using Harvesting Accounts for Collecting Correct Answers in a MOOC

Alexandron, G., Ruipérez-Valiente, J. A., Chen, Z.-Z., Muñoz-Merino, P., & Pritchard, D. E. (2017) · Comput. Educ. · doi:10.1016/j.compedu.2017.01.015 · Cited by 36 · see citing works

The Role of Content Knowledge in Ill-Structured Problem Solving for High School Physics Students

Milbourne, J., & Wiebe, E. (2017) · Research in Science Education · doi:10.1007/s11165-016-9564-4 · Cited by 36 · see citing works

A systematic review of empirical studies using log data from open‐ended learning environments to measure science and engineering practices

Wang, K. D., Cock, J. M., Käser, T., & Bumbacher, E. W. (2022) · British Journal of Educational Technology · doi:10.1111/bjet.13289 · Cited by 36 · see citing works

An accurate and practical method for assessing science and engineering problem-solving expertise

Price, A., Salehi, S., Burkholder, E. W., Kim, C. J., Isava, V., Flynn, M., & Wieman, C. (2022) · International Journal of Science Education · doi:10.1080/09500693.2022.2111668 · Cited by 25 · see citing works

TIME TO COMPLETION OF WEB‐BASED PHYSICS PROBLEMS WITH TUTORING

Warnakulasooriya, R., Palazzo, D. J., & Pritchard, D. E. (2007) · Journal of the Experimental Analysis of Behavior · doi:10.1901/jeab.2007.70-06 · Cited by 24 · see citing works

Rethinking time-on-task estimation with outlier detection accounting for individual, time, and task differences

Nguyen, Q. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375538 · Cited by 17 · see citing works

Automating the Assessment of Problem-solving Practices Using Log Data and Data Mining Techniques

Wang, K. D., Salehi, S., Arseneault, M., Nair, K. K., & Wieman, C. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460127 · Cited by 11 · see citing works

Measuring the level of homework answer copying during COVID-19 induced remote instruction

Chen, Z. (2022) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.18.010126 · Cited by 10 · see citing works

Relationship between Students

Avar, Z., & Sadi (2020) · FIRE Forum for International Research in Education · doi:10.32865/fire202062188 · Cited by 9 · see citing works

ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills

Pardos, Z. A., & Bhandari, S. (2024) · PLOS ONE · doi:10.1371/journal.pone.0304013 · Cited by 97 · see citing works

Math Education With Large Language Models: Peril or Promise?

Kumar, H., Rothschild, D. M., Goldstein, D. G., & Hofman, J. M. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-98459-4_5 · Cited by 54 · see citing works

Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education

Wang, K. D., Wu, Z., Tufts, L., Wieman, C., Salehi, S., & Haber, N. (2025) · 2025 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016406 · Cited by 28 · see citing works

“It’s Not My Fault”: Exploring Attributional Patterns in eSports Player Toxicity

Kordyaka, B. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3748622 · Cited by 4 · see citing works

Too Many Zombies: Exploring Challenges and Motivations for (Not) Deleting Unused Online Accounts

Bumiller, F., Rodriguez, S. D., Mecke, L., Distler, V., & Alt, F. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790497 · Cited by 1 · see citing works

Testing the testing effect on prolific: when retrieval practice fails to boost learning

Sigayret, K., Parmentier, J.-F., & Silvestre, F. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1727423

WSC : A Crowd‐Powered Framework for Mapping Decomposable Complex‐Task With Worker‐Set

Kumar, S., & Pandey, S. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8305

Unlocking Cultural Heritage: The Gamified Digitisation Project of SMA-UniGe

Tessa, L. L., Coccoli, M., Schiaparelli, S., & Zolezzi, D. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-85663-1_5

Are we collaborative yet? A Usability Perspective on Mixnet Latency for Real-Time Applications

Davitt, K., Ristea, D., & Murdoch, S. J. (2026) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2026-0029

What Shapes Learner Perceptions of LA Technologies? Demographics, Privacy Dispositions and Contexts

Poquet, O., Salta, S., Longin, L., & Viberg, O. (2026) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3785022.3785102

Caution when Crowdsourcing: Prolific as a Superior Platform Compared with MTurk

O’Connell, D., Bautista, A., Johnson, C., & Venta, A. C. (2025) · Graduate Student Journal of Psychology · doi:10.52214/gsjp.v25i1.14129 · Cited by 2 · see citing works

Crowdsourcing in Computing Education Research: Case Amazon MTurk

Hellas, A., Zavgorodniaia, A., & Sorva, J. (2020) · doi:10.1145/3428029.3428062 · Cited by 8 · see citing works

Comparing Crowdsourcing Platforms for Behavioral Research: An Examination of MTurk, Prolific and Testable MindsPreprint

Aube, J. (2025) · OSF Preprints (OSF Preprints) · doi:10.17605/osf.io/8deja

Crowdsourcing as a platform for social studies

Шпак, С. И., & Shpak, Z. (2015)

Where's the Learning in Education Crowdsourcing?

Nguyen, H., Ahn, J., Young, W., & Campos, F. (2020) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3386527.3406734 · Cited by 5 · see citing works