Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS
Rianne Conijn, Chris Snijders, Ad Kleingeld, et al.
IEEE Transactions on Learning Technologies (2016) Vol. 10, Iss. 1, pp. 17-29
Closed Access | Times Cited: 321

Showing 1-25 of 321 citing articles:

Utilising learning analytics to support study success in higher education: a systematic review
Dirk Ifenthaler, Jane Yin-Kim Yau
Educational Technology Research and Development (2020) Vol. 68, Iss. 4, pp. 1961-1990
Open Access | Times Cited: 284
Postdigital Education in Design and Practice
Tim Fawns
Postdigital Science and Education (2018) Vol. 1, Iss. 1, pp. 132-145
Open Access | Times Cited: 276
Predicting academic performance: a systematic literature review
Arto Hellas, Petri Ihantola, Andrew Petersen, et al.
(2018), pp. 175-199
Open Access | Times Cited: 274
A systematic review on trends in using Moodle for teaching and learning
Sithara H. P. W. Gamage, Jennifer R. Ayres, Monica Behrend
International Journal of STEM Education (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 233
Study on student performance estimation, student progress analysis, and student potential prediction based on data mining
Fan Yang, Frederick W. B. Li
Computers & Education (2018) Vol. 123, pp. 97-108
Open Access | Times Cited: 178
Student performance analysis and prediction in classroom learning: A review of educational data mining studies
Anupam Khan, Soumya K. Ghosh
Education and Information Technologies (2020) Vol. 26, Iss. 1, pp. 205-240
Closed Access | Times Cited: 153
The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study
Mohammed Saqr, Sonsoles López‐Pernas, Satu Helske, et al.
Computers & Education (2023) Vol. 199, pp. 104787-104787
Open Access | Times Cited: 51
AI student success predictor: Enhancing personalized learning in campus management systems
Muhammad Shoaib, Nasir Sayed, Jaiteg Singh, et al.
Computers in Human Behavior (2024) Vol. 158, pp. 108301-108301
Closed Access | Times Cited: 33
Profiling students’ learning engagement in MOOC discussions to identify learning achievement: An automated configurational approach
Zhi Liu, Qianhui Tang, Fan Ouyang, et al.
Computers & Education (2024) Vol. 219, pp. 105109-105109
Closed Access | Times Cited: 30
Massive LMS log data analysis for the early prediction of course-agnostic student performance
Moises Riestra-González, María Puerto Paule Ruiz, Francisco Ortín
Computers & Education (2020) Vol. 163, pp. 104108-104108
Closed Access | Times Cited: 125
Student Performance Prediction Based on Blended Learning
Zhuojia Xu, Hua Yuan, Qishan Liu
IEEE Transactions on Education (2020) Vol. 64, Iss. 1, pp. 66-73
Closed Access | Times Cited: 101
Predicting student academic performance using multi-model heterogeneous ensemble approach
Olugbenga Adejo, Thomas Connolly
Journal of Applied Research in Higher Education (2017) Vol. 10, Iss. 1, pp. 61-75
Closed Access | Times Cited: 98
Predicting student final performance using artificial neural networks in online learning environments
Şeyhmus Aydoğdu
Education and Information Technologies (2019) Vol. 25, Iss. 3, pp. 1913-1927
Closed Access | Times Cited: 98
Finding traces of self-regulated learning in activity streams
Analía Cicchinelli, Eduardo Veas, Abelardo Pardo, et al.
(2018), pp. 191-200
Open Access | Times Cited: 97
Predicting student performance in a blended MOOC
Rianne Conijn, Antoine van den Beemt, Pjl Pieter Cuijpers
Journal of Computer Assisted Learning (2018) Vol. 34, Iss. 5, pp. 615-628
Open Access | Times Cited: 95
DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era
Sofia B. Dias, Sofia J. Hadjileontiadou, José Alves Diniz, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 95
Detecting students-at-risk in computer programming classes with learning analytics from students’ digital footprints
David Azcona, I‐Han Hsiao, Alan F. Smeaton
User Modeling and User-Adapted Interaction (2019) Vol. 29, Iss. 4, pp. 759-788
Closed Access | Times Cited: 93
Implementing AutoML in Educational Data Mining for Prediction Tasks
Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis, et al.
Applied Sciences (2019) Vol. 10, Iss. 1, pp. 90-90
Open Access | Times Cited: 90
Students matter the most in learning analytics: The effects of internal and instructional conditions in predicting academic success
Jelena Jovanović, Mohammed Saqr, Srécko Joksimovíc, et al.
Computers & Education (2021) Vol. 172, pp. 104251-104251
Open Access | Times Cited: 88
Empowering online teachers through predictive learning analytics
Christothea Herodotou, Martin Hlosta, Avinash Boroowa, et al.
British Journal of Educational Technology (2019) Vol. 50, Iss. 6, pp. 3064-3079
Closed Access | Times Cited: 84
Early prediction of undergraduate Student's academic performance in completely online learning: A five-year study
Javier Bravo, Sonia Janeth Romero Martínez, Sonia Pamplona
Computers in Human Behavior (2020) Vol. 115, pp. 106595-106595
Closed Access | Times Cited: 74
Predicting achievement and providing support before STEM majors begin to fail
Matthew L. Bernacki, Michelle Chavez, P. Merlin Uesbeck
Computers & Education (2020) Vol. 158, pp. 103999-103999
Open Access | Times Cited: 71
Educational Big Data: Predictions, Applications and Challenges
Xiaomei Bai, Fuli Zhang, Jinzhou Li, et al.
Big Data Research (2021) Vol. 26, pp. 100270-100270
Open Access | Times Cited: 59
Practical early prediction of students’ performance using machine learning and eXplainable AI
Yeonju Jang, Seongyune Choi, Heeseok Jung, et al.
Education and Information Technologies (2022) Vol. 27, Iss. 9, pp. 12855-12889
Closed Access | Times Cited: 47
Early Predicting of Students Performance in Higher Education
Essa Alhazmi, Abdullah Sheneamer
IEEE Access (2023) Vol. 11, pp. 27579-27589
Open Access | Times Cited: 41
Page 1 - Next Page

Scroll to top