Different learning predictors and their effects for Moodle Machine Learning models. - László Bognár; Tibor Fauszt (CogInfoCom, 2020)

Analysis of Conditions for Reliable Predictions by Moodle Machine Learning Models. - László Bognár; Tibor Fauszt; Gábor Zsolt Nagy (Int. J. Emerg. Technol. Learn., 2021)

The Impact of Online Quizzes on Student Success. - László Bognár; Tibor Fauszt; Mariann Váraljai (Int. J. Emerg. Technol. Learn., 2021)

Increasing the Prediction Power of Moodle Machine Learning Models with Self-defined Indicators. - Tibor Fauszt; László Bognár; Ágnes Sándor (Int. J. Emerg. Technol. Learn., 2021)

Factors and conditions that affect the goodness of machine learning models for predicting the success of learning. - László Bognár; Tibor Fauszt (Comput. Educ. Artif. Intell., 2022)

Design of a Machine Learning Model to Predict Student Attrition. - Tibor Fauszt; Katalin Erdélyi; Dóra Dobák; László Bognár; Endre Kovács (Int. J. Emerg. Technol. Learn., 2023)

The shifting landscape of student engagement: A pre-post semester analysis in AI-enhanced classrooms. - László Bognár; Myint Swe Khine (Comput. Educ. Artif. Intell., 2025)