Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works

Classification and Regression Trees.

Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works

A Tutorial on Support Vector Machines for Pattern Recognition

Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909 · see citing works

Classification and regression trees

Praagman, J. (1985) · European Journal of Operational Research · doi:10.1016/0377-2217(85)90321-2 · Cited by 10,053 · see citing works

Machine learning: Trends, perspectives, and prospects

Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works

Reviews: Lord, Frederic M., and Novick, Melvin R. (with contributions by Allan Birnbaum). Statistical Theories of Mental Test Scores. Reading, Mass.: Addison-Wesley, 1968. xii + 568 pp. $14.95.

Rozeboom, W. W., Lord, F. M., Novick, M. R., & Birnbaum, A. (1969) · American Educational Research Journal · doi:10.2307/1162101 · Cited by 8,012 · see citing works

The relationship between Precision-Recall and ROC curves

Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559 · see citing works

Test Theory: A Unified Treatment

Zwick, R., & McDonald, R. P. (2000) · Journal of the American Statistical Association · doi:10.2307/2669496 · Cited by 3,637 · see citing works

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv · doi:10.48550/arxiv.1702.08608 · Cited by 3,121 · see citing works

Proceedings of the 23rd international conference on Machine learning

Cohen, W. W., & Moore, A. (2006) · Cited by 2,647 · see citing works

Multi-Label Classification

Tsoumakas, G., & Katakis, I. (2007) · International Journal of Data Warehousing and Mining · doi:10.4018/jdwm.2007070101 · Cited by 2,512 · see citing works

Handbook of Modern Item Response Theory

van der Linden, W. J., & Hambleton, R. K. (1997) · Springer eBooks · doi:10.1007/978-1-4757-2691-6 · Cited by 2,331 · see citing works

Knowledge tracing: Modeling the acquisition of procedural knowledge

Corbett, A. T., & Anderson, J. R. (1995) · User Modelling and User-Adapted Interaction · doi:10.1007/bf01099821 · Cited by 2,324 · see citing works

Data Mining and Knowledge Discovery Handbook

(2010) · doi:10.1007/978-0-387-09823-4 · Cited by 2,190 · see citing works

Do we need hundreds of classifiers to solve real world classification problems

Fernández-Delgado, M., Cernadas, E., Barro, S., & AmorimDinani (2014) · Journal of Machine Learning Research · doi:10.5555/2627435.2697065 · Cited by 2,131 · see citing works

Untitled work

(1968) · Addison-Wesley eBooks · Cited by 2,001 · see citing works

Introduction to Measurement Theory

Allen, M. J. (1979) · Cited by 1,971 · see citing works

Methods and metrics for cold-start recommendations

Schein, A. I., Popescul, A., Ungar, L., & Pennock, D. M. (2002) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/564376.564421 · Cited by 1,805 · see citing works

Graph-based approach for airborne light detection and ranging segmentation

Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works

Mining Multi-label Data

Tsoumakas, G., Katakis, I., & Vlahavas, I. P. (2009) · Springer eBooks · doi:10.1007/978-0-387-09823-4_34 · Cited by 1,466 · see citing works

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining

Yang, Q., Agarwal, D., & Pei, J. (2012) · doi:10.1145/2339530 · Cited by 1,456 · see citing works

ltm : An R Package for Latent Variable Modeling and Item Response Theory Analyses

Rizopoulos, D. (2006) · Journal of Statistical Software · doi:10.18637/jss.v017.i05 · Cited by 1,246 · see citing works

Deep Knowledge TracingPreprint

Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl‐Dickstein, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.05908 · Cited by 875 · see citing works

An NCME Instructional Module on

Hambleton, R. K., & Jones, R. W. (1993) · Educational Measurement Issues and Practice · doi:10.1111/j.1745-3992.1993.tb00543.x · Cited by 736 · see citing works

Top-down induction of first-order logical decision trees

Blockeel, H., & De Raedt, L. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00034-4 · Cited by 685 · see citing works

Facing the cold start problem in recommender systems

Lika, B., Kolomvatsos, K., & Hadjiefthymiades, S. P. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.09.005 · Cited by 670 · see citing works

Decision trees for hierarchical multi-label classification

Vens, C., Struyf, J., Schietgat, L., Džeroski, S., & Blockeel, H. (2008) · Machine Learning · doi:10.1007/s10994-008-5077-3 · Cited by 640 · see citing works

Sentiment analysis in Facebook and its application to e-learning

Ortigosa, Á., Martin, J., & Carro, R. M. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.05.024 · Cited by 594 · see citing works

Handbook of Modern Item Response Theory.

mw, van der Linden, W. J., & Hambleton, R. K. (1997) · Journal of the American Statistical Association · doi:10.2307/2965612 · Cited by 591 · see citing works

Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals

Boyd, K., Eng, K., & Page, D. (2013) · ECML/PKDD · doi:10.1007/978-3-642-40994-3_29 · Cited by 549 · see citing works

Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities

Truong, H. M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.02.014 · Cited by 531 · see citing works

ltm: An R package for latent variable modeling and item response theory analyses

Rizopoulos, D. (2011) · Cited by 529 · see citing works

Introduction to measurement with theory

Barnett, W. A., Diewert, W. E., & Zellner, A. (2010) · Journal of Econometrics · doi:10.1016/j.jeconom.2010.09.001 · Cited by 514 · see citing works

A collaborative filtering approach to mitigate the new user cold start problem

Bobadilla, J., Ortega, F., Hernando, A., & Bernal, J. (2011) · Knowledge-Based Systems · doi:10.1016/j.knosys.2011.07.021 · Cited by 493 · see citing works

A review of recent advances in learner and skill modeling in intelligent learning environments

Desmarais, M. C., & Baker, R. S. (2011) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9106-8 · Cited by 457 · see citing works

Dropout prediction in e-learning courses through the combination of machine learning techniques

Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. G. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.05.010 · Cited by 401 · see citing works

Ratings meet reviews, a combined approach to recommend

Ling, G., Lyu, M. R.-T., & King, I. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645728 · Cited by 360 · see citing works

Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval

Järvelin, K., Beaulieu, M., Baeza-Yates, R. A., & Myaeng, S. H. (2002) · Cited by 287 · see citing works

Tree ensembles for predicting structured outputs

Kocev, D., Vens, C., Struyf, J., & Džeroski, S. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.09.023 · Cited by 279 · see citing works

Adaptive E-Learning

Shute, V. J., & Towle, B. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3802_5 · Cited by 267 · see citing works

Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning

Kalyuga, S., & Sweller, J. (2005) · Educational Technology Research and Development · doi:10.1007/bf02504800 · Cited by 265 · see citing works

Computer adaptive practice of Maths ability using a new item response model for on the fly ability and difficulty estimation

Klinkenberg, S., Straatemeier, M., & van der Maas, H. L. J. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.02.003 · Cited by 261 · see citing works

Examining competitive, collaborative and adaptive gamification in young learners' math learning

Jagušt, T., Botički, I., & So, H. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.06.022 · Cited by 248 · see citing works

Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades

Kotsiantis, S. B. (2011) · Artificial Intelligence Review · doi:10.1007/s10462-011-9234-x · Cited by 247 · see citing works

A comparison of item-level and scale-level multiple imputation for questionnaire batteries

Gottschall, A. C., West, S. G., & Enders, C. K. (2012) · Multivariate Behavioral Research · doi:10.1080/00273171.2012.640589 · Cited by 225 · see citing works

IRTrees: Tree-Based Item Response Models of the GLMM Family

Boeck, P. D., & Partchev, I. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.c01 · Cited by 215 · see citing works

A novel Bayesian similarity measure for recommender systems

Guo, G., Zhang, J., & Yorke‐Smith, N. (2013) · Scopus · Cited by 206 · see citing works

Proceedings of the 8th ACM Conference on Recommender systems

(2014) · doi:10.1145/2645710 · Cited by 205 · see citing works

Advances in Intelligent Systems and Computing

Girish, S. V., Prakash, R., Swetha, S., Pareek, G., & Kumar, T. S. (2016) · Cited by 199 · see citing works

Comparative recommender system evaluation

Said, A., & Bellogín, A. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645746 · Cited by 196 · see citing works

Erratum: Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals

Boyd, K., Eng, K. H., & Page, C. D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40994-3_55 · Cited by 155 · see citing works

A generalized item response tree model for psychological assessments

Jeon, M., & de Boeck, P. A. L. (2015) · Behavior Research Methods · doi:10.3758/s13428-015-0631-y · Cited by 151 · see citing works

Data-driven system to predict academic grades and dropout

Rovira, S. C., Puertas, E., & Igual, L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171207 · Cited by 143 · see citing works

Simultaneous co-clustering and learning to address the cold start problem in recommender systems

Pereira, A. L. V., & Hruschka, E. R. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.02.016 · Cited by 142 · see citing works

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

Wauters, K., Desmet, P., & Noortgate, W. V. D. (2010) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2010.00368.x · Cited by 137 · see citing works

Response prediction using collaborative filtering with hierarchies and side-information

Menon, A. K., Chitrapura, K. P., Garg, S. K., Agarwal, D., & Kota, N. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020436 · Cited by 137 · see citing works

Alleviating the new user problem in collaborative filtering by exploiting personality information

Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci⋆, F., & Iván Cantador, A. (2016) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-016-9172-z · Cited by 127 · see citing works

Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation

Forsati, R., Mahdavi, M., Shamsfard, M., & Sarwat, M. (2014) · ACM Transactions on Information Systems · doi:10.1145/2641564 · Cited by 122 · see citing works

MODELING NONIGNORABLE MISSING DATA WITH ITEM RESPONSE THEORY (IRT)

Rose, N., von Davier, M., & Xu, X. (2010) · ETS Research Report Series · doi:10.1002/j.2333-8504.2010.tb02218.x · Cited by 114 · see citing works

Constrained Adaptive Testing with Shadow Tests

van der Linden, W. J. (2000) · Springer eBooks · doi:10.1007/0-306-47531-6_2 · Cited by 112 · see citing works

Constraining Item Exposure in Computerized Adaptive Testing With Shadow Tests

van der Linden, W. J., & Veldkamp, B. P. (2004) · Journal of Educational and Behavioral Statistics · doi:10.3102/10769986029003273 · Cited by 107 · see citing works

DESPRO: A method based on roles to provide collaboration analysis support adapted to the participants in CSCL situations

Marcos-García, J. A., Martínez‐Monés, A., & Dimitriadis, Y. A. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.10.027 · Cited by 94 · see citing works

Factorization Models for Forecasting Student Performance

Thai-Nghe, N., Horváth, T., & Schmidt-Thieme, L. (2011) · Educational Data Mining · Cited by 88 · see citing works

Item difficulty estimation: An auspicious collaboration between data and judgment

Wauters, K., Desmet, P., & Noortgate, W. V. D. (2011) · Computers & Education · doi:10.1016/j.compedu.2011.11.020 · Cited by 74 · see citing works

Cold-Start Recommendation with Provable Guarantees: A Decoupled Approach

Barjasteh, I., Forsati, R., Ross, D. M., Esfahanian, A., & Radha, H. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2522422 · Cited by 73 · see citing works

Utilizing Learners' Negative Ratings in Semantic Content-Based Recommender System for e-Learning Forum.

Albatayneh, N. A., Ghauth, K. I., & Chua, F.-F. (2018) · Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) · Cited by 54 · see citing works

Course planning of extension education to meet market demand by using data mining techniques – an example of Chinkuo technology university in Taiwan

Hsia, T.-C., Shie, A., & Chen, L.-C. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.09.025 · Cited by 53 · see citing works

Computers and Education

Fernández‐Manjón, B., Vega-Rodríguez, M. A., Sánchez-Pérez, J. M., Gomez-Pulido, J. A., & Bravo, J. (2007) · Springer eBooks · doi:10.1007/978-1-4020-4914-9 · Cited by 49 · see citing works

Decision Tree Modeling of Wheel- Spinning and Productive Persistence in Skill Builders

Kai, S., Almeda, M. V., Baker, R. S., Heffernan, C., & Heffernan, N. T. (2018) · Journal of Educational Data Mining · doi:10.5281/zenodo.3344810 · Cited by 47 · see citing works

An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments

Park, J. Y., Joo, S., Frederik Cornillie, B., van der Maas, H. L. J., & Noortgate, W. V. D. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1166-9 · Cited by 45 · see citing works

Sentiment Analysis of Social Network Data for Cold-Start Relief in Recommender Systems

Contratres, F. G., Alves-Souza, S. N., Filgueiras, L. V. L., & DeSouza, L. S. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77712-2_12 · Cited by 42 · see citing works

From smart testing to smart learning: how testing technology can assist the new generation of education

Zhang, S., & Chang, H. (2016) · International Journal of Smart Technology and Learning · doi:10.1504/ijsmarttl.2016.078162 · Cited by 39 · see citing works

Utilizing Artificial Learners to Help Overcome the Cold-Start Problem in a Pedagogically-Oriented Paper Recommendation System

Tang, T. Y., & McCalla, G. I. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27780-4_28 · Cited by 37 · see citing works

Representing Adaptive and Adaptable Units of Learning

Burgos, D., Tattersall, C., & Koper, R. (2007) · Springer eBooks · doi:10.1007/978-1-4020-4914-9_4 · Cited by 28 · see citing works

Comparative recommender system evaluation: Benchmarking recommendation frameworks

Said, A., & Bellogín, A. (2014) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 22 · see citing works

An application of fuzzy geographically clustering for solving the Cold-Start problem in recommender systems

Sơn, L. H., Cuong, K. M., Minh, N. T. H., & Canh, N. V. (2013) · International Conference of Soft Computing and Pattern Recognition (SoCPaR) · doi:10.1109/socpar.2013.7054096 · Cited by 16 · see citing works

Evaluating a Smart Recommender for an Evolving E-learning System: A Simulation-Based Study

Tang, T. Y., & McCalla, G. I. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24840-8_34 · Cited by 15 · see citing works

A Heuristic Approach for New-Item Cold Start Problem in Recommendation of Micro Open Education Resources

Sun, G., Cui, T., Dongming Xu, A., Shen, J., & Chen, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91464-0_21 · Cited by 13 · see citing works

Automated Test Assembly for Handling Learner Cold-Start in Large-Scale Assessments

Vie, J.-J., Popineau, F., Bruillard, É., & Bourda, Y. (2018) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-017-0163-y · Cited by 12 · see citing works

Modeling Growth in Electronic Learning Environments Using a Longitudinal Random Item Response Model

Kadengye, D. T., Ceulemans, E., & Noortgate, W. V. D. (2014) · The Journal of Experimental Education · doi:10.1080/00220973.2014.907226 · Cited by 10 · see citing works

Motivating Learning in the Age of the Adaptive Tutor

Ostrow, K. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19773-9_131 · Cited by 2 · see citing works

Untitled work

(1999)

Application and theory gaps during the rise of artificial intelligence in education

Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100002 · Cited by 1,003 · see citing works

Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023) · Education Sciences · doi:10.3390/educsci13121216 · Cited by 980 · see citing works

AI-enabled adaptive learning systems: A systematic mapping of the literature

Kabudi, T., Pappas, I., & Olsen, D. H. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100017 · Cited by 734 · see citing works

A systematic literature review of personalized learning terms

Shemshack, A., & Spector, J. M. (2020) · Smart Learning Environments · doi:10.1186/s40561-020-00140-9 · Cited by 539 · see citing works

A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda

Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, et al. (2024) · Smart Learning Environments · doi:10.1186/s40561-024-00350-5 · Cited by 235 · see citing works

Adaptive gamification in E‐learning: A literature review and future challenges

Bennani, S., Maalel, A., & Ghézala, H. B. (2021) · Computer Applications in Engineering Education · doi:10.1002/cae.22477 · Cited by 198 · see citing works

Personalised and Adaptive Learning: Emerging Learning Platforms in the Era of Digital and Smart Learning

Kem, D. (2022) · International Journal of Social Science and Human Research · doi:10.47191/ijsshr/v5-i2-02 · Cited by 163 · see citing works

A comprehensive analysis of personalized learning components

Shemshack, A., Kinshuk, & Spector, J. (2021) · Journal of Computers in Education · doi:10.1007/s40692-021-00188-7 · Cited by 133 · see citing works

Harnessing AI for education 4.0: Drivers of personalized learning.

Barrera Castro, G. P., Chiappe, A., Becerra Rodriguez, D. F., & Sepulveda, F. G. (2024) · Electronic Journal of e-Learning · doi:10.34190/ejel.22.5.3467 · Cited by 132 · see citing works

A systematic literature review on personalised learning in the higher education context

Fariani, R. I., Junus, K., & Santoso, H. B. (2023) · Technology, Knowledge and Learning · doi:10.1007/s10758-022-09628-4 · Cited by 141 · see citing works

A two-phase machine learning approach for predicting student outcomes

Iatrellis, O., Savvas, I. Κ., Fitsilis, P., & Gerogiannis, V. C. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10260-x · Cited by 110 · see citing works

New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests

Herce-Zelaya, J., Porcel, C., Bernabé-Moreno, J., Tejeda-Lorente, Á., & Herrera‐Viedma, E. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.071 · Cited by 105 · see citing works

A Learning Analytics Theoretical Framework for STEM Education Virtual Reality Applications

Christopoulos, A., Πέλλας, Ν., & Laakso, M. (2020) · Education Sciences · doi:10.3390/educsci10110317 · Cited by 68 · see citing works

Smart Classrooms: How Sensors and AI Are Shaping Educational Paradigms

Zhang, X., Ding, Y., Huang, X., Li, W., Long, L., & Ding, S. (2024) · Sensors · doi:10.3390/s24175487 · Cited by 66 · see citing works

Multi-Sensor Eye-Tracking Systems and Tools for Capturing Student Attention and Understanding Engagement in Learning: A Review

Wang, Y., Lu, S., & Harter, D. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3105706 · Cited by 64 · see citing works

Implementation of Adaptive Learning Systems: Current State and Potential

Imhof, C., Bergamin, P. B., & McGarrity, S. (2020) · Cognition and exploratory learning in the digital age · doi:10.1007/978-3-030-48190-2_6 · Cited by 65 · see citing works

In-service teachers' TPACK development through an adaptive e-learning environment (ALE)

Elmaadaway, M. A. N., & Abouelenein, Y. A. M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11477-8 · Cited by 67 · see citing works

The Integration of Advanced AI-Enabled Emotion Detection and Adaptive Learning Systems for Improved Emotional Regulation

Shi, L. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241296890 · Cited by 67 · see citing works

Dynamic Cognitive Diagnosis: An Educational Priors-Enhanced Deep Knowledge Tracing Perspective

Wang, F., Huang, Z., Liu, Q., Chen, E., Yin, Y., Ma, J., & Wang, S. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3254544 · Cited by 47 · see citing works

Learning analytics for student modeling in virtual reality training systems: Lineworkers case

Santamaría-Bonfil, G., Ibáñez, M. B., Pérez-Ramírez, M., Figueroa, G., & Alvarez, F. M. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103871 · Cited by 47 · see citing works

Ontology-based E-learning Content Recommender System for Addressing the Pure Cold-start Problem

Joy, J., Raj, N. S., & Renumol, V. G. (2021) · Journal of Data and Information Quality · doi:10.1145/3429251 · Cited by 43 · see citing works

Artificial Intelligence in Special Education

Harkins-Brown, A. R., Carling, L. Z., & Peloff, D. C. (2025) · Encyclopedia · doi:10.3390/encyclopedia5010011 · Cited by 41 · see citing works

Predicting the percentage of student placement: A comparative study of machine learning algorithms

Çakıt, E., & Dağdeviren, M. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10655-4 · Cited by 36 · see citing works

Evaluating the Potential of Artificial Intelligence in Islamic Religious Education

Abubakari, M. S., Shafik, W., & Hidayatullah, A. F. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2728-9.ch010 · Cited by 35 · see citing works

Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Abdullah, N. A., Rasheed, R. A., Nasir, M. H. N. M., & Rahman, M. M. (2021) · Applied Sciences · doi:10.3390/app11209608 · Cited by 34 · see citing works

Virtual reality as a pedagogical tool: An experimental study of English learner in lower elementary grades

Chang, H., Park, J., & Suh, J. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11988-y · Cited by 43 · see citing works

The Effect of AI ‐Based Systems on Mathematics Achievement in Rural Context: A Quantitative Study

Khazanchi, R., Mitri, D. D., & Drachsler, H. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13098 · Cited by 33 · see citing works

A Taxonomy of Various Applications of Artificial Intelligence in Education

Rana, P. P., Gupta, L. R., Kumar, G., & Dubey, M. K. (2021) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem51511.2021.9445339 · Cited by 31 · see citing works

AI-Based Adaptive Learning: A Systematic Mapping of the Literature

Ezzaim, A., Dahbi, A., Haidine, A., & Aqqal, A. (2023) · JUCS - Journal of Universal Computer Science · doi:10.3897/jucs.90528 · Cited by 46 · see citing works

Alleviating the Cold Start Problem in Adaptive Learning using Data-Driven Difficulty Estimates

van der Velde, M., Sense, F., Borst, J. P., & van Rijn, H. (2021) · Computational Brain & Behavior · doi:10.1007/s42113-021-00101-6 · Cited by 30 · see citing works

Adaptive learning: a cluster-based literature review (2011-2022)

Фадєєва, Л. О. (2023) · Educational Technology Quarterly · doi:10.55056/etq.613 · Cited by 27 · see citing works

Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions

Li, X., Ouyang, F., & Chen, W. (2022) · Journal of Computing in Higher Education · doi:10.1007/s12528-022-09321-6 · Cited by 26 · see citing works

Smart Natural Disaster Relief: Assisting Victims with Artificial Intelligence in Lending

Liu, Y., Li, X., & Zheng, Z. (2023) · Information Systems Research · doi:10.1287/isre.2023.1230 · Cited by 26 · see citing works

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Bai, Y., Li, X., Liu, Z., Huang, Y., Guo, T., Hou, M., Xia, F., & Luo, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125988 · Cited by 24 · see citing works

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Cao, W., Wang, Q., Sbeih, A. H., & Shibly, F. H. A. (2020) · INTELIGENCIA ARTIFICIAL · doi:10.4114/intartif.vol23iss66pp112-123 · Cited by 24 · see citing works

AI-based multidisciplinary framework to assess the impact of gamified video-based learning through schema and emotion analysis

Vidanaralage, A. J., Dharmaratne, A. T., & Haque, S. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100109 · Cited by 30 · see citing works

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Gao, Q., Zhang, S., Cai, Z., Liu, K., Hui, N., & Tong, M. (2022) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2022.101097 · Cited by 23 · see citing works

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Konca, A. S., Simsar, A., Alhajji, R., & Almansoori, A. G. A. M. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2488143 · Cited by 20 · see citing works

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Park, J. Y., Dedja, K., Pliakos, K., Kim, J., Joo, S., Frederik Cornillie, B., Vens, C., & Noortgate, W. V. D. (2022) · Behavior Research Methods · doi:10.3758/s13428-022-01910-8 · Cited by 18 · see citing works

The Classical Test or Item Response Measurement Theory: The Status of the Framework at the Examination Council of Lesotho

Ayanwale, M. A., Chere-Masopha, J., & Morena, M. C. (2022) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.21.8.22 · Cited by 16 · see citing works

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Imamah, I., Yuhana, U. L., Djunaidy, A., & Purnomo, M. H. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100280 · Cited by 16 · see citing works

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Ruipérez‐Valiente, J. A., Kim, Y. J., Baker, R. S., Martínez, P. A., & Lin, G. C. (2022) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2022.3203912 · Cited by 14 · see citing works

A Methodological Review of Machine Learning in Applied Linguistics

Lin, Z. (2020) · English Language Teaching · doi:10.5539/elt.v14n1p74 · Cited by 12 · see citing works

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Anupama, V., & Elayidom, M. S. (2022) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs54159.2022.9785353 · Cited by 12 · see citing works

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Kang, N., Liu, L., & Zhang, S. (2025) · Journal of Nursing Management · doi:10.1155/jonm/6689213 · Cited by 11 · see citing works

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Abbakumov, D., Desmet, P., & Noortgate, W. V. D. (2020) · Psychologica Belgica · doi:10.5334/pb.515 · Cited by 11 · see citing works

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Yuliansyah, H., Othman, Z. A., & Bakar, A. A. (2022) · International Journal of Advances in Intelligent Informatics · doi:10.26555/ijain.v8i3.882 · Cited by 10 · see citing works

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Faresta, R. A. (2024) · Path of Science · doi:10.22178/pos.104-19 · Cited by 24 · see citing works

Digital twin of patient in clinical workflow

Wickramasinghe, N. S., Ulapane, N. N. B., Andargoli, A., Shuakat, M. N., Nguyen, T. A., Zelcer, J., & Vaughan, S. (2023) · Proceedings of the Royal Society of Victoria · doi:10.1071/rs23013 · Cited by 9 · see citing works

Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Kline, A. S., Kline, T. J. B., Abad, Z. S. H., & Lee, J. (2020) · Journal of Medical Internet Research · doi:10.2196/20268 · Cited by 9 · see citing works

A new modification and application of item response theory‐based feature selection for different machine learning tasks

Çoban, Ö. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7282 · Cited by 9 · see citing works

IT Higher Education Teachers and Trust in AI-Enabled Ed-Tech: Implications for Adoption of AI in Higher Education

Aladi, C. C. (2024) · ACM SIGCPR Conference on Computer Personnel Research (SIGCPR) · doi:10.1145/3632634.3655852 · Cited by 10 · see citing works

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van der Velde, M., Sense, F., Borst, J. P., & van Rijn, H. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09401-5 · Cited by 9 · see citing works

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Niemeijer, K., Feskens, R. C. W., Krempl, G., Koops, J., & Brinkhuis, M. J. S. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375486 · Cited by 7 · see citing works

Behavior testing of load forecasting models using BuildChecks

Deng, Y., Fan, J., Jiang, H., He, F., Wang, D., Li, A., & Xiao, F. (2022) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3538637.3538841 · Cited by 8 · see citing works

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Plass, J. L., & Froehlich, F. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102813 · Cited by 11 · see citing works

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Vermeiren, H., Hofman, A. D., Bolsinova, M. A., van der Maas, H. L. J., & Noortgate, W. V. D. (2025) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-025-09439-z · Cited by 7 · see citing works

Self-Attention Gated Cognitive Diagnosis For Faster Adaptive Educational Assessments

Pei, X., Yang, S., Huang, J., & Xu, C. (2022) · Proceedings · doi:10.1109/icdm54844.2022.00051 · Cited by 7 · see citing works

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Pearson, B. M., Lacombe, D. J., & Khatun, N. (2024) · Econometrics · doi:10.3390/econometrics12040033 · Cited by 7 · see citing works

Item response theory as a feature selection and interpretation tool in the context of machine learning

Kline, A. S., Kline, T. J. B., & Lee, J. (2021) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02301-x · Cited by 7 · see citing works

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Huda, A., Firdaus, F., Irfan, D., Hendriyani, Y., Almasri, A., & Sukmawati, M. (2024) · JOIV International Journal on Informatics Visualization · doi:10.62527/joiv.8.1.2217 · Cited by 6 · see citing works

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Malasaga, E. V., Arguson, A. C., Andales, J. K. F., Ambat, S. C., Paculanan, R. S., & Pablo, M. F. D. S. (2023) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem60674.2023.10589068 · Cited by 6 · see citing works

The Uses of Machine Learning (ML) in Teaching and Learning English Language: A Methodical Review.

AlHarbi, A. A. (2022) · المجلة التربوية لکلية التربية بسوهاج · doi:10.21608/edusohag.2022.212355 · Cited by 5 · see citing works

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Ramani, P. (2022) · World Journal of Educational Research · doi:10.22158/wjer.v9n2p56 · Cited by 5 · see citing works

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Cansiz, S., Sudan, B., Ogretici, E., & Aktaş, M. S. (2020) · Annals of Computer Science and Information Systems · doi:10.15439/2020f138 · Cited by 5 · see citing works

Development of Text Classification Based on Difficulty Level in Adaptive Learning System using Convolutional Neural Network

Imamah, I., Yuhana, U. L., Djunaidy, A., & Purnomo, M. H. (2021) · International Electronics Symposium (IES) · doi:10.1109/ies53407.2021.9594021 · Cited by 5 · see citing works

AI and Competency-Based Education: Moving Beyond Traditional Approaches

Yahaya, W. A. J. W., & Zaini, K. M. (2025) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-56848-0_4 · Cited by 6 · see citing works

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Srivastav, G., Kant, S., Srivastava, D., & Vats, S. (2023) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf58270.2023.10235135 · Cited by 5 · see citing works

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Денищева, Л. О., Safuanov, I., & Semenyachenko, Y. A. (2024) · The Education and science journal · doi:10.17853/1994-5639-2024-3-40-68 · Cited by 4 · see citing works

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Islam, M. R. (2024) · SN Social Sciences · doi:10.1007/s43545-024-00966-x · Cited by 4 · see citing works

Adaptive task selection in automated educational software: a comparative study

Azoulay, R., David, E., Avigal, M., & Hutzler, D. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-823410-5.00008-5 · Cited by 4 · see citing works

Is the Area Under Curve Appropriate for Evaluating the Fit of Psychometric Models?

Han, Y., Zhang, J., Jiang, Z., & Shi, D. (2022) · Educational and Psychological Measurement · doi:10.1177/00131644221098182 · Cited by 4 · see citing works

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Emrani, S. E., Palomo‐Duarte, M., Mota, J. M., & Dodero, J. M. (2022) · ICST Transactions on Scalable Information Systems · doi:10.4108/eetsis.v9i6.2713 · Cited by 4 · see citing works

Sequential Rule Mining on the Student Behavior Data of an E-Learning Platform in the Field of Financial Sciences: Case Study

Aktas, D. E., & Aktaş, M. S. (2021) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce52056.2021.9514230 · Cited by 4 · see citing works

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He, L., Tang, J., Li, X., & Wang, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62005-9_22 · Cited by 4 · see citing works

Prediction of musculoskeletal discomfort through integration of item response theory and machine learning

da Silva, J. M. N., Santos, A. O., Tavares, J., Vieira, E. M. D. A., Leite, W. K. D. S., Bispo, L. G. M., & Martins, V. W. B. (2026) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2026.103899 · Cited by 3 · see citing works

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Bennani, S., Maalel, A., Ghézala, H. B., & Daouahi, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16014-1_6 · Cited by 3 · see citing works

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<p>Guo Xinyu, C. C. (2023) · Frontiers in Educational Research · doi:10.25236/fer.2023.062405 · Cited by 3 · see citing works

Artificial Intelligence-Supported Teacher Tools to Increase Participation in Online Courses

Üstün, A. G., & Yavuz, M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-4268-8.ch005 · Cited by 3 · see citing works

A Note on Equivalent and Nonequivalent Parametrizations of the Two-Parameter Logistic Item Response Model

Robitzsch, A. (2024) · Information · doi:10.3390/info15110668 · Cited by 3 · see citing works

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Şimşek, A. C., Anders, G., Göth, J., Specht, L., & Huff, M. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100512 · Cited by 6 · see citing works

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Riofrío-Luzcando, D., Ramírez, J., & Berrocal‐Lobo, M. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131158 · Cited by 2 · see citing works

LSTM-Based Top N Recommendation System using Cognitive Data

Dey, A. K., Chauhan, V. K., Singh, P. K., & Choudhury, P. (2022) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart55829.2022.10046838 · Cited by 2 · see citing works

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Liang, C., Horikoshi, I., Majumdar, R., & Ogata, H. (2024) · Research and Practice in Technology Enhanced Learning · doi:10.58459/rptel.2025.20012 · Cited by 2 · see citing works

A knowledge recommendation algorithm based on tacit knowledge relation calculation

Zhou, Y., Hong, L., & Zhu, W. (2020) · IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC) · doi:10.1109/itaic49862.2020.9339106 · Cited by 2 · see citing works

Continuous Student Knowledge Tracing Using SVD and Concept Maps

Teodorescu, O. M., Popescu, P. Ș., MOCANU, L. M., & Mihăescu, M. C. (2021) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2021.01008 · Cited by 2 · see citing works

Machine Learning Predictive Model of Academic Achievement Efficiency based on Data Envelopment Analysis

Razi, N. F. M., Baharun, N., & Omar, N. (2022) · Mathematical Sciences and Informatics Journal · doi:10.24191/mij.v3i1.18284 · Cited by 2 · see citing works

Knowledge-Concept Diagnosis from fMRIs by Using a Space-Time Embedding Graph Convolutional Network

Ye, L., Zhang, Y., Lin, Y., & Shang, X. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-6222-8_9 · Cited by 2 · see citing works

Model of the Cold-Start Recommender System Based on the Petri-Markov Nets

Чипчагов, М. С., & Kublik, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77964-1_7 · Cited by 2 · see citing works

Creating and Visualising Cognitive Maps of Knowledge Diagnosis During the Processing of Learning Digital Footprint

Uglev, V. A., & Александрович, С. О. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80421-3_11 · Cited by 2 · see citing works

Position Papers of the 2020 Federated Conference on Computer Science and Information Systems

(2020) · Annals of Computer Science and Information Systems · doi:10.15439/978-83-959183-0-8 · Cited by 2 · see citing works

Attendance and learning platforms in college course success

Chang, I. (2025) · Active Learning in Higher Education · doi:10.1177/14697874251347189 · Cited by 1 · see citing works

Explaining Person-by-Item Responses using Person- and Item-Level Predictors via Random Forests and Interpretable Machine Learning in Explanatory Item Response Models

Cho, S., Amanda, G., Salas, J. A., & Mueller, S. (2025) · Psychometrika · doi:10.1017/psy.2025.10032 · Cited by 2 · see citing works

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Liyanawatta, M., Yang, S., Cai, M.-Y., Yu, S.-Y., Wang, J., Chang, C.-K., & Chen, G. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13806-z · Cited by 1 · see citing works

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Unterschemmann, S., Christiansen, H., & Kettemann, B. (2025) · Child Psychiatry & Human Development · doi:10.1007/s10578-025-01932-1 · Cited by 1 · see citing works

What makes listening comprehension difficult?: A feature-based machine learning approach to understanding item difficulty

Cai, H., Yan, X., Chuang, P.-L., Pan, Y., & Huo, M. (2025) · Applied Linguistics · doi:10.1093/applin/amaf079 · Cited by 1 · see citing works

FuMoE-csKT: fusing disentangled and self-attention in a personalized mixture-of-experts framework for cold-start knowledge tracing

Li, S., Noah, S. A. M., Adam, A., & Majid, N. A. A. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3922 · Cited by 1 · see citing works

AI and student motivation: A scoping review

Li, J., King, R. B., Chai, C. S., Lee, V. W. Y., & Li, R. (2026) · Journal of Research on Technology in Education · doi:10.1080/15391523.2026.2672515 · Cited by 1 · see citing works

Fifty years of topic development, thematic combination, and AI's evolving reach in Computers & Education

Wu, J., Shih, A.-C., Lee, Y., Chai, C. S., & Tsai, C. (2026) · Computers & Education · doi:10.1016/j.compedu.2026.105755 · Cited by 1 · see citing works

Use of Different Variants of Item Response Theory-Based Feature Selection Method for Text Categorization

Çoban, Ö. (2022) · International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) · doi:10.1109/ictacse50438.2022.10009653 · Cited by 1 · see citing works

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Merembayev, T., Amirgaliyeva, S. N., & Kozhaly, K. B. (2021) · IEEE International Conference on Smart Information Systems and Technologies (SIST) · doi:10.1109/sist50301.2021.9465896 · Cited by 5 · see citing works

An explanatory item response theory method for alleviating the cold-start problem in adaptive learning environments

Park, J. Y., Joo, S., Frederik Cornillie, B., van der Maas, H. L. J., & Noortgate, W. V. D. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1166-9 · Cited by 45 · see citing works

A Hybrid Neural-IRT Framework for Addressing Cold-Start Challenges in Computerized Adaptive Testing

Искакова, А. М., Salykova, O., Дидарбекова, Н. А., Иванова, И., Akmoldina, A., & Zhumadillayeva, A. (2026) · Computers · doi:10.3390/computers15020132 · Cited by 1 · see citing works

A Learning Ability Evaluation Method Based On Item Response Theory and Machine Learning Method

Xu, Y., Li, Q., Dong, H., & Chen, Y. (2020) · International Conference on Big Data and Education (ICBDE) · doi:10.1145/3396452.3396455 · Cited by 5 · see citing works

Adaptive item‐based learning environments based on the item response theory: possibilities and challenges

Wauters, K., Desmet, P., & Noortgate, W. V. D. (2010) · Journal of Computer Assisted Learning · doi:10.1111/j.1365-2729.2010.00368.x · Cited by 137 · see citing works