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

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

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 25,543

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 21,030

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 16,616

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

Classification and regression trees

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

Machine learning: Trends, perspectives, and prospects

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

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

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

Test Theory: A Unified Treatment

Zwick, R., & McDonald, R. P. (2000) · Journal of the American Statistical Association · doi:10.2307/2669496 · Cited by 3,641

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

Untitled work

(1999) · Cited by 2,653

Proceedings of the 23rd international conference on Machine learning

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

Multi-Label Classification

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

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

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 2,323

Data Mining and Knowledge Discovery Handbook

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

Introduction to Measurement Theory

Allen, M. J. (1979) · Cited by 2,124

Untitled work

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

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

Graph-based approach for airborne light detection and ranging segmentation

Vilarino, 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

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 1,540

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

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

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

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

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

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

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

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

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

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

Rizopoulos, D. (2011) · Cited by 529

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

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

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 477

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_29 · Cited by 470

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 416

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

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

Adaptive E-Learning

Shute, V. J., & Towle, B. (2003) · Educational Psychologist · doi:10.1207/s15326985ep3802_5 · Cited by 286

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 285

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

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

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

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

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

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

A novel Bayesian similarity measure for recommender systems

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

Proceedings of the 8th ACM Conference on Recommender systems

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

Advances in Intelligent Systems and Computing

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

Comparative recommender system evaluation

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

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 157

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 149

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 149

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

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

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

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

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 126

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

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

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

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

Factorization Models for Forecasting Student Performance

Thai-Nghe, N., Horváth, T., & Schmidt-Thieme, L. (2011) · Educational Data Mining · Cited by 92

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Deep knowledge tracing

Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., & Sohl-Dickstein, J. (2015) · arXiv · doi:10.48550/arxiv.1506.05908

Untitled work

(2014) · doi:10.5555/2627435.2697065

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

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

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

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 740

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

Using item response theory in machine learning algorithms for student response data

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

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

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

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

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