Publications Using
Public Datasets

Dr Heffernan is proud that his data sets have been used in over 100 papers, written by others.  This page organizes what are the common used data sets and also lists the first 100 we are aware of. (We have had to stop counting)

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Datasets that are used by these publications:

1.  ASSISTments 2009-10; ASSIST2009-10. (63 papers below)

2.  ASSISTments 2012-13; ASSIST2012-13. (18 papers below)

3. Competition predicting students' future careers (70 people competed) ASSISTments2017CompetitionSTEMCarerer (Was called PREDICT-STEM-CAREER & ASSISTments2017Competition but these names caused Confusion)  This data set is referred to as ASSISTCHALL and ASSIST2017 interchangeably. (29 papers below)

4.  ASSISTments 2015-16; ASSIST2015-16. (26 papers below)

5.  Dataset on 22 experiments in ASSISTments ; 22Experiments. (2+ paper below)

6.  Earlier datasets are stored at the PSLC in what is called the WPI-ASSISTments project that contains three data sets; ASSIST2004-05 (912 students), ASSIST2005-06 (3136 students), and ASSIST2006-07 (5046 students). (4 papers below)

7.  Dr. Zach Pardos used datasets referred to as G6.207, G7.233, G6.217. (2+ papers below). Dr Heffernan has lost track of that data.

8. ASSISTments2012-13WithBaker'sAffect;  (1+ papers below) 

9. ASSISTmentsSkillbuildersFrom2016-17 (1 paper)

10.  The TeachASSIST data from Patikorn & Heffernan (2020). (0 papers below)

Patikorn, T. & Heffernan, N. T. (2020, August 12) Effectiveness of Crowd-Sourcing On-Demand Tutoring from Teachers in Online Learning Platforms. Proceedings of the Seventh ACM Conference on Learning @ Scale (L@S). Pages 115–124. https://doi.org/10.1145/3386527.3405912. Best Student Paper Awardee. Video of talk

11. Toward-Personalizing-2021 (0 Papers below)

Prihar, E., Patikorn, T., Botelho, A., Sales, A., & Heffernan, N. (2021). Towards Personalizing Students' Education with Crowdsourced Tutoring. Learning@Scale 2021. Pages 37–45 https://doi.org/10.1145/3430895.3460130  Camera Ready Copy 

12.  CODE-2022 Release: Presented at CODE2022

This paper used the data set from the 89-Experiment-2021 release.  The 250 number comes from the fact that experiments had more than 2 conditions. 

Sales, A., Prihar, E., Gagnon-Bartsch, J., Gurung, A & Heffernan, N. (2022) The More Powerful A/B Testing using Auxiliary Data and Deep earning. Conference on Digital Experimentation (CODE 2022). 

13. The-89-Experiments-2022 release;  (0 papers below but some are coming) 

Prihar, E., Syed, M., Ostrow, K., Shaw, S., Sales, A., & Heffernan, N. (2022). Exploring Common Trends in Online Educational Experiments. Proceedings of the 15th International Educational Data Mining Conference. Held in Durham, England., July 2022. Winner of "Best Data Set" Award.  

14. "ASSIST-L@S-22" : Learning at Scale paper that won best data set.

Prihar, E., Haim, A., Sales, A., & Heffernan, N. (2022). Automatic Interpretable Personalized Learning. Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S ’22), June 1–3, 2022, New York City, NY, USA.11 pages. PDF. https://doi.org/10.1145/3491140.3528267  Nominated for Best Paper and won "Best Data Set" for releasing a valuable dataset that lets external researchers try out their personalization models. 

15. Neil calls this the "Problem Bodies 2012 v2" dataset.  WPI considers this data proprietary and only gives it away with a signed license. See here  

16. Student Engagement During Remote Learning 

Prihar, E., Botelho, A., Yuen, J., Corace, M., Shanaj, A., Dia, Z., & Heffernan, N. (April, 2021). Student Engagement During Remote Learning. In Companion Proceedings 11th International Conference on Learning Analytics & Knowledge (pp. 49-51).

17. This data set with 33 experiment first appeared in : 

Gagnon-Bartsch, J. A., A. C. Sales*, J. A.,  Wu, E.,  Botelho,  A. F.,  Erickson,  J. A., Miratrix,  L. W. & Heffernan, N. T. (Accepted 2023) Precise unbiased estimation in randomized experiments using auxiliary observational data. Journal of Casual Inference.  Volume and page numbers.  Data set is here: https://osf.io/j6esa/ 

The list below is the first 100 or so paper but the above views in Airtable is where I moving my maintance of these papers.  So look into airtable instead of this list.  

104. Ghodai Abdelrahman, Qing Wang, and Bernardo Nunes. 2023. Knowledge Tracing: A Survey. ACM Comput. Surv. 55, 11, Article 224 (November 2023), 37 pages. https://doi.org/10.1145/3569576

103. Zhang, K., & Yao, Y. (2018). A three learning states Bayesian knowledge tracing model. Knowledge-Based Systems, 148, 189-201. This paper used three datasets: ASSIST2004-05 (912 students), ASSIST2005-06 (3136 students), and ASSIST2006-07 (5046 students).

102. Later published as  Hang Su, Xin Liu, Shanghui Yang, and Xuesong Lu*. Deep knowledge tracing with learning curves. Frontiers in Psychology 14 (2023). 

101. Benedetto, L., Aradelli, G., Cremonesi, P., Cappelli, A., Giussani, A., & Turrin, R. (2021, April). On the application of Transformers for estimating the difficulty of Multiple-Choice Questions from text. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications (pp. 147-157).  https://aclanthology.org/2021.bea-1.16/  This paper uses the  "Problem Bodies 2012 v1" dataset.

100. Cai, Y., Xu, B., & Wu, H. (2022, February). Research on time enhanced cognitive diagnosis in intelligent education system. In Proceedings of the 5th International Conference on Big Data and Education (pp. 22-29). https://doi.org/10.1145/3524383.3524430. Uses the ASSIST 2009-10 and ASSIST 2008-09 datasets.

99. He, Z., Li, W., & Yan, Y. (2022). Modeling knowledge proficiency using multi-hierarchical capsule graph neural network. Applied Intelligence, 52(7), 7230-7247. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSIST 2017 datasets. 

98. Luo, Y., Xiao, B., Jiang, H., & Ma, J. (2022, January). Heterogeneous graph based knowledge tracing. In 2022 11th International Conference on Educational and Information Technology (ICEIT) (pp. 226-231). IEEE. https://doi.org/10.1109/ICEIT54416.2022.9690737. Uses the ASSIST 2009-10 and ASSIST 2017 datasets. 

97. Lyu, L., Wang, Z., Yun, H., Yang, Z., & Li, Y. (2022). Deep Knowledge Tracing Based on Spatial and Temporal Representation Learning for Learning Performance Prediction. Applied Sciences, 12(14), 7188. https://doi.org/10.3390/app12147188. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSISTCHALL datasets.

96. Shen, S., Huang, Z., Liu, Q., Su, Y., Wang, S., & Chen, E. (2022, July). Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 427-437). https://doi.org/10.1145/3477495.3531939. Uses the ASSIST 2012-13 data set.

95. Su, Y., Cheng, Z., Wu, J., Dong, Y., Huang, Z., Wu, L., ... & Xie, F. (2022). Graph-based cognitive diagnosis for intelligent tutoring systems. Knowledge-Based Systems, 253, 109547. https://doi.org/10.1016/j.knosys.2022.109547. Uses the ASSIST 2009-10 and ASSIST 2017 datasets.

94. Tsutsumi, E., Guo, Y., & Ueno, M. (2022). DeepIRT with a hypernetwork to optimize the degree of forgetting of past data. In A. Mitrovic and N. Bosch, editors, Proceedings of the 15th International Conference on Educational Data Mining, (pp. 543–548). International Educational Data Mining Society. Uses the ASSIST 2009-10 and ASSIST 2017 datasets.

93. Wang, X., Zheng, Z., Zhu, J., & Yu, W. (2022). What is wrong with deep knowledge tracing? Attention-based knowledge tracing. Applied Intelligence, 1-12. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSISTCHALL datasets.

92. Zhao, Z., Liu, Z., Wang, B., Ouyang, L., Wang, C., & Ouyang, Y. (2022, May). Research on Deep Knowledge Tracing Model Integrating Graph Attention Network. In 2022 Prognostics and Health Management Conference (PHM-2022 London) (pp. 389-394). IEEE. https://doi.org/10.1109/PHM2022-London52454.2022.00074. Uses the ASSIST 2009-10 and ASSIST 2017 datasets. 

91. Abdelrahman, G., & Wang, Q. (2021). Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing. https://doi.org/10.48550/arXiv.2108.08105. Uses the ASSIST 2009-10 data set. 

90. Abdelrahman, G., & Wang, Q. (2021). Learning Data Teaching Strategies Via Knowledge Tracing. https://doi.org/10.48550/arXiv.2111.07083. Uses the ASSIST 2009-10 data set.

89. Adjei, S.A., Baker, R.S., & Bahel, V. (2021). Seven-Year Longitudinal Implications of Wheel Spinning and Productive Persistence. In Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V. (eds). Artificial Intelligence in Education.  AIED. Lecture Notes in Computer Science, vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_2. Uses the ASSISTCHALL data set. 

88. Guo, X., Huang, Z., Gao, J., Shang, M., Shu, M., & Sun, J. (2021, October). Enhancing Knowledge Tracing via Adversarial Training. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 367-375). https://doi.org/10.1145/3474085.3475554. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSIST 2017 datasets.

87. He, L. (2021, July). Integrating performance and side factors into embeddings for deep Learning-Based knowledge tracing. In 2021 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE. https://doi.org/10.1109/ICME51207.2021.9428154. Uses the ASSIST 2009-10 and ASSIST 2017 datasets. 

86. He, L., Li, X., Tang, J., & Wang, T. (2021, April). EDKT: an extensible deep knowledge tracing model for multiple learning factors. In International Conference on Database Systems for Advanced Applications (pp. 340-355). Springer, Cham. Uses the ASSIST 2009-10 and ASSIST 2017 datasets. 

85. He, Z., Kuang, J., Li, W., & Yan, Y. (2021, July). Using Cognitive Interest Graph and Knowledge-activated Attention for Learning Resource Recommendation. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 93-102). IEEE. Uses the ASSIST 2009-10 and ASSIST 2017 datasets.

84. Jiang, C., Gan, W., Su, G., Sun, Y., & Sun, Y. (2021) Improving Knowledge Tracing through Embedding based on Metapath. In Proceedings of the 29th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education. Uses the ASSIST 2009-10 and ASSIST 2012-13 datasets. 

83. Kim, S., Kim, W., Jung, H., & Kim, H. (2021, June). DiKT: Dichotomous Knowledge Tracing. In International Conference on Intelligent Tutoring Systems (pp. 41-51). Springer, Cham. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

82. Lee, S., Choi, Y., Park, J., Kim, B., & Shin, J. (2021). Consistency and monotonicity regularization for neural knowledge tracing. Uses the ASSIST 2015-16 and ASSISTCHALL datasets.

81. Li, Z., Ren, C., Li, X., Pardos, Z. A. (2021). Learning Skill Equivalencies Across Platform Taxonomies. In Learning Analytics and Knowledge (LAK). Irvine, CA, USA. ACM.  Uses the ASSIST 2012-13 data set.

80. Liu, C., & Li, X. (2021, August). Memory Attentive Cognitive Diagnosis for Student Performance Prediction. In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (pp. 79-90). Springer, Singapore. Uses the ASSIST 2009-10 data set. 

79. Liu, C., & Li, X. (2021, November). Multi-factor memory attentive model for knowledge tracing. Proceedings of The 13th Asian Conference on Machine Learning in Proceedings of Machine Learning Research 157 (pp. 856-869). PMLR. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSIST 2017 datasets.

78. Liu, Q., Shen, S., Huang, Z., Chen, E., & Zheng, Y. (2021). A Survey of Knowledge Tracing. They did not release their data set. 

77. Long, T., Liu, Y., Shen, J., Zhang, W., & Yu, Y. (2021, July). Tracing knowledge state with individual cognition and acquisition estimation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 173-182). https://doi.org/10.1145/3404835.3462827. Uses the ASSIST 2009-10 and ASSIST 2012-13 datasets.

76. Ma, R., Zhang, L., Li, J., Mei, B., Ma, Y., & Zhang, H. (2021, August). DTKT: An Improved Deep Temporal Convolutional Network for Knowledge Tracing. In 2021 16th International Conference on Computer Science & Education (ICCSE) (pp. 794-799). IEEE. https://doi.org/10.1109/ICCSE51940.2021.9569258. Uses the ASSIST 2009-10 and ASSIST 2012-13 datasets.

75. Ma, Y., & Lu, W. (2021, November). Design and Implementation of Learning System Based on T-LSTM. In International Conference on Web-Based Learning (pp. 148-153). Springer, Cham. Uses the ASSIST 2009-10 data set. 

74. Ouyang, Y., Zhou, Y., Zhang, H., Rong, W., & Xiong, Z. (2021, June). Pakt: A position-aware self-attentive approach for knowledge tracing. In International Conference on Artificial Intelligence in Education (pp. 285-289). Springer, Cham. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets.

73. Pu S., Converse G., Huang Y. (2021). Deep Performance Factors Analysis for Knowledge Tracing. In Roll I., McNamara D., Sosnovsky S., Luckin R., Dimitrova V. (eds) Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12748. Springer, Cham. https://doi.org/10.1007/978-3-030-78292-4_27. Uses the ASSISTCHALL data set.

72. Qin, X., Li, Z., Gao, Y., & Xue, T. (2021, December). Knowledge Tracing With Learning Memory and Sequence Dependence. In 2021 IEEE International Conference on Engineering, Technology & Education (TALE) (pp. 01-06). IEEE. https://doi.org/10.1109/TALE52509.2021.9678654. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

71. Shen, S., Liu, Q., Chen, E., Huang, Z., Huang, W., Yin, Y., ... & Wang, S. (2021, August). Learning process-consistent knowledge tracing. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1452-1460). https://doi.org/10.1145/3447548.3467237. Uses the ASSIST 2012-13 and ASSISTCHALL datasets.

70. Sheng, D., Yuan, J., & Zhang, X. (2021). Grasping or forgetting? MAKT: A dynamic model via multi-head self-attention for knowledge tracing. In 33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021 (pp. 399-404). Uses the ASSIST 2009-10 data set. 

69. Tsutsumi, E., Kinoshita, R., & Ueno, M. (2021). Deep-IRT with Independent Student and Item Networks. International Educational Data Mining Society. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets

68. Wang, M., Peng, C., Yang, R., Wang, C., Chen, Y., & Yu, X. (2021, August). GASKT: A Graph-Based Attentive Knowledge-Search Model for Knowledge Tracing. In International Conference on Knowledge Science, Engineering and Management (pp. 268-279). Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_22. Uses the ASSIST 2009-10 and ASSISTCHALL datasets. 

67. Wu, H., Xu, B., & Cai, Y. (2021, April). Exponent-Enhanced Attentive Knowledge Tracing based Online Learning Reinforcing. In 2021 2nd International Conference on Big Data and Informatization Education (ICBDIE) (pp. 147-150). IEEE. https://doi.org/10.1109/ICBDIE52740.2021.00041. Uses the ASSIST 2009-10 and ASSIST 2017 datasets. 

66. Zeng, J., Zhang, Q., Xie, N., & Yang, B. (2021). Application of deep self-attention in knowledge tracing. Uses the ASSIST 2009-10 and ASSIST 2012-13 datasets. 

65. Zhang, J., Mo, Y., Chen, C., & He, X. (2021, July). GKT-CD: Make Cognitive Diagnosis Model Enhanced by Graph-based Knowledge Tracing. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533298. Uses the ASSIST 2009-10 data set. 

64. Zhang, M., Zhu, X., & Ji, Y. (2021, January). Input-Aware Neural Knowledge Tracing Machine. In International Conference on Pattern Recognition (pp. 345-360). Springer, Cham. Uses the ASSIST 2009-10 and ASSIST 2012-13 datasets. 

63. Zhang, M., Zhu, X., Zhang, C., Ji, Y., Pan, F., & Yin, C. (2021, October). Multi-factors aware dual-attentional knowledge tracing. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (pp. 2588-2597). https://doi.org/10.1145/3459637.3482372. Uses the ASSIST 2009-10 data set. 

62. Zhang, X., Zhang, J., Lin, N., Yang, X. (2021). Sequential Self-Attentive Model for Knowledge Tracing. In Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science, vol 12891. Springer, Cham. https://doi.org/10.1007/978-3-030-86362-3_26. Uses the ASSIST 2009-10 and ASSISTCHALL datasets. 

61. Agarwal, D., Baker, R.S., Muraleedharan, A. (2020). Dynamic knowledge tracing through data driven recency weights. In Proceedings of The 13th International Conference on Educational Data Mining, EDM 2020 (pp. 725 - 729). Uses the ASSISTments-G6_207, G7_233, and G6_217 datasets.

60. Cheng, S., Liu, Q., & Chen, E. (2020). Domain adaption for knowledge tracing. https://doi.org/10.48550/arXiv.2001.04841. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSIST 2017 datasets. 

59. Clavie, B., Gal, K. (2020). Deep Embeddings of Contextual Assessment Data for Improving Performance Prediction. In Proceedings of The 13th International Conference on Educational Data Mining, EDM 2020 (pp. 374 - 380). Uses the ASSIST 2009-10 and ASSISTCHALL datasets. 

58. He, L., Tang, J., Li, X., & Wang, T. (2020, October). ADKT: adaptive deep knowledge tracing. In International Conference on Web Information Systems Engineering (pp. 302-314). Springer, Cham. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

57. King, D.R. (2020). Production Implementation of Recurrent Neural Networks in Adaptive Instructional Systems. In Sottilare, R., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science, vol 12214. Springer, Cham. https://doi.org/10.1007/978-3-030-50788-6_25.  Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets.

56. Li, Z., Yee, L., Sauerberg, N., Sakson, I., Williams, J.J., Rafferty, A. (2020). Getting too personal(ized): The importance of feature choice in online adaptive algorithms In Proceedings of The 13th International Conference on Educational Data Mining, EDM 2020 (pp. 159 - 170). Uses the 22 Experiments data set. 

55. Lu, Y., Wang, D., Meng, Q., & Chen, P. (2020). Towards Interpretable Deep Learning Models for Knowledge Tracing. Uses the ASSIST 2009-10 data set. 

54. Pandey, S. & Srivastava, J. (2020). RKT: Relation-Aware Self-Attention for Knowledge Tracing. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM '20). Association for Computing Machinery, New York, NY, USA, 1205–1214. https://doi.org/10.1145/3340531.3411994. Uses the ASSIST 2012-13 data set. 

53. Pavlik, P. Jr., Eglington, L. & Harrell-Williams, L. M. (2020). Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling. Uses the ASSIST 2004-05 data set.

52. Sergent, T., Bouchet, F., & Carron, T. (2020). Towards Temporality-Sensitive Recurrent Neural Networks through Enriched Traces. In Proceedings of The 13th International Conference on Educational Data Mining, EDM 2020 (pp. 658 - 661). Uses the ASSIST 2012-13 and ASSIST 2017 datasets. 

51. Shen, S., Liu, Q., Chen, E., Wu, H., Huang, Z., Zhao, W., Su, Y., Ma, H., & Wang, S. (2020). Convolutional Knowledge Tracing: Modeling Individualization in Student Learning Process. In SIGIR ’20, July 25–30, 2020, Virtual Event, China. Uses the  ASSIST 2009-10, ASSIST 2015-16 and ASSISTCHALL datasets.

50. Sonkar, S., Waters, A. E., Lan, A. S., Grimaldi, P. J., & Baraniuk, R. G. (2020). qDKT: Question-centric Deep Knowledge Tracing. In Proceedings of The 13th International Conference on Educational Data Mining, EDM 2020 (pp. 677-681). Uses the ASSIST 2009-10 and ASSIST 2017 datasets. 

49. Tong, S., Liu, Q., Huang, W., Hunag, Z., Chen, E., Liu, C., ... & Wang, S. (2020, November). Structure-based knowledge tracing: an influence propagation view. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 541-550). IEEE. Uses the ASSIST 2014-15 data set.

48. Wang, W., Liu, T., Chang, L., Gu, T., & Zhao, X. (2020, October). Convolutional Recurrent Neural Networks for Knowledge Tracing. In 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) (pp. 287-290). IEEE. https://doi.org/10.1109/CyberC49757.2020.00054. Uses the ASSIST 2009-10 and ASSISTCHALL datasets.

 47. Wang, Y., Kai, S., Baker, R.S. (2020). Early Detection of Wheel-Spinning in ASSISTments. In Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-7_46. Uses the ASSIST 2015-16 data set. 

46. Xie, B., Fu, L., Jiang, B., & Chen, L. (2020, November). Dynamic Key-Value Gated Recurrent Network for Knowledge Tracing. In CCF Conference on Computer Supported Cooperative Work and Social Computing (pp. 169-183). Springer, Singapore. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSISTCHALL datasets.

45. Xu, L. & Davenport M. (2020) Dynamic knowledge embedding and tracing. Uses the ASSIST 2009-10 and ASSIST 2012-13 datasets.  

44. Yang, S., Zhu, M., Hou, J., & Lu, X. (2020). Deep knowledge tracing with convolutions. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

43. Yang, S., Zhu, M., & Lu, X. (2020). Deep Knowledge Tracing with Learning Curves. https://doi.org/10.48550/arXiv.2008.01169. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSIST 2017 datasets. 

42. Zhang, J., Mo, Y., Chen, C., & He, X. (2020, August). Neural Attentive Knowledge Tracing Model for Student Performance Prediction. In 2020 IEEE International Conference on Knowledge Graph (ICKG) (pp. 641-648). IEEE. Uses the ASSIST 2009-10 and ASSIST 2014-15 datasets. 

41. Zhang, N., Du, Y., Deng, K., Li, L., Shen, J., & Sun, G. (2020, August). Attention-based knowledge tracing with heterogeneous information network embedding. In International Conference on Knowledge Science, Engineering and Management (pp. 95-103). Springer, Cham. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

40. Zhu, J., Yu, W., Zheng, Z., Huang, C., Tang, Y., & Fung, G. P. C. (2020, July). Learning from Interpretable Analysis: Attention-Based Knowledge Tracing. In International Conference on Artificial Intelligence in Education (pp. 364-368). Springer, Cham. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

39. Abdelrahman, G., & Wang, Q. (2019, July). Knowledge tracing with sequential key-value memory networks. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 175-184). https://doi.org/10.1145/3331184.3331195. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

38. Choffin, B., Popineau, F., Bourda, Y., & Vie, J. (2019). DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills. In Proceedings of the 12th International Conference on Educational Data Mining (pp. 29-38). Montreal, Canada. Uses the ASSIST 2012-13 data set and won "Best Paper" at EDM. Dr. Heffernan is proud of the fact that the best paper at EDM2019 used ASSIStments Data. (Photo)

37. Abidi, S.M.R., Hussain, M., Xu, Y., & Zhang, W. (2019). Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development. Sustainability, 11(1), 105.  Uses the ASSIST 2009-2010 data set.

36. Botelho, A., Varatharaj,  Patikorn, T., Doherty,  Adjei, S. & Beck, J. E. (2019). Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning. In IEEE Transactions on Learning Technologies, vol. 12, no. 2 (pp. 158-170). https://doi.org/10.1109/TLT.2019.2912162. Uses the ASSIST 2016-17 data set. 

35. Ding, X. & Larson, E. (2019). Why Deep Knowledge Tracing has less Depth than Anticipated. In Proceedings of the 12th International Conference on Educational Data Mining (pp. 282-287). Montreal, Canada. Pages Uses the ASSIST 2015-16 data set.

34. Jiang, N., & Pardos, Z. A. (2019). Binary Q-matrix Learning with dAFM. In Proceedings of the 12th International Conference on Educational Data Mining (pp. 588-590). Montreal, Canada. Uses the ASSIST 2009-10 and ASSIST 2012-13 datasets.

33. Lalwani A., & Agrawal S. (2019). What Does Time Tell? Tracing the Forgetting Curve Using Deep Knowledge Tracing. In Artificial Intelligence in Education AIED 2019, Lecture Notes in Computer Science, vol 11626  (pp. 158-162). Springer, Cham. Uses the ASSIST 2012-13 data set. 

32. Lee, J., & Yeung, D. Y. (2019, March). Knowledge query network for knowledge tracing: How knowledge interacts with skills. In Proceedings of the 9th international conference on learning analytics & knowledge (pp. 491-500). https://doi.org/10.1145/3303772.3303786. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets. 

31. Mandalapu, V., Gong, J. (2019). Studying Factors Influencing the Prediction of Student STEM and Non-STEM Career Choice. In Proceedings of the 12th International Conference on Educational Data Mining (pp. 607-610). Montreal, Canada. Uses the ASSISTCHALL data set. 

30. Pandey, P, & Karypis, G. (2019). A Self Attentive model for Knowledge Tracing. Proceedings of the 12th International Conference on Educational Data Mining. Montreal, Canada. Pages 384-389. Uses the ASSIST 2009-10, ASSIST 2015-16, and ASSISTCHALL datasets.

29. Rafferty, A., Ying, H., & Williams, J. (2019). Statistical Consequences of using Multi-armed Bandits to Conduct Adaptive Educational Experiments. In Journal of Educational Data Mining, 11(1), (pp. 47-79). Uses the 22 Experiments data set. 

28. Wang, T., Ma, F., & Gao, J. (2019). Deep Hierarchical Knowledge Tracing. In Proceedings of the 12th International Conference on Educational Data Mining (pp. 67-670). Montreal, Canada. Uses the ASSIST2009-10 and ASSIST 2012-13 datasets.

27. Wang, S. (2019). Improving Computer-Assisted Language Learning through Hierarchical Knowledge Structures. Cornell University, ProQuest Dissertations Publishing. 13866099. They did not release their data set. 

26. Yeung, C. K. (2019). Deep-IRT: Make deep learning based knowledge tracing explainable using item response theory. https://doi.org/10.48550/arXiv.1904.11738. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets.

25. Chaudhry, R., Singh, H., Doggay, P., & Saini, S. K. (2018). Modeling Hint-Taking Behavior and Knowledge State of Students with Multi-Task Learning. In The 11th International Conference on Educational Data Mining. EDM 2018. Uses the ASSIST 2009-10 data set. 

24. Montero, S., Arora, A., Kelly, S., Milne, B., Mozer, M. (2018). Does Deep Knowledge Tracing Model Interactions Among Skills? In The 11th International Conference on Educational Data Mining, EDM 2018. Uses the ASSIST 2009-10 data set.

23. Yeung, C. K., & Yeung, D. Y. (2018). Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In Proceedings of the Fifth Annual ACM Conference on Learning at Scale (p. 5). Uses the ASSIST 2009-10 data set. 

22. Liu, R., Walker, E. &, Solovey, E. (2017). Toward Neuroadaptive Personal Learning Environments. In The First Biannual Neuroadaptive Technology Conference. Used ASSISTments to collect their own data connected to brain scanning. They did not release their data set.

21. Pardos, Z. A., Dadu, A. (2017). Imputing KCs with Representations of Problem Content and Context. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP'17) (pp. 148-155). Bratislava, Slovakia. Uses the ASSIST 2012-13 data set.

20. Rochelle, J., Feng, M., Murphy, R., Mason, C. & Fairman, J. (2017). Rigor and Relevance in an Efficacy Study of an Online Mathematics Homework Intervention Intervention. In The Society for Research on Educational Effectiveness Spring Conference. Presented March 2nd 2017. Slides. Used data that was never released.

19. Sha L. & Hong P. (2017). Neural Knowledge Tracing. In Frasson C., Kostopoulos G. (eds) Brain Function Assessment in Learning. BFAL 2017. Lecture Notes in Computer Science, vol 10512. Springer, Cham. Uses the ASSIST 2009-10 data set.

18. Song Y., Cai H., Zheng X., Qiu Q., Jin Y., & Zhao, X. (2017). FTGWS: Forming Optimal Tutor Group for Weak Students Discovered in Educational Settings. In Benslimane D., Damiani E., Grosky W., Hameurlain A., Sheth A., Wagner R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science, vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_33. Uses the ASSIST 2012-13 data set.

17. Feng, M. & Roschelle, J. (2016). Predicting Students' Standardized Test Scores Using Online Homework. In L@S 2016 (pp. 213-216). They did not release their data set.

16. Feng, M., Roschelle, J., Mason, C. & Bhanot, R. (2016) Investigating Gender Difference on Homework in Middle School Mathematics. In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining (pp. 364-369). They did not release their data set.

15. Khajah, M., Lindsey, R., & Mozer, M. (2016). How Deep is Knowledge Tracing? In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining (pp 94-101). Uses the ASSIST 2009-10 data set.

14. Rochelle, J., Feng, M., Murphy, R. & Mason, C. (2016). Online Mathematics Homework Increases Student Achievement. In AERA OPEN. October-December 2016, Vol. 2, No. 4, (pp. 1–12). https://doi.org/10.1177/2332858416673968. They did not release their data set. 

13. Wilson, K., Karklin, Y., Han, B., & Ekanadham, C. (2016). Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation. In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining (pp 539-544). Uses the ASSSIT 2009-10 data set.

12. Xiong, X., Zhao, S., Vaninwegen, E. & Beck, J. (2016). Going Deeper with Deep Knowledge Tracing. In Barnes, Chi & Feng (eds) The 9th International Conference on Educational Data Mining (pp. 94-101).  Uses ASSIST 2009-10 and ASSIST 2014-15 datasets. 

11. Zhang, J., Shi, X., King, I., & Yeung, D. (2016). Dynamic Key-Value Memory Network for Knowledge Tracing. Uses the ASSIST 2009-10 and ASSIST 2015-16 datasets.

10. Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. & Sohl-Dickstein, J. (2015). Deep Knowledge Tracing. In Neural Information Processing Systems (NIPS) 2015. Uses the ASSIST 2009-10 data set. 

9. Song Y., Jin Y., Zheng X., Han H., Zhong Y., & Zhao X. (2015). PSFK: A Student Performance Prediction Scheme for First-Encounter Knowledge in ITS. In Zhang S., Wirsing M., Zhang Z. (eds) Knowledge Science, Engineering and Management. Lecture Notes in Computer Science, vol 9403 (pp. 639-650). Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_58. Author Copy. Uses the ASSIST 2009-10 data set.

8. Tang, S., Gogel, H., McBride, E., & Pardos, Z.A. (2015). Desirable Difficulty and Other Predictors of Effective Item Orderings. In Romero, C. and Pechenizkiy, M. (eds.) Proceedings of the 8th International Conference on Educational Data Mining (pp. 416-419). Madrid, Spain. Uses the ASSIST 2012-13 data set and a data set that they never released. 

7. Wan, H. & Beck, J. (2015). Considering the influence of prerequisite performance on wheel spinning. In Romero, C. and Pechenizkiy, M. (eds.) Proceedings of the 8th International Conference Educational Data Mining. Madrid, Spain. They never released their data set. 

6. Xing, W., & Goggins, S. (2015, March). Learning analytics in outer space: a Hidden Naïve Bayes model for automatic student off-task behavior detection. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 176-183). ACM. Free version. They did not release their data set. 

5. Feng, M. (2014). Towards Uncovering the Mysterious World of Math Homework. In Proceedings of the 7th International Conference on Educational Data Mining (pp. 425-426). EDM 2014. Uses a data set that was never released.

4. Galyardt, A. & Goldin, I. (2014). Recent-Performance Factors Analysis. In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining (pp. 411-412). They did not release their data set. 

3. Schultz, S. & Arroyo, I. (2014). Tracing Knowledge and Engagement in Parallel in an Intelligent Tutoring System. In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining (pp. 312-315). Uses the ASSIST 2009-10 data set.

2. Tan, L., Sun, X., & Kho, S. T. (2014). Can Engagement be Compared? Measuring Academic Engagement for Comparison. In Stamper, J., Pardos, Z., Mavrikis, M., McLaren, B.M. (eds.) Proceedings of the 7th International Conference on Educational Data Mining (pp. 213-216). Uses the ASSIST 2005-06 data set. 

1. Pardos, Z., Wang, Q., & Trivedi, S. (2012). The real world significance of performance prediction. In EDM 2012: 192-195.  Uses the ASSIST 2009-10 data set.