EduStudio: towards a unified library for student cognitive modeling
Le WU1, Xiangzhi CHEN1(), Fei LIU1(), Junsong XIE1, Chenao XIA1, Zhengtao TAN1, Mi TIAN1, Jinglong LI1, Kun ZHANG1, Defu LIAN2, Richang HONG1, Meng WANG1
. Key Laboratory of Knowledge Engineering with Big Data, Hefei University of Technology, Hefei 230601, China . School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
Student cognitive modeling is a fundamental task in the intelligence education field. It serves as the basis for various downstream applications, such as student profiling, personalized educational content recommendation, and adaptive testing. Cognitive Diagnosis (CD) and Knowledge Tracing (KT) are two mainstream categories for student cognitive modeling, which measure the cognitive ability from a limited time (e.g., an exam) and the learning ability dynamics over a long period (e.g., learning records from a year), respectively. Recent efforts have been dedicated to the development of open-source code libraries for student cognitive modeling. However, existing libraries often focus on a particular category and overlook the relationships between them. Additionally, these libraries lack sufficient modularization, which hinders reusability. To address these limitations, we have developed a unified PyTorch-based library EduStudio, which unifies CD and KT for student cognitive modeling. The design philosophy of EduStudio is from two folds. From a horizontal perspective, EduStudio employs the modularization that separates the main step pipeline of each algorithm. From a vertical perspective, we use templates with the inheritance style to implement each module. We also provide eco-services of EduStudio, such as the repository that collects resources about student cognitive modeling and the leaderboard that demonstrates comparison among models. Our open-source project is available at the website of edustudio.ai.
Fig.1 Introduction to student cognitive modeling, including CD and KT
Fig.2 Data description
Fig.3 EduStudio’s design philosophy incorporates horizontal modularization and vertical templatization to enhance flexibility and reusability. Horizontal modularization: We decompose the general algorithmic pipeline into six modules to enhance flexibility. Vertical templatization: We implement reusable templates within the modules for Steps 2–5 to achieve high-level management of complex elements. Since all models share the same configuration reading method and log storage path management, there is no need for the template-based design for them
Fig.4 The overall architecture of EduStudio
Fig.5 Data Preparation includes the loading, processing, and delivery stages. We have established a set of standardized protocols and developed a series of atomic data operations for data processing (Section 4.2.1). We utilize data templates (Section 4.2.2) to manage and control the three stages, enabling reusable data preparation
M2C operation type
M2C operation name
Description
Data cleaning
M2C_FilterRecords4CD
Filter some students or exercises according specific conditions
M2C_Label2Int
Binarization for answering response
Data conversion
M2C_ReMapId
Identifier remapping of discrete features
M2C_BuildSeqInterFeats
Build sample format for KT
Data partition
M2C_DataSplit4CD
Data partition for CD
M2C_DataSplit4KT
Data partition for KT
Data generation
M2C_GenQMat
Generate Q-matrix
M2C_BuildKCRelation
Build knowledge component relation graph
Tab.1 Representative M2C atomic data operations that transform data from middata to cachedata
Template type
Template name
Parent template
Description
Data templates
BaseDataTPL
/
The basic class of data templates
GeneralDataTPL
BaseDataTPL
Implement all protocols for data processing
EduDataTPL
GeneralDataTPL
Load extra student-side and excise-side features based on GeneralDataTPL
Model templates
BaseModel
/
The basic class of model templates
GDBaseModel
BaseModel
Provide utilities for gradient descent models based on BaseModel
Training templates
BaseTrainTPL
/
The basic class of training templates
GDBaseTrainTPL
BaseTrainTPL
Provide utilities for gradient descent models based on BaseTrainTPL
Tab.4 Path Management in EduStudio. We normalize the user’s working directory
Fig.6 Code example of EduStudio usage
Fig.7 Frontend of leaderboard. (a) Task selection; (b) detailed leaderboard
Library
#CD Models
#KT Models
#Datasets
Modularization
Templatization
Eco-services
Release year
EduCDM [26]
9
0
0
Low
No
Datasets
2021
EduKTM [27]
0
9
0
Low
No
Datasets
2021
pyKT [28]
0
27
13
Low
No
No
2022
EduStudio
16
29
18
High
Yes
Datasets, papersJournals & conferencesLeaderboard
2023
Tab.5 Comparison with existing libraries
1
A, Roy S, Kim C, Christensen M Cincebeaux . Detecting educational content in online videos by combining multimodal cues. In: Proceedings of the NeurIPS’ 2023 Workshop on Generative AI for Education. 2023
2
Khayi N Ait . Deep knowledge tracing using temporal convolutional networks. In: Proceedings of the Workshop Artificial Intelligence for Education. 2021
3
Caines A, Benedetto L, Taslimipoor S, Davis C, Gao Y, Andersen Ø E, Yuan Z, Elliott M, Moore R, Bryant C, Rei M, Yannakoudakis H, Mullooly A, Nicholls D, Buttery P. On the application of large language models for language teaching and assessment technology. In: Caines A, Benedetto L, Taslimipoor S, Davis C, Gao Y, Andersen Ø E, Yuan Z, Elliott M, Moore R, Bryant C, Rei M, Yannakoudakis H, Mullooly A, Nicholls D, Buttery P. 2023
4
F P, Deek S R, Hiltz H, Kimmel N Rotter . Cognitive assessment of students’ problem solving and program development skills. Journal of Engineering Education, 1999, 88( 3): 317–326
5
L V, Huang A N, Bardos R C D’Amato . Identifying students with learning disabilities: composite profile analysis using the cognitive assessment system. Journal of Psychoeducational Assessment, 2010, 28( 1): 19–30
6
W Widada . Profile of cognitive structure of students in understanding the concept of real analysis. Infinity Journal, 2016, 5( 2): 83–98
7
F, Liu X, Hu S, Liu C, Bu L Wu . Meta multi-agent exercise recommendation: a game application perspective. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023, 1441–1452
8
B, Jiang X, Li S, Yang Y, Kong W, Cheng C, Hao Q Lin . Data-driven personalized learning path planning based on cognitive diagnostic assessments in MOOCs. Applied Sciences, 2022, 12( 8): 3982
9
P Lou . Learning path recommendation of intelligent education based on cognitive diagnosis. International Journal of Emerging Technologies in Learning, 2023, 18( 13): 104–119
10
D, Cai Y, Zhang B Dai . Learning path recommendation based on knowledge tracing model and reinforcement learning. In: Proceedings of the 5th IEEE International Conference on Computer and Communications. 2019, 1881–1885
11
F, Ai Y, Chen Y, Guo Y, Zhao Z, Wang G, Fu G Wang . Concept-aware deep knowledge tracing and exercise recommendation in an online learning system. In: Proceedings of the 12th International Educational Data Mining Society. 2019
12
D, Lian Y, Wu Y, Ge X, Xie E Chen . Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020, 2009–2019
13
H, Wang T, Xu Q, Liu D, Lian E, Chen D, Du H, Wu W Su . MCNE: an end-to-end framework for learning multiple conditional network representations of social network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 1064–1072
14
C, Gao S, Wang S, Li J, Chen X, He W, Lei B, Li Y, Zhang P Jiang . CIRS: bursting filter bubbles by counterfactual interactive recommender system. ACM Transactions on Information Systems, 2024, 42( 1): 14
15
Y, Zhuang Q, Liu G, Zhao Z, Huang W, Huang Z A, Pardos E, Chen J, Wu X Li . A bounded ability estimation for computerized adaptive testing. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2024, 111
16
Q Liu . Towards a new generation of cognitive diagnosis. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence. 2021, 4961–4964
17
Y, Liu T, Zhang X, Wang G, Yu T Li . New development of cognitive diagnosis models. Frontiers of Computer Science, 2023, 17( 1): 171604
18
G, Abdelrahman Q, Wang B Nunes . Knowledge tracing: a survey. ACM Computing Surveys, 2023, 55( 11): 224
19
S, Shen Q, Liu Z, Huang Y, Zheng M, Yin M, Wang E Chen . A survey of knowledge tracing: models, variants, and applications. IEEE Transactions on Learning Technologies, 2024, 17: 1898–1919
20
H, Liu T, Zhang F, Li M, Yu G Yu . A probabilistic generative model for tracking multi-knowledge concept mastery probability. Frontiers of Computer Science, 2024, 18( 3): 183602
21
A Gurría . PISA 2015 results in focus. Paris: Organisation for Economic Co-operation and Development (OECD), 2016
22
A Schleicher . PISA 2018: insights and interpretations. Paris: Organisation for Economic Co-operation and Development (OECD), 2019
23
Ş, İdil S, Gülen İ Dönmez . What should we understand from PISA 2022 results?. Journal of STEAM Education, 2024, 7( 1): 1–9
24
C, Piech J, Bassen J, Huang S, Ganguli M, Sahami L, Guibas J Sohl-Dickstein . Deep knowledge tracing. In: Proceedings of the 28th International Conference on Neural Information Processing Systems. 2015, 505–513
25
Z, Huang Q, Liu C, Zhai Y, Yin E, Chen W, Gao G Hu . Exploring multi-objective exercise recommendations in online education systems. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 1261–1270
26
BigData-USTC. EduCDM. See Github.com/bigdataustc/EduCDM website, 2021
27
BigData-USTC. EduKTM. See Github.com/bigdataustc/EduKTM website, 2021
28
Z, Liu Q, Liu J, Chen S, Huang J, Tang W Luo . pyKT: a Python library to benchmark deep learning based knowledge tracing models. In: Proceedings of the 36th International Conference on Neural Information Processing Systems. 2022, 18542–18555
29
K, Nagatani Q, Zhang M, Sato Y Y, Chen F, Chen T Ohkuma . Augmenting knowledge tracing by considering forgetting behavior. In: Proceedings of the World Wide Web Conference 2019. 2019, 3101–3107
30
S, Shen Q, Liu E, Chen Z, Huang W, Huang Y, Yin Y, Su S Wang . Learning process-consistent knowledge tracing. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021, 1452–1460
31
S, Shen E, Chen Q, Liu Z, Huang W, Huang Y, Yin Y, Su S Wang . Monitoring student progress for learning process-consistent knowledge tracing. IEEE Transactions on Knowledge and Data Engineering, 2023, 35( 8): 8213–8227
32
la Torre J de . Dina model and parameter estimation: a didactic. Journal of Educational and Behavioral Statistics, 2009, 34( 1): 115–130
33
F, Wang Z, Huang Q, Liu E, Chen Y, Yin J, Ma S Wang . Dynamic cognitive diagnosis: an educational priors-enhanced deep knowledge tracing perspective. IEEE Transactions on Learning Technologies, 2023, 16( 3): 306–323
34
G Rasch . Probabilistic Models for Some Intelligence and Attainment Tests. Chicago: University of Chicago Press, 1980
35
R F DeVellis . Classical test theory. Medical Care, 2006, 44( 11): S50–S59
36
B W, Junker K Sijtsma . Cognitive assessment models with few assumptions, and connections with nonparametric item response theory. Applied Psychological Measurement, 2001, 25( 3): 258–272
37
J P, Leighton M J, Gierl S M Hunka . The attribute hierarchy method for cognitive assessment: a variation on tatsuoka’s rule-space approach. Journal of Educational Measurement, 2004, 41( 3): 205–237
38
F, Wang Q, Liu E, Chen Z, Huang Y, Chen Y, Yin Z, Huang S Wang . Neural cognitive diagnosis for intelligent education systems. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 6153–6161
39
F, Wang Q, Liu E, Chen Z, Huang Y, Yin S, Wang Y Su . NeuralCD: a general framework for cognitive diagnosis. IEEE Transactions on Knowledge and Data Engineering, 2023, 35( 8): 8312–8327
40
X, Wang C, Huang J, Cai L Chen . Using knowledge concept aggregation towards accurate cognitive diagnosis. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 2010–2019
41
H, Ma M, Li L, Wu H, Zhang Y, Cao X, Zhang X Zhao . Knowledge-sensed cognitive diagnosis for intelligent education platforms. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022, 1451–1460
42
S, Li Q, Guan L, Fang F, Xiao Z, He Y, He W Luo . Cognitive diagnosis focusing on knowledge concepts. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022, 3272–3281
43
Y, Zhou Q, Liu J, Wu F, Wang Z, Huang W, Tong H, Xiong E, Chen J Ma . Modeling context-aware features for cognitive diagnosis in student learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021, 2420–2428
44
Z, Zhang L, Wu Q, Liu J, Liu Z, Huang Y, Yin Y, Zhuang W, Gao E Chen . Understanding and improving fairness in cognitive diagnosis. Science China Information Sciences, 2024, 67( 5): 152106
45
Z, Zhang Q, Liu H, Jiang F, Wang Y, Zhuang L, Wu W, Gao E Chen . FairLISA: fair user modeling with limited sensitive attributes information. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2023, 41432–41450
46
J, Huang Q, Liu F, Wang Z, Huang S, Fang R, Wu E, Chen Y, Su S Wang . Group-level cognitive diagnosis: a multi-task learning perspective. In: Proceedings of 2021 IEEE International Conference on Data Mining. 2021, 210–219
47
W, Gao Q, Liu Z, Huang Y, Yin H, Bi M C, Wang J, Ma S, Wang Y Su . RCD: relation map driven cognitive diagnosis for intelligent education systems. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 501–510
48
J, Li F, Wang Q, Liu M, Zhu W, Huang Z, Huang E, Chen Y, Su S Wang . HierCDF: a Bayesian network-based hierarchical cognitive diagnosis framework. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022, 904–913
49
X, Chen L, Wu F, Liu L, Chen K, Zhang R, Hong M Wang . Disentangling cognitive diagnosis with limited exercise labels. In: Proceedings of the 37th International Conference on Neural Information Processing Systems. 2024, 792
50
B, Xu Z, Huang J, Liu S, Shen Q, Liu E, Chen J, Wu S Wang . Learning behavior-oriented knowledge tracing. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023, 2789–2800
51
A T, Corbett J R Anderson . Knowledge tracing: modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 1994, 4( 4): 253–278
52
T, Käser S, Klingler A G, Schwing M Gross . Dynamic Bayesian networks for student modeling. IEEE Transactions on Learning Technologies, 2017, 10( 4): 450–462
53
H, Cen K, Koedinger B Junker . Learning factors analysis – a general method for cognitive model evaluation and improvement. In: Proceedings of the 8th International Conference on Intelligent Tutoring Systems. 2006, 164–175
54
P I, Pavlik H, Cen K R Koedinger . Performance factors analysis – a new alternative to knowledge tracing. In: Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling. 2009, 531–538
55
J J, Vie H Kashima . Knowledge tracing machines: factorization machines for knowledge tracing. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence. 2019, 750–757
56
C K, Yeung D Y Yeung . Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In: Proceedings of the 5th Annual ACM Conference on Learning at Scale. 2018, 5
57
S, Minn Y, Yu M C, Desmarais F, Zhu J J Vie . Deep knowledge tracing and dynamic student classification for knowledge tracing. In: Proceedings of 2018 IEEE International Conference on Data Mining. 2018, 1182–1187
58
S, Sonkar A S, Lan A E, Waters P, Grimaldi R G Baraniuk . qDKT: question-centric deep knowledge tracing. In: Proceedings of the 13th International Conference on Educational Data Mining. 2020
59
J, Zhang X, Shi I, King D Y Yeung . Dynamic key-value memory networks for knowledge tracing. In: Proceedings of the 26th International Conference on World Wide Web. 2017, 765–774
60
G, Abdelrahman Q Wang . Knowledge tracing with sequential key-value memory networks. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2019, 175–184
61
Q, Liu Z, Huang Y, Yin E, Chen H, Xiong Y, Su G Hu . EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Transactions on Knowledge and Data Engineering, 2021, 33( 1): 100–115
62
S, Pandey G Karypis . A self attentive model for knowledge tracing. In: Proceedings of the 12th International Conference on Educational Data Mining. 2019, 384–389
63
Y, Choi Y, Lee J, Cho J, Baek B, Kim Y, Cha D, Shin C, Bae J Heo . Towards an appropriate query, key, and value computation for knowledge tracing. In: Proceedings of the 7th ACM Conference on Learning@Scale. 2020, 341–344
64
D, Shin Y, Shim H, Yu S, Lee B, Kim Y Choi . SAINT+: Integrating temporal features for EdNet correctness prediction. In: Proceedings of the 11th International Learning Analytics and Knowledge Conference. 2021, 490–496
65
H, Nakagawa Y, Iwasawa Y Matsuo . Graph-based knowledge tracing: modeling student proficiency using graph neural network. In: Proceedings of 2019 IEEE/WIC/ACM International Conference on Web Intelligence. 2019, 156–163
66
Y, Yang J, Shen Y, Qu Y, Liu K, Wang Y, Zhu W, Zhang Y Yu . GIKT: a graph-based interaction model for knowledge tracing. In: Proceedings of the Machine Learning and Knowledge Discovery in Databases: European Conference. 2020, 299–315
67
Z, Huang Q, Liu Y, Chen L, Wu K, Xiao E, Chen H, Ma G Hu . Learning or forgetting? A dynamic approach for tracking the knowledge proficiency of students. ACM Transactions on Information Systems, 2020, 38( 2): 19
68
C K Yeung . Deep-IRT: make deep learning based knowledge tracing explainable using item response theory. In: Proceedings of the 12th International Conference on Educational Data Mining. 2019
69
W, Gan Y, Sun Y Sun . Knowledge interaction enhanced sequential modeling for interpretable learner knowledge diagnosis in intelligent tutoring systems. Neurocomputing, 2022, 488: 36–53
70
R L, McKinley M D Reckase . The use of the general rasch model with multidimensional item response data. Iowa City: American College Testing, 1982
71
Y, Su Q, Liu Q, Liu Z, Huang Y, Yin E, Chen C, Ding S, Wei G Hu . Exercise-enhanced sequential modeling for student performance prediction. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018
72
J, Lee D Y Yeung . Knowledge query network for knowledge tracing: how knowledge interacts with skills. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge. 2019, 491–500
73
A, Ghosh N, Heffernan A S Lan . Context-aware attentive knowledge tracing. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020, 2330–2339
74
S, Shen Q, Liu E, Chen H, Wu Z, Huang W, Zhao Y, Su H, Ma S Wang . Convolutional knowledge tracing: modeling individualization in student learning process. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 1857–1860
75
S, Pandey J Srivastava . RKT: relation-aware self-attention for knowledge tracing. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020, 1205–1214
76
X, Guo Z, Huang J, Gao M, Shang M, Shu J Sun . Enhancing knowledge tracing via adversarial training. In: Proceedings of the 29th ACM International Conference on Multimedia. 2021, 367–375
77
T, Long Y, Liu J, Shen W, Zhang Y Yu . 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. 2021, 173–182
78
C, Wang W, Ma M, Zhang C, Lv F, Wan H, Lin T, Tang Y, Liu S Ma . Temporal cross-effects in knowledge tracing. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 2021, 517–525
79
H, Ma J, Wang H, Zhu X, Xia H, Zhang X, Zhang L Zhang . Reconciling cognitive modeling with knowledge forgetting: a continuous time-aware neural network approach. In: Proceedings of the 31st International Joint Conference on Artificial Intelligence. 2022, 2174–2181
80
W, Lee J, Chun Y, Lee K, Park S Park . Contrastive learning for knowledge tracing. In: Proceedings of the ACM Web Conference 2022. 2022, 2330–2338
81
S, Shen Z, Huang Q, Liu Y, Su S, Wang E Chen . 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. 2022, 427–437
82
J, Chen Z, Liu S, Huang Q, Liu W Luo . Improving interpretability of deep sequential knowledge tracing models with question-centric cognitive representations. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence. 2023, 14196–14204
83
Z, Liu Q, Liu J, Chen S, Huang W Luo . simpleKT: a simple but tough-to-beat baseline for knowledge tracing. In: Proceedings of the 11th International Conference on Learning Representations. 2023
84
Y, Yin L, Dai Z, Huang S, Shen F, Wang Q, Liu E, Chen X Li . Tracing knowledge instead of patterns: stable knowledge tracing with diagnostic transformer. In: Proceedings of the ACM Web Conference 2023. 2023, 855–864
85
J, Li Q, Liu F, Wang J, Liu Z, Huang F, Yao L, Zhu Y Su . Towards the identifiability and explainability for personalized learner modeling: an inductive paradigm. In: Proceedings of the ACM Web Conference 2024. 2024, 3420–3431
86
C, Dwork M, Hardt T, Pitassi O, Reingold R Zemel . Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. 2012, 214–226
87
M, Hardt E, Price N Srebro . Equality of opportunity in supervised learning. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016, 3323–3331
88
J, Yu Y, Wang Q, Zhong G, Luo Y, Mao K, Sun W, Feng W, Xu S, Cao K, Zeng Z, Yao L, Hou Y, Lin P, Li J, Zhou B, Xu J, Li J, Tang M Sun . MOOCCubeX: a large knowledge-centered repository for adaptive learning in MOOCs. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021, 4643–4652
89
L, Song M, He X, Shang C, Yang J, Liu M, Yu Y Lu . A deep cross-modal neural cognitive diagnosis framework for modeling student performance. Expert Systems with Applications, 2023, 230: 120675
90
J, Zhao S, Bhatt C, Thille N, Gattani D Zimmaro . Cold start knowledge tracing with attentive neural turing machine. In: Proceedings of the 7th ACM Conference on Learning @ Scale. 2020, 333–336
91
W, Gao H, Wang Q, Liu F, Wang X, Lin L, Yue Z, Zhang R, Lv S Wang . Leveraging transferable knowledge concept graph embedding for cold-start cognitive diagnosis. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2023, 983–992
92
S, Liu J, Shen H, Qian A Zhou . Inductive cognitive diagnosis for fast student learning in web-based intelligent education systems. In: Proceedings of the ACM Web Conference 2024. 2024, 4260–4271
93
W, Gao Q, Liu H, Wang L, Yue H, Bi Y, Gu F, Yao Z, Zhang X, Li Y He . Zero-1-to-3: domain-level zero-shot cognitive diagnosis via one batch of early-bird students towards three diagnostic objectives. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence. 2024, 8417–8426
94
H, Jung J, Yoo Y, Yoon Y Jang . CLST: cold-start mitigation in knowledge tracing by aligning a generative language model as a students’ knowledge tracer. 2024, arXiv preprint arXiv: 2406.10296
95
G, Liu H, Zhan J J Kim . Question difficulty consistent knowledge tracing. In: Proceedings of the ACM Web Conference 2024. 2024, 4239–4248
96
R, Das J, Zhang R S, Baker R Scruggs . A new interpretation of knowledge tracing models’ predictive performance in terms of the cold start problem. In: Proceedings of the 14th International Conference on Educational Data Mining. 2021
97
H, Zhang Z, Liu S, Huang C, Shang B, Zhan Y Jiang . Improving low-resource knowledge tracing tasks by supervised pre-training and importance mechanism fine-tuning. 2024, arXiv preprint arXiv: 2403.06725
98
B, Zhan T, Guo X, Li M, Hou Q, Liang B, Gao W, Luo Z Liu . Knowledge tracing as language processing: a large-scale autoregressive paradigm. In: Proceedings of the 25th International Conference on Artificial Intelligence in Education. 2024, 177–191
99
L, Fu H, Guan K, Du J, Lin W, Xia W, Zhang R, Tang Y, Wang Y Yu . SINKT: a structure-aware inductive knowledge tracing model with large language model. 2024, arXiv preprint arXiv: 2407.01245
100
U, Lee J, Bae D, Kim S, Lee J, Park T, Ahn G, Lee D, Stratton H Kim . Language model can do knowledge tracing: simple but effective method to integrate language model and knowledge tracing task. 2024, arXiv preprint arXiv: 2406.02893
101
S, Caton C Haas . Fairness in machine learning: a survey. ACM Computing Surveys, 2024, 56( 7): 166
102
P, Shao L, Wu K, Zhang D, Lian R, Hong Y, Li M Wang . Average user-side counterfactual fairness for collaborative filtering. ACM Transactions on Information Systems, 2024, 42( 5): 140
103
L, Chen L, Wu K, Zhang R, Hong D, Lian Z, Zhang J, Zhou M Wang . Improving recommendation fairness via data augmentation. In: Proceedings of the ACM Web Conference 2023. 2023, 1012–1020
104
D, Zhang K, Zhang L, Wu M, Tian R, Hong M Wang . Path-specific causal reasoning for fairness-aware cognitive diagnosis. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024
105
S, Tschiatschek M, Knobelsdorf A Singla . Equity and fairness of Bayesian knowledge tracing. In: Proceedings of the 15th International Conference on Educational Data Mining. 2022
106
J, Barrett A, Day K Gal . Improving model fairness with time-augmented Bayesian knowledge tracing. In: Proceedings of the 14th Learning Analytics and Knowledge Conference. 2024, 46–54
107
S, Doroudi E Brunskill . Fairer but not fair enough on the equitability of knowledge tracing. In: Proceedings of the 9th International Conference on Learning Analytics & Knowledge. 2019, 335–339
108
C, Wu X, Wang D, Lian X, Xie E Chen . A causality inspired framework for model interpretation. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2023, 2731–2741
109
H, Wan B, Che H, Luo X Luo . Learning path recommendation based on knowledge tracing and reinforcement learning. In: Proceedings of 2023 IEEE International Conference on Advanced Learning Technologies. 2023, 55–57
110
X, Tian F Liu . Capacity tracing-enhanced course recommendation in MOOCs. IEEE Transactions on Learning Technologies, 2021, 14( 3): 313–321
111
Q, Ban W, Wu W, Hu H, Lin W, Zheng L He . Knowledge-enhanced multi-task learning for course recommendation. In: Proceedings of the 27th International Conference on Database Systems for Advanced Applications. 2022, 85–101
112
H, Ma Z, Huang W, Tang X Zhang . Exercise recommendation based on cognitive diagnosis and neutrosophic set. In: Proceedings of the 25th IEEE International Conference on Computer Supported Cooperative Work in Design. 2022, 1467–1472
113
Y, Cheng M, Li H, Chen Y, Cai H, Sun H, Zou G Zhang . Exercise recommendation method combining NeuralCD and NeuMF models. In: Proceedings of the 7th Annual International Conference on Network and Information Systems for Computers. 2021, 646–651
114
F, Wang W, Gao Q, Liu J, Li G, Zhao Z, Zhang Z, Huang M, Zhu S, Wang W, Tong E Chen . A survey of models for cognitive diagnosis: new developments and future directions. 2024, arXiv preprint arXiv: 2407.05458
115
J, Yu Y, Zhuang Z, Huang Q, Liu X, Li R, Li E Chen . A unified adaptive testing system enabled by hierarchical structure search. In: Proceedings of the 41st International Conference on Machine Learning. 2024
116
H, Bi Q, Liu H, Wu W, He Z, Huang Y, Yin H, Ma Y, Su S, Wang E Chen . Model-agnostic adaptive testing for intelligent education systems via meta-learned gradient embeddings. ACM Transactions on Intelligent Systems and Technology, 2024
