Figure 1.
Framework for causal deep learning (CDL) (Cs is the student input embedded with causal interventions; Cs is the exercise input embedded with causal interventions)
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May
2022, 2(2): 157-172.
doi: 10.3934/steme.2022011
Personalized exercise recommendation method based on causal deep learning: Experiments and implications
| 1. | Department of Computer Science, Changchun Humanities and Sciences College, Changchun, 130117, China; wangsuhua@ccrw.edu.cn; mazq@nenu.edu.cn; zhaodawei@ccrw.edu.cn |
| 2. | School of Information Science and Technology, Northeast Normal University, Changchun 130117, China; jihj328@nenu.edu.cn; liut790@nenu.edu.cn; chenaq669@nenu.edu.cn |
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The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student′s knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students′ score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students′ knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps.
Citation:
Suhua Wang, Zhiqiang Ma, Hongjie Ji, Tong Liu, Anqi Chen, Dawei Zhao. Personalized exercise recommendation method based on causal deep learning: Experiments and implications. STEM Education,
2022, 2
(2)
: 157-172.
doi: 10.3934/steme.2022011
![]()
References:
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Segal, A., Katzir, Z. and Shapira, B., EduRank: A collaborative filtering approach to personalization in e-learning. Proceedings of the 7th International Conference on Educational Data Mining, 2014, 68–75. |
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Toledo, R. and Mota, Y., An e-learning collaborative filtering approach to suggest problems to solve in programming online judges. International Journal of Distance Education Technologies, 2014, 12(2): 51–65. https://doi.org/10.4018/ijdet.2014040103
doi: 10.4018/ijdet.2014040103. |
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Dwivedi, P. and Bharadwaj, K., Effective trust-aware e-learning recommender system based on learning styles and knowledge levels. Journal of Educational Technology & Society, 2013, 16(4): 201–216. |
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Jiang, C., Feng, J. and Sun, X., Personalized exercises recommendation algorithm based on knowledge hierarchical graph, ReKHG. Computer Engineering and Applications, 2018, 54(10): 234–240. |
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Gong, T. and Yao, X., Deep exercise recommendation model. International Journal of Modeling and Optimization, 2019, 9(1): 18–23. https://doi.org/10.7763/IJMO.2019.V9.677
doi: 10.7763/IJMO.2019.V9.677. |
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Piech, C., Bassen, J. and Huang, J., Deep knowledge tracing. NIPS'15 Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015, 505–513. |
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Zhang, L., Xiong, X. and Zhao, S., Incorporating rich features into deep knowledge tracing. Proceedings of the Fourth ACM Conference on Learning at Scale, 2017, 169–172. https://doi.org/10.1145/3051457.3053976
doi: 10.1145/3051457.3053976. |
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Su, Y., Liu, Q. and Liu, Q., Exercise-enhanced sequential modeling for student performance prediction. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2018, 2435–2443. |
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Wang, L., Angela, S. and Liu L., Deep knowledge tracing on programming exercises. Proceedings of the Fourth. ACM Conference on Learning at Scale, 2017, 201–204. https://doi.org/10.1145/3051457.3053985
doi: 10.1145/3051457.3053985. |
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Yeung, C. and Yeung, D., Addressing two problems in deep knowledge tracing via prediction-consistent regularization. Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018, 41–50. https://doi.org/10.1145/3231644.3231647
doi: 10.1145/3231644.3231647. |
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Didelez, V. and Pigeot, I., Judea Pearl: Causality: Models, reasoning, and inference. Politische Vierteljahresschrift, 2001, 42(2): 313–315. https://doi.org/10.1007/s11615-001-0048-3
doi: 10.1007/s11615-001-0048-3. |
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Louizos, C., Shalit, U. and Mooij, J., Causal effect inference with deep latent-variable models. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, 6449–6459. |
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Van, D., Causal reasoning and inference making in judging the importance of story statements. Child Development, 1989, 60(2): 286–297. |
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Joachims, T., Swaminathan, A. and De, R., Deep learning with logged bandit feedback. International Conference on Learning Representations, 2018, 1–12. |
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Swaminathan, A. and Joachims, T., Counterfactual risk minimization: Learning from logged bandit feedback. Proceedings of the 32nd International Conference on Machine Learning, 2015, 814–823. https://doi.org/10.1145/2740908.2742564
doi: 10.1145/2740908.2742564. |
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Swaminathan, A. and Joachims, T., The self-normalized estimator for counterfactual learning. Advances in Neural Information Processing Systems, 2015, 3231–3239. |
| [22] |
Lisa, W., Angela, S., Larry. L. and Chris, P., Deep knowledge tracing on programming exercises. Proceedings of the Fourth ACM Conference on Learning, 2017, 201–204. https://doi.org/10.1145/3051457.3053985
doi: 10.1145/3051457.3053985. |
| [23] |
Guy, S. and Asela, G., Evaluating recommendation systems. Recommender Systems Handbook, 2011, 257–297. https://doi.org/10.1007/978-0-387-85820-3_8
doi: 10.1007/978-0-387-85820-3_8. |
| [24] |
Gang, L. and Tianyong, H., User-based question recommendation for question answering system. International Journal of Information and Education Technology, 2012, 2(3): 243–246. https://doi.org/10.7763/IJIET.2012.V2.120
doi: 10.7763/IJIET.2012.V2.120. |
| [25] |
Shah, K., Zafar, A. and Irfan, U., Recommender systems: Issues, challenges, and research opportunities. Information science and applications (ICISA) 2016, 2016, 1179–1189. https://doi.org/10.1007/978-981-10-0557-2_112
doi: 10.1007/978-981-10-0557-2_112. |
| [26] |
Ming, Z., De-sheng, Z., Ran, T., You-Qun, S., Xiang, Y. and Qian, W., Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. Proceedings of the 14th International Conference of the Knowledge Management in Organizations, 2019, 122–134. https://doi.org/10.1007/978-3-030-21451-7_11
doi: 10.1007/978-3-030-21451-7_11. |
show all references
References:
| [1] |
Vie, J. and Kashima, H., Knowledge tracing machines: Factorization machines for knowledge tracing. The Thirty-Third AAAI Conference on Artificial Intelligence, 2019, 33(01): 750–757. https://doi.org/10.1609/aaai.v33i01.3301750
doi: 10.1609/aaai.v33i01.3301750. |
| [2] |
Walker, A., Recker, M. and Lawless, K., Collaborative information filtering: A review and an educational application. International Journal of Artificial Intelligence in Education, 2004, 14(1): 3–28. |
| [3] |
Hsu, M., A personalized English learning recommender system for ESL students. Expert Systems with Applications, 2008, 34(1): 683–688. https://doi.org/10.1016/j.eswa.2006.10.004
doi: 10.1016/j.eswa.2006.10.004. |
| [4] |
Milicevic, A., Vesin, B. and Ivanovic, M., E-learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 2011, 56(3): 885–899. https://doi.org/10.1016/j.compedu.2010.11.001
doi: 10.1016/j.compedu.2010.11.001. |
| [5] |
Segal, A., Katzir, Z. and Shapira, B., EduRank: A collaborative filtering approach to personalization in e-learning. Proceedings of the 7th International Conference on Educational Data Mining, 2014, 68–75. |
| [6] |
Toledo, R. and Mota, Y., An e-learning collaborative filtering approach to suggest problems to solve in programming online judges. International Journal of Distance Education Technologies, 2014, 12(2): 51–65. https://doi.org/10.4018/ijdet.2014040103
doi: 10.4018/ijdet.2014040103. |
| [7] |
Wu, D., Lu, J. and Zhang G., A fuzzy tree matching-based personalized e-learning recommender system. IEEE Transactions on Fuzzy Systems, 2015, 23(6): 2412–2426. https://doi.org/10.1109/TFUZZ.2015.2426201
doi: 10.1109/TFUZZ.2015.2426201. |
| [8] |
Dwivedi, P. and Bharadwaj, K., Effective trust-aware e-learning recommender system based on learning styles and knowledge levels. Journal of Educational Technology & Society, 2013, 16(4): 201–216. |
| [9] |
Jiang, C., Feng, J. and Sun, X., Personalized exercises recommendation algorithm based on knowledge hierarchical graph, ReKHG. Computer Engineering and Applications, 2018, 54(10): 234–240. |
| [10] |
Gong, T. and Yao, X., Deep exercise recommendation model. International Journal of Modeling and Optimization, 2019, 9(1): 18–23. https://doi.org/10.7763/IJMO.2019.V9.677
doi: 10.7763/IJMO.2019.V9.677. |
| [11] |
Piech, C., Bassen, J. and Huang, J., Deep knowledge tracing. NIPS'15 Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015, 505–513. |
| [12] |
Zhang, L., Xiong, X. and Zhao, S., Incorporating rich features into deep knowledge tracing. Proceedings of the Fourth ACM Conference on Learning at Scale, 2017, 169–172. https://doi.org/10.1145/3051457.3053976
doi: 10.1145/3051457.3053976. |
| [13] |
Su, Y., Liu, Q. and Liu, Q., Exercise-enhanced sequential modeling for student performance prediction. Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2018, 2435–2443. |
| [14] |
Wang, L., Angela, S. and Liu L., Deep knowledge tracing on programming exercises. Proceedings of the Fourth. ACM Conference on Learning at Scale, 2017, 201–204. https://doi.org/10.1145/3051457.3053985
doi: 10.1145/3051457.3053985. |
| [15] |
Yeung, C. and Yeung, D., Addressing two problems in deep knowledge tracing via prediction-consistent regularization. Proceedings of the Fifth Annual ACM Conference on Learning at Scale, 2018, 41–50. https://doi.org/10.1145/3231644.3231647
doi: 10.1145/3231644.3231647. |
| [16] |
Didelez, V. and Pigeot, I., Judea Pearl: Causality: Models, reasoning, and inference. Politische Vierteljahresschrift, 2001, 42(2): 313–315. https://doi.org/10.1007/s11615-001-0048-3
doi: 10.1007/s11615-001-0048-3. |
| [17] |
Louizos, C., Shalit, U. and Mooij, J., Causal effect inference with deep latent-variable models. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, 6449–6459. |
| [18] |
Van, D., Causal reasoning and inference making in judging the importance of story statements. Child Development, 1989, 60(2): 286–297. |
| [19] |
Joachims, T., Swaminathan, A. and De, R., Deep learning with logged bandit feedback. International Conference on Learning Representations, 2018, 1–12. |
| [20] |
Swaminathan, A. and Joachims, T., Counterfactual risk minimization: Learning from logged bandit feedback. Proceedings of the 32nd International Conference on Machine Learning, 2015, 814–823. https://doi.org/10.1145/2740908.2742564
doi: 10.1145/2740908.2742564. |
| [21] |
Swaminathan, A. and Joachims, T., The self-normalized estimator for counterfactual learning. Advances in Neural Information Processing Systems, 2015, 3231–3239. |
| [22] |
Lisa, W., Angela, S., Larry. L. and Chris, P., Deep knowledge tracing on programming exercises. Proceedings of the Fourth ACM Conference on Learning, 2017, 201–204. https://doi.org/10.1145/3051457.3053985
doi: 10.1145/3051457.3053985. |
| [23] |
Guy, S. and Asela, G., Evaluating recommendation systems. Recommender Systems Handbook, 2011, 257–297. https://doi.org/10.1007/978-0-387-85820-3_8
doi: 10.1007/978-0-387-85820-3_8. |
| [24] |
Gang, L. and Tianyong, H., User-based question recommendation for question answering system. International Journal of Information and Education Technology, 2012, 2(3): 243–246. https://doi.org/10.7763/IJIET.2012.V2.120
doi: 10.7763/IJIET.2012.V2.120. |
| [25] |
Shah, K., Zafar, A. and Irfan, U., Recommender systems: Issues, challenges, and research opportunities. Information science and applications (ICISA) 2016, 2016, 1179–1189. https://doi.org/10.1007/978-981-10-0557-2_112
doi: 10.1007/978-981-10-0557-2_112. |
| [26] |
Ming, Z., De-sheng, Z., Ran, T., You-Qun, S., Xiang, Y. and Qian, W., Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. Proceedings of the 14th International Conference of the Knowledge Management in Organizations, 2019, 122–134. https://doi.org/10.1007/978-3-030-21451-7_11
doi: 10.1007/978-3-030-21451-7_11. |
Figure 3.
Influence of the length of the knowledge path
Figure 4.
RMSE with different embedding sizes
Figure 5.
RMSE with different epochs
Figure 6.
Impact of interaction layers
Table 1.
Exercise-knowledge matrix
| Exercise | Knowledge | |||||||||||
| k1 | k2 | k3 | k4 | k5 | k6 | k7 | k8 | k9 | k10 | … | kN | |
| E1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| E2 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| … | ||||||||||||
| Ei | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | |
| Exercise | Knowledge | |||||||||||
| k1 | k2 | k3 | k4 | k5 | k6 | k7 | k8 | k9 | k10 | … | kN | |
| E1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| E2 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| … | ||||||||||||
| Ei | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | |
Table 2.
Summary of the PAM database
| Types of PAM exercises | |||
| Multiple choice | Judgement | Filling the blank | Calculation |
| 917 | 326 | 384 | 591 |
| Types of PAM exercises | |||
| Multiple choice | Judgement | Filling the blank | Calculation |
| 917 | 326 | 384 | 591 |
Table 3.
Summary of the PAM and Algebra 2005-2006 datasets for experiments
| Dataset | Number of students | Number of exercises | Knowledge concepts | Records |
| PAM | 450 | 2218 | 368 | 1264 |
| Algebra 2005-2006 | 300 | 1085 | 437 | 3000 |
| Dataset | Number of students | Number of exercises | Knowledge concepts | Records |
| PAM | 450 | 2218 | 368 | 1264 |
| Algebra 2005-2006 | 300 | 1085 | 437 | 3000 |
Table 4.
RMSE and comparison
| Method | Algebra 2005-2006 | PAM | ||
| RMSE | CDL improvement | RMSE | CDL improvement | |
| User-CF | 0.8441 | 10.95% | 0.8718 | 14.44% |
| KS-CF | 0.8033 | 6.42% | 0.7989 | 6.63% |
| DKT+ | 0.7892 | 4.75% | 0.7602 | 1.88% |
| KGEB-CF | 0.7768 | 3.23% | 0.7633 | 2.28% |
| CDL | 0.7617 | - | 0.7459 | - |
| Average improvement | 6.33% | 6.31% | ||
| Method | Algebra 2005-2006 | PAM | ||
| RMSE | CDL improvement | RMSE | CDL improvement | |
| User-CF | 0.8441 | 10.95% | 0.8718 | 14.44% |
| KS-CF | 0.8033 | 6.42% | 0.7989 | 6.63% |
| DKT+ | 0.7892 | 4.75% | 0.7602 | 1.88% |
| KGEB-CF | 0.7768 | 3.23% | 0.7633 | 2.28% |
| CDL | 0.7617 | - | 0.7459 | - |
| Average improvement | 6.33% | 6.31% | ||
Table 5.
Comparison of precision and recall on PAM
| Method | PAM | |||||||
| P@5 | CDL improvement | P@10 | CDL improvement | R@5 | CDL improvement | R@10 | CDL improvement | |
| User-CF | 0.493 | 15.82% | 0.481 | 11.43% | 0.049 | 14.29% | 0.079 | 10.13% |
| KS-CF | 0.514 | 11.09% | 0.496 | 8.06% | 0.049 | 14.29% | 0.081 | 7.41% |
| DKT+ | 0.529 | 7.94% | 0.497 | 7.85% | 0.053 | 5.67% | 0.085 | 2.35% |
| KGEB-CF | 0.547 | 4.39% | 0.512 | 4.69% | 0.054 | 3.70% | 0.085 | 2.35% |
| CDL | 0.571 | - | 0.536 | - | 0.056 | - | 0.087 | - |
| Average improvement | 9.81% | 8.01% | 9.49% | 5.56% | ||||
| Method | PAM | |||||||
| P@5 | CDL improvement | P@10 | CDL improvement | R@5 | CDL improvement | R@10 | CDL improvement | |
| User-CF | 0.493 | 15.82% | 0.481 | 11.43% | 0.049 | 14.29% | 0.079 | 10.13% |
| KS-CF | 0.514 | 11.09% | 0.496 | 8.06% | 0.049 | 14.29% | 0.081 | 7.41% |
| DKT+ | 0.529 | 7.94% | 0.497 | 7.85% | 0.053 | 5.67% | 0.085 | 2.35% |
| KGEB-CF | 0.547 | 4.39% | 0.512 | 4.69% | 0.054 | 3.70% | 0.085 | 2.35% |
| CDL | 0.571 | - | 0.536 | - | 0.056 | - | 0.087 | - |
| Average improvement | 9.81% | 8.01% | 9.49% | 5.56% | ||||
Table 6.
Comparison of precision and recall on Algebra 2005-2006
| Method | Algebra 2005-2006 | |||||||
| P@5 | CDL improvement | P@10 | CDL improvement | R@5 | CDL improvement | R@10 | CDL improvement | |
| User-CF | 0.502 | 8.23% | 0.496 | 6.65% | 0.048 | 12.50% | 0.069 | 14.50% |
| KS-CF | 0.518 | 5.60% | 0.512 | 3.32% | 0.048 | 12.50% | 0.072 | 9.72% |
| DKT+ | 0.532 | 2.82% | 0.516 | 2.52% | 0.050 | 8.00% | 0.077 | 2.78% |
| KGEB-CF | 0.538 | 1.67% | 0.523 | 1.15% | 0.053 | 2.00% | 0.074 | 6.76% |
| CDL | 0.547 | - | 0.529 | - | 0.054 | - | 0.079 | - |
| Average improvement | 4.58% | 3.41% | 8.75% | 8.44% | ||||
| Method | Algebra 2005-2006 | |||||||
| P@5 | CDL improvement | P@10 | CDL improvement | R@5 | CDL improvement | R@10 | CDL improvement | |
| User-CF | 0.502 | 8.23% | 0.496 | 6.65% | 0.048 | 12.50% | 0.069 | 14.50% |
| KS-CF | 0.518 | 5.60% | 0.512 | 3.32% | 0.048 | 12.50% | 0.072 | 9.72% |
| DKT+ | 0.532 | 2.82% | 0.516 | 2.52% | 0.050 | 8.00% | 0.077 | 2.78% |
| KGEB-CF | 0.538 | 1.67% | 0.523 | 1.15% | 0.053 | 2.00% | 0.074 | 6.76% |
| CDL | 0.547 | - | 0.529 | - | 0.054 | - | 0.079 | - |
| Average improvement | 4.58% | 3.41% | 8.75% | 8.44% | ||||
Table 7.
Comparison of performances of CDL with/without causal inference
| Dataset | Metric | Method | |
| CDL-Without-CI | CDL-CI(CDL) | ||
| PAM | P@5 |
0.572 | 0.582 |
| P@10 |
0.507 | 0.545 | |
| R@5 |
0.052 | 0.058 | |
| R@10 |
0.078 | 0.091 | |
| Algebra 2005-2006 | P@5 |
0.569 | 0.578 |
| P@10 |
0.499 | 0.539 | |
| R@5 |
0.051 | 0.055 | |
| R@10 |
0.073 | 0.089 | |
| Dataset | Metric | Method | |
| CDL-Without-CI | CDL-CI(CDL) | ||
| PAM | P@5 |
0.572 | 0.582 |
| P@10 |
0.507 | 0.545 | |
| R@5 |
0.052 | 0.058 | |
| R@10 |
0.078 | 0.091 | |
| Algebra 2005-2006 | P@5 |
0.569 | 0.578 |
| P@10 |
0.499 | 0.539 | |
| R@5 |
0.051 | 0.055 | |
| R@10 |
0.073 | 0.089 | |
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