Abstract
Neural and symbolic architectures are key techniques in AI for learner modelling, enhancing adaptive educational services. Symbolic models offer explanation and reasoning for decisions but require significant human effort. On the other hand, neural architectures demand less human input and yield better predictions, yet lack interpretability. Given the high-risk nature of education and that incorrectly tailored support can negatively affect learning outcomes, the integration of neural and symbolic architectures becomes crucial. This research proposes a novel neural-symbolic AI approach for temporal learner modelling, called TemporaLM, that leverages unsupervised deep neural networks (i.e., autoencoders enriched with symbolic educational knowledge) and dynamic Bayesian networks for learners’ knowledge tracing over time. The approach employs a dynamic Bayesian network for temporal knowledge tracking in learners' computational thinking and employs a knowledge-based autoencoder to enhance predictive performance through synthetic data augmentation. Our findings from both cross-validation and practical application demonstrate that the TemporaLM approach, trained on the neural-symbolic AI augmented dataset, achieves better generalizability, yielding an accuracy of 85% and an F1 score of 87%. This surpasses the dynamic Bayesian network trained solely on original and autoencoder-augmented data. Notably, by leveraging the transformed dataset for model training, improvements of up to 8% in F1 score and 5% in accuracy were achieved compared to the original dataset, observed in both cross-validation and application stages. The augmented prediction capabilities, coupled with interpretable knowledge tracing, cultivate trust among educators and learners in data-driven decisions. These findings underline the potential of neural-symbolic family of AI to improve limitation of existing (symbolic) AI methods in education, advancing AI's potential in education and enabling trustworthy and interpretable applications.
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Data availability
The datasets generated and analysed during the current study are not publicly available due to privacy or ethical restrictions. The corresponding author can provide sample of the dataset on reasonable request.
References
Abyaa, A., Khalidi Idrissi, M., & Bennani, S. (2019). Learner modelling: Systematic review of the literature from the last 5 years. Educational Technology Research and Development, 67, 1105–1143.
Almond, R. G., Mislevy, R. J., Steinberg, L. S., Yan, D., & Williamson, D. M. (2015). Bayesian networks in educational assessment. Springer.
Babaei, K., Chen, Z., & Maul, T. (2019). Data augmentation by autoencoders for unsupervised anomaly detection. Ar**v Preprint Ar**v:1912.13384.
Baker, R. S. D., Corbett, A. T., & Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. 406–415.
Besold, T., A. D. Garcez, S. Bader, H. Bowman, P. Domingos, P. Hitzler, K.-U. Kühnberger, LC Lamb, D. Lowd, PMV Lima et al. (2017),“Neural-symbolic learning and reasoning: A survey and interpretation,.” Ar**v Preprint Ar**v:1711.03902.
Bhanuse, R., & Mal, S. (2021). A systematic review: Deep learning based e-learning recommendation system. 190–197.
Carmona, C., Castillo, G., & Millán, E. (2008). Designing a dynamic bayesian network for modeling students’ learning styles. 346–350.
Castillo, E., Gutiérrez, J. M., & Hadi, A. S. (1997). Sensitivity analysis in discrete Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 27(4), 412–423.
Conati, C., Porayska-Pomsta, K., & Mavrikis, M. (2018). AI in education needs interpretable machine learning: Lessons from open learner modelling. Ar**v Preprint Ar**v:1807.00154.
Cui, Y., Chu, M.-W., & Chen, F. (2019). Analyzing student process data in game-based assessments with Bayesian knowledge tracing and dynamic Bayesian networks. Journal of Educational Data Mining, 11(1), 80–100.
Culbertson, M. J. (2016). Bayesian networks in educational assessment: The state of the field. Applied Psychological Measurement, 40(1), 3–21.
de Wit, J., Schodde, T., Willemsen, B., Bergmann, K., De Haas, M., Kopp, S., Krahmer, E., & Vogt, P. (2018). The effect of a robot’s gestures and adaptive tutoring on children’s acquisition of second language vocabularies. 50–58.
Demir, S., Mincev, K., Kok, K., & Paterakis, N. G. (2021). Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting. Applied Energy, 304, 117695.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22.
El Mawas, N., Hooshyar, D., & Yang, Y. (2020). Investigating the learning impact of Autothinking educational game on adults: A case study of France. 188–196.
Garcez, A. d.’ A., Bader, S., Bowman, H., Lamb, L. C., de Penning, L., Illuminoo, B., Poon, H., & Zaverucha, C. G. (2022). Neural-symbolic learning and reasoning: A survey and interpretation. Neuro-Symbolic Artificial Intelligence: The State of the Art, 342(1), 327.
Garcez, A. d.’ A., & Lamb, L. C. (2023). Neurosymbolic AI: The 3 rd wave. Artificial Intelligence Review, 1–20.
Grawemeyer, B., Mavrikis, M., Holmes, W., & Gutierrez-Santos, S. (2015). Adapting feedback types according to students’ affective states. 586–590.
Hernández, Y., Sucar, L. E., & Arroyo-Figueroa, G. (2013). Affective modeling for an intelligent educational environment. In Intelligent and adaptive educational-learning systems: Achievements and trends (pp. 3–24). Springer.
Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019). A systematic review of deep learning approaches to educational data mining. Complexity, 2019.
Hooshyar, D. (2022). Effects of technology-enhanced learning approaches on learners with different prior learning attitudes and knowledge in computational thinking. Computer Applications in Engineering Education, 30(1), 64–76.
Hooshyar, D., Ahmad, R. B., Yousefi, M., Fathi, M., Horng, S.-J., & Lim, H. (2016). Applying an online game-based formative assessment in a flowchart-based intelligent tutoring system for improving problem-solving skills. Computers & Education, 94, 18–36.
Hooshyar, D., Huang, Y.-M., & Yang, Y. (2022). GameDKT: Deep knowledge tracing in educational games. Expert Systems with Applications, 196, 116670.
Hooshyar, D., Pedaste, M., Yang, Y., Malva, L., Hwang, G.-J., Wang, M., Lim, H., & Delev, D. (2021). From gaming to computational thinking: An adaptive educational computer game-based learning approach. Journal of Educational Computing Research, 59(3), 383–409.
Hooshyar, D., & Yang, Y. (2021). Neural-symbolic computing: A step toward interpretable AI in education. Bulletin of the Technical Committee on Learning Technology (ISSN: 2306-0212), 21(4), 2–6.
Hooshyar, D., Yousefi, M., & Lim, H. (2019). A systematic review of data-driven approaches in player modeling of educational games. Artificial Intelligence Review, 52, 1997–2017.
Hu, Z., Ma, X., Liu, Z., Hovy, E., & **ng, E. (2016). Harnessing deep neural networks with logic rules. ar**v preprint ar**v:1603.06318.
Jensen, F. V., & Nielsen, T. D. (2007). Bayesian networks and decision graphs (Vol. 2). Springer.
Käser, T., Klingler, S., Schwing, A. G., & Gross, M. (2014). Beyond knowledge tracing: Modeling skill topologies with bayesian networks. 188–198.
Käser, T., Klingler, S., Schwing, A. G., & Gross, M. (2017). Dynamic Bayesian networks for student modeling. IEEE Transactions on Learning Technologies, 10(4), 450–462.
Kautz, H. (2022). The third ai summer: Aaai robert s. Engelmore memorial lecture. AI Magazine, 43(1), 105–125.
Lauritzen, S. L. (1995). The EM algorithm for graphical association models with missing data. Computational Statistics & Data Analysis, 19(2), 191–201.
Lenat, D. B., Prakash, M., & Shepherd, M. (1985). CYC: Using common sense knowledge to overcome brittleness and knowledge acquisition bottlenecks. AI Magazine, 6(4), 65–65.
Levy, R. (2019). Dynamic Bayesian network modeling of game-based diagnostic assessments. Multivariate Behavioral Research, 54(6), 771–794.
Li, T., & Srikumar, V. (2019). Augmenting neural networks with first-order logic. Ar**v Preprint Ar**v:1906.06298.
Meltzer, J. P., & Tielemans, A. (2022). The European Union AI act: Next steps and issues for building international cooperation in AI.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan kaufmann.
Pelánek, R. (2017). Bayesian knowledge tracing, logistic models, and beyond: An overview of learner modeling techniques. User Modeling and User-Adapted Interaction, 27, 313–350.
Permanasari, A. E., Hidayah, I., & Nugraha, S. (2014). A student modeling based on bayesian network framework for characterizing student learning style. Advanced Science Letters, 20(10–11), 1936–1940.
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Sohl-Dickstein, J. (2015). Deep knowledge tracing. Advances in Neural Information Processing Systems, 28.
Reichenberg, R. E., Levy, R., & Clark, A. (2022). Considerations for fitting dynamic Bayesian networks with latent variables: A Monte Carlo study. Applied Psychological Measurement, 46(2), 116–135.
Rowe, J., & Lester, J. (2010). Modeling user knowledge with dynamic Bayesian networks in interactive narrative environments., 6(1), 57–62.
Saldanha, J., Chakraborty, S., Patil, S., Kotecha, K., Kumar, S., & Nayyar, A. (2022). Data augmentation using Variational autoencoders for improvement of respiratory disease classification. PLoS One, 17(8), e0266467.
Sarker, M. K., Zhou, L., Eberhart, A., & Hitzler, P. (2021). Neuro-symbolic artificial intelligence. AI Communications, 34(3), 197–209.
Serafini, L., & d’Avila Garcez, A. S. (2016). Learning and reasoning with logic tensor networks. 334–348.
Shakya, A., Rus, V., & Venugopal, D. (2021). Student strategy prediction using a Neuro-symbolic approach. International Educational Data Mining Society.
Ting, C.-Y., Cheah, W.-N., & Ho, C. C. (2013). Student engagement modeling using bayesian networks. 2939–2944.
Ting, C.-Y., & Phon-Amnuaisuk, S. (2012). Properties of Bayesian student model for INQPRO. Applied Intelligence, 36, 391–406.
Tran, S. N., Garcez, A. S. D. A. (2016). Deep logic networks: Inserting and extracting knowledge from deep belief networks. IEEE Transactions on Neural Networks and Learning Systems, 29(2), 246–258.
Vincent-Lancrin, S., & Van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges.
Yudelson, M. V., Koedinger, K. R., & Gordon, G. J. (2013). Individualized bayesian knowledge tracing models. 171–180.
Acknowledgments
This work was supported by Tallinn University project entitled “Fostering the research strand in Artificial Intelligence in Education at TLU” with number TF/1422. Bayesian network simulation software GeNIe Modeler (https://www.bayesfusion.com/), the data science platform RapidMiner (https://rapidminer.com/), and the Python programming language were utilized in this work.
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Hooshyar, D. Temporal learner modelling through integration of neural and symbolic architectures. Educ Inf Technol 29, 1119–1146 (2024). https://doi.org/10.1007/s10639-023-12334-y
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DOI: https://doi.org/10.1007/s10639-023-12334-y