Résumés
Abstract
Over the past decade, opportunities for online learning have dramatically increased. Learners around the world now have digital access to a wide array of corporate trainings, certifications, comprehensive academic degree programs, and other educational and training options. Some organizations are blending traditional instruction methods with online technologies. Blended learning generates large volumes of data about both the content (quality and usage) and the learners (study habits and learning outcomes). Correspondingly, the need to properly process voluminous, continuous, and often disparate data has prompted the advent of cognification. Cognification techniques design complex data analytic models that allow natural intelligence to engage artificial smartness in ways that can enhance the learning experience. Cognification is the approach to make something increasingly, ethically, and regulatably smarter. This article highlights how emerging trends in cognification could disrupt online education.
Keywords:
- Cognification,
- AI in Education,
- Fourth Industrial Revolution,
- Educational Technology
Résumé
Au cours de la dernière décennie, les possibilités d'apprentissage en ligne ont augmenté de façon remarquable. Les apprenants du monde entier ont maintenant un accès numérique à un large éventail de formations d'entreprise, de certifications, de programmes universitaires complets et d'autres options d'éducation et de formation. Certaines organisations combinent les méthodes d'enseignement traditionnelles avec les technologies en ligne. L'apprentissage hybride génère d'importants volumes de données concernant à la fois le contenu (qualité et utilisation) et les apprenants (habitudes d'étude et résultats d'apprentissage). En conséquence, la nécessité de traiter correctement des données volumineuses, continues et souvent divergentes a entraîné l'avènement de la cognification. Les techniques de cognification conçoivent des modèles d'analyse de données complexes qui permettent à l'intelligence naturelle de mobiliser l'intelligence artificielle de manière à améliorer l'expérience d'apprentissage. La cognification est l'approche qui consiste à rendre quelque chose de plus en plus intelligent, de manière éthique et régulée. Cet article souligne comment les tendances émergentes en matière de cognification pourraient bouleverser l'enseignement en ligne.
Mots-clés :
- Cognification,
- IA dans l'éducation,
- quatrième révolution industrielle,
- technologie éducative
Parties annexes
Bibliography
- Abbass, H. A. (2019). Social integration of artificial intelligence: Functions, automation allocation logic and human-autonomy trust. Cognitive Computation, 11(2), 159–171. https://doi.org/10.1007/s12559-018-9619-0
- Alikaniotis, D., Yannakoudakis, H., & Rei, M. (2016). Automatic text scoring using neural networks. ArXiv Preprint ArXiv:1606.04289.
- Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., & Benjamins, R. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
- Astle, J. (2018). Do schools really kill creativity? RSA Blog, April 2018. https://www.thersa.org/blog/2018/04/do-schools-kill-creativity
- Brokenshire, D., & Kumar, V. (2009). Discovering Causal Models of Self-Regulated Learning. AIED, 257–264. https://doi.org/0.3233/978-1-60750-028-5-257
- Dong, F., Zhang, Y., & Yang, J. (2017). Attention-based recurrent convolutional neural network for automatic essay scoring. Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), 153–162.
- Gysegom, W., Houthuys, S., Khanna, S., Migliorini, R., Misljencevic, D., & Robertson, M. (2021). 4IR capability building: Opportunities and solutions for lasting impact. McKinsey & Company. https://www.mckinsey.com/capabilities/operations/our-insights/4ir-capability-building-opportunities-and-solutions-for-lasting-impact
- Jankowska, M., Conrad, C., Harris, J., & Kešelj, V. (2018). N-gram based approach for automatic prediction of essay rubric marks. In E. Bagheri & J. C. K. Cheung (Eds.), Advances in artificial intelligence (pp. 298–303). Cham: Springer International Publishing.
- Kumar, V. S. (1996). Computer-Supported Collaborative Learning: Issues for Research. Proceedings of the 8th Annual Graduate Symposium on Computer Science, University of Saskatchewan, Canada.
- Kumar, V. S., & Boulanger, D. (2020a). Automated Essay Scoring and the Deep Learning Black Box: How are rubric scores determined? International Journal of Artificial Intelligence in Education, 31, 538-584. https://doi.org/10.1007/S40593-020-00211-5
- Kumar, V. S., & Boulanger, D. (2020b). Explainable Automated Essay Scoring: Deep Learning Really Has Pedagogical Value, Frontiers in Education, 5. https://doi.org/10.3389/feduc.2020.572367
- Kumar, V. S., Fraser, S., & Boulanger, D. (2017). Discovering the predictive power of five baseline writing competences. Journal of Writing Analytics, 1(1), 176–226.
- Majumder, S., Chakraborty, J., Bai, G. R., Stolee, K. T., & Menzies, T. (2021). Fair enough: Searching for sufficient measures of fairness. ArXiv Preprint ArXiv:2110.13029. https://arxiv.org/abs/2110.13029
- Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35. https://doi.org/10.1145/3457607
- Mohkami, M., Noorian, Z., & Vassileva, J. (2015). Dynamic credibility threshold assignment in trust and reputation mechanisms using PID Controller. In N. Baloian, Y. Zorian, P. Taslakian, & S. Shoukouryan (Eds.), Collaboration and technology, 9334 (pp. 148–163). Springer International Publishing. https://doi.org/10.1007/978-3-319-22747-4_12
- Penprase, B. E. (2018). The fourth industrial revolution and higher education. In Nancy W. Gleason (Ed.), Higher education in the era of the fourth industrial revolution (pp. 208-229). Palgrave Macmillan. https://doi.org/10.1007/978-981-13-0194-0_9
- Scholtz, J., & Bahrami, S. (2003). Human-robot interaction: Development of an evaluation methodology for the bystander role of interaction. SMC’03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 4, 3212–3217. https://doi.org/10.1109/ICSMC.2003.1244385
- Schwab, K. (2016). The fourth industrial revolution (First U.S. edition). Crown Business.
- Sheridan, T. B. (1992). Telerobotics, automation, and human supervisory control. MIT press.
- Shermis, M. D. (2014). State-of-the-art automated essay scoring: Competition, results, and future directions from a United States demonstration. Assessing Writing, 20, 53–76. https://doi.org/10.1016/j.asw.2013.04.001
- Verma, S., & Rubin, J. (2018). Fairness definitions explained. Proceedings of the International Workshop on Software Fairness, 1–7. https://doi.org/10.1145/3194770.3194776
- Wagner, G., Lukyanenko, R., & Paré, G. (2022). Artificial intelligence and the conduct of literature reviews. Journal of Information Technology, 37(2), 209–226. https://doi.org/10.1177/02683962211048201
- Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Routledge.
- Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., & Zhu, J. (2019). Explainable AI: A brief survey on history, research areas, approaches and challenges. In J. Tang, M.-Y. Kan, D. Zhao, S. Li, & H. Zan (Eds.), Natural language processing and Chinese computing, 11839 (pp. 563–574). Springer International Publishing. https://doi.org/10.1007/978-3-030-32236-6_51
- Yusuf, B., Walters, L. M., & Sailin, S. N. (2020). Restructuring educational institutions for growth in the fourth industrial revolution (4IR): A systematic review. International Journal of Emerging Technologies in Learning, 15(3), 93–109. https://doi.org/10.3991/ijet.v15i03.11849
- Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 2–27. https://doi.org/10.1186/s41239-019-0171-0
- Zupanc, K., & Bosnić, Z. (2017). Automated essay evaluation with semantic analysis. Knowledge-Based Systems, 120, 118–132. https://doi.org/10.1016/j.knosys.2017.01.006