Abstract
The need for qualified people is growing exponentially, requiring limited resources allocated to education/training to be used most efficiently. However some problems can occur: (1) relying on learning theories, it is crucial to improve the learning process and mitigate the issues that may arise from technologically enhanced learning environments; (2) each student presents a particular way of assimilating knowledge, i.e. his/her learning procedure. It’s essential that these systems adapt to the learning preferences of the students. In the present study, we propose an intelligent learning system able to monitor the patterns of students’ behaviour during e-assessments, to support the teaching procedure within school environments. Results show that there are still mechanisms that can be explored to understand better the complex relationship between human behaviour, attention, and assessment which could be used for the implementation of better learning strategies. These results may be crucial for improving learning systems in an e-learning environment and for predicting students’ behaviour in an exam, based on their interaction with technological devices.
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References
Aarts, E., Wichert, R.: Ambient intelligence. In: Bullinger, H.J. (ed.) Technology Guide, pp. 244–249. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-88546-7_47
Bouton, M.E.: Behaviourism, thoughts, and actions. Br. J. Psychol. 100(S1), 181–183 (2009)
Durães, D., Cardoso, C., Bajo, J., Novais, P.: Learning frequent behaviors patterns in intelligent environments for attentiveness level. In: De la Prieta, F., et al. (eds.) PAAMS 2017. AISC, vol. 619, pp. 139–147. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_13
Duraes, D., Carneiro, D., Jimenez, A., Novais, P.: Characterizing attentive behavior in intelligent environments. Neurocomputing 272, 46–54 (2018)
Ford, N., Chen, S.Y.: Individual differences, hypermedia navigation, and learning: an empirical study. J. Educ. Multimed. Hypermedia 9(4), 281–311 (2000)
Novais, P., Carneiro, D.: Interdisciplinary Perspectives on Contemporary Conflict Resolution. Advances in Linguistics and Communication Studies. IGI Global (2016). https://books.google.pt/books?id=irFjjwEACAAJ
Pimenta, A., Carneiro, D., Neves, J., Novais, P.: A neural network to classify fatigue from human-computer interaction. Neurocomputing 172, 413–426 (2016)
Yampolskiy, R.V., Govindaraju, V.: Behavioural biometrics: a survey and classification. Int. J. Biom. 1(1), 81–113 (2008)
Acknowledgement
This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT- Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.
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Novais, P., Gonçalves, F., Durães, D. (2018). Forecasting Student’s Preference in E-learning Systems. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science(), vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_18
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DOI: https://doi.org/10.1007/978-3-030-04191-5_18
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