Abstract
The paper presents an exploration of the role of mobile technology for the realization of personalized learning and for improvement the students’ learning performance within an intelligent educational environment. The two of the created predictive models show the patterns and anomalies in students’ learning behavior and their learning performance. The utilized supervised machine learning algorithms: Random Forest, ID3, Naïve Bayes, Deep learning, k-NN are evaluated, and the results points out that the most suitable algorithms for solving these classification tasks are decision tree-based Random Forest and ID3. A multi-layer perceptron is used for predicting the students’ group learning performance at whole.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Klimova, B., Poulova, P.: Mobile learning in higher education. Adv. Sci. Lett. 22(5–6), 1111–1114 (2016). https://doi.org/10.1166/asl.2016.6673
Krull, G., Duart, J.: Research trends in mobile learning in education: a systematic review of articles (2011–2015). International Review of Research in Open and Distributed Learning 18(7) (2017). http://openaccess.uoc.edu/webapps/o2/bitstream/10609/93100/1/researchtrends.pdf
Costa, C., Cardoso, A.P., Lima, M.P., Ferreira, M., Abrantes, J.L.: Pedagogical interaction and learning performance as determinants of academic achievement. Procedia. Soc. Behav. Sci. 171, 874–881 (2015)
AdrianChin, Y.K., JosephNg, P.S., Shibghatullah, A.S., Loh, Y.F.: JomDataMining: learning behavior affecting their academic performance, really?. In: 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS), pp. 1–4 (2019). https://doi.org/10.1109/ICETAS48360.2019.9117453
Zhiyenbayeva, N., Belyanova, E., Petunina, I., Dmitrichenkova, S., Dolzhich, E.: Personalized computer support of performance rates and education process in high school: case study of engineering students. Int. J. Eng. Pedagogy 11(2), 135–153 (2021)
Kosasi, S., Vedyanto, Kasma, U., Ayu Eka Yuliani, I.D.: The mediating role of learning analytics to improve student academic performance. In: 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS), pp. 1–6 (2020). https://doi.org/10.1109/ICORIS50180.2020.9320802
Vital, T.P., Sangeeta, K., Kumar, K.: Student classification based on cognitive abilities and predicting learning performances using machine learning models. Int. J. Comput. Dig. Syst. 10(1), 63–75 (2021). http://dx.doi.org/10.12785/ijcds/100107
Zacharis, N.: Predicting student academic performance in blended learning using artificial neural networks. Int. J. Artif. Intell. Appl. 7, 17–29 (2016)
Van Eck, N.J., Waltman, L.: Text mining and visualization using VOSviewer. ISSI Newsl. 7(3), 50–54 (2011)
Wang, C., Fang, T., Miao, R.: Learning performance and cognitive load in mobile learning: Impact of interaction complexity. J. Comput. Assist. Learn. (2018). https://doi.org/10.1111/jcal.12300
Lin, Y.-T.: When mobile technology meets traditional classroom learning environment: How does it improve students’ learning performances? In: Learning Environments: Emerging Theories, Applications and Future Directions, pp. 143–158 (2016)
Talan, T.: The effect of mobile learning on learning performance: a meta-analysis study. Educ. Sci. Theory Pract. 20(1), 79–103 (2020). https://doi.org/10.12738/jestp.2020.1.006
Chen, C.-M., Chen, L.-C., Yang, S.-M.: An English vocabulary learning app with self-regulated learning mechanism to improve learning performance and motivation. Comput. Assist. Lang. Learn. 32(3), 237–260 (2019). https://doi.org/10.1080/09588221.2018.1485708
Acknowledgments
This research is supported by the Bulgarian FNI fund through the project “Modeling and Research of Intelligent Educational Systems and Sensor Networks (ISOSeM)”, contract КП-06-H47/4 from 26.11.2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ivanova, M., Ivanova, T., Terzieva, V., Todorova, K. (2022). Modeling Students’ Learning Performance and Their Attitudes to Mobile Learning. In: Auer, M.E., Tsiatsos, T. (eds) New Realities, Mobile Systems and Applications. IMCL 2021. Lecture Notes in Networks and Systems, vol 411. Springer, Cham. https://doi.org/10.1007/978-3-030-96296-8_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-96296-8_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96295-1
Online ISBN: 978-3-030-96296-8
eBook Packages: EngineeringEngineering (R0)