Data science in education: interdisciplinary contributions

Authors

DOI:

https://doi.org/10.47909/anis.978-9916-9760-3-6.94

Keywords:

Computer Science, Education, Data Science, Machine Learning, Interdisciplinarity

Abstract

The present paper aims to identify the contributions of Information Science (IS) and Computer Science (CS) to meet the informational needs of the educational context. The methodology used is exploratory, descriptive, and applied. A qualitative approach is used to process data retrieved through bibliographic research on data science applied to education. The study brought together the theoretical and practical contributions of IS and CS techniques capable of addressing the main informational challenges of educational environments reported in the literature. The preliminary results obtained consist of connections between concepts and techniques from the two knowledge areas that propose solutions for informational needs in education. Data mining, fuzzy logic, natural computing, text mining, data warehouse, ontologies, semantic web, and information retrieval were the most evident contributions identified when analyzing the demands of students, teachers, and managers. The conclusion reached is that articulating interdisciplinary knowledge of CS and IS in proposing data-driven approaches in the educational context is possible and necessary. These contributions proved capable of dealing with the challenge of supporting education in different ways by enabling even more robust theoretical and methodological solutions.

Downloads

Download data is not yet available.

Author Biographies

Patrícia Takaki, Universidade Federal de Santa Catarina - UFSC/Universidade Estadual de Montes Claros-UNIMONTES, Brasill

Doutoranda do Programa de Pós-Graduação em Ciência da Informação (PGCIn/CED/UFSC)

Docente do Departamento de Ciência da Computação (DCC/CCET/UNIMONTES)

Moisés Dutra, Universidade Federal de Santa Catarina - UFSC, Brasil

Programa de Pós-Graduação em Ciência da Informação (PGCIn/CED/UFSC)

References

Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational Data Mining and Learning Analytics for 21st Century higher education: A review and synthesis. Telematics and Informatics, 37, 13-49. https://doi.org/doi:10.1016/j.tele.2019.01.007

Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers & Education, 113(113), 226–242. https://doi.org/10.1016/j.compedu.2017.05.021

Araújo, C. A. Á. (2009). Correntes teóricas da ciência da informação. Ciência Da Informação, 38(3), 192–204. https://doi.org/10.1590/s0100-19652009000300013

Artífice, A. F. V. P., Sarraipa, J., & Jardim-Goncalves, R. (2021). Improvement of Student Attention Monitoring Supported by Precision Sensing in Learning Management Systems. In I. Dei (Ed.), Computer-Mediated Communication. https://doi.org/10.5772/intechopen.98764

Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2020). Big data in education: a state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-00223-0

Borko, H. (1968). Information Science: What is it? American Documentation, 19(1), 3–5. https://doi.org/10.1002/asi.5090190103

Buckland, M. K. (1991). Information as thing. Journal of the American Society for Information Science, 42(5), 351–360. https://doi.org/3.0.CO;2-3">10.1002/(SICI)1097-4571(199106)42:5<351::AID-ASI5>3.0.CO;2-3

Coneglian, C. S., Valentim, M. L. P., & Santarem Segundo, J. E. (2021). Muli e interdisciplinaridade entre a Ciência da Informação e a Ciência da Computação no âmbito da Web Semântica. Informação & Sociedade: Estudos, 31(1), 1–18. https://doi.org/10.22478/ufpb.1809-4783.2021v31n1.52120

Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73(73), 247–256. https://doi.org/10.1016/j.chb.2017.01.047

Daniel, B. K. (2017). Big Data in Higher Education: The Big Picture. In B. K. Daniel (Ed.), Big Data and Learning Analytics in Higher Education (pp. 19–28). https://doi.org/10.1007/978-3-319-06520-5_3

Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., … Warschauer, M. (2020). Mining Big Data in Education: Affordances and Challenges. Review of Research in Education, 44(1), 130–160. https://doi.org/10.3102/0091732x20903304

Gambo, Y., & Shakir, M. Z. (2021). WIP: Model of Self-Regulated Smart Learning Environment. 2021 IEEE World Conference on Engineering Education (EDUNINE), 1–4. https://doi.org/10.1109/edunine51952.2021.9429090

Karlos, S., Kostopoulos, G., & Kotsiantis, S. (2020). Predicting and Interpreting Students’ Grades in Distance Higher Education through a Semi-Regression Method. Applied Sciences, 10(23), 8413. https://doi.org/10.3390/app10238413

Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1), 205395171452848. https://doi.org/10.1177/2053951714528481

Morin, E. (2011). Os sete saberes necessários a educação do futuro (2nd ed.; C. Eleonora & Jeanne Sawaya, Trans.). Brasília/DF: Unesco.

Morin, E. (2015). Introdução ao pensamento complexo (5th ed.; E. Lisboa, Trans.). Porto Alegre: Sulina.

OECD. (2019). Recommendation of the Council on Artificial Intelligence - OECD Legal Instruments 0449. Retrieved from Organisation for Economic Co-operation and Development website. Obtido de: https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449

Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational Data Mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701–1717. https://doi.org/10.1016/j.tele.2018.04.015

Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), 1–21. https://doi.org/10.1002/widm.1355

Saracevic, T. (1996). Ciência da informação: origem, evolução e relações. Perspectivas Em Ciência Da Informação, 1(1), 41–62. Obtido de http://portaldeperiodicos.eci.ufmg.br/index.php/pci/article/view/235/22

Tomasevic, N., Gvozdenovic, N., & Vranes, S. (2020). An overview and comparison of supervised data mining techniques for student exam performance prediction. Computers & Education, 143(143), 103676. https://doi.org/10.1016/j.compedu.2019.103676

UNESCO. (2017). Ensure quality education for all: Sustainable development goal 4; ten targets. In UNESDOC. Obtido de https://unesdoc.unesco.org/ark:/48223/pf0000259784

UNESCO. (2021). AI and education-Guidance for policymakers. Obtido de https://unesdoc.unesco.org/ark:/48223/pf0000376709

Published

2022-05-31

How to Cite

Takaki, P., & Dutra, M. (2022). Data science in education: interdisciplinary contributions. Advanced Notes in Information Science, 2, 149-160. https://doi.org/10.47909/anis.978-9916-9760-3-6.94