Coping with Access Difficulties and Absenteeism through Data Visualization: A Case Study from a Rural Vocational School in Northern Greece
Source abstract
Abstract
Absenteeism and early school leaving (ESL) constitute two main problems in education with a significant impact on adolescents’ life. Early leavers from education and training may face considerable difficulties in the labor market later as adults. Motivated by the necessity of minimizing this phenomenon, we developed a novel application that uses big data, generated from a student attendance management system in a vocational senior high school in Greece. This application first automatically conducts data preprocessing and data transformation and saves the processed data on a cloud data warehouse. Then, an online analytical processing (OLAP) analysis is performed, resulting in a real-time visualization that provides a variety of different graphs. In this study, we demonstrate the need for real-time visualization of the analyzed data. Such a type of presentation provides information on the spot regarding potential early leavers’ behavior, helping school administrators gain time for prompt and effective actions. Through the data processing and analysis, the application provides instructors with constant information, in addition to those acquired by the formal student attendance management systems. Our research provides indicative evidence in favor of the use of such applications, as by adequately reacting to the observed patterns in real-time, we observed a significant decrease in students’ unnecessary absences and a reduction of the ELS phenomenon in a three-year school period.
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Research context
OpenAlex topics: Cardiovascular Health and Risk Factors
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Science and Technology, Hellenic Open University, 263 35 Patras, Greece
Department of Mathematics, University of Patras, 265 04 Patras, Greece
References (48)
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1. Tawil, S., and Cougoureux, M. (2013). Revisiting Learning: The Treasure Within
2. Assessing the Influence of the 1996 Delors Report, UNESCO. Available online: https://unesdoc.unesco.org/ark:/48223/pf0000220050.
3. Kolyvas, G., Kotsifakos, D., Douligeris, C., and Modeling, C. (2020). Designing and implementing an open personalized learning environment for the electrical engineering training course in vocational education. Eur. J. Eng. Res. Sci.10.24018/ejeng.2020.0.CIE.2308 ↗
4. Kotsifakos, D., Kostis, B., and Douligeris, C. (2017, January 25–28). Science, technology, engineering, and mathematics (STEM) for vocational education in Greece. Proceedings of the Global Engineering Education Conference (EDUCON), Athens, Greece.10.1109/EDUCON.2017.7943099 ↗
5. Thibert, R. (2013). Le décrochage scolaire: Diversité des approches, diversité des dispositive. Dossier d’actualité, ENS de Lyon, Veille & Analyses IFÉ.
6. Including adolescent migrants in school through VET approach: Evidence from a pilot action in Italy10.1186/s40461-020-00092-x ↗
7. The phenomenon of early school leaving (ESL) in Greek educational system10.46609/IJSSER.2020.v05i09.006 ↗
8. Can educational engagement prevent Early School Leaving? Unpacking the school’s effect on educational success
9. UNESCO UIS (2022, February 01). Available online: https://en.unesco.org/news/unesco-warns-without-urgent-action-12-million-children-will-never-spend-day-school-0.
10. Eurostat (2022, February 01). Statistics Explained. Early Leavers from Education and Training. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php/Early_leavers_from_education_and_training#Analysis_by_degree_of_urbanisation.
11. Ranking and classification of EU countries regarding their levels of implementation of the Europe 2020 strategy10.1016/j.jclepro.2017.07.088 ↗
12. Institute of Education Policy (IEP) (2022, February 01). Student Dropout in Greek Primary and Secondary Education. Available online: http://www.iep.edu.gr/images/IEP/EPISTIMONIKI_YPIRESIA/Paratiritirio/2017/2017-03-28_paratiritirio_epiteliki_synopsi.pdf.
13. Likas, A., Blekas, K., and Kalles, D. (2014). A learning analytics methodology for student profiling. Artificial Intelligence: Methods and Applications, Springer. Lecture Notes in Computer Science, 8445.
14. A learning analytics methodology for detecting sentiment in student fora: A case study in distance education10.1515/eurodl-2015-0014 ↗
15. Gkontzis, A., Kotsiantis, S., Tsoni, R., and Verykios, V. (December, January 29). An effective LA approach to predict student achievement. Proceedings of the 22nd Pan-Hellenic Conference on Informatics, Athens, Greece.
16. Polarity, emotions, and online activity of students and tutors as features in predicting grades
17. On the equivalence between bimodal and unimodal students’ collaboration networks in Distance Learning
18. Learning analytics in education: Literature review and case examples from vocational education10.1080/00313831.2019.1649718 ↗
19. A complete validated learning analytics framework: Designing issues from data preparation perspective10.4018/IJICTE.2018040101 ↗
20. Data preparation in predictive learning analytics (PLA) for student dropout10.35940/ijitee.C1027.0193S20 ↗
21. Grasso, V.F. (2009). Early Warning Systems: State-of-Art Analysis and Future Directions, United Nations. Draft Report United Nations Environment Programme (UNEP).
22. Early dropout prediction using data mining: A case study with high school students10.1111/exsy.12135 ↗
23. Greek Ministry of Education (2022, February 01). Strategic Policy Framework for the Reduction of Early School Leaving in Greece, Available online: https://www.minedu.gov.gr/publications/docs2015/14.%CE%A3%CF%87%CE%AD%CE%B4%CE%B9%CE%BF_%CE%94%CF%81%CE%AC%CF%83%CE%B7%CF%82_%CE%A0%CE%95%CE%A3_2014-2020.pdf.
24. Esch, P., Bocquet, V., Pull, C., Couffignal, S., Lehnert, T., Graas, M., and Ansseau, M. (2014). The downward spiral of mental disorders and educational attainment: A systematic review on early school leaving. BMC Psychiatry, 14.10.1186/s12888-014-0237-4 ↗
25. Causes of early school leaving in secondary education
26. Internalizing disorders as risk factors for early school leaving: A systematic review10.1007/s40894-016-0024-1 ↗
27. The chronic absenteeism assessment project: Using biometrics to evaluate the magnitude of and reasons for student chronic absenteeism in rural India10.1016/j.ijedudev.2019.102140 ↗
28. Hippe, R., and Jakubowski, M. (2018). Immigrant Background and Expected Early School Leaving in Europe: Evidence from PISA, Publications Office of the European Union. EUR 28866 EN.
29. Risk factors for school absenteeism and dropout: A meta-analytic review10.1007/s10964-019-01072-5 ↗
30. Does anybody notice? On the impact of improved truancy reporting on school dropout10.1080/09645292.2012.672555 ↗
31. Bembich, C. (2019). Social network learning analytics: Identification of students at risk of early school leaving. Ital. J. Educ. Res., 174–186.
32. Early school-leaving in The Netherlands: The role of family resources, school composition and background characteristics in early school-leaving in lower secondary education10.1080/03323315.2011.535975 ↗
33. Family environment variables as predictors of school absenteeism severity at multiple levels: Ensemble and classification and regression tree analysis10.3389/fpsyg.2019.02381 ↗
34. Lyche, C. (2010). Taking on the Completion Challenge: A Literature Review on Policies to Prevent Dropout and Early School Leaving, OECD Publishing. OECD Education Working Papers No. 53.
35. Early school leaving in Greece and Europe and educational inequality: Actions and policies against educational and social exclusion10.26417/ejser.v12i1.p229-236 ↗
36. Tomcsik, D., Joksimovic, J., Juhász, J., and Mihályi, K. (2014). Early Warning Systems in Six European Countries, Tempus Közalapítvány. Available online: https://cpi.si/wp-content/uploads/2020/08/12-Sistemi-zgodnjega-opozarjanja-v-EU-EN.pdf.
37. Dropout prediction in e-learning courses through the combination of machine learning techniques10.1016/j.compedu.2009.05.010 ↗
38. Prediction of students’ early dropout based on their interaction logs in the online learning environment
39. A predictive analytics framework as a countermeasure for attrition of students
40. Delis, V., Papaioannou, I., Valeontis, E., Kalaitzis, D., Giannakopoulos, C., and Voukelatos, M. (2012, January 14–15). Experiences from the development of the Greek school profiling system. Proceedings of the 12th European Conference on eGovernment, Barcelona, Spain.
41. Eurostat (2022, February 01). Region of Anatoliki Makedonia. Available online: https://ec.europa.eu/eurostat/documents/2995521/9618249/1-26022019-AP-EN.pdf/f765d183-c3d2-4e2f-9256-cc6665909c80.
42. The influence of age, schooling, literacy, and socioeconomic status on serial-order memory10.1007/s41809-020-00056-3 ↗
43. An improved join-free snowflake schema for ETL and OLAP of data warehouse10.1002/cpe.5519 ↗
44. Scheduling strategies for efficient ETL execution10.1016/j.is.2012.12.001 ↗
45. Beyond roll-up’s and drill-down’s: An intentional analytics model to reinvent OLAP10.1016/j.is.2019.03.011 ↗
46. Predicting the response of laminated composite beams: A comparison of machine learning algorithms10.3389/fbuil.2022.855112 ↗
47. Sara, N.-B., Halland, R., Igel, C., and Alstrup, S. (2015, January 22–24). High-school dropout prediction using machine learning: A Danish large-scale study. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning, Bruges, Belgium.
48. Statistical learning for predicting school dropout in elementary education: A comparative study