Framtida forskning

För att fortsätta forskningen inom EDM hade det varit intressant att genomföra samma studie fast på andra kommuners frånvarostatistik för att se om det finns några likheter eller skillnader i mönstren. Det hade varit intressant att fortsätta studien genom att plocka fram andra attribut som eventuellt kan påverka närvaro i skolan för att kunna få en djupare och mer rättvisande bild av verkligheten. Dessa attribut hade (utöver de som nämns i studien) kunnat innehålla information om elever som t.ex. årsklass, ålder, postnummer, betyg, antal klassmedlemmar m.m. Genom att studera flera faktorer kommer det resultat som tas fram visa på en större variation i mönster som kan kompletteras med redan upptäckta mönster inom EDM.

38

Referenser

ACM. (2019). The ACM Guide to Computing Literature. Retrieved from Association for Computing Machinery: https://libraries.acm.org/digital-library/acm-guide-to-computing-literature

Allen, M., Titsworth, S., & Hunt, S. K. (2009). Quantitative research in communication. London: SAGE Publications.

Alsuwaiket, M., Dawson, C., & Batmaz, F. (2018). Measuring the Credibility of Student Attendance Data in. International Journal of Information and Education Technology, 121-127.

Arnold, D. E. (2002). Block Schedule and Traditional Schedule Achievement: A Comparison. NASSP Bulletin, 42.

Baradwaj, B. K., & Pal, S. (2011). Mining Educational Data to Analyze Students' Performance. International Journal of Advanced Computer Science and Applications, 63-69.

Bernard, H. R., & Ryan, G. W. (2010). Analyzing Qualitative Data. SAGE.

Berndtsson, M., Hansson, J., Olsson, B., & Lundell, B. (2008). Thesis project. Springer. Blazer, C. (2009). Delayed High School Starting Times. Miami: Research Services. Cheng, J. (2017). Data-Mining Research in Education. arXiv.

Cuadra B, S. (2012). Forskningsmetodik - Idéhistoriska utblickar och teoretiska perspektiv. Malmö: Liber.

Durairaj, M., & Vijitha, C. (2014). Educational Data mining for Prediction of Student Performance Using Clustering Algorithms. International Journal of Computer Science and Information Technologies, 5987-5991.

Eggeby, E., & Söderberg, J. (1999). Kvantitativa metoder - för samhällsvetare och humanister. Lund: Studentlitteratur.

Elakia, Gayathri, Aarthi, & Naren, J. (2014). Application of Data Mining in Educational. International Journal of Computer Science and Information Technologies, 4649-4652.

Eliasson, A. (2010). Kvantitativ metod från början. Lund: Studentlitteratur AB.

Eliasson, A., Eliasson, A., King, J., Gould, B., & Eliasson, A. (2002). Association of sleep and academic performance. Sleep and Breathing, 45.

Ervasti, M., Isomursu, M., & Marianne, K. (2009). Bringing technology into school: NFC-enabled school attendance supervision. MUM '09 Proceedings of the 8th

39 International Conference on Mobile and Ubiquitous Multimedia, (pp. 1-10). Cambridge.

Gagnon, Y.-C. (2010). The case study as research method: a practical handbook. Academic Collection - Worldwide.

Gendron, J. (2016). Introduction to R for Business Intelligence. Birmingham: Packt Publishing.

Goyal, M., & Hatami, H. (2012, September 05). Mining Big Data to Find New Markets. Harvard Business Review.

Goyal, M., & Vohra, R. (2012). Applications of Data Mining in Higher Education . International Journal of Computer Science Issues, 113-120.

Grabmeier, J., & Rudolph, A. (2002). Techniques of Cluster Algorithms in Data Mining. Data Mining and Knowledge Discovery, 303-360.

Green, J., Willis, K., Hughes, E., Small, R., Welch, N., Gibbs, L., & Daly, J. (2007). Generating best evidence from qualitative research: the role of data analysis. Journal of the Public Health Association of Australia, 545-550.

Havik, T., Bru, E., & Ertesvåg, S. K. (2015). School factors associated with school refusal- and truancy-related reasons for school non-attendance. Dordrecht: Springer Science+Business Media.

Jain, A. K., Duin, R. P., & Mao, J. (2000). Statistical Pattern Recognition: A Review. IEEE. Joazeiro de Baker, R. S., Barnes, T., & Beck, J. E. (2008). Eduactional Data Mining. The 1st

International Conference on Educational Data Mining, (pp. 8-17). Montréal, Québec, Canada.

Klasander, C., & Ginner, T. (2017, April). Mönster i teknisk förändring. Retrieved from Skolverket: https://larportalen.skolverket.se/LarportalenAPI/api-

v2/document/path/larportalen/material/inriktningar/7- teknik/Grundskola/702-Teknisk-forandring-och-dess-konsekvenser-arskurs_4-

6/del_02/Material/Flik/Del_02_MomentA/Artiklar/NT2_4-6_02_A_forandring.docx

Kronholz, J. (2011). Truants: The Challenges of Keeping Kids in School. Education Next, 32-38.

Kulkarni, M. (2017, September 7). Decision Trees for Classification: A Machine Learning Algorithm. Retrieved from Xoriant: https://www.xoriant.com/blog/product-engineering/decision-trees-machine-learning-algorithm.html

Larose, D. T. (2005). Discovering Knowledge in Data : An Introduction to Data Mining. John Wiley & Sons.

40 Marx, R., Tanner-Smith, E. E., Davison, C. M., Ufholz, L.-A., Freeman, J., Shankar, R., . . . Hendrikx, S. (2017). Later School Start Times for Supporting the Education, Health, and Well-Being of High School Students: A Systematic Review. Campbell Collaboration.

Merceron, A., & Yacef, K. (2005). Educational Data Mining: a Case Study . Naisbitt, J. (1984). Megatrends.

Patton, M. Q. (2015). Qualitative Research & Evaluation Methods. SAGE.

Phyu, T. N. (2009). Survey of Classification Techniques in Data. International MultiConference of Engineers and Computer Scientists. Hong Kong.

PMC. (2018). What is a predatory journal? A scoping review. Retrieved from US National Library of Medicine National Institutes of Health: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6092896/

Pudil, P., Somol, P., & Haindl, M. (2013). Introduction to Statistical Pattern Recognition. Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005.

Elsevier.

Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review. Romero, C., & Ventura, S. (2013). Data mining in education.

Romero, C., & Ventura, S. (2019). Educational Data Mining: A Review.

Saa, A. A. (2016). Educational Data Mining & Students’ Performance. International Journal of Advanced Computer Science and Applications, 212-220.

Sanchez, M. (2012). Truancy and Chronic Absence in Redwood City. California.

Shafique, U., & Qaiser, H. (2014). A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA). International Journal of Innovation and Scientific Research, 217-222.

Sharda, R., Delen, D., & Turban, E. (2014). Business Intelligence and Analytics. Pearson Education Limited.

Skolinspektionen. (2016). Omfattande ogiltig frånvaro.

Skollag (2010:800). (2010, 06 23). Retrieved from Sveriges Riksdag:

https://www.riksdagen.se/sv/dokument-lagar/dokument/svensk-forfattningssamling/skollag-2010800_sfs-2010-800

41 Skolverket. (2012). Arbetet med att främja närvaro och attt uppmärksamma, utreda och

åtgärda frånvaro i skolan. Stockholm: Fritzes.

Skolverket. (2019, Februari 04). Scheman och lärotider. Retrieved from Skolverket: https://www.skolverket.se/regler-och-ansvar/ansvar-i-skolfragor/scheman-och-larotider

Skolverket. (2019, Januari 30). Timplan för grundskolan. Retrieved from Skolverket:

https://www.skolverket.se/undervisning/grundskolan/laroplan-och-kursplaner-for-grundskolan/timplan-for-grundskolan

Smink, J., & Reimer, M. S. (2005). Fifteen Effective Strategies for Improving Student Attendance and Truancy Prevention.

Socialstyrelsen. (2010). Social rapport. Västerås: Socialstyrelsen.

Sutphen, R. D., Ford, J. P., & Flaherty, C. (2010). Truancy Interventions: A Review of the Research Literature. Sage Publications, 161.

Svenska Akademien. (2006). Svensk ordbok utgiven av Svenska Akademien. Stockholm: Svenska Akademien.

Tripathi, A., & Kumar, A. (2019). Analysis of Educational Data. International Journal of Computer Science and Mobile Computing, 8-15.

Wang, H., Weng, C., & Yuan, J. (2017). Visual Pattern Discovery and Recognition. Singapore: Springer.

Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 13-23.

Weller, D. R., & McLeskey, J. (2001). Block Scheduling and Inclusion in a High School: Teacher Perceptions of the Benefits and Challenges. Remedial and Special Education, 209-218.