Application of Data Science to Access Classroom Management Information
Keywords:
Big Data, Classroom Management, Data Analytics, Data Mining, Data Science
Abstract
This study explores emerging trends in educational data science with a focus on smart learning and research areas. Information fusion, soft computing, machine learning, and the Internet of things are just a few of the cutting-edge frameworks and techniques in data science that are applied to education in the finest manuscripts reviewed. Here it is discussed about searching the data using data science and analyzing the necessary paper from it. This study focuses on data analysts and emphasizes the importance of evaluating technical areas of data science based on their ability to learn from data. The benefits of a field can be direct or indirect, where the tools used by data analysts provide direct benefits and the theories serve as the basis for developing the tools, the review studies provide indirect benefits. Despite getting accurate results, data science (DS) methods often produce complex models. Despite this, the multiple opportunities presented in research through DS have increased the demand for research today.
References
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Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wires Data Mining and Knowledge Discovery, 10, e1355. https://doi.org/10.1002/widm.1355
Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River: Prentice Hall.
Sathi, A. (2013). Big data analytics: Disruptive technologies for changing the game. Boise, ID, USA: MC Press: IBM Corporation.
Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B. J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., & McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences of the USA, 117(15), 8398–8403. https://doi.org/10.1073/pnas.1915006117
Samaddar, R. & Sikdar, D. P. (2023). Development in Teaching Style on Classroom Management. Asian Research Journal of Arts & Social Sciences, 20(1), 47-58, https://doi.org/10.9734/arjass/2023/v20i1441
Samaddar, R., Mukherjee, S. & Sikdar, D.P. (2023). Documentary Analysis on Challenges of Classroom Management. International Journal of Creative Research Thoughts, 11(3), c613-c622. https://ijcrt.org/papers/IJCRT2303292.pdf
Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5, 13–22. https://www.academia.edu/42079490/CRISP_DM_The_New_Blueprint_for_Data_Mining_Colin_Shearer_Fall_2000_
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Strang, K., & Sun, Z. (2015). Analyzing relationships in terrorism big data using Hadoop and statistics. Journal of Computer and Information Systems (JCIS), in Press.
Sueb, R. (2013). Pre-Service Teacher’s Classroom Management in Secondary School: Managing for Success in Teaching and Learning. 6th International Conference on University Learning and Teaching, 90(2013), 670-676. http://dx.doi.org/10.1016/j.sbspro.2013.07.139
Sun, Z., Sun, L., & Strang, K. (2018). Big Data Analytics Services for Enhancing Business Intelligence. Journal of Computer Information Systems (JCIS), 58(2), 162-169. https://doi.org/10.1080/08874417.2016.1220239
Sun, Z. & Huo, Y. (2021). The spectrum of big data analytics. Journal of Computer Information Systems, 61(2), 154-162. https://doi.org/10.1080/08874417.2019.1571456
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Aljawarneh, S. & Lara, A, J. (2021). Data science for analyzing and improving educational processes. Journal of Computing in Higher Education, 33, 545–550. https://doi.org/10.1007/s12528-021-09299-7
Baker, R. (2010). Data Mining. In P. Peterson, E. Baker, & B. McGaw (Eds.), International Encyclopedia of Education (3rd ed., Vol. 33, pp. 112–118). Elsevier. http://doi.org/10.1016/B978-0-08-044894-7.01318-X
Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3–16. https://www.researchgate.net/publication/256309431_The_State_of_Educational_Data_Mining_in_2009_A_Review_and_Future_Visions
Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational Data Mining and Learning Analytics: Applications to Constructionist Research. Technology, Knowledge and Learning, 19, 205–220. http://doi.org/10.1007/s10758-014-9223-7
Bichsel, J., (2012). Analytics in Higher Education, Benefits, Barriers, Progress & Recommendations (Research Report). Louisville, CO: EDUCAUSE Center for Applied Research. http://www.educause.edu/ecar
Bird, K. A., Castleman, B. L., Mabel, Z., & Song, Y. (2021). Bringing Transparency to Predictive Analytics: A Systematic Comparison of Predictive Modeling Methods in Higher Education. AERA Open, 7(1). Advance online presentation. https://doi.org/10.1177/23328584211037630
Bowen, G. A. (2009). Document Analysis as a Qualitative Research Method. Qualitative Research Journal, 9(2), 27-40. https://doi.org/10.3316/QRJ0902027
Cao, L. (2017). Data science: challenges and directions. CACM, 60(8), 59-68. https://doi.org/10.1145/3015456
Chaturapruek, S., Dalberg, T., Thompson, M. E., Giebel, S., Harrison, M. H., Johari, R., Stevens, M. L., & Kizilcec, R. F. (2021). Studying undergraduate course consideration at scale. AERA Open, 7(1). https://doi.org/10.1177/2332858421991148
Cleveland W. S. (2001). Data science: an action plan for expanding the technical areas of the field of statistics. ISI Rev., 69, 21–26. https://zhanksun.github.io/files/DataScience.pdf
Clow, D. (2013, April). MOOCs and the funnel of participation [Conference session]. Third International Conference on Learning Analytics and Knowledge, Leuven, Belgium. https://doi.org/10.1145/2460296.2460332
Daniel, B. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. http://doi.org/10.1111/bjet.12230
Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73. https://doi.org/10.1145/2500499
Donoho, D. (2017). 50 years of data science. Journal of Computational and Graphical Statistics, 26(4), 745–766. https://doi.org/10.1080/10618600.2017.1384734
Doroudi, S. (2020). The bias-variance tradeoff: How data science can inform educational debates. AERA Open, 6(4). https://doi.org/10.1177/2332858420977208
Dowell, N. M. M., McKay, T. A., & Perrett, G. (2021). It’s not that you said it, it’s how you said it: Exploring the linguistic mechanisms underlying values affirmation interventions at scale. AERA Open, 7(1). https://doi.org/10.1177/23328584211011611
Eaton, C., Deroos, D., Deutsch, T., Lapis, G. & Zikopoulos, P.C. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Companies, 978-0-07-179053-6,
Emmer, E. & Sabornie, E. J. (Eds.) (2015). A Handbook for Classroom Management (2nd ed.). New York, NY: Routledge. https://doi.org/10.4324/9780203074114
Evertson, C. M., & Weinstein, C. S. (Eds.). (2006). Handbook of classroom management: Research, practice, and contemporary issues. Lawrence Erlbaum Associates Publishers. https://psycnet.apa.org/record/2006-01816-000
Escoufier, Y., Hayashi, C., & Fichet, B. (Eds.). (1995). Data science and its applications. Academic Press/Harcourt Brace. https://wordminer.org/wp-content/uploads/2014/04/Data-Science-and-Its-Applications-Preface-Academic-Press-1995.pdf
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework. AI magazine, 17, 82–88.
Fiadh, J. (2014). The Next Step for Learning Analytics, Lakehead University Published by the IEEE Computer Society. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6908909
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
Jiang, W., & Pardos, Z. A. (2021). Towards equity and algorithmic fairness in student grade prediction. In B. Kuipers, S. Lazar, D. Mulligan, & M. Fourcade (Eds.), Proceedings of the Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (pp. 608–617). ACM.
Kalidindi, S.R. & Graef, M.D. (2015). Materials Data Science: Current Status and Future Outlook. Annu. Rev. Mater. Res., 45, 171–93. https://www.annualreviews.org/doi/pdf/10.1146/annurev-matsci-070214-020844
Kabakchieva, D. (2013). Predicting student performance by using data mining methods for classification. Cybernetics and Information Technologies, 13(1), 61–72. http://doi.org/10.2478/cait-2013-0006
Martin, F., & Ndoye, A. (2016). Using learning analytics to assess student learning in online courses. Journal of University Teaching & Learning Practice, 13(3), 1–20. http://doi.org/10.1177/0047239516656369
McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90, 60-68. https://hbr.org/2012/10/big-data-the-management-revolution
Martschenko, D., Trejo, S., & Domingue, B. W. (2019). Genetics and education: Recent developments in the context of an ugly history and an uncertain future. AERA Open, 5(1). https://doi.org/10.1038/s41587-021-00940-5
Manyika, J., Chui, M., & Bughin, J. e. (2011, May). Big data: The next frontier for innovation, competition, and productivity. Retrieved from McKinsey Global Institute. http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation
McFarland, D. A., Lewis, K., & Goldberg, A. (2015). Sociology in the era of big data: The ascent of forensic social science. The American Sociologist, 47(1), 12–35. https://doi.org/10.1007/s12108-015-9291-8
McFarland, A. D., Khanna, S., Domingue, W. B. & Pardos, A. Z. (2021). Education Data Science: Past, Present, Future. AERA Open, 7(1), 1-12. https://doi.org/10.1177/23328584211052055
Mitrofanova, Y. S., Sherstobitova, A. A., & Filippova, O. A. (2019). Modeling smart learning processes based on educational data mining tools. In V. Uskov, R. Howlett, & L. Jain (Eds.), Smart Education and e-Learning 2019. Smart Innovation, Systems and Technologies (Vol. 144). https://doi.org/10.1007/978-981-13-8260-4_49
Moltudal, S., Krumsvik, R., Jones, L., Eikeland, O. J., & Johnson, B. (2019). The Relationship Between Teachers’ Perceived Classroom Management Abilities and Their Professional Digital Competence. Des. Learn., 11(1), 80–98. https://doi.org/10.16993/dfl.128
National Research Council. (2013). Frontiers in Massive Data Analysis. Washington DC: The National Research Press.
Naur, P. (1974). Concise survey of computer methods. Petrocelli Books.
NIST Big Data Public Working Group. (2015). NIST Big Data Interoperability Framework: Volume 1, Definitions. Technical Report NIST SP 1500-1. National Institute of Standards and Technology. https://doi.org/10.6028/NIST.SP.1500-1
Oliver, R. M. (2007). Effective Classroom Management: Teacher Preparation and Professional Development. TQ Connection Issue Paper on Improving Student Outcomes in General and Special Education. https://files.eric.ed.gov/fulltext/ED543769.pdf
Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9–20. http://doi.org/10.24059/olj.v16i3.267
Piety, P. J., Hickey, D. T., & Bishop, M. J. (2014, March 24–28). Educational data sciences: Framing emergent practices for analytics of learning, organizations, and systems [Conference session]. Fourth International Conference on Learning Analytics and Knowledge, Indianapolis, IN, United States.
Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. C. (2016). Student attitudes toward learning analytics in higher education: “The Fitbit version of the learning world.” Frontiers in Psychology, 7, 1–11. http://doi.org/10.3389/fpsyg.2016.01959
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wires Data Mining and Knowledge Discovery, 10, e1355. https://doi.org/10.1002/widm.1355
Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River: Prentice Hall.
Sathi, A. (2013). Big data analytics: Disruptive technologies for changing the game. Boise, ID, USA: MC Press: IBM Corporation.
Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B. J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., & McLanahan, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences of the USA, 117(15), 8398–8403. https://doi.org/10.1073/pnas.1915006117
Samaddar, R. & Sikdar, D. P. (2023). Development in Teaching Style on Classroom Management. Asian Research Journal of Arts & Social Sciences, 20(1), 47-58, https://doi.org/10.9734/arjass/2023/v20i1441
Samaddar, R., Mukherjee, S. & Sikdar, D.P. (2023). Documentary Analysis on Challenges of Classroom Management. International Journal of Creative Research Thoughts, 11(3), c613-c622. https://ijcrt.org/papers/IJCRT2303292.pdf
Shearer, C. (2000). The CRISP-DM Model: The New Blueprint for Data Mining. Journal of Data Warehousing, 5, 13–22. https://www.academia.edu/42079490/CRISP_DM_The_New_Blueprint_for_Data_Mining_Colin_Shearer_Fall_2000_
Solemani, N. & Razmjoo, S. A. (2016). Classroom Management Challenges: An Account Of EFL Teachers At Private Language Institutes. Anatolian Journal of Education, 1(1), 2547-9652. http://dx.doi.org/10.29333/aje.2016.114a
Silver, N. (2020, August 23). What I need from statisticians. Stats and Data Science Views. https://www.statisticsviews.com/article/nate-silver-what-i-need-from-statisticians/
Singh, S. & Kumar, Dr. V. (2013). Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques. IJCSET, 3, (31-37). http://ijcset.net/docs/Volumes/volume3issue2/ijcset2013030202.pdf
Strang, K., & Sun, Z. (2015). Analyzing relationships in terrorism big data using Hadoop and statistics. Journal of Computer and Information Systems (JCIS), in Press.
Sueb, R. (2013). Pre-Service Teacher’s Classroom Management in Secondary School: Managing for Success in Teaching and Learning. 6th International Conference on University Learning and Teaching, 90(2013), 670-676. http://dx.doi.org/10.1016/j.sbspro.2013.07.139
Sun, Z., Sun, L., & Strang, K. (2018). Big Data Analytics Services for Enhancing Business Intelligence. Journal of Computer Information Systems (JCIS), 58(2), 162-169. https://doi.org/10.1080/08874417.2016.1220239
Sun, Z. & Huo, Y. (2021). The spectrum of big data analytics. Journal of Computer Information Systems, 61(2), 154-162. https://doi.org/10.1080/08874417.2019.1571456
Wu, C. F. J. (1986). Future directions of statistical research in China: A historical perspective. Application of Statistics and Management, 1, 1–7.
Wu, C., Buyya, R., & Ramamohana, K. (2016). Big Data Analytics = Machine Learning + Cloud Computing. https://arxiv.org/ftp/arxiv/papers/1601/1601.03115.pdf
Yuan, L., & Powell, S. J. (2013). MOOCs and open education: Implications for higher education. JISC CETIS. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.354.108&rep=rep1&type=pdf
Published
2024-04-25
How to Cite
Samaddar, R., & Sikdar, D. P. (2024). Application of Data Science to Access Classroom Management Information. European Journal of Science, Innovation and Technology, 4(2), 310-320. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/418
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Copyright (c) 2024 Ritu Samaddar, Deb Prasad Sikdar
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