Thanks to advancements in communication and online social media, there has been a surge of useful online educational resources across the Internet. In addition to supplementing educational materials, these resources could be used in varying education research and potentially advance the quality of education. Nevertheless, conducting such research projects requires using big data techniques and approaches to find meaningful resources and harnessing them in an effective way. In this chapter, we present a roadmap for how to incorporate online social media in education research projects. The roadmap consists of three major components: project initialization, data collection, and data utilization. Furthermore, we present some learned lessons, tips, and tricks, as well as case studies from the Teachers in Social Media project (www.teachersinsocialmedia.com/). We believe this chapter can be used as a practical reference point for many researchers whose concern is connecting data to their education research endeavors.
Get full access to this article
View all access options for this article.
References
1.
AbeS. (2010). Feature selection and extraction. In Support vector machines for pattern classification (pp. 331–341). London, England: Springer.
2.
AgasistiT., & BowersA. J. (2017). Data analytics and decision making in education: Towards the educational data scientist as a key actor in schools and higher education institutions. In Handbook of contemporary education economics (pp. 184–210). Northampton, MA: Edward Elgar.
3.
AggarwalC. C., & YuP. S. (2001, May). Outlier detection for high dimensional data. In ACM Sigmod Record (Vol. 30, No. 2, pp. 37–46). New York, NY: ACM.
AustinC. C. (2018). A path to big data readiness. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4844–4853). New York, NY: IEEE.
6.
AustinC. C., BloomT., Dallmeier-TiessenS., KhodiyarV. K., MurphyF., NurnbergerA., … WhyteA. (2017). Key components of data publishing: Using current best practices to develop a reference model for data publishing. International Journal on Digital Libraries, 18(2), 77–92.
7.
BaeplerP., & MurdochC. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2). Article 17.
8.
BagdyL. M., DennenV. P., RutledgeS. A., RowlettJ. T., & BurnickS. (2018). Teens and social media: A case study of high school students’ informal learning practices and trajectories. In Proceedings of the 9th International Conference on Social Media and Society (pp. 241–245). New York, NY: ACM.
9.
BakerR. S. (2010). Data mining for education. International Encyclopedia of Education, 7(3), 112–118.
10.
BakerR. S., & InventadoP. S. (2014). Educational data mining and learning analytics. In LarussonJ., & WhiteB. (Eds.), Learning analytics (pp. 61–75). New York, NY: Springer.
11.
BakerR. S., & KoedingerK. R. (2018). Towards demonstrating the value of learning analytics for K-12 education. In NiemiD., PeaR.D., SaxbergB., & ClarkR.E. (Eds.), Learning analytics in education (pp. 49–62). Charlotte, NC: Information Age.
12.
BakerR. S., & YacefK. (2009). The state of educational data mining in 2009: A review and future visions. JEDM Journal of Educational Data Mining, 1(1), 3–17.
13.
Bello-OrgazG., JungJ. J., & CamachoD. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45–59.
14.
BishopC. M. (2006). Pattern recognition and machine learning. New York, NY: Springer.
15.
BleiD. M., NgA. Y., & JordanM. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
16.
BouchardT. J.Jr. (1971). Whatever happened to brainstorming. The Journal of Creative Behavior, 5(3), 182–189.
17.
BowersA. J., BangA. H., PanY., & GravesK. E. (2019). Education Leadership Data Analytics (ELDA): A White Paper Report on the 2018 ELDA Summit. Teachers College, Columbia University, New York, NY.
18.
boydD., & CrawfordK. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679.
19.
BuchananV., LuY., McNeeseN., SteptoeM., MaciejewskiR., & CookeN. (2017). The role of teamwork in the analysis of big data: A study of visual analytics and box office prediction. Big Data, 5(1), 53–66.
20.
CalarcoJ. M. (2011). “I need help!”: Social class and children's help-seeking in elementary school. American Sociological Review, 76(6), 862–882.
ChenP. M., LeeE. K., GibsonG. A., KatzR. H., & PattersonD. A. (1994). RAID: High-performance, reliable secondary storage. ACM Computing Surveys, 26(2), 145–185.
23.
DalyA. J., LiouY.-H., Del FresnoM., RehmM., & BjorklundP.Jr. (2019). Educational leadership in the Twitterverse: Social media, social networks and the new social continuum. Teachers College Record, 121(14). Retrieved from https://www.tcrecord.org/Content.asp?ContentId=23044
24.
DanielB. K. (2017). Big Data and data science: A critical review of issues for educational research. British Journal of Educational Technology, 50(1), 101–113.
25.
DharV. (2012). Data science and prediction. Communications of the ACM, 56(12), 64–73.
26.
DondersA. R. T., Van Der HeijdenG. J., StijnenT., & MoonsK. G. (2006). A gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59(10), 1087–1091.
FrankK. A., & TorphyK. T.* (2019). Social media, who cares? A dialogue between a millennial and a curmudgeon. *Equal authorship. Teachers College Record, 121(14). Retrieved from https://www.tcrecord.org/Content.asp?ContentId=23064
29.
GandomiA., & HaiderM. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
30.
GibsonC., & VermeulenF. (2003). A healthy divide: Subgroups as a stimulus for team learning behavior. Administrative Science Quarterly, 48(2), 202–239.
31.
GoodfellowI., Pouget-AbadieJ., MirzaM., XuB., Warde-FarleyD., OzairS., … BengioY. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680). Red Hook, NY: Curran Associates.
32.
GrayJ., ChaudhuriS., BosworthA., LaymanA., ReichartD., VenkatraoM., … PiraheshH. (1997). Data cube: A relational aggregation operator generalizing group-by, cross-tab, and subtotals. Data Mining and Knowledge Discovery, 1(1), 29–53.
33.
GreenhowC., RobeliaB., & HughesJ. E. (2009). Learning, teaching, and scholarship in a digital age: Web 2.0 and classroom research: What path should we take now?Educational Researcher, 38(4), 246–259.
34.
GrusJ. (2019). Data science from scratch: First principles with python. Sebastopol, CA: O'Reilly Media.
35.
HalversonR. (2014). Data-driven leadership for learning in the age of accountability. In BowersA.J., ShohoA.R., & BarnettB.G. (Eds.), Using data in schools to inform leadership and decision making (pp. 255–267). Charlotte, NC: Information Age.
36.
HalversonR. (2018). A distributed leadership perspective on information technologies for teaching and learning. In Second handbook of information technology in primary and secondary education (pp. 1–17). Cham, Switzerland: Springer.
37.
HanJ., PeiJ., & KamberM. (2011). Data mining: Concepts and techniques. Waltham, MA: Elsevier.
38.
HoA. (2017). Advancing educational research and student privacy in the “big data” era. In Workshop on big data in education: Balancing the benefits of educational research and student privacy (pp. 1–18). Washington, DC: National Academy of Education.
39.
HodgeV., & AustinJ. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review, 22(2), 85–126.
40.
HoraM. T., Bouwma-GearhartJ., & ParkH. J. (2017). Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning. The Review of Higher Education, 40(3), 391–426.
41.
JolliffeI. (2011). Principal component analysis. In International encyclopedia of statistical science (pp. 1094–1096). Berlin, Germany: Springer.
42.
JordanM. I., & MitchellT. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
43.
KarimiH., RoyP., Saba-SadiyaS., & TangJ. (2018). Multi-source multi-class fake news detection. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 1546–1557). Stroudsburg, PA: Association for Computational Linguistics.
44.
KarimiH., & TangJ. (2019). Learning hierarchical discourse-level structure for fake news detection. In Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 3432–3442). Stroudsburg, PA: Association for Computational Linguistics.
45.
KarimiH., TangJ., & LiY. (2018). Toward end-to-end deception detection in videos. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 1278–1283). New York, NY: IEEE.
KumarR., & ChadrasekaranR. M. (2011). Attribute correction-data cleaning using association rule and clustering methods. International Journal Data Mining & Knowledge Management Process, 1(2), 22–32.
48.
LenzeriniM. (2002). Data integration: A theoretical perspective. In Proceedings of the Twenty-First ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (pp. 233–246). New York, NY: ACM.
49.
LukoianovaT., & RubinV. L. (2014). Veracity roadmap: Is big data objective, truthful and credible?Advances in Classification Research Online, 24(1), 4–15.
50.
MitchellR. (2018). Web scraping with Python: Collecting more data from the modern web. Sebastopol, CA: O'Reilly Media.
51.
NielsenM. A. (2015). Neural networks and deep learning (Vol. 25). USA: Determination Press.
52.
OwensJ. D., HoustonM., LuebkeD., GreenS., StoneJ. E., & PhillipsJ. C. (2008). GPU computing. Proceedings of the IEEE, 96(5), 879–899.
53.
PaetschF., EberleinA., & MaurerF. (2003). Requirements engineering and agile software development. In WET ICE 2003. Proceedings. Twelfth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, 2003 (pp. 308–313). doi:10.1109/ENABL.2003.1231428
54.
ParnesS. J., & MeadowA. (1959). Effects of “brainstorming” instructions on creative problem solving by trained and untrained subjects. Journal of Educational Psychology, 50(4), 171–176.
55.
PempekT. A., YermolayevaY. A., & CalvertS. L. (2009). College students’ social networking experiences on Facebook. Journal of Applied Developmental Psychology, 30(3), 227–238.
56.
Ramírez-GallegoS., GarcíaS., Mouriño-TalinH., Martinez-RegoD., Bolón-CanedoV., Alonso-BetanzosA., … HerreraF. (2016). Data discretization: Taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 6(1), 5–21.
57.
RawlinsonJ. G. (2017). Creative thinking and brainstorming. London, England: Routledge.
58.
ReardonS. F., KalogridesD., & ShoresK. (2019). The geography of racial/ethnic test score gaps. American Journal of Sociology, 124(4), 1164–1221.
59.
RomeroC., & VenturaS. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.
60.
RossiniF. A., & PorterA. L. (1979). Frameworks for integrating interdisciplinary research. Research Policy, 8(1), 70–79.
61.
RoystonP. (2004). Multiple imputation of missing values. The Stata Journal, 4(3), 227–241.
62.
RoystonP. (2005). Multiple imputation of missing values: Update of ice. The Stata Journal, 5(4), 527–536.
63.
RutledgeS. A., DennenV. P., & BagdyL. M. (2019). Exploring adolescent social media use in a high school: Tweeting teens in a bell schedule world. Teachers College Record, 121(14). Retrieved from https://www.tcrecord.org/Content.asp?ContentId=23038
64.
SalganikM. J. (2017). Bit by bit: social research in the digital age. Princeton, NJ: Princeton University Press.
65.
SiemensG., & BakerR. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252–254). New York, NY: ACM.
66.
SoloveD. J. (2008). Understanding privacy (Vol. 173). Cambridge, MA: Harvard University Press.
67.
SorzanoC. O. S., VargasJ., & MontanoA. P. (2014). A survey of dimensionality reduction techniques. arXiv:1403.2877. arXiv preprint.
68.
SutskeverI., MartensJ., & HintonG. E. (2011). Generating text with recurrent neural networks. In Proceedings of the 28th International Conference on Machine Learning (ICML-11) (pp. 1017–1024).
TorphyK. T., & DrakeC. (2019). Educators meet the Fifth Estate: The role of social media in teacher training. Teachers College Record, 121(14).
71.
Van BuurenS. (2018). Flexible imputation of missing data. Boca Raton, FL: Chapman and Hall/CRC.
72.
Van der AalstW. (2016). Data science in action. In Process Mining (pp. 3–23). Berlin, Germany: Springer.
73.
WangY. (2013). Social media in schools: A treasure trove or hot potato?Journal of Cases in Educational Leadership, 16(1), 56–64.
74.
WangY. (2016). Big opportunities and big concerns of big data in education. TechTrends, 60(4), 381–384.
75.
WangY. (2017a). Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges. Education Policy Analysis Archives, 25(94), 1–21.
76.
WangY. (2017b). The social networks and paradoxes of the opt-out movement amid the Common Core State Standards implementation: The case of New York. Education Policy Analysis Archives/Archivos Analíticos de Políticas Educativas, 25(34), 1–24.
77.
WardM. O., GrinsteinG., & KeimD. (2015). Interactive data visualization: foundations, techniques, and applications. Natick, MA: AK Peters/CRC Press.
78.
WellmanB. (2001). Computer networks as social networks. Science, 293(5537), 2031–2034.
79.
WellmanB., & HaythornthwaiteC. (Ed.). (2008). The Internet in everyday life. Hoboken, NJ: Wiley.
80.
ZeideE. (2017). The structural consequences of big data-driven education. Big Data, 5(2), 164–172.
