Valerie Drew
Beiträge in merz
Anna Wilson, Terrie Lynn Thompson, Cate Watson, Valerie Drew and Sarah Doyle: Big data and learning analytics: Singular or plural?
Recent critiques of both the uses of and discourse surrounding big data have raised important questions as to the extent to which big data and big data techniques should be embraced. However, while the context-dependence of data has been recognized, there remains a tendency among social theorists and other commentators to treat certain aspects of the big data phenomenon, including not only the data but also the methods and tools used to move from data as database to data that can be interpreted and assigned meaning, in a homogenizing way. In this paper, we seek to challenge this tendency, and to explore the ways in which explicit consideration of the plurality of big data might inform particular instances of its exploitation. We compare one currently popular big data-inspired innovation - learning analytics - with three other big data contexts - the physical sciences, business intelligence and public health. Through these comparisons, we highlight some dangers of learning analytics implemented without substantial theoretical, ethical and design effort. In so doing, we also highlight just how plural data, analytical approaches and intentions are, and suggest that each new big data context needs to be recognized in its own singularity.
Literature
Anderson, Chris (2008). The end of theory, will the data deluge makes the scientific method obsolete? Edge (30 June). edge.org/3rd_culture/anderson08/anderson08_index.html [accessed: 1803.2017]
Arnold, Kimberly E./Pistilli, Matthew D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In: LAK '12: Proceedings of the Second International Conference on Learning Analytics and Knowledge, pp. 267-270. doi: dx.doi.org/10.1145/2330601.2330666, accessed 20 March 2017
Baker, Ryan Shaun/Inventado, Paul Salvador (2014). Educational data mining and learning analytics. In: Learning analytics: From research to practice. New York: Springer, pp. 61-75.doi: dx.doi.org/10.1007/978-1-4614-3305-7_4 [accessed: 20.03.2017]
Boellstorff, Tom (2013). Making big data, in theory,” First Monday, 18 (10). firstmonday.org/article/view/4869/3750. doi: dx.doi.org/10.5210/fm.v18i10.4869 [accessed: 20.03.2017]boyd, danah/Crawford, Kate (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. In: Information, Communication & Society, 15 (5), pp. 662-679. doi: dx.doi.org/10.1080/1369118X.2012.678878 [accessed: 20.03.2017]
Brownstein, John S./Freifeld, Clark C./Madoff, Lawrence C. (2009). Digital disease detectionharnessing the Web for public health surveillance. In: New England Journal of Medicine, 360 (21 May), pp. 2.153-2.157. doi: dx.doi.org/10.1056/NEJMp0900702 [accessed: 20.03.2017]
Carter, Carter (2011). Big data analytics: Future architectures, skills, 2011. Big data analytics: Future architectures, skills and roadmaps for the CIO. IDC white paper. www.sas.com/resources/asset/BigDataAnalytics-FutureArchitectures-Skills-RoadmapsfortheCIO.pdf [accessed: 20.03.2017]
Clow, Doug (2013). An overview of learning analytics, 2013. An overview of learning analytics. In: Teaching in Higher Education, 18 (6), pp. 683-695. doi: dx.doi.org/10.1080/13562517.2013.827653 [accessed: 20.03.2017]
Cowburn, Gill/Stockley, Lynn (2005). Consumer understanding and use of nutrition labelling: A systematic review. In: Public Health Nutrition, 8 (1), pp. 21-28. doi: dx.doi.org/10.1079/PHN2004666 [accessed: 20.03.2017]
Crampton, Jeremy W. (2015). Collect it all: National security, big data and governance. In: GeoJournal, 80 (4), pp. 519-531. doi: dx.doi.org/10.1007/s10708-014-9598-y [accessed: 20.03.2017]
Crawford, Kate/Gray, Mary L./Miltner, Kate (2014). Critiquing big data: Politics, ethics, epistemology. In: International Journal of Communication, 8. ijoc.org/index.php/ijoc/article/view/2167 [accessed: 20.03.2017]
De Mauro, Andrea/Greco, Marco/Grimaldi, Michele (2015). What is big data? A consensual definition and a review of key research topics. In: AIP Conference Proceedings, 1644 (1). aip.scitation.org/doi/abs/10.1063/1.4907823, accessed 20 March 2017. doi: dx.doi.org/10.1063/1.4907823 [accessed: 20.03.2017]
Dietz-Uhler, Beth/Hurn, Janet E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. In: Journal of Interactive Online Learning, 12 (1), pp. 17-26. www.ncolr.org/issues/jiol/v12/n1/using-learning-analytics-to-predict-and-improve-student-success [accessed: 20.03.2017]
Ebeling, Mary F. E. (2016). Healthcare and big data: Digital specters and phantom objects. New York: Palgrave Macmillan. doi: dx.doi.org/10.1057/978-1-137-50221-6 [accessed: 20.03.2017]Gartner (2013). Big data. IT glossary. www.gartner.com/it-glossary/big-data [accessed: 20.03.2017]Greller, Wolfgang/Drachsler, Hendrik (2012). Translating learning into numbers: A generic framework for learning analytics. In: Educational Technology & Society, 15 (3), pp. 42-57. ifets.info/journals/15_3/4.pdf [accessed: 20.03.2017]
Grunert, Klaus G./Wills, Josephine M./Fernández-Celemin, Laura (2010). Nutrition knowledge, and use and understanding of nutrition information on food labels among consumers in the UK. In: Appetite, 55 (2), pp. 177-189. doi: dx.doi.org/10.1016/j.appet.2010.05.045 [accessed: 20.03.2017]
Helles, Rasmus/Jensen, Klaus Bruhn (2013). Making data”-”Big data and beyond: Introduction to the special issue. In: First Monday, 18 (10). firstmonday.org/article/view/4860/3748. doi: dx.doi.org/10.5210/fm.v18i10.4860 [accessed: 20.03.2017]
Hornik, Robert C. (2002). Public health communication: Making sense of contradictory evidence. In: Hornik, Robert C. (Eds.), Public health communication: Evidence for behavior change. Mahwah, N.J.: L. Erlbaum Associates, pp. 1-22.
Hosanagar, Kartik/Fleder, Daniel/Lee, Dokyun/Buja, Andreas (2013). Will the global village fracture into tribes? Recommender systems and their effects on consumer fragmentation. In: Management Science, 60 (4), pp. 805-823. doi: dx.doi.org/10.1287/mnsc.2013.1808 [accessed: 20.03.2017]
Lee, Dokyun/Hosanagar, Kartik (2014). Impact of recommender systems on sales volume and diversity. Proceedings of ICIS 2014: International Conference on Information Systems (Auckland, New Zealand). aisel.aisnet.org/icis2014/proceedings/EBusiness/40 [accessed: 20.03.2017]
Lewis, Seth C./Westlund, Oscar (2015). Big data and journalism: Epistemology, expertise, economics, and ethics. In: Digital Journalism, 3 (3), pp. 447-466. doi: dx.doi.org/10.1080/21670811.2014.976418 [accessed: 20.03.2017]
Markham, Annette N. (2013). Undermining ‘data': A critical examination of a core term in scientific inquiry. In: First Monday, 18 (10). firstmonday.org/article/view/4868/3749, accessed. doi: dx.doi.org/10.5210/fm.v18i10.4868 [accessed: 20.03.2017]
Murdoch, Travis B./Detsky, Allan S.”(2013). The inevitable application of big data to health care. In: Journal of the American Medical Association, 309 (13), pp. 1.351-1.352. doi: dx.doi.org/10.1001/jama.2013.393 [accessed: 20.30.2017]
Nelson, Karen J./Creagh, Tracy A. (2013). A good practice guide: Safeguarding student learning engagement. Sydney: Australian Government, Office for Learning and Teaching. www.olt.gov.au/system/files/resources/CG10_1730_Nelson_Good_Practice_Guide_2012.pdf [accessed: 20.03.2017]
Nunan, Dan/Di Domenico, MariaLaura (2013). Market research and the ethics of big data. In: International Journal of Market Research, 55 (4), pp. 505-520. www.mrs.org.uk/ijmr_article/article/98860 [accessed: 20.03.2017]
Ovadia, Steven (2013). The role of big data in the social sciences. In: Behavioral & Social Sciences Librarian, 32 (2), pp. 130-134.
Pathak, Bhavik/Garfinkel, Robert/Gopal, Ram D./Venkatesan, Rajkumar/Yin, Fang (2010). Empirical analysis of the impact of recommender systems on sales. In: Journal of Management Information Systems, 27 (2), pp. 159-188. doi: dx.doi.org/10.2753/MIS0742-1222270205 [accessed: 20.03.2017]
Provost, Foster/Fawcett, Tom (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, Calif.: O'Reilly.
Randolph, Whitney/Viswanath. K. (2004). Lessons learned from public health mass media campaigns: Marketing health in a crowded media world. In: Annual Review of Public Health, 25, pp. 419-437. doi: dx.doi.org/10.1146/annurev.publhealth.25.101802.123046 [accessed: 20.03.2017]
Sacks, Gary/Rayner, Mike/Swinburn, Boyd (2009). Impact of front-of-pack ‘traffic-light' nutrition labelling on consumer food purchases in the UK. In: Health Promotion International, 24 (4), pp. 344-352. doi: doi.org/10.1093/heapro/dap032 [accessed: 20.03.2017]
Sacks, Gary/Tikellis, Kim/Millar, Lynne/Swinburn, Boyd (2011). Impact of ‘traffic-light' nutrition information on online food purchases in Australia. In: Australian and New Zealand Journal of Public Health, 35 (2), pp. 122-126. doi: doi.org/10.1111/j.1753-6405.2011.00684.x [accessed: 20.03.2017]
Science & Technology Facilities Council (STFC), n.d. Large Hadron Collider. www.stfc.ac.uk/research/particle-physics-and-particle-astrophysics/large-hadron-collider//a>. [accessed: 20.03.2017] ATLAS.”www.stfc.ac.uk/research/particle-physics-and-particle-astrophysics/large-hadron-collider/atlas [accessed: 20.03.2017].
CMS.”www.stfc.ac.uk/research/particle-physics-and-particle-astrophysics/large-hadron-collider/cms [accessed: 20.03.2017]
Sclater, Niall (2014). Effective learning analytics: Using data and analytics to support students (3 October). analytics.jiscinvolve.org/wp/2014/10/03/engagement-reporting-tools-for-blackboard-and-moodle [accessed: 20.03.2017]
Shin, Don Donhee/Choi, Min Jae (2015). Ecological views of big data: Perspectives and issues. In: Telematics and Informatics, 32 (2), pp. 311-320. doi: dx.doi.org/10.1016/j.tele.2014.09.006 [accessed: 20.03.2017]
Sunstein, Cass R. (2007). Republic.com 2.0. Princeton, N.J.: Princeton University Press.
Townsend, Joy/Roderick, Paul/Cooper, Jacqueline (1994). Cigarette smoking by socioeconomic group, sex, and age: Effects of price, income, and health publicity. In: British Medical Journal, 309 (6959), pp. 923-927. doi: doi.org/10.1136/bmj.309.6959.923 [accessed: 20.03.2017]
VanderPlas, Jake (2014). Frequentism and bayesianism: A practical introduction. jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro [accessed: 20.03.2017]
Vis, Farida (2013). A critical reflection on big data: Considering APIs, researchers and tools as data makers. In: First Monday, 18 (10). firstmonday.org/article/view/4878/3755 doi: dx.doi.org/10.5210/fm.v18i10.4878 [accessed: 20.03.2017]
Wamba, Samuel Fosso/Akter, Shahriar/Edwards, Andrew/Chopin, Geoffrey/Gnanzou, Denis (2015). How ‘big data' can make big impact: Findings from a systematic review and a longitudinal case study. In: International Journal of Production Economics, 165, pp. 234-246. doi: dx.doi.org/10.1016/j.ijpe.2014.12.031 [accessed: 20.03.2017]
Wilson, Anna/Watson, Cate/Drew, Valerie/Thompson, Terrie Lynn/Doyle, Sarah (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education. under review.Zelenkauskaite, Asta (2016). Remediation, convergence, and big data: Conceptual limits of cross-platform social media. Convergence. doi: dx.doi.org/10.1177/1354856516631519 [accessed: 20.03.2017]
Zelenkauskaite, Asta/Bucy, Erik P. (2016). A scholarly divide: Social media, big data, and unattainable scholarship. First Monday, 21 (5). firstmonday.org/article/view/6358/5511 doi: dx.doi.org/10.5210/fm.v21i5.6358 [accessed: 20.03.2017]
The paper was originally published in First Monday in the April 2017 issue, at http://firstmonday.org.
Der Artikel ist ursprünglich in dem Fachjournal First Monday in der Ausgabe April 2017 unter firstmonday.org erschienen.