Location in the multiverse of methods: measuring online users’ contexts

References

• Alcorn, B., Christensen, G., & Kapur, D. (2015). Higher education and MOOCs in India and the Global South. Change: The Magazine of Higher Learning, 47(3), 42–49. https://doi.org/10.1080/00091383.2015.1040710
   Google Scholar
• American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing.
   Google Scholar
• Anghel, E., Littenberg-Tobias, J., & Reich, J. (2021). Do the rich get richer? Studying differences in online professional learning participation by school demographics. American educational research association annual conference (remote).
   Google Scholar
• Bakhshi, S., Kanuparthy, P., & Gilbert, E. (2014, April). Demographics, weather and online reviews: A study of restaurant recommendations. In Proceedings of the 23rd international conference on world wide web (pp. 443–454). https://doi.org/10.1145/2566486.2568021
   Google Scholar
• Bao, J., Zheng, Y., Wilkie, D., & Mokbel, M. (2015). Recommendations in location-based social networks: A survey. GeoInformatica, 19(3), 525–565. https://doi.org/10.1007/s10707-014-0220-8
   Web of Science ®Google Scholar
• Ben-Shachar, M. S., Makowski, D., Lüdecke, D., Patil, I., Kelley, K., Stanley, D., Burnett, J., & Karreth, J. (2021). Effectsize: Indices of effect size and standardized parameters. R package Version 0.4.5. https://cran.r-project.org/web/packages/effectsize/effectsize.pdf
   Google Scholar
• Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom research into edX’s first MOOC. Research & Practice in Assessment, 8, 13–25. https://files.eric.ed.gov/fulltext/EJ1062850.pdf
   Google Scholar
• Bryman, A. (2004). Triangulation and measurement. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.9785&rep=rep1&type=pdf
   Google Scholar
• Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. https://doi.org/10.1037/h0046016
   PubMed Web of Science ®Google Scholar
• Campbell, B., Patel, D., Riise, S., & Pai, R. (2011). U.S. Patent No. 8,024,454. U.S. Patent and Trademark Office.
   Google Scholar
• Colvin, C., Rogers, T., Wade, A., Dawson, S., Gasevic, D., Buckingham Shum, S., Nelson, K., Alexander, S., Lockyer, L., Kennedy, G., Corrin, L., & Fisher, J. (2016). Student retention and learning analytics: A snapshot of Australian practices and a framework for advancement. Australian Government Office for Learning and Teaching. http://www.olt.gov.au/project-student-retention-and-learning-analytics-snapshot-current-australian-practices-and-framework
   Google Scholar
• Cranshaw, J., Toch, E., Hong, J., Kittur, A., & Sadeh, N. (2010, September). Bridging the gap between physical location and online social networks. In Proceedings of the 12th ACM international conference on ubiquitous computing (pp. 119–128).
   Google Scholar
• Diver, P., & Martinez, I. (2015). Moocs as a massive research laboratory: Opportunities and challenges. Distance Education, 36(1), 5–25. https://doi.org/10.1080/01587919.2015.1019968
   Web of Science ®Google Scholar
• Evans, B. J., Baker, R. B., & Dee, T. S. (2016). Persistence patterns in massive open online courses (MOOCs). The Journal of Higher Education, 87(2), 206–242. https://doi.org/10.1353/jhe.2016.0006
   Web of Science ®Google Scholar
• Foy, P., Arora, A., & Stanco, G. M. (2017). TIMSS 2015 user guide for the international database. TIMSS & PIRLS International Study Center.
   Google Scholar
• Ganelin, D., & Chuang, I. (2019). IP geolocation underestimates regressive economic patterns in MOOC usage. Proceedings of the 2019 11th international conference on education technology and computers. https://doi.org/10.1145/3369255.3369301.
   Google Scholar
• Hansen, J. D., & Reich, J. (2015, March). Socioeconomic status and MOOC enrollment: Enriching demographic information with external datasets. In Proceedings of the fifth international conference on learning analytics and knowledge (pp. 59–63). https://doi.org/10.1145/2723576.2723615
   Google Scholar
• Harder, J. A. (2020). The multiverse of methods: Extending the multiverse analysis to address data-collection decisions. Perspectives on Psychological Science, 15(5), 1158–1177. https://doi.org/10.1177/1745691620917678
   Web of Science ®Google Scholar
• Heale, R., & Forbes, D. (2013). Understanding triangulation in research. Evidence-Based Nursing, 16(4), 98. https://doi.org/10.1136/eb-2013-101494
   PubMedGoogle Scholar
• Jacobsen, D. Y. (2019). Dropping out or dropping in? A connectivist approach to understanding participants’ strategies in an e-learning MOOC pilot. Technology, Knowledge and Learning, 24(1), 1–21. https://doi.org/10.1007/s10758-017-9298-z
   Web of Science ®Google Scholar
• Joksimović, S., Poquet, O., Kovanović, V., Dowell, N., Mills, C., Gašević, D., … Brooks, C. (2017). How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1), 43–86. https://doi.org/10.3102/0034654317740335
   Web of Science ®Google Scholar
• Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., & Mascolo, C. (2013, August). Geo-spotting: Mining online location-based services for optimal retail store placement. In Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 793–801).
   Google Scholar
• Kelly, A. E., & Seppälä, M. (2016). Changing policies concerning student privacy and ethics in online education. International Journal of Information and Education Technology, 6(8), 652–655. https://doi.org/10.7763/ijiet.2016.v6.768
   Google Scholar
• Kizilcec, R. F., & Schneider, E. (2015). Motivation as a lens to understand online learners. ACM Transactions on Computer-Human Interaction, 22(2), 1–24. https://doi.org/10.1145/2699735
   Web of Science ®Google Scholar
• Landers, R. N., Brusso, R. C., Cavanaugh, K. J., & Collmus, A. B. (2016). A primer on theory-driven web scraping: Automatic extraction of big data from the internet for use in psychological research. Psychological Methods, 21(4), 475–492. https://doi.org/10.1037/met0000081
   PubMed Web of Science ®Google Scholar
• Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
   Google Scholar
• Livadariu, I., Dreibholz, T., Al-Selwi, A. S., Bryhni, H., Lysne, O., Bjørnstad, S., & Elmokashfi, A. (2020, July). On the accuracy of country-level IP geolocation. In Proceedings of the applied networking research workshop (pp. 67–73).
   Google Scholar
• Luo, H., Rocco, S., & Schaad, C. (2015). Using Google analytics to understand online learning: A case study of a graduate-level online course. 2015 international conference of educational innovation through technology (EITT). https://doi.org/10.1109/EITT.2015.62
   Google Scholar
• MaxMind, Inc. (2018). GeoPl2. https://www.maxmind.com/en/geoip2-city
   Google Scholar
• Meinel, C., Willems, C., Renz, J., & Staubitz, T. (2014). Reflections on enrollment numbers and success rates at the openhpi MOOC platform. Proceedings of the European MOOC stakeholder summit (pp. 101–106).
   Google Scholar
• Mullis, I. V. S., & Martin, M. O. (Eds.). (2013). TIMSS 2015 assessment frameworks. TIMSS & PIRLS International Study Center website. http://timssandpirls.bc.edu/timss2015/frameworks.html
   Google Scholar
• National Center for Education Statistics. (2015). School district boundaries [shapefile]. https://nces.ed.gov/programs/edge/Geographic/DistrictBoundariesF
   Google Scholar
• Nesterko, S. O., Dotsenko, S., Han, Q., Seaton, D., Reich, J., Chuang, I., & Ho, A. D. (2013). Evaluating the geographic data in MOOCs. The 2013 conference on Neural Information Processing System. http://nesterko.com/files/papers/nips2013-nesterko.pdf
   Google Scholar
• Nieuwenhuis, J., & Hooimeijer, P. (2016). The association between neighbourhoods and educational achievement, a systematic review and meta-analysis. Journal of Housing and the Built Environment, 31(2), 321–347. https://doi.org/10.1007/s10901-015-9460-7
   PubMed Web of Science ®Google Scholar
• Oest, S. E., Hightower, M., & Krasowski, M. D. (2018). Activation and utilization of an electronic health record patient portal at an academic medical center—impact of patient demographics and geographic location. Academic Pathology, 5, 237428951879757. https://doi.org/10.1177/2374289518797573
   Google Scholar
• Pozón-López, I., Kalinic, Z., Higueras-Castillo, E., & Liébana-Cabanillas, F. (2020). A multi-analytical approach to modeling of customer satisfaction and intention to use in Massive Open Online Courses (MOOC). Interactive Learning Environments, 28(8), 1003–1021. https://doi.org/10.1080/10494820.2019.1636074
   Web of Science ®Google Scholar
• Quercia, D., Lathia, N., Calabrese, F., Di Lorenzo, G., & Crowcroft, J. (2010, December). Recommending social events from mobile phone location data. In 2010 IEEE international conference on data mining (pp. 971–976). IEEE. https://doi.org/10.1109/ICDM.2010.152
   Google Scholar
• R Core Team. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/
   Google Scholar
• Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D., Jang, H., & Chavez, B. (2021). Stanford education data archive (Version 4.1). http://purl.stanford.edu/db586ns4974
   Google Scholar
• Reich, J. (2015). Rebooting MOOC research. Science, 347(6217), 34–35. https://doi.org/10.1126/science.1261627
   PubMed Web of Science ®Google Scholar
• Ruipérez-Valiente, J. A., Halawa, S., & Reich, J. (2019, June). Multiplatform MOOC analytics: Comparing global and regional patterns in edX and Edraak. In Proceedings of the sixth (2019) ACM conference on learning@ scale (pp. 1–9). https://doi.org/10.1145/3330430.3333616
   Google Scholar
• Sax, L. J., Gilmartin, S. K., Lee, J. J., & Hagedorn, L. S. (2008). Using web surveys to reach community college students: An analysis of response rates and response bias. Community College Journal of Research and Practice, 32(9), 712–729. https://doi.org/10.1080/10668920802000423
   Google Scholar
• Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712. https://doi.org/10.1177/1745691616658637
   Web of Science ®Google Scholar
• Thurmond, V. A. (2001). The point of triangulation. Journal of Nursing Scholarship, 33(3), 253–258. https://doi.org/10.1111/j.1547-5069.2001.00253.x
   PubMed Web of Science ®Google Scholar
• Vygotskij, L. S. (19342012). Thought and language. MIT Press.
   Google Scholar
• Waggoner, P., Kennedy, R., & Clifford, S. (2019). Detecting fraud in online surveys by tracing, scoring, and visualizing IP addresses. Journal of Open Source Software, 4(37), 1285. https://doi.org/10.21105/joss.01285
   Google Scholar