{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:09:21Z","timestamp":1762506561449,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,6,2]],"date-time":"2015-06-02T00:00:00Z","timestamp":1433203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>We describe how statistical predictive models might play an expanded role in educational analytics by giving students automated, real-time information about what their current performance means for eventual success in eLearning environments. We discuss how an online messaging system might tailor information to individual students using predictive analytics. The proposed system would be data-driven and quantitative; e.g., a message might furnish the probability that a student will successfully complete the certificate requirements of a massive open online course. Repeated messages would prod underperforming students and alert instructors to those in need of intervention. Administrators responsible for accreditation or outcomes assessment would have ready documentation of learning outcomes and actions taken to address unsatisfactory student performance. The article\u2019s brief introduction to statistical predictive models sets the stage for a description of the messaging system. Resources and methods needed to develop and implement the system are discussed.<\/jats:p>","DOI":"10.3390\/fi7020170","type":"journal-article","created":{"date-parts":[[2015,6,2]],"date-time":"2015-06-02T11:38:36Z","timestamp":1433245116000},"page":"170-183","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Output from Statistical Predictive Models as Input to  eLearning Dashboards"],"prefix":"10.3390","volume":"7","author":[{"given":"Marlene","family":"Smith","sequence":"first","affiliation":[{"name":"Business School, University of Colorado Denver, 1475 Lawrence Street, Denver, CO 80202, USA"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,2]]},"reference":[{"key":"ref_1","unstructured":"Hardesty, L. Lessons learned from MITx\u2019s prototype course. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/newsoffice.mit.edu\/2012\/mitx-edx-first-course-recap-0716."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"202","DOI":"10.19173\/irrodl.v14i3.1455","article-title":"MOOCs: A systematic study of the published literature 2008\u20132012","volume":"14","author":"Liyanagunawardena","year":"2013","journal-title":"Int. Rev. Res. Open Distance Learn."},{"key":"ref_3","unstructured":"Yang, D., Sinha, T., Adamson, D., and Rose, C.P. (2013, January 9\u201310). Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. Proceedings of the 2013 NIPS Data-Driven Education Workshop, Lake Tahoe, NV, USA. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/lytics.stanford.edu\/datadriveneducation\/papers\/yangetal.pdf."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ho, A.D., Reich, J., Nesterko, S., Seaton, D.T., Mullaney, T., Waldo, J., and Chaung, I. HarvardX and MITx: The First Year of Open Online Courses. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/dx.doi.org\/10.2139\/ssrn.2381263.","DOI":"10.2139\/ssrn.2381263"},{"key":"ref_5","unstructured":"Liyanagunawardena, T.R., Parslow, P., and Williams, S.A. (2014, January 10\u201312). Dropout: MOOC participants\u2019 perspective. Proceedings of the European MOOC Stakeholder\u2019s Summit 2014, Lausanne, Switzerland."},{"key":"ref_6","unstructured":"Rosen, R.J. Overblown-Claims-of-Failure Watch: How not to gauge the success of online courses. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/www.theatlantic.com\/technology\/archive\/2012\/07\/overblown-claims-of-failure-watch-how-not-to-gauge-the-success-of-online-courses\/260159\/."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Daniel, J. (2012). Making Sense of MOOCs: Musings in a Maze of Myth, Paradox and Possibility. J. Interact. Med. Educ., 3.","DOI":"10.5334\/2012-18"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.compedu.2004.12.004","article-title":"Comparing dropouts and persistence in e-learning courses","volume":"48","author":"Levy","year":"2007","journal-title":"Comput. Educ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/S8755-4615(01)00067-6","article-title":"Fault lines in the terrain of distance education","volume":"18","author":"Brady","year":"2001","journal-title":"Comput. Compos."},{"key":"ref_10","unstructured":"Carr, S. As distance education comes of age, the challenge is keeping the students. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/chronicle.com\/article\/As-Distance-Education-Comes-of\/14334."},{"key":"ref_11","unstructured":"Parker, A. (1999). A study of variables that predict dropout from distance educations. Int. J. Educ. Technol., 2, Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/education.illinois.edu\/ijet\/v1n2\/parker\/index.html."},{"key":"ref_12","unstructured":"Jaggars, S.S., Edgecombe, N., and Stacey, G.W. What We Know about Online Course Outcomes. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/ccrc.tc.columbia.edu\/publications\/what-we-know-online-course-outcomes.html."},{"key":"ref_13","unstructured":"Boettcher, J.V. (2004). Online Course Development: What Does It Cost?. Campus Technol., Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/campustechnology.com\/Articles\/2004\/06\/Online-Course-Development-What-Does-It-Cost.aspx?aid=39863&Page=1."},{"key":"ref_14","unstructured":"Finkelstein, M.J., Frances, C., Jewett, F.I., and Scholz, B.W. (2000). Dollars, Distance, and Online Education: The New Economics of College Teaching and Learning, Oryx Press."},{"key":"ref_15","unstructured":"Young, J.R. (2012). Inside the Coursera contract: How an upstart company might profit from free courses. Chron. High. Educ., Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/chronicle.com\/article\/How-an-Upstart-Company-Might\/133065\/?cid=at&utm_source=at&utm_medium=en."},{"key":"ref_16","unstructured":"Campaign for the Future of Higher Education The \u201cPromises\u201d of Online Education: Reducing Costs. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/futureofhighered.org\/wp-content\/uploads\/2013\/10\/Promises-of-Online-Higher-Ed-Reducing-Costs1.pdf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/MIS.2013.66","article-title":"MOOCs: So many learners, so much potential","volume":"28","author":"Kay","year":"2013","journal-title":"IEEE Intell. Syst."},{"key":"ref_18","first-page":"935","article-title":"Differentiated instruction: A research basis","volume":"7","author":"Subban","year":"2006","journal-title":"Int. Educ. J."},{"key":"ref_19","unstructured":"Siefert, J.W. Data mining and homeland security: An overview. Congressional Research Service Report for Congress. Available online:https:\/\/linproxy.fan.workers.dev:443\/https\/epic.org\/privacy\/fusion\/crs-dataminingrpt.pdf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/S0160-791X(02)00038-6","article-title":"Data mining techniques for customer relationship management","volume":"24","author":"Rygielski","year":"2002","journal-title":"Technol. Soc."},{"key":"ref_21","unstructured":"Nichols, J. (2013). Not Just the NSA: Politicians are Data Mining the American Electorate. Nation, Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/www.thenation.com\/blog\/174759\/not-just-nsa-politicians-are-data-mining-american-electorate."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1109\/TSMCC.2010.2053532","article-title":"Educational data mining: A review of the state of the art","volume":"40","author":"Romero","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev."},{"key":"ref_23","unstructured":"Arnold, K. Signals: Applying academic analytics. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/www.educause.edu\/ero\/article\/signals-applying-academic-analytics."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S1096-7516(01)00036-7","article-title":"Critical factors for successful delivery of online programs","volume":"3","author":"Lieblein","year":"2000","journal-title":"Internet High. Educ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Smith, M.A., and Kellogg, D.L. (2015). Required collaborative work in online courses: A predictive modeling approach. Decis. Sci. J. Innov. Educ., in press.","DOI":"10.1111\/dsji.12078"},{"key":"ref_26","unstructured":"Pappano, L. The Year of the MOOC. Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/edinaschools.org\/cms\/lib07\/MN01909547\/Centricity\/Domain\/272\/The%20Year%20of%20the%20MOOC%20NY%20Times.pdf."},{"key":"ref_27","unstructured":"Bali, M. (2014). MOOC pedagogy: Gleaning good practice from existing MOOCs. MERLOT J. Online Learn. Teach., 10, Available online:https:\/\/linproxy.fan.workers.dev:443\/http\/jolt.merlot.org\/vol10no1\/bali_0314.pdf."},{"key":"ref_28","first-page":"412","article-title":"Using peer feedback to enhance the quality of student online postings: An exploratory study","volume":"12","author":"Ertmer","year":"2007","journal-title":"J. Comput.-Med. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1214\/10-STS330","article-title":"To explain or to predict?","volume":"25","author":"Shmueli","year":"2010","journal-title":"Stat. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"553","DOI":"10.2307\/23042796","article-title":"Predictive analytics in information systems research","volume":"35","author":"Shmueli","year":"2011","journal-title":"MIS Q."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1177\/002224378302000203","article-title":"The significance of statistical significance tests in marketing research","volume":"20","author":"Sawyer","year":"1983","journal-title":"J. Mark. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1287\/isre.2013.0480","article-title":"Research commentary\u2014Too big to fail: Large samples and the p-value problem","volume":"24","author":"Lin","year":"2013","journal-title":"Inf. Syst. Res."},{"key":"ref_33","unstructured":"Hand, D.J., Manilla, H., and Smyth, P. (2001). Principles of Data Mining, The MIT Press."},{"key":"ref_34","unstructured":"Linoff, G.S., and Berry, M.J.A. (2011). Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, Wiley. [3rd ed.]."},{"key":"ref_35","unstructured":"Shmueli, G., Patel, N.R., and Bruce, P.C. (2010). Data Mining for Business Intelligence: Concepts, Techniques, and Applications, Wiley. [2nd ed.]."},{"key":"ref_36","unstructured":"Walsh, S. (2005). Applying Data Mining Techniques Using SAS Enterprise Miner, SAS Publishing."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2414","DOI":"10.1016\/j.compedu.2011.05.016","article-title":"Using Signals for appropriate feedback: Perceptions and practices","volume":"57","author":"Tanes","year":"2011","journal-title":"Comput. Educ."},{"key":"ref_38","first-page":"29","article-title":"Administrative perspectives of data-mining software signals: Promoting student success and retention","volume":"6","author":"Arnold","year":"2010","journal-title":"J. Acad. Adm. High. Educ."},{"key":"ref_39","first-page":"674","article-title":"Learning and teaching styles in engineering education","volume":"78","author":"Felder","year":"1988","journal-title":"Eng. Educ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.paid.2005.01.011","article-title":"Goldberg\u2019s \u201cIPIP\u201d big-five factor markers: Internal consistency and concurrent validation in Scotland","volume":"39","author":"Gow","year":"2005","journal-title":"Personal. Individ. Differ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1037\/a0017371","article-title":"Confirmatory factor analysis of the M5\u201350: An implementation of the International Personality Item Pool Set","volume":"22","author":"Socha","year":"2010","journal-title":"Psychol. Assess."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1999-5903\/7\/2\/170\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:47:21Z","timestamp":1760215641000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/1999-5903\/7\/2\/170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,2]]},"references-count":41,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,6]]}},"alternative-id":["fi7020170"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/fi7020170","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2015,6,2]]}}}