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Ninety-eight information science students were tracked during an online masters degree program. At their initial orientation, each student completed a demographic data form and the Kolb Learning Style Inventory. Because of their major, it was hypothesized that students would fall into Kolb's Converger and Assimilator categories and these learning styles would be predictive of success in the program. Results indicated that 79.6 percent (n = 78) of all students graduated from the program. Seventy-three students (74.5%) fell into the predicted categories and maintained an 83.6 percent (n = 61) graduation rate. Students not falling into the predicted categories maintained a 68 percent (n = 17) graduation rate. The implications are clear. First, the majority of students can succeed in an online learning environment regardless of their learning style. Care has to be taken, however, since a trend existed in this study for students with learning styles different from predicted to drop out in higher numbers. Institutions offering online programs should be aware of this and be prepared to address learning style issues.

This essay is written to present a prospective stance on how learning analytics, as a core evaluative approach, must help instructors uncover the important trends and evidence of quality learner data in the online course. A critique is presented of strategic and tactical issues of learning analytics. The approach to the critique is taken through the lens of questioning the current status of applying learning analytics to online courses. The goal of the discussion is twofold: (1) to inform online learning practitioners (e.g., instructors and administrators) of the potential of learning analytics in online courses and (2) to broaden discussion in the research community about the advancement of learning analytics in online learning. In recognizing the full potential of formalizing big data in online coures, the community must address this issue also in the context of the potentially "harmful" application of learning analytics.