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Potentials and Possibilities for Online Education
S. Baker and P. S. Inventado
Editor’s Note: The following was reprinted from Emergence and Innovation in Digital Learning, an open textbook edited by George Veletsianos.
Baker, S., & Inventado, P. S. (2016). Educational data mining and learning analytics: Potentials and possibilities for online education. In G. Veletsianos (Ed.), Emergence and Innovation in Digital Learning (83–98). doi:10.15215/aupress/9781771991490.01
Over the last decades, online and distance education has become an increasingly prominent part of the higher educational landscape (Allen & Seaman, 2008; O’Neill et al., 2004; Patel & Patel, 2005). Many learners turn to distance education because it works better for their schedule, and makes them feel more comfortable than traditional face-to-face courses (O’Malley & McCraw, 1999). However, working with distance education presents challenges for both learners and instructors that are not present in contexts where teachers can work directly with their students. As learning is mediated through technology, learners have fewer opportunities to communicate to instructors about areas in which they are struggling. Though discussion forums provide an opportunity that many students use, and in fact some students are more comfortable seeking help online than in person (Kitsantas & Chow, 2007), discussion forums depend upon learners themselves realizing that they are facing a challenge, and recognizing the need to seek help. Further, many students do not participate in forums unless given explicit prompts or requirements (Dennen, 2005). Unfortunately, the challenges of help-seeking are general: many learners, regardless of setting, do not successfully recognize the need to seek help, and fail to seek help in situations where it could be extremely useful (Aleven et al., 2003). Without the opportunity to interact with learners in a face-to-face setting, it is therefore harder for instructors as well to recognize negative affect or disengagement among students.
Beyond a student not participating in discussion forums, ceasing to complete assignments is a clear sign of disengagement (Kizilcec, Piech, & Schneider, 2013), but information on these disengaged behaviors is not always available to instructors, and more subtle forms of negative affect (such as boredom) are difficult for an unaided distance instructor to identify and diagnose. As such, a distance educator has additional challenges compared to a local instructor in identifying which students are at-risk, in order to provide individual attention and support. This is not to say that face-to-face instructors always take action when a student is visibly disengaged, but they have additional opportunities to recognize problems.
In this chapter, we discuss educational data mining and learning analytics (Baker & Siemens, 2014) as a set of emerging practices that may assist distance education instructors in gaining a rich understanding of their