Blended learning and research issues
Blended learning (BL), or the integration of face-to-face and online instruction (Graham
2013), is widely adopted across higher education with some scholars referring to it as the “new traditional model” (Ross and Gage
2006, p. 167) or the “new normal” in course delivery (Norberg et al.
2011, p. 207). However, tracking the accurate extent of its growth has been challenging because of definitional ambiguity (Oliver and Trigwell
2005), combined with institutions’ inability to track an innovative practice, that in many instances has emerged organically. One early nationwide study sponsored by the Sloan Consortium (now the Online Learning Consortium) found that 65.2% of participating institutions of higher education (IHEs) offered blended (also termed
hybrid) courses (Allen and Seaman
2003). A 2008 study, commissioned by the U.S. Department of Education to explore distance education in the U.S., defined BL as “a combination of online and in-class instruction
with reduced in-class seat time for students” (Lewis and Parsad
2008, p. 1, emphasis added). Using this definition, the study found that 35% of higher education institutions offered blended courses, and that 12% of the 12.2 million documented distance education enrollments were in blended courses.
The 2017 New Media Consortium Horizon Report found that blended learning designs were one of the short term forces driving technology adoption in higher education in the next 1–2 years (Adams Becker et al.
2017). Also, blended learning is one of the key issues in teaching and learning in the EDUCAUSE Learning Initiative’s 2017 annual survey of higher education (EDUCAUSE
2017). As institutions begin to examine BL instruction, there is a growing research interest in exploring the implications for both faculty and students. This modality is creating a community of practice built on a singular and pervasive research question, “How is blended learning impacting the teaching and learning environment?” That question continues to gain traction as investigators study the complexities of how BL interacts with cognitive, affective, and behavioral components of student behavior, and examine its transformation potential for the academy. Those issues are so compelling that several volumes have been dedicated to assembling the research on how blended learning can be better understood (Dziuban et al.
2016; Picciano et al.
2014; Picciano and Dziuban
2007; Bonk and Graham
2007; Kitchenham
2011; Jean-François
2013; Garrison and Vaughan
2013) and at least one organization, the Online Learning Consortium, sponsored an annual conference solely dedicated to blended learning at all levels of education and training (2004–2015). These initiatives address blended learning in a wide variety of situations. For instance, the contexts range over K-12 education, industrial and military training, conceptual frameworks, transformational potential, authentic assessment, and new research models. Further, many of these resources address students’ access, success, withdrawal, and perception of the degree to which blended learning provides an effective learning environment.
Currently the United States faces a widening educational gap between our underserved student population and those communities with greater financial and technological resources (Williams
2016). Equal access to education is a critical need, one that is particularly important for those in our underserved communities. Can blended learning help increase access thereby alleviating some of the issues faced by our lower income students while resulting in improved educational equality? Although most indicators suggest “yes” (Dziuban et al.
2004), it seems that, at the moment, the answer is still “to be determined.” Quality education presents a challenge, evidenced by many definitions of what constitutes its fundamental components (Pirsig
1974; Arum et al.
2016). Although progress has been made by initiatives, such as, Quality Matters (
2016), the OLC OSCQR Course Design Review Scorecard developed by Open SUNY (Open SUNY
n.d.), the Quality Scorecard for Blended Learning Programs (Online Learning Consortium
n.d.), and SERVQUAL (Alhabeeb
2015), the issue is by no means resolved. Generally, we still make quality education a perceptual phenomenon where we ascribe that attribute to a course, educational program, or idea, but struggle with precisely why we reached that decision. Searle (
2015), summarizes the problem concisely arguing that quality does not exist independently, but is entirely observer dependent. Pirsig (
1974) in his iconic volume on the nature of quality frames the context this way,
“There is such thing as Quality, but that as soon as you try to define it, something goes haywire. You can’t do it” (p. 91).
Therefore, attempting to formulate a semantic definition of quality education with syntax-based metrics results in what O’Neil (O'Neil
2017) terms
surrogate models that are rough approximations and oversimplified. Further, the derived metrics tend to morph into goals or benchmarks, losing their original measurement properties (Goodhart
1975).
Blended learning forces us to consider the characteristics of digital technology, in general, and information communication technologies (ICTs), more specifically. Floridi (
2014) suggests an answer proffered by Alan Turing: that digital ICTs can process information on their own, in some sense just as humans and other biological life. ICTs can also communicate information to each other, without human intervention, but as linked processes designed by humans. We have evolved to the point where humans are not always “in the loop” of technology, but should be “on the loop” (Floridi
2014, p. 30), designing and adapting the process. We perceive our world more and more in informational terms, and not primarily as physical entities (Floridi
2008). Increasingly, the educational world is dominated by information and our economies rest primarily on that asset. So our world is also blended, and it is blended so much that we hardly see the individual components of the blend any longer. Floridi (
2014) argues that the world has become an “infosphere” (like biosphere) where we live as “inforgs.” What is real for us is shifting from the physical and unchangeable to those things with which we can interact.
Floridi also helps us to identify the next blend in education, involving ICTs, or specialized artificial intelligence (Floridi
2014, 25; Norberg
2017, 65). Learning analytics, adaptive learning, calibrated peer review, and automated essay scoring (Balfour
2013) are advanced processes that, provided they are good interfaces, can work well with the teacher— allowing him or her to concentrate on human attributes such as being caring, creative, and engaging in problem-solving. This can, of course, as with all technical advancements, be used to save resources and augment the role of the teacher. For instance, if artificial intelligence can be used to work along with teachers, allowing them more time for personal feedback and mentoring with students, then, we will have made a transformational breakthrough. The Edinburg University manifesto for teaching online says bravely, “Automation need not impoverish education – we welcome our robot colleagues” (Bayne et al.
2016). If used wisely, they will teach us more about ourselves, and about what is truly human in education. This emerging blend will also affect curricular and policy questions, such as the
what? and
what for? The new normal for education will be in perpetual flux. Floridi’s (
2014) philosophy offers us tools to understand and be in control and not just sit by and watch what happens. In many respects, he has addressed the new normal for blended learning.