write an essay on social stigma related to online learning
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Answer:
It is time to stop asking if online learning is as “effective” as traditional face-to-face learning. The question has been asked and answered. Asking it over and over again will not yield the responsive, localized, and collaborative answers we need to facilitate meaningful education for an increasingly diverse body of learners.
Teaching and learning work best when faculty, instructional staff, and students are invested and engaged. What is often lost when we ask the “effective” question is that investment and engagement are modality-independent qualities. The educational ecosystem teachers and students need in the decades to come must involve both online and face-to-face expressions. Tethering “effective” teaching and learning to a particular modality restricts the strategic implementation of research-driven pedagogy across college and university offerings.
To support my claim, I pursue two points. First, online education is growing, and this growth poses no substantive risk to student learning. Second, a logic of legitimacy drives the stigma that bars integration of online learning into higher education’s core mission.
The “effective” question is rooted in fear of disruption. We need to recognize and acknowledge continuity instead.
Online education is growing. Fast. And there is no established scholarly consensus that online education poses substantive risk to the attainment of student learning outcomes.
In College (Un)Bound, Jeffrey Selingo reports the number of students enrolled in at least one online course grew from 1.6 million in 2002 to 6.1 million on 2010 (Selingo 97). I. Elaine Allen and Jeff Seaman’s Grade Change presents similar data, showing enrollment in at least one online course reaching 7.1 million in 2012 (Allen and Seaman 15).
Rapid growth means online learning, like distance before it, feels the burden of proving its effectiveness in relation to traditional practice. This burden has taken many forms. Most visible are the meta-analyses, aggregating data from dozens or hundreds of studies exploring various metrics of “effective
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Filteration
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