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A memory-based approach to learning shallow natural language patterns

By John Sener

Abstract

Online learning has entered the mainstream of American higher education. Millions of students are taking online courses, and enrollments are projected to triple over the next several years (Symonds 2001). The majority of American college students are now using the Internet for their course work (Jones 2002), and more than one-third of all college courses use online course management tools (Green 2003). Although its rapid growth and increasing acceptance has somewhat muted the once-loud voices of its critics, online learning still struggles with lingering perceptions that it is somehow inferior, unproven, and limited in application relative to traditional classroom instruction. For this reason, online learning programs and courses receive closer scrutiny than their traditional counterparts. For instance, accrediting agencies often demand more extensive documentation for online degree programs or notifications of substantive change, and online learning programs usually require more detailed financial and assessment plans than traditionally delivered programs. As a result, significant energy has been put into establishing the "equivalent " quality of online courses and programs relative to traditional ones, as evidenced by the compilation of hundreds of distance education studies that document the well-known "no significant difference " phenomenon. Other compilations suggest that online learning is often better than classroom instruction (Hiltz, Zhang, and Turoff 2001). Beyond establishing a ballpark equivalence, comparing traditional and online learning is problematic on man

Year: 1999
OAI identifier: oai:CiteSeerX.psu:10.1.1.186.6321
Provided by: CiteSeerX
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