A Content Analytic Comparison of Learning Processes in Online and Face-to-Face Case Study Discussions

Abstract

While much research has shown that asynchronous learning networks (ALNs) can produce learning equivalent to face‐to‐face (FTF) classrooms, there has been little research that explicitly explores similarities and differences between the learning processes that occur in ALN and FTF activities. This study used a content analytic framework (derived primarily from previous work of Anderson, Archer, Garrison, and Rourke) to analyze transcripts from eight case study discussions, four FTF and four ALN. While previous authors developed a model that studies cognitive, social, and teaching processes in ALN discussions, the current scheme also considers characteristics of the discourse process. The findings provide evidence that ALNs generate high levels of cognitive activity, at least equal to, and in some cases superior to, the cognitive processes in the FTF classroom. The findings also suggest that students assume some aspects of the teacher's role in ALNs, and that student‐to‐student interactions contain a greater proportion of high‐level cognitive indicators than do student‐to‐teacher interactions.

Student ratings of the importance of survey items, multiplicative factor analysis, and the validity of the community of inquiry survey

“…Assuming some degree of association among importance ratings for items addressing teaching, social, and cognitive presence (Heckman & Annabi, 2005;Garrison et al, 2004), oblique rotation (Direct Obliminal in SPSS) was utilized with the default value δ=0 specified to reasonably limit the level of correlation among the factors. The use of an oblique rotation was justified on the theoretical grounds that the three presences are considered to be interdependent, thus their multiplicative scores would be considered independent as well.…”

Comparing Perceived Formal and Informal Learning in Face-to-Face versus Online Environments

“…Differences between learning environments (online vs. face-to-face) may influence learners' achievements and satisfaction (Allen, Bourhis, Burrell, & Mabry, 2002;Heckman & Annabi, 2005;Jahng, Krug, & Zhang, 2007;Russell, 1999). Recent meta-analysis (Means, Toyama, Murphy, Bakia, & Jones, 2009) found more than a thousand empirical research studies concerning online learning.…”

Gender, Identity, and Language Use in Web Discussion Forum and Mobile Phone Txt Messages

“…Word, category and conceptual frequency analyses are some of its primary areas of focus. At the most basic level, the main purpose of this method is to locate the nature of the relative patterns within and between sets of data (Heckman & Annabi, 2005;Huffaker & Calvert, 2005;Lowe, 2003;Pachler & Daly, 2009;Petrina, 1998). For its part conversational analysis studies conversation and deals with such aspects as talk, conversation structure, adjacency pairs, preference, repairs, floor, turn-taking, and participants (Levinson, 1983;Norrick, 1991;Sacks, 1994).…”

“…For its part conversational analysis studies conversation and deals with such aspects as talk, conversation structure, adjacency pairs, preference, repairs, floor, turn-taking, and participants (Levinson, 1983;Norrick, 1991;Sacks, 1994). Similarly, discourse analysis in this case deals with features of discourse or language such as discourse markers, markers of similarities and differences between men's and women's language, and aspects of discourse or language reflecting the gender and identity of language users (Coates &Cameron, 1988;Eckert & McConnell-Ginet, 1999;Heckman & Annabi, 2005;Herring, 2000;Huffaker & Calvert, 2005;Lee, 2003;Lewis & Mills, 2003;Mills, 2003a, c).…”

“…Regarding the procedure used in this study, all the three types of messages (web discussion forum -WDF -messages, personal mobile phone txt messages and viewers' mobile phone txt messages) were coded, scored and analysed for personally identifiable information, emotive features, sexual identity, and gendered language (see Calcutt, 2001;Heckman & Annabi, 2005;Huffaker & Calvert, 2005;Katz & Aakhus, 2002;Ling & Yttri, 2002). Tropes V6.2 and DICTION 5.0, two content analysis software packages designed to analyse documents in terms of word counts as well as content types and language tone, were used to determine the tone and semantic features of words (Heckman & Annabi, 2005;Huffaker & Calvert, 2005).…”