Complexity in Asynchronous CMC Agreement and Disagreement

Complexity in Asynchronous CMC Agreement and Disagreement CALICO 2011 Greg Kessler Ohio University General research into Agreement & Disagreement • General preference for agreement (or appearance of agreement) in oral exchanges (Brown & Levinson, 1987) • Foppa (1995) Disagreement always requires jusQficaQon…& it contains no informaQon, but rather the informaQon is contained within the jusQficaQon. • Disagreement in online exchanges can easily be seen as impolite (Angouri & Tseliga, 2010) Agreement & Disagreement Research • Morand and Ocker (2003) idenQfy disagreement as one of many face threatening act. • Scollon & Scollon (1983) idenQfy the significance of solidarity in agreement. • Holtgraves (1997) employed politeness theory to explore agreement, avoidance of disagreement, and seeking common ground in discussion of controversial topics. Disagreement in Online CommunicaQon • Chu‐Carroll & Carberry (1998) observed a]empts at reconciliaQon following disagreement. • Walker (1996) focused on the acceptance and rejecQon of disagreements. • Di‐Eugenio (2001) uQlized a holisQc perspecQve to idenQfy soluQons to disagreements. Agreement & Disagreement in Language Learning • Discussion boards can support students’ ability to agree and disagree within language learning contexts (Opp‐ Beckman & Kieffer, 2004). • SoQllo (2000) Students in ACMC devoted 49% of their posQngs to responding to the teacher. • Student responses to each other, “suggested agreement and disagreement in indirect ways” (p. 104). • Anecdotal observaQon of superficial ACMC • No explicit previous research into agreement and disagreement in online language learning Guiding Research Regarding Complexity • Various approaches to invesQgaQng CAF • ACMC and SCMC comparison, SoQllo (2000) – ACMC Promotes reflecQon & opportunity for elaboraQon • MulQdimensional “organic” perspecQves, Norris & Ortega (2009) – Comparison of measures – CauQon in interpretaQon/extrapolaQon • Balanced complexity, Schulze (2010) – Combining measures QuesQons • What is the nature of (dis)agreement and directness in online ACMC? • What is the relaQonship between syntacQc complexity and (dis)agreement in ACMC? • How do complexity measures compare across these discourse funcQons? • How does human vs automated complexity compare? Methodology • 40 NNES in 15 week online course • Weekly discussions on culture related topics • All response posts were included • 1662 response posts consisQng of 228,060 words • Two independent trained raters idenQfied direct and indirect agreement and disagreement acts (as outlined by B & L,1998) Methodology • SyntacQc complexity was observed in terms of three convenQonal measures: – General Complexity (Mean length of T‐unit) – Hunt, 1965 – Phrasal ElaboraQon (Mean length of clause) – Elder & Iwashita, 2005 – SubordinaQon (Clauses per T‐unit) – Sco], 1988 • Anova across the agreement types & measures • MulQcollinearity correlaQon comparing measures • Discriminant analysis Results • 1010 instances of agreement and disagreement – 964 of agreement • 831 direct • 133 indirect – 46 of disagreement • 31 indirect • 15 direct Results Results • Significant difference in complexity between agreement and disagreement (sub: clauses/ t‐ unit) • Indirect agreement was more syntacQcally complex than direct agreement across all measures • Indirect disagreement was more syntacQcally complex than direct disagreement Results • Results vary according to selected measure • Consistent with Norris & Ortega, 2009 • Complexity measures demonstrated significant mulQcollinearity • Mean length of T‐unit and mean length of clause correlated at .665 • Comparing complexity measures revealed redundancy of use of measures • Length of clause removed from study • Discriminant analysis provided insight into comparison across agreement types and measures Conclusions Q1 • Students may benefit from expectaQons for demonstraQng disagreement • Students may benefit from suggesQons & guidance on how to appropriately disagree • Students may benefit from explicit exposure to findings of SoQllo (2000) Conclusions Q2 • Complexity varies across acts of agreement and disagreement • Statements of disagreement are more complex than statements of agreement • Indirect statements are more complex than direct statements Conclusions Q3 • Different complexity measures offer different results • Varied measures should be employed • CombinaQons of complexity measures can be beneficial or redundant • Measure choice should be influenced by nature of discourse funcQons Looking at the entire text… …or organized by discourse function Scale and orientation can also influence interpretation Conclusions Q4 • Automated analysis of complexity is reliable Length T unit Length Clause Clauses per T unit IRR 0.95 IRR 0.97 IRR 0.98 Automated SyntacQc Complexity h]p://aihaiyang.com/synlex/syntacQc/ Lu, Xiaofei (2010). Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics, 15(4):474-496. " Thanks! Questions? http:call.ohio.edu/calico11a.ppt References • Angouri, J., & Tseliga, T. (2010). “you HAVE NO IDEA WHAT YOU ARE TALKING ABOUT!” A study of impoliteness strategies in two online fora. Angouri & Tseliga. Journal of Politeness Research 6(1), 57‐82. Special Issue on Politeness and Computer‐ Mediated CommunicaQon. Edited by Miriam Locher. • Brown, P. and Levinson, S.C. (1987) Politeness: Some Universals in Language Use. • Cambridge: Cambridge University Press. • Foppa, K. (1995). On mutual understanding and agreement in dialogues. In I. Marková, C. Graumann, & K. Foppa (Eds.), MutualiQes in dialogue (pp. 149–175). Cambridge, UK: Cambridge University Press. • Lu, Xiaofei (2010). AutomaQc analysis of syntacQc complexity in second language wriQng. InternaQonal Journal of Corpus LinguisQcs, 15(4):474‐496. • Norris, J. & Ortega, L. (2009). 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