Collaborative learning in Facebook: Can argument structure facilitate academic opinion change?

Social networking services (SNS), such as Facebook, are an increasingly important platform for computer supported collaborative learning (CSCL). However, little is known about whether and how academic opinion change and argumentative knowledge construction (AKC) can be facilitated in SNS. Existing argumentation practice in informal SNS discussions typically lacks elaboration and argumentative quality. We investigate the potential benefits of argument structure provided through individual computer-supported argument diagramming to foster academically sound opinions in the context of Facebook. In a quasi-experimental lab study, we found evidence of academic opinion change along with correlations of opinion change with knowledge gains. Argumentative Knowledge Construction in SNS Social networking services (SNS), such as Facebook, Twitter, Google+ etc., are rapidly growing communication platforms. SNS provide easy platform-independent access and almost unrestricted interactivity for sharing ideas and opinions, and may therefore be conducive to argumentative knowledge construction (AKC; Weinberger & Fischer, 2006). AKC is the deliberate practice of elaborating learning material by constructing formally and semantically sound arguments with the goal of gaining argumentative and domain knowledge. Argument structure provided through individual argument diagramming is among the most prominent approaches to foster AKC in CSCL environments (Scheuer et al, 2010). However, there is little known about the extent these approaches can be applied to learning in SNS (McLoughin, & Lee, 2010; Tsovaltzi et al, 2012). Current argumentation practice in informal SNS discussions lacks argumentative quality. Elaboration, evidence testing, and the evaluation of new knowledge are rare (Kanuka, & Anderson, 1998). This may not be surprising, since SNS were created for interactions at the personal level with users typically airing private opinions. However, academic opinions, i.e. opinions about academic and school subject matters are also shared, and potentially formed, through SNS (Roblyer, McDaniel, Webb, Herman, & Witty, 2010). Existing studies show that purposeful use of social media can support information sharing, communication and collaboration (Dabner, 2011), as well as learning (Laru, Näykki, & Järvelä, 2011). Yet, there is little systematic research on the educational potential of SNS for academic opinion change or formation, and the facilitation of learning. Research results learning suggest that argumentative elaboration can promote individual knowledge construction, and can greatly benefit from additional support through scripting, i.e. socio-cognitive structures that specify what learners are to do in collaborative learning scenarios (e.g. Baker & Lund, 1997; Weinberger, Stegmann & Fischer, 2010). Learners, for instance, can be prompted to provide support or counterarguments for their claims. This can help them elaborate the task, gain argumentative knowledge, understand multiple perspectives, and promote knowledge convergence (Weinberger et al., 2010). An alternative way to script learners is to let them first work on a task individually and then compare and combine their individual solutions (e.g., Weinberger, 2011; Asterhan & Schwarz, 2007). Such approaches may prevent process losses of simultaneously following diverse instructions, also characterized as over-scripting (Dillenbourg, 2002), which can hinder AKC. Moreover, learners in online discussions often dismiss conflicting opinions and inconsistencies rather than try to resolve them. Raising awareness of opinion conflict is one way to foster critical argumentative elaboration during collaboration and take advantage of the dialogic potential of SNS (Bodemer, 2011). In this paper, we investigate the potential benefits of argument structure provided through individual computer-supported argument diagramming for academic opinion change in Facebook and its influence on learning, compared to standard SNS discussions. We hypothesize that collaborators will resolve opinion conflicts productively by building on sound argumentation and attain higher knowledge gains after individual argument preparation as opposed to no individual preparation (Darnon et al, 2006). Our hypotheses are: H1: Argument structure provided before SNS discussions will foster more academic opinion change than standard SNS discussions. H2: Opinion change will correlate with knowledge gains. Methods To test our hypotheses, we conducted a quasi-experimental lab study. We compared two conditions: argument structure (ArgStr), which included individual construction of computer supported argument diagrams prior to the collaborative discussion in Facebook, vs. no argument structure (NoArgStr), which included collaborative discussion in Facebook only. The participants were randomly assigned to condition. Forty (40) students at a German university – ten dyads per condition – took part in the study. Dyads were chosen to maximize conflict on ethical aspects of the discussion topic (behaviorism in the classroom) based on a questionnaire (see Section 2.2). Socio-demographic data, reported on an 1-5 Likert scale and analyzed with the MannWhitney-U-Test, showed no significant differences between conditions in frequency of SNS use, purpose of Facebook use (e.g., social contact and information exchange), ambiguity tolerance, interest or prior knowledge, self-assessed domain knowledge, and familiarity with SNS and computer. Figure 1. Abbreviated example of argument representation in LASAD Due to privacy concerns, data collection for the experiment was done through specially created Facebook accounts. To maintain the effect of opinion conflict awareness, which is native in Facebook, we posted strong statements on behaviorism along two dimensions (effectiveness and ethics of behavioristic principles) and asked participants in dyads to use the “like” button to indicate agreement with these statements. An example statement on the ethical aspect is “The free will of a child must be facilitated at all costs.” Students in the argument structure condition used the web-based system, LASAD, to create individual argument diagrams (Loll, Pinkwart, Scheuer, & McLaren, 2012; see Fig. 1). Two types of boxes were available, one to represent "claims" and the other to represent "evidence". Students could choose from a dropdown menu whether (a) claims relate to the "effectiveness" or "ethics" of Behaviorism as a teaching method, and (b) whether evidence was based on "scientific results," "examples," or "everyday knowledge". Boxes could be related to one another through color-coded arrows, which indicated "support" (green arrows) or "opposition" (red arrows). The diagrams aimed at helping students to construct arguments for and against Behaviorism as a teaching method, contemplate the validity of arguments, as well as share and discuss the related evidence. All students took an online pretest prior to the intervention in the Lab and a posttest. They also briefly read an essay on Behaviorism that could be used as reference during the intervention. The duration of the intervention was 55 minutes. NoArgStr used the entire time discussing on Facebook and trying to reach an agreement on the topic "Should behavioristic principles be applied in the classroom?" ArgStr used the first 25 minutes prior to the Facebook discussion to individually create an argument diagram with LASAD on the topic. Opinion Conflict, Formation and Change Opinion conflict, used to group dyads, was measured with a questionnaire in which participants had to state their agreement with statements on the effectiveness and ethical aspects of the principles of behaviorism for learning on a 5-point Likert scale. A statement on effectiveness, translated from the German original, is “Behaviorism can be applied with learning success on simple tasks”, and on ethics, “It is potentially wrong to use negative reinforcement on kids.” To analyze opinion formation independent of the direction of opinion change (that is, opinions becoming more or less favorable), we used a t-test of the absolute difference between the mean prestatement score and the mean post-statement score of each participant. Knowledge Test Our knowledge test comprised twenty-four multiple-choice and two open questions (“Name some weak/strong points of behaviorism.”), evaluated by two raters. The inter-rater-reliability was substantial for pre and posttest for the first question, Cohen’s kpre =.86; Cohen’s kpost =.86, and moderate for the second question, Cohen’s kpre 51; Cohen’s kpost =.44. To compare the knowledge scores we used the GLM Univariate procedure.