Effects Of Textual And Graphical - Textual Argumentation Software

or tacit thinking as well as facilitating group discussion by providing them with the opportunity to track down each others opinions (van Gelder, 2001; Tan, 2000). Most of these software combines visual clues such as different colour, different shapes for different argumentation structures with the verbal argumentation content to ease understanding. Veerman, Andriessen & Kanselaar (1999) compared Netmeeting chatting tool, Belvedere CSCA tool, Allaire BBS system. They reported that Belvedere is better to support an argumentation since students in this group, control and counter more frequently each others’ statement than students in other groups. Carr (1999) researched Quest-Map in problem-solving context. The researcher found no significant difference between groups in developing argumentation skills. He explained the participants were law students and had already developed argumentation skills. Tan (2000) researched Quest-Map, a constraint-based CSCA conversation systems, (scaffolding by pre-structured forms of conversation systems) in problem-solving context. The researcher concluded that students in Quest-Map performed significantly better in stating grounds (data) in Toulmin’s model of argumentation. Cho (2001) used BBS and Belvedere in his research about problem solving, and found that graphical group is better than textual in use of data and claim. In Turkey, researches in critical thinking is dated mostly in last ten years. It is more difficult to find publications in argumentation or in teaching argumentation, except classical logic. No research is found related to argumentation software use. There are many unresolved issues left by the limited amount of researches in teaching and learning argumentation. 2. Purpose of the study This study is conducted to understand whether teaching an explicit model of argumentation structures will be effective on learning the structures, and how effective will be if the graphical cognitive tool is combined with the direct instruction. Thus, the purpose of this research is defined as “Covariating preargumentation scores, is there a significant difference between textual, textualgraphical and control groups’ scores of post-argumentation structures? ”. 3. Method 3.1 Participants The pilot study with the prospective teachers showed computer literacy can be confounding variable considering treatment results. Thus, participants are selected from Department of Computers and Instructional Technology, Faculty of Education, Çukurova University-Turkey since they have the computer literacy skills. 112 undergraduate students signed up; 102 students from Computers and Instructional Technology Department are voluntarily joined in the study. Two students participated in the beginning of study, but were absent following classes. Thus, research completed with a total of 100 students. Among the participants 63 is male and 37 is female. There was no significant difference between groups in terms of sex (X2= .328, P> .05). Table1 shows the distribution of students in terms of sex and groups. Students of control group were 3rd and 4th year undergraduates, while students of experimental groups 2nd year undergraduates. Students of experimental group were registered for Learning Theories course. Participation was voluntary. Registered students were free to choose alternative assignments or grading other than joining to study. None registered student choose the alternative assignment. Students were assured of confidentiality. (Table 1) Table 1. Distribution of participants in terms of gender and groups 3.2 Research Design Non-equivalent control-group design is the most common method used in quasiexperimental research (Gall, Borg and Gall, 1996). This research design is appropriate when researcher has no control over when or to whom they might apply treatment (Campbell and Stanley, 1963 in Cohen, Manion and Morrison, 2000). The difference between nonequivalent control group design and experimental design is that participants is not selected randomly to the groups (Borg etc., 1996). In this research there are two experimental groups and a control group. Participants in experimental groups are selected randomly from second year students of Instructional Technology Department according to their pre-argumentation scores. They were very close to each other in terms of learning background, age and sex distribution. Beside, participants from the second year students were registered for a class; therefore, they were the most appropriate participants for the experimental treatment. However, control group is chosen from the third year and forth year voluntary students. They were not registered for the course, but they agreed to join learning theories seminars for this study. Students in the control group were direct instructed only about the learning theories. Thus, nonequivalent control-group design is selected as research design since control group is not selected randomly. Table2. shows nonequivalent control-group design for this study. (Table 2) Table 2. Nonequivalent design for the study E1: Textual experimental group E2: Textual-Graphical experimental group C: Control group R: Randomly assignment to the groups O1.1, O2.1, O3.1: Pre-argumentation scores X1: Only textual software use, arguing with word processor X2: Textual and graphical software use, arguing by Belvedere mapping and word processor O1.2, O2.2, O3.2: Post-argumentation scoresTreatments are applied only to experimental groups while no treatment applied to the control group. Dependent variables are claim, data, data support, warrant, warrant support, probability qualifier, condition of strength, condition of constraint, rebuttal, counter-rebuttal and total score for the analytic level. Holistic argumentation level is also identified as dependent variable.3. 3 Teaching Instruments First teaching instrument, argumentation courseware in Turkish is developed to support direct instruction of argumentation components to textual and textualgraphical groups. Students were able to reach to courseware trough internet connection (Figure1). Second teaching instrument, Belvedere is a graphical a r g u m e n t a t i o n t o o l d e v e l o p e d b y P i t t s b u r g U n i v e r s i t y (http://advlearn/Irde.pitt.edu/belvedere/). This tool aims to provide an opportunity for arguers to establish or to examine the relationship among argumentation structures. Belvedere is selected since it is an appropriate tool for functionalizing argumentation structures of Toulmin model. Belvedere is used only in textual graphical group for organizing group argument before writing it in textual form in this study. Figure 2 gives an example of students’ argumentation in Turkish about learning theories. Books and articles about theories are provided to each group. F i g u r e 2 . I l l u s t r a t i o n o f Argumentat ion Components Teaching Tool F i g u r e 3 . I l l u s t r a t i o n o f Argumentation in Graphical Tool Belvedere 3.4 Assessment Instruments A holistic and analytic rubric (1. and 2. appendix) developed for the analysis of argumentation based on argumentation components model shown below. Rubric defines the students’ performance level or learning goals which students are accepted to attain during learning process. Rubrics designed to assess learning (McColskey & O’Sullivan, 1993, p.41) can be classified as holistic or analytic rubrics in terms of how criteria or performance level are constructed (Luft, 1997; 1999). The performance level of learning skill is defined holistically in holistic rubrics whereas learning skill is divided into subskills and these subskills are the ones defined for the performance level in analytic rubric.Holistic and analytic argumentation rubrics are developed to measure students’ use of argumentation structures before and after treatment in this study. The researcher has six years of experience in developing rubrics as an instructional designer and instructor. Argumentation rubrics are developed based on Toulmin model of argumentation structures. This model with the field independent quality is applied to many different fields of study (Crammond, 1998) for teaching argumentation skills. Toulmin model also shed light into developing rubrics to assess argumentation skills. The results of persuasive writing studies show this model is appropriate for analyzing arguments (Connor & Lauer, 1985, 1988; Knudson, 1992; McCann, 1989; Scardamalia & Paris, 1985, Lunsford, 2002). However, model is criticized for its shortcomings in analysis (for example Driver etc., 2000; van Eemeren etc., 1996; Simosi, 2003). For this reason, Toulmin model of argumentation structures is adapted based on purposes in this research. Figure 4 shows the argumentation components model with additional structures to Toulmin model which is used for developing rubrics in this study. Figure 4 . An Argumentat ion Components Model Based on Toulmin’s Conceptual Model of Argumentation Structures First draft of rubrics and argumentation components model were developed based on Toulmin model of argumentation structures, related literature and trial composition analysis. Then, first drafts is tested in a pilot study with the participants whose background similar to participants of this study. Second draft of the model and rubrics for the study was organized based on pilot study results. Then, two experts are asked for their opinions on the model and whether the rubrics were able to functionalize the structures of the model for analysis. The experts were experienced in instructional design and teaching thinking. The argumentation component model given below was the conceptual base on which argumentation structures are functionalized for assessment purposes in the final version of holistic and analytic rubrics.Definitions of argumentation structures in argumentation component model: • Claim is the position being argued for; the conclusion of the argument. • Warrant is defined