January 2012 Volume 15 Number 1 - Educational Technology ...

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Hou, H.-T. (2012). Analyzing the Learning Process of an Online Role-Playing Discussion Activity. Educational Technology & Society, 15 (1), 211–222. Analyzing the Learning Process of an Online Role-Playing Discussion Activity Huei-Tse Hou Graduate Institute of Engineering, National Taiwan University of Science and Technology, Taiwan // hthou@mail.ntust.edu.tw ABSTRACT Instructional activities based on online discussion strategies have gained prevalence in recent years. Within this context, a crucial research topic is to design innovative and appropriate online discussion strategies that assist learners in attaining a deeper level of interaction and higher cognitive skills. By analyzing the process of online discussion in depth using a specific instructional strategy, we may discover the characteristics and limitations of this strategy. This case study utilizes an online discussion activity adopting a role-playing strategy in a college course and conducts an empirical analysis to explore and evaluate both the content structure and behavioral patterns in the discussion process. We propose and adopt a new method of multi-dimensional process analysis that integrates both content and sequential analysis, whereby the dimension of interaction and cognition are analyzed simultaneously. Furthermore, we discuss the patterns, characteristics, and limitations of the roleplaying discussions and provide suggestions as references for teachers who utilize online role-playing discussion activities. Keywords Online discussion, Role-playing, Behavioral pattern, Instructional strategy Introduction Instructional Activity Utilizing Online Role-playing Discussion Online discussion instructional activities have been widely applied in higher education courses. Furthermore, their effectiveness has been extensively discussed by several researchers (e.g., Gilbert & Dabbagh, 2005; Hou et al., 2007; Yeh, 2010). Instructional activities based on online discussions and appropriate teaching strategies may include the development of the learner’s argumentation skills (Driver et al., 2000; Oh & Jonassen, 2007). This approach may help learners gain a deeper understanding and develop higher cognitive skills. As a result, a crucial research topic relates to designing customized, innovative online discussion strategies that facilitate teaching and allow learners to reach a deeper level of knowledge construction and develop advanced cognitive skills. The quality of online discussions is often influenced by the design of the underlying interactive mechanisms (Gilbert & Dabbagh, 2005). There are several interactive learning strategies that are used in discussion-based online teaching activities, such as peer assessment (e.g., Hou et al., 2007) and problem solving (Hou et al., 2008; Oh & Jonassen, 2007). By analyzing the process of online discussion within these strategies, the characteristics and limitations of each strategy can be identified because teachers may not be able to predict the ideal timing for intervention during online discussions (Mazzolini & Maddison, 2007). The findings of the process analysis may help teachers choose better strategies, intervene in discussions at more opportune times, and develop improved facilitative mechanisms that address known limitations. Many behavioral analyses of online discussion activities (Hou, 2010; Hou et al., 2007, 2008; Jeong, 2003) provide insight into process limitations when students conduct discussions without teacher guidance. They also offer teacher guidance strategies based on behavioral patterns such as interactive mechanism design and proper timing of guidance. Furthermore, developers of educational software may also refer to these findings to develop appropriate tools, such as designs with automatic behavioral analysis technology, which can be used to automatically detect behavioral patterns (Hou et al., 2010). In addition to problem solving and peer-assessment interactive strategies, another teaching strategy involves asking students to learn through role-playing. This approach has been increasingly researched in recent years (e.g., Bos & Shami, 2006; Hou, 2011; Wishart et al., 2007). This type of interactive learning is commonly used to help learners develop skills to handle group decision-making (Bos & Shami, 2006; Pata et al., 2005). Some studies suggest that role-playing also keeps learners motivated (Wishart et al., 2007) and improves communication skills among professionals (Chien et al., 2003). Furthermore, this strategy helps students develop abilities in problem solving by requiring them to assume different roles and confront unstructured problems in scenarios involving the professional domain of the given role. ISSN 1436-4522 (online) and 1176-3647 (print). © International Forum of Educational Technology & Society (IFETS). The authors and the forum jointly retain the copyright of the articles. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear the full citation on the first page. Copyrights for components of this work owned by others than IFETS must be honoured. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from the editors at kinshuk@ieee.org. 211
Several studies explore the roles that students play during the process of collaborative learning (e.g., De Wever et al., 2008; Hara et al., 2000; Strijbos et al., 2004, etc.), such as source searcher, theoretician, summarizer and moderator (De Wever et al., 2008). However, these studies rarely focus on the cognitive process of online teaching activities in which students simulate real-life scenarios in assorted roles designated by teachers. The behavioral pattern of cognitive dimension and knowledge interaction in this role-playing learning process requires a more in-depth exploration. Role-playing should also be amenable to applications in discussion-based online teaching in which a teacher assigns roles and tasks to students (or students decide for themselves), and the students play the roles (such as requiring students to role-play different positions in a company) and discuss a given task such as solving the company’s management issues. The purpose of this approach is to increase student interactions during social knowledge construction and produce higher cognitive skills through realistic discussions. There are limited studies on the behavioral patterns of discussion-based online teaching involving role-play tasks. Hou (2011) examined the cognitive dimension of online role-play discussion activities, and concluded that students are better at the diversity of the cognitive aspect when role-playing in problem-solving tasks/scenarios. However, this study focused only on cognitive dimension analysis, and not the in-depth research on knowledge interactive behavior between the roles. Therefore, an important and interesting topic for research is to explore the characteristics and limitations of both the interaction and cognitive patterns in online role-playing discussion activities. The research presented in this paper comprises an empirical case study exploring the use of role-play in discussion-based online learning in a higher education course. To analyze both the content of the discussion and the interactive behavior of its participants from multiple dimensions, we propose an approach that integrates the dimensions of interaction and cognition. A Multi-dimensional Process Analysis of the Interactions and Cognition of Online Discussions Many studies have explored the process of computer-assisted collaborative learning and developed various analytical methods and frameworks (e.g., Daradoumis et al., 2006; Hou et al., 2008). Furthermore, the analytical methods for online learning processes have gradually shifted towards the integration of multiple and diverse trends, such as the layered framework for evaluating online collaborative learning interactions proposed by Daradoumis et al. (2006). This framework includes a multidimensional analysis of social interaction and learning achievement. Hou et al. (2008) also explored the behavioral patterns in online teaching and learning activities that involve knowledge construction and the problem-solving process. Numerous studies have analyzed the process of online discussion as a teaching tool (e.g., Hou et al., 2008; Jeong, 2003). Furthermore, several studies have explored the frequency of interaction in online learning (Black et al., 2008), or conducted social network analysis to understand the state of the learners’ social interaction (e.g., Zhu, 2006). However, when focusing merely on the analysis of discussion frequency or social interaction status of online learning activities, the exploration on the “content of interaction” of the discussion is limited. To address this issue, an analysis of the content of discussions may help us gain a deeper understanding of a given discussion activity and the behavior of its participants. Quantitative content analysis has been used in several studies to explore the online discussion process (e.g., Gunawardena et al., 1997; Jeong, 2003). In those studies in which the messages of a learning community’s discussion were coded, the collected data allows for further analysis that can increase the understanding of the content structure of the entire discussion. However, simply understanding the content of discussions does not reveal much about behavioral patterns and does not allow us to understand the process of “content-related interactions”. To explore both content structure and behavioral patterns, we have therefore integrated content analysis and the lag sequential analysis (Bakeman & Gottman, 1997) as a way to infer sequential behavioral patterns based on discussion content. During the overall discussion process, sequential analysis allows us to determine whether students would further conduct a certain cognitive aspect of discussion after completing another cognitive aspect of discussion. Several studies apply sequential analysis to behavioral analysis regarding teaching and learning (e.g., Hou et al., 2007, 2009; Jeong, 2003; Sung et al., 2008). However, most of these studies only involve sequential analysis of behavioral patterns using a single coding scheme, and they rarely involve a sequential analysis in which additional dimensions are coded simultaneously. Using more dimensional schemes may better demonstrate behavior sequences when the discussion is influenced by multiple factors or may enable improved interpretation of the characteristics and limitations of the given discussion strategy. Furthermore, sequential analyses of online discussions that cover both “interactive” and “cognitive” dimensions simultaneously are limited. Most of the analyses that have focused on knowledge construction interaction tend to address the learning community’s “interactive” behaviors such as sharing, coordination, and joint knowledge construction (e.g., Gunawardena et al., 1997; Hou et al., 2007). This paper provides an in-depth exploration of the “cognitive” processes during discussions including the phases of remembering, understanding, and analyzing, as 212
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January 2012 Volume 15 Number 1
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Supporting Organizations Centre for
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Analyzing the Learning Process of a
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Angeli, C., & Valanides, N. (2012).
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ased on an integration and evaluati
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ased on the assumption that no sing
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Diagram type D thinking (shown in F
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collaborative task in terms of inte
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Table 4. Descriptive statistics for
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Hong, N. S., Jonassen, D. H., & McG
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commercial off-the-shelf digital ga
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The post-questionnaire was used aft
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Phase Table 1. Phases and activitie
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Research results Describing student
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Assessment results for each one of
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References Annetta, L.A., Minogue,
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Huang, T.-W. (2012). Aberrance Dete
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Detection power Typically, the rela
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Results Detection rates As seen in
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2 .00 .05 3 .02 .10 1 .60 .40 ECI 4
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their easily understandable devices
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Ifenthaler, D. (2012). Determining
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activities is assumed to be reflect
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that the problem representations (i
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deductive reasoning inventory (33 m
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HIMATT structural measures The part
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outcomes, r = .297, p < .01. Accord
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Azevedo, R. (2009). Theoretical, co
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Seel, N. M. (1999b). Educational se
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Initially this paper explores defin
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gamers were boys, and 35% were girl
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Teachers underwent eight hours of p
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A correlation analysis was used to
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playing the game. This would mean P
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Yu, F. Y. (2012). Any Effects of Di
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Considering that identity concealme
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In the nickname group, the student
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Attitudes toward assessors Percepti
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Liu, Z. F., Chiu, C. H., Lin, S. S.
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Appendix A: Attitudes toward Peer-A
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Appendix C: Perception toward the I
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Han, H., & Johnson, S. D. (2012). R
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Research participants The target po
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that its value of cross-loading is
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The canonical variable for the emot
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Literature in the field of online l
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Cornelius, R. R. (1996). The scienc
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Shih, W.-C., Tseng, S.-S., Yang, C.
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To provide personalized (e-)learnin
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Figure 3. An illustrative example o
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elementary schools. Thus they are b
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Table 5. Descriptive statistics for
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References Abowd, G. D., & Atkeson,
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Tseng, K.-H., Chang, C.-C., Lou, S.
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Figure 1. The five stages of KT (Gi
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The use of learning strategies, mon
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Additionally, the above two scales
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Figure 3. Canonical structure betwe
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highly positive perception of CM an
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Langan-Fox, J., Platania-Phung, C.,
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Gömleksiz, M. N. (2012). Elementar
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any statistically significant diffe
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consideration in an educational set
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conditions of a learning environmen
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Kahle, J. B. (1983). The disadvanta
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Appendix A Science and Technology S
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(Guuawardena, Nola, Wilson, Lopez-I
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Questionnaire on student satisfacti
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G4 53 (12.5) 48.9 (13.4) 0.39 G5 51
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across the five levels of knowledge
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Santhanam, R., Sasidharan, S., Webs
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This paper reports only the early e
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Figure 1 illustrates how the abovem
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teacher/instructor continues to dom
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The instructor plays a significant
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Data collection and data sources Fi
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y turning over the responsibility t
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Brophy, J. (1999). Toward a model o
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Lawanto, O., Santoso, H. B., & Liu,
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such as grades and evaluation by ot
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students’ interest, expectancy fo
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constructs that measure students’
Page 165 and 166: Bandura, A. (1978). Reflections on
Page 167 and 168: Lin, J. M.-C., & Liu, S.-F. (2012).
Page 169 and 170: Table 1. The MSWLogo commands learn
Page 171 and 172: other words, we had prepared a set
Page 173 and 174: lab. When she returned and found th
Page 175 and 176: Attitudes toward Programming and Co
Page 177 and 178: Table 5 successfully, it took four
Page 179 and 180: Kuter, S., Altinay Gazi, Z., & Alti
Page 181 and 182: not only the means for trainees’
Page 183 and 184: Data Collection Techniques and Anal
Page 185 and 186: organizations was highlighted by on
Page 187 and 188: provides the means for professional
Page 189 and 190: Kohonen, V. (2001). Towards experie
Page 191 and 192: As Rogers (1995) postulates in his
Page 193 and 194: Instrument and data collection Afte
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Page 197 and 198: teachers deploy ICT tools in langua
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Page 201 and 202: APPENDIX A. Questionnaire for Dista
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Page 211 and 212: Table 3 shows that the mean frequen
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Page 219 and 220: understand the content structures o
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Page 229 and 230: 2009; Bernard & Cathryn, 2006) or p
Page 231 and 232: In this study, the focus of the ins
Page 233 and 234: The student’s degree of mastery i
Page 235 and 236: The questionnaire for the acceptanc
Page 237 and 238: the t-test result, it is found that
Page 239 and 240: Scale Questionnaire item Mean S.D.
Page 241 and 242: Hwang, G. J. (2003). A conceptual m
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Page 245 and 246: Virtual learning environment In the
Page 247 and 248: Class Table 1. The survey questions
Page 249 and 250: “…the distance and the lack of
Page 251 and 252: collaboration within EVS, thus prev
Page 253 and 254: Huang, T.-H., Liu, Y.-C., & Chang,
Page 255 and 256: y personalised context examples in
Page 257 and 258: Students’ Problem-Solving Guidanc
Page 259 and 260: Research Method Figure 8. The scree
Page 261 and 262: Table 4. Abstract of Pairwise Compa
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Page 265 and 266: Lee, J. (2012). Patterns of Interac
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This study focused on online fora i
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Results and Discussion Due date-cen
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In addition, all groups developed 8
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future. Therefore, they had an oppo
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surprising that most interaction wa
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Pawan, F., Paulus, T. M., Yalcin, S
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systems by using 18 personalization
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Short-Term Sensory Memory is a temp
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with respect to articles by using a
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system also computes a review value
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DFL. d ( yi ) is the degree of mem
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the experimental group can understa
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Finally, Figure 10(A) and Figure 10
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Appendix # Question Description Par
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mental models, are cognitive struct
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Based on the theoretical implicatio
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Data analysis To test the model fit
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may be considered an effective inte
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Hsu, I.-C. (2012). Intelligent Disc
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(Gradinarova, Zhelezov et al. 2006)
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RLO denotes a remote learning objec
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Figure 2. The flow-oriented LOFinde
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if x include with y, and y include
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Similarly, the relation metadata of
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(defrule student-advisor (triple (p
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References ADL. (2006). Sharable Co
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determinants of how people think, b
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Mediating effect of learning flow A
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The instrument used to measure lear
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addition, the cross-loadings of the
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and ease of use had significant eff
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Connell, J. P., Spencer, M. B., & A
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Despotović-Zrakić, M., Marković,
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Learning is a cognitive activity th
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The survey consisted of 30 question
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Created data mining model needs to
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dealt with the matter taught at the
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Conclusion Conducted research showe
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Romero, C., Ventura S., García E.,
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prompts as scaffolding strategy to
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Reflection Types This study attempt
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All the three groups completed the
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Results Learning Outcomes Pre- and
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Map Analysis in Transfer Test Figur
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Generic Prompts and Specific Prompt
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Biswas, G., Schwartz, D., Bransford
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Chen, Y.-H., Looi, C.-K., Lin, C.-P
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of correct response, answer until c
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Figure 7 shows a screenshot of one
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Collaboration Questionnaire results
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following diagrams. The double-arro
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“Most students were encouraged to
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Bangert-Drowns, R.L., Kulick, C. C.
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primary research questions. First,
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Individual learning Figure 2. Scree
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Experimental Tools This study emplo
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Table 3 shows that almost all items
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I usually engaged myself in listeni
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References Bloom, B. S. (Ed.). (195
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APPENDIX 2. Taxonomy for Informatio
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understand how we can effectively u
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5. Is there a relationship between
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correlation between teachers’ IWB
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Q7. IWB provides advantages to me t
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training regarding this topic. This
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Conclusion This study provides a so
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Smith, H. J., Higgins, S., Wall, K.
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