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

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Jo, I.-H. (2012). Shared Mental Models on the Performance of e-Learning Content Development Teams. Educational Technology & Society, 15 (1), 289–297. Shared Mental Models on the Performance of e-Learning Content Development Teams Il-Hyun Jo Department of Educational Technology, Ewha Womans University, 11-1, Daehyun-dong, Seodaemun-gu, Seoul, 120-750 South Korea // ijo@ewha.ac.kr ABSTRACT The primary purpose of the study was to investigate team-based e-Learning content development projects from the perspective of the shared mental model (SMM) theory. The researcher conducted a study of 79 e-Learning content development teams in Korea to examine the relationship between taskwork and teamwork SMMs and the performance of the teams. Structural equation modeling (SEM) was used to analyze the parameter estimations. As hypothesized, the results indicated that interaction among e-Learning ID team members led to higher SMMs (Ed- this acronym has already been defined above) which in turn improved the team performance. Meanwhile, the interaction decreased with the progression of ID projects and with the role differentiation. The implications of the findings and directions for instructional design (ID) practices are discussed. Keywords Shared mental model, role division, team performance, e-learning Introduction In real world instructional design (ID) situations, team-based approaches are common. In e-Learning ID projects where a variety of expertise – e.g., ID, graphic design, and programming - is required, it is often difficult or impractical to find an all-in-one expert instructional designer. In this regard, Jo (2008) suggested that an e-Learning content development project is a typically team-based, ill-structured problem solving task that involves a series of complex, problem solving activities. However, ID settings as considered by most traditional ID theories and models are more logical or individual than collaborative or team-based (Jo, 2008). Most ID theorists, regardless of their epistemological perspectives, assume that their typical research targets are individual designers, not teams. This discrepancy between the theories and real world practices may generate severe challenges to the ID research in e-Learning. Without the provision of relevant theories that explain the team aspects of ID practices, our credibility as application scientists might be challenged. There is growing evidence that the existence of shared mental models (SMMs) among the members of a work team has a great impact on team processes and task effectiveness (Klimoski & Mohammed, 1994; Mathieu, Heffner, Goodwin, Cannon-Bowers, & Salas, 2005). SMMs are socially constructed cognitive structures that represent shared knowledge or beliefs about an environment and its expected behavior (Klimoski & Mohammed, 1994). They influence team member behavior and improve coordination by enabling members to anticipate one another’s actions and needs (Cannon-Bowers, Salas, & Converse, 2005). This notion is particularly important when work events are unpredictable or when frequent communication is difficult (Mathieu et al., 2005), such as in the development of an e-Learning instructional design project (Jo, 2008). Some empirical studies have examined the relationship between SMMs and the team-based software design processes (e.g., Espinosa, Kraut, Lerch, Slaughter, Herbsleb, & Mockus, 2001). However, no reported research has investigated the effect of SMMs in team-based e-Learning ID projects. The purposes of this study are; 1) to suggest a theoretical Model to explain the team-based ID processes in e- Learning content development project settings using the SMM perspective, and 2) to empirically validate the Model and investigate the path relationships among the Model’s structural factors. The results will provide theoretical and practical implications to the increasingly popular team-based ID practices. Literature review and hypotheses Shared Mental Model (SMM) and Team Work Humans create representations of their worlds that are simpler than the entities they represent (Johnson-Laird, 1983) in order to reduce uncertainty in their lives (Klimoski & Mohammed, 1994). These representations, which are called 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. 289
mental models, are cognitive structures that include specific types of knowledge humans use to describe, explain, and predict their surroundings (Rouse & Morris, 1986). Uncertainty is reduced through a heuristic function that individuals use to classify and retrieve the most salient pieces of information about situations, objects, and environments from their mental models (Cannon-Bowers et al., 1993). This process of identifying potential outcomes further reduces uncertainty. A collection of individuals working together as a team also needs mental representations, or SMMs, in order to effectively accomplish their assigned tasks. Thus, the key for a team with diverse expertise to process information more effectively is to generate common understandings or SMMs. SMMs are `knowledge [and belief] structures held by members of a team that enable them to form accurate explanations and expectations for the task, and in turn, to coordinate their actions and adapt their behavior to demands of the task and other team members' (Cannon-Bowers et al., 1993; 228). These cognitive structures are expected to influence the way in which individuals cognitively process new information, both the content of what they process and the speed with which they are able to process it (Walsh, 1995). Thus, by shifting their focus from the individual level to the team level, team members can be better able to complete the project in a manner that is desirable for themselves, their teammates, and the organization. Empirical studies that investigated the SMMs (e.g., Marks, Zaccaro, & Mathieu, 2000) suggest that team SMMs allow members to anticipate one another’s actions and coordinate their behaviors, especially when time and circumstances do not permit overt and lengthy communication and strategizing among team members. Teams who share mental models are expected to have common expectations of the task and team, allowing them to predict the behavior and resource needs of team members more accurately (Cannon-Bowers et al, 2005).Under the team and task circumstances such as e-Learning content development projects, members in teams must rely on preexisting knowledge to predict the actions of their team members and respond in a coordinated fashion to urgent and novel task demands in order to be more productive (Jo, 2008). Multiple SMMs: Taskwork and Teamwork The theoretical literature on SMMs suggests that the members of a team are likely to hold not one, but multiple SMMs (Klimoski & Mohammed, 1994). Although there are many detailed breakdowns of mental-model types, these models can be viewed as reflecting two major content domains: those about taskwork and those about teamwork (Cooke, Salas, Cannon-Bowers, & Stout, 2000; Klimoski and Mohamed, 1994). Taskwork encompasses all activities related to the execution of the task, while teamwork encompasses all activities necessary for teammates to work with each other. Each of these may have different effects on coordination depending on the task. A taskwork SMM describes the content and structure of the team’s specific tasks. A teamwork SMM refers to how team members should interact with each other to accomplish the task and has been adopted by many researchers for representation because different types of projects have similar teamwork SMM content (e.g., Johnson & Lee, 2008). This division is also consistent with the idea that teams develop two tracks of behavior: a teamwork track and a taskwork track (McIntyre & Salas, 1995). Research Hypotheses As previous studies suggest, in project teams, members with different mental models about how tasks should be completed experience difficulty in coordinating their activities. To resolve these differences, team members need to exchange enough information in order to negotiate a mutually agreed upon solution and the means of achieving it. As information is accumulated through interactions such as observation, hearing others' explanations, or adapting one's own models, group mental models are thought to converge over time (Johnson-Laird, 1989; Klimoski & Mohammed, 1994; Mathieu et al., 2005). Thus, researchers insist that interactions among members are a strong facilitator for the creation of the SMMs. The more team members communicate with each other, the more likely they are to form a common frame of reference and develop an SMM (Klimoski & Mohammed, 1994; Lurey & Raisinghani, 2001). Empirical research indicates that interactions among organizational members lead to similar interpretations of team- and task-events (e.g., Schein, 1992). In summary, relevant theories and empirical studies suggest that as team members develop experience with 290
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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’
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Bandura, A. (1978). Reflections on
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Lin, J. M.-C., & Liu, S.-F. (2012).
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Table 1. The MSWLogo commands learn
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other words, we had prepared a set
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lab. When she returned and found th
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Attitudes toward Programming and Co
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Table 5 successfully, it took four
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Kuter, S., Altinay Gazi, Z., & Alti
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not only the means for trainees’
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Data Collection Techniques and Anal
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organizations was highlighted by on
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provides the means for professional
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Kohonen, V. (2001). Towards experie
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As Rogers (1995) postulates in his
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Instrument and data collection Afte
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“Raising our computer skills in C
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teachers deploy ICT tools in langua
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Ma, W., Anderson, R., & Streith, K.
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APPENDIX A. Questionnaire for Dista
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consistency with other ideas, and a
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conceptions, justifying their belie
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each group. The experimental group
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specifically, instructional approac
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Table 3 shows that the mean frequen
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previous Web-based instructional le
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Millar, R., & Osborne, J.F. (1998).
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Several studies explore the roles t
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understand the content structures o
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teacher was not allowed to provide
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two coding schemes, as illustrated
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also very limited. Teachers and sof
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learning activities. British Journa
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2009; Bernard & Cathryn, 2006) or p
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In this study, the focus of the ins
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The student’s degree of mastery i
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The questionnaire for the acceptanc
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the t-test result, it is found that
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Scale Questionnaire item Mean S.D.
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Hwang, G. J. (2003). A conceptual m
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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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Page 269 and 270: Results and Discussion Due date-cen
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Page 291 and 292: Finally, Figure 10(A) and Figure 10
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Page 299 and 300: Data analysis To test the model fit
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Page 303 and 304: Hsu, I.-C. (2012). Intelligent Disc
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Page 317 and 318: References ADL. (2006). Sharable Co
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Page 321 and 322: Mediating effect of learning flow A
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Page 333 and 334: Learning is a cognitive activity th
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Page 341 and 342: Conclusion Conducted research showe
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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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