Research and Development in Higher Education: [Re] Valuing Higher Education

Volume 41

Refereed papers from the

41st HERDSA Annual International Conference

2-5 July 2018

Convention Centre, Adelaide, Australia

Lim, L., Andrew, Jane., Lewis, S. & Gao, J. (2018). Interdisciplinary teamwork in an authentic project-based learning environment. In D. Wache and D. Houston (Eds.), Research and Development in Higher Education: (Re)Valuing Higher Education, 41 (pp 1 - 13). Adelaide, Australia, 2-5 July 2018. Published 2018 by the Higher Education Research and Development Society of Australasia, Inc PO Box 6106, Hammondville, NSW 2214, Australia www.herdsa.org.au ISSN 1441 001X ISBN 978-0-908557-96-7

This research paper was reviewed using a double blind peer review process that meets DIISR requirements. Two reviewers were appointed on the basis of their independence and they reviewed the full paper devoid of the authors’ names and institutions in order to ensure objectivity and anonymity. Papers were reviewed according to specified criteria, including relevance to the conference theme and audience, soundness of the research methods and critical analysis, originality and contribution to scholarship, and clear and coherent presentation of the argument. Following review and acceptance, this full paper was presented at the international conference.

Copyright © 2018 HERDSA and the authors. Apart from any fair dealing for the purposes of research or private study, criticism or review, as permitted under the Copyright, Designs and Patent Act, 2005, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers at the address above.

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Interdisciplinary teamwork in an authentic project-based learning environment

Lisa Lim

University of South Australia, Adelaide, Australia Lisa.Lim@unisa.edu.au

Jane Andrew

University of South Australia, Adelaide, Australia Jane.Andrew@unisa.edu.au

Sally Lewis

University of South Australia, Adelaide, Australia Sally.Lewis@unisa.edu.au

Jing Gao

University of South Australia, Adelaide, Australia Jing.Gao@unisa.edu.au

Abstract A recent trend in higher education relates to the promotion of creative skills through the adoption of interdisciplinary group work pedagogies. Although research has documented the benefits of teamwork on students’ learning outcomes, what is less well-known is how students from different disciplines interact with one another to develop a creative product. This paper reports on a study examining group interactions occurring within an interdisciplinary project involving Graphic Design, Communication, Information Technology and Psychology students. Insights into the group dynamics were determined from student self-reports; team interaction data (collected from sociometric devices) and online engagement activity (derived from the online collaboration platform). Analyses of the sociometric data demonstrated significant interdisciplinary interactions occurring in face-to-face sessions, and that over time, verbal participation became more equal among students in the various disciplines. Analysis of online collaboration activity suggested that students from a Graphic Design background were the most active, while Communication students, though very active in face-to-face sessions, were the least active in online collaboration. Self-reports suggest that students had a positive experience of the course, and reported learning gains with respect to group work skills, learning gains, and helping them to be work-ready. A further contribution of the paper is in the use of learning analytics to complement student perceptions of the interdisciplinary teamwork experience. Implications for future implementations of the interdisciplinary teamwork approach are discussed.

Keywords: interdisciplinary teamwork, group work, learning analytics,

sociometrics

Introduction To work effectively in multi-disciplinary teams is an important graduate skill (Trilling & Fadel, 2009). However universities are often criticised for not adequately equipping students with the

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prerequisite teamwork skills to be quickly productive in the work place (Bridgstock, 2017). While higher education is seeing an increase in the use of group work pedagogies (Bravo, Lucia-Palacios, & Martin, 2016), this has been received with mixed responses by students. Research suggests that while students recognise the benefits of teamwork in preparing them for professional practice and developing important skills to work with others, these benefits are marred by negative experiences in undertaking assigned group work (Tucker & Abbasi, 2016). This paper reports on student interactions occurring in an interdisciplinary course employing group work. The aim of this study was to examine the student’s experience of interdisciplinary teamwork and interactions between team members whilst undertaking a client focused, project-based learning course. The framing of this work is referenced to Bridgstock’s (2017) networked approach to graduate employability, which highlights the importance of social activities such as collaborative problem-solving in developing important graduate employability skills.

Background and context Interdisciplinary teamwork as a pedagogical approach Historically in education, group-based learning has been implemented in various ways. More recently, there has been an emerging interest in employing interdisciplinary group work in educational settings (Heikkinen & Isomöttönen, 2015). This trend reflects the growing prevalence of interdisciplinary teams in organisations, as a solution to increasingly dynamic environments (Benoliel & Somech, 2015) and to foster creative skills (Yong, Sauer, & Mannix, 2014). Research on the outcomes of interdisciplinary teamwork pedagogy has shown a number of benefits of this approach including, but not limited to, contextualising and building knowledge within and outside of students’ own discipline (Hirsch et al., 2001; Lau, Jones, Ng, & Shum, 2012; Rhee, Cordero, & Quill, 2010), and developing ‘soft’ skills such as communication and teamwork (Jaccheri & Sindre, 2007), as well as professional identity (Crosby & Morgan, 2017). Whilst students generally find working with such diverse teams to be a largely positive experience, they also report that it can be challenging (Kruck & Teer, 2009; Öncü & Özdilek, 2013). Group conflict can be seen to be a major challenge when working in diverse team settings. Conflict among team members is an obvious obstacle to overcome when leveraging the benefits associated with interdisciplinarity. Research by Yong et al (2014) on graduate-level project teams extrapolates this point further in noting that not all group conflict is negative. The authors distinguish between task conflict (i.e., differing viewpoints) and relationship conflict (i.e., interpersonal clashes), reporting that task conflict, not relationship conflict, is beneficial for creativity. Further teamwork challenges to be addressed relate to the divergent values, goals, and interests of members that can make up an interdisciplinary team. This divergence of views, values and modes of operation can create a form of social barrier that stifles productive communications and prevents what might otherwise be a synergistic relationship (Benoliel & Somech, 2015). The capacity for group members to quickly build productive working relations in a short period of time is necessarily a trait of effective interdisciplinary teams (González-Carrasco et al., 2016). These challenges highlight the importance of monitoring team processes in these settings, to ensure that the interdisciplinary experience is optimised. Advances in research on teamwork processes The studies described above have typically relied on students’ self-report to describe team processes and experience in working in an interdisciplinary team. Given the many self -report

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instruments available to capture different aspects of teamwork processes and outcomes, measuring teamwork engagement by students in field settings can become an arduous task. Furthermore, accuracy in recall of events may be affected by delay, making self-reports, though valuable, prone to error. With the advancement of technology, new methods of capturing teamwork processes have been developed, which could complement retrospective self-reports. Sociometric badges, for example, are wearable devices that collect minute-by-minute data on the wearer’s movement, interactions, and speech data through in-built accelerometers, Bluetooth and infrared sensors, and microphones (see Jayagopi, Kim, Pentland, & Gatica-Perez, 2012). The badges do not collect the verbal content of the participants, thereby safeguarding the privacy of conversations within the team. Each sociometric badge is numbered to provide further anonymity in the collection process. Sociometric badges are an emerging area in team research, from which data collected are potentially powerful for gleaning insights into real-time team process dynamics (Santoro, Dixon, Chang, & Kozlowski, 2015). To the best of our knowledge, no research has employed sociometric badges to capture student interactions in an interdisciplinary teamwork pedagogy. While sociometric badges collect information about team processes in face-to-face settings, online learning interactions are missed. The rise of Web2.0, social media, and advances in information and communication technologies have provided the affordances of synchronous and asynchronous online teamwork collaborations. In addition to the learning management system (LMS) which is being increasingly used in higher education as a collaborative learning platform, other online project management web-based applications are emerging, such as Basecamp and Slack, that provide discussion spaces, facilitate synchronous chats, and allow file sharing among team members in one space. All user activity data are captured and documented in the online environment. The extraction of these activity logs can allow for the monitoring and review of team and individual team members’ progress. With the growing availability of ‘big data’ in education settings, research in Learning Analytics (LA) emerged from the related disciplines of business intelligence and data mining, and computer science. An attraction of LA is the capacity to automate the collection and analysis of vast amounts of data in order to generate insights into learning. To date, much LA research has focused on interpreting and modelling online user activity logs or trace data to predict student academic performance (Dawson, et al 2014). LA work on social learning has also explored the LMS interaction data mined from discussion forums. The focus of such research is on understanding learner networks through the use of social network analysis (SNA) (Bakharia & Dawson, 2011; De Laat, Lally, Lipponen, & Simons, 2007; Dowell et al., 2015). This approach can be used to provide insights into the impact of social learning processes on student academic performance, sense of community and teaching quality for example. However, few studies have leveraged LA approaches to understand teamwork processes and outcomes. For example, Zhang et al. (2017) analysed teamwork engagement in Slack but did not extract the actual clickstream data to examine students’ teamwork engagement. Instead, the authors relied on students’ self-reports to measure perceived effectiveness of teamwork engagement. Potentially more insights into the team’s interactions and processes could have been gleaned by interrogating the actual log data. Further research is needed to explore the potential of online user engagement data for measuring and understanding teamwork processes.

Course description: Peer2Peer at University of South Australia In this section, we introduce the interdisciplinary course - the focus of the present paper. First conceived in 2016, Peer2Peer is an annual, interdisciplinary student project-based course drawing on Bridgstock’s (2017) tenets of the experiential, networked approach to learning. The

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main learning objective of the course was to develop important graduate employability skills such as collaboration skills. The present iteration of the program brings together students from the Communication, Graphic Design, IT and Psychology disciplines to research, develop and implement a social media campaign, in order to meet the requirements of real clients. In 2016 students addressed the challenge of young people becoming involved in online violent extremism, while in 2017 students worked with the South Australian Police to address growing public concern on the issue of safe partying. Students were divided into interdisciplinary teams for the duration of the course. During the course, students were required to attend two-hour weekly seminars and workshops, facilitated by course coordinators and invited experts to contextualise and inform the teams’ approach to the project task. During workshops, students worked within their teams to complete the project; the course coordinator delivered a short seminar on an aspect of Design Thinking and spoke with each team on their progress. The teams were also expected to work independently by continuing their collaboration online and self-organised face to face sessions. Throughout the course, students opted to use either Basecamp or Facebook, or both, to communicate with each other via discussion forums and chats, coordinate team tasks, and share documents. Both platforms kept records of all activities, and could be seen only by team members. If the course coordinators or researcher wanted access to the online group activities, requests had to be made to the individual team leaders; in such cases, team leaders were free to either decline the request or grant access. In the final week of the course, teams were required to pitch their social media campaign to an expert panel and given a team score. At this point, student feedback on the course was collected through an online survey. Students were also assessed individually at discipline level based on their participation and specific learning outcomes. Two weeks before the formal pitch, teams were also given the opportunity to present their project ideas to other teams, as a way to get feedback and to improve on their project and final presentation to the client.

Aim of the study The main aim of this study was to examine the effectiveness of this new model of interdisciplinary project course. This paper reports on the second offering of the course in 2017, with the following research questions guiding the study: 1. To what extent do students from different disciplines interact with each other in this

interdisciplinary environment? 2. What is the impact of this interdisciplinary project course on students’ attitudes toward

group work? 3. What is the impact of this interdisciplinary project course on students’ skills in group

work? 4. What are students’ perceptions of this interdisciplinary, team-based approach to

learning?

Method To achieve the aim and research questions, a one-group quasi-experimental pre-test, post-test design was employed. In this design, careful measurement of attitudes to teamwork and teamwork skills was done before the teamwork commenced, and then again at the end of the course at Week 11. The advantage of this methodology is that changes in the measured variables may be attributed to the project-based learning experience, though with less certainty because of the lack of a control group. To compensate for this, additional data on team processes were collected to provide further insights into the results.

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Participants The second offering of this course saw a total enrolment of 40 students across 4 different disciplines, divided into 5 teams. Table 1 provides a breakdown of students by discipline for each team. This course was offered at an undergraduate level. Approval for this study was obtained under the institution’s ethical guidelines for the evaluation of standard educational practices.

Table 1: Breakdown of students by discipline and team

Team Discipline Total

Communications Graphic Design

Information Technology

Psychology/Social Work

1 4 1 3 1 9

2 3 1 2 1 7

3 4 1 3 1 9

4 4 1 2 1 8

5 3 1 2 1 7

Total 18 5 12 5 40

Data collection The study collected data on student interactions with each other, as well as data on perceptions of group work attitudes, skills for teamwork, and the course. As such, data from a number of online and offline sources were collected. Sociometric badges and a mobile app, Speakernizer, were used to investigate how students’ interactions during face-to-face sessions. The Speakernizer app collects data on who is speaking, and generates a list of the speaker by second. Data from these two sources were collected at three points in time: at Week 2 (Session 1), Week 5 (Session 2), and Week 8 (Session 3). Students were informed about the purpose of the badges and Speakernizer during the workshop in Week 2. Individual students were assigned one sociometric badge each, while one team member from each team downloaded the Speakernizer app into their own mobile phone. Students could elect to abstain from participation in the data collection process by not using the devices. Non-participating students were not penalised for their decision. Activity data from Basecamp were collected to measure how team members interacted online. Only data from Teams 1, 3, and 5 were collected, as the remaining two teams chose not to share access to their Basecamp information with the researcher. On the user’s page, Basecamp provides a vertical timeline that records every activity performed by team members; each activity is timestamped, providing a ready logbook of how each team member is interacting and contributing within their team (See Figure 1).

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Figure 1: Sample of Basecamp activity timeline as seen on a user’s page Attitude to group work was measured by a teamwork attitudes survey (Vance, Konak, Kulturel-Konak, Kremer, & Esparragoza, 2015). This 9-item scale elicits students’ level of agreement with statements such as “I like to participate in teamwork” and “Teamwork improves the quality of final project outcomes”. Skills for teamwork was measured by the self-efficacy for teamwork scale (Eby & Dobbins, 1997). This scale comprises 8 items, for example, “I can work very effectively in a group setting” and “I am able to resolve conflicts between individuals effectively”. In this study, both scales used a 5-point Likert scale. These surveys were administered through the survey platform Qualtrics, at Week 1 and then again at Week 11. Survey completion was anonymous. Students’ course perceptions were sought at the end-of-course survey administered with the two attitude surveys, at Week 11. This survey comprised 8 items asking students to rate their level of satisfaction with the course with respect to their learning, the teaching, workload, and feedback. Responses were anonymous and on a 5-point Likert scale. The survey also asked for the respondent’s team membership and discipline, as these were variables of interest in the study. An additional question asked students to indicate the extent (in %) the team used the online platforms for their collaboration during the course. Three options were provided: Basecamp, Facebook, and Other students had to respond such that the options equated to 100%. Finally, the survey also asked an open question about perceived learning gains from participating in the interdisciplinary project.

Data analysis Quantitive survey data were analysed using SPSS. Pre- and post- attitudes data were compared using independent samples t-tests. Where possible, differences between teams and disciplines were explored using non-parametric procedures, due to the small subsamples (see Table 1). Interactions data were visualised using Excel and social network analysis (SNA) in Gephi. Aligning with the purposes of this study, only the adjacency matrix from the turn-taking output of the sociometric badges was analysed, with further explorations of the resulting SNA

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metrics conducted in SPSS. Speakernizer data were available for all teams in Session 1, however, in Sessions 2 and 3, students from a few of the teams who were in possession of the app were absent, and therefore no data were available for those teams. In addition, due to technical issues with a few of the sociometric badges, no data were available for those individuals. In the end, only Team 4 had Speakernizer data for all 3 sessions.

Results Team interactions Face to face sessions Figure 2 shows an example sociogram for Team 4, generated from the turn-taking output derived from the badges for Sessions 1 and 2. Node colour represents the discipline of the student, node size reflects the extent to which the student is connected (number of interactions with others), and line thickness represents the frequency of interactions between two students. The figure shows interactions occurring between all members of the team. Edge (i.e., line) thickness also shows how interactions between team members have become more frequent between Session 1 and Session 2. Similar patterns were found for all teams except for Team 5, which had missing data due to technical issues. A closer look at the edges shows how, initially in Session 1, there was frequent communication between the two IT students (P22 and P25). However, in Session 2 the interactions were extended to other team members. The relative node sizes also show that P22 (IT) tended to be more interactive, whereas the team leader (P32) was not as connected as other team members.

Figure 2: Interdisciplinary interactions for Team 4 across Sessions 1 and 2 To measure how teams interacted differently, the average weighted degree (normalised) for each team was calculated for Sessions 1 and 2 (see Figure 3). The weighted degree statistic indicates the number of connections that a participant has, and therefore is a measure of popularity or influence of the individual (De Laat et al., 2007). When averaged across team members, this provides the weighted average for the team, providing a useful comparison across teams. A higher average weighted degree indicates greater level of shared interactions. One-way between-groups ANOVA showed that the teams differed in their average weighted degree for Session 1 (F(4,19) = 22.53, p < .001, η2 = .83), as well as for Session 2 (F(4,25) = 47.561, p < .001, η2 = .884). For both sessions, Team 4 had the highest average weighted degree. Disciplinary differences had no impact on average weighted degree.

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Figure 3: Average weighted degree (normalised) by team for Sessions 1 & 2 Next, ANOVA was conducted on Speakernizer data, to explore disciplinary differences in verbal participation during face-to-face meetings, as measured by proportion of speaking time. Figure 4 shows the percentage of speaking time by discipline, during the 3 sessions. The analysis found significant differences for Session 1 (F(3,31) = 3.852, p = .02, η2 = .27; Session 2 (F(3,13) = 6.325, p = .007, η2 = .59. There was found to be no impact of disciplinary difference for Session 3 (F(3,9) = 1.47, p = .29). Thus, Psychology (i.e., team leaders) and Communications students initially dominated the first 2 sessions, however, by the 3rd session, participation had evened out.

Figure 4: Average proportion of verbal participation by discipline over 3 sessions

Notes. 1. Numbers are in percentages.

2. Only 1 Graphic Design student was present in each of Sessions 2 and 3.

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Online collaborative activities Table 2 shows the mean percentage of each online platform used for each team, as reported in the end-of-course survey. Basecamp engagement data for Teams 1, 3, and 5 were used to measure the degree inter-member interactions. The main metric for this analysis was the frequency of posts. A total of 278 posts was recorded from Team 1, 114 posts from Team 3, and 274 posts from Team 5.

Table 2: Online platform used for collaboration (average team percentage)

Team No. of respondents

Basecamp Facebook Other

1 6 89% 9% 2%

2 7 22% 74% 4%

3 5 36% 48% 6%

4 3 23% 77% 0%

5 4 98% 0% 2%

To explore disciplinary differences in posting behaviours, an ANOVA on frequency of posts with team was conducted (see Table 3). This analysis found that the difference in posting frequency was significant (F(3,21) = 5.857, p = .005, η2 = .45). On average, Communications students posted the least (M = 5.05, SD = 3.37), while Graphic Design students posted the most (M = 25.1, SD = 8.99).

Table 3: Percentage contribution to Basecamp activities by discipline

Discipline N Mean percentage

contribution SD

Graphic design 3 25.1 8.99

IT 8 13.34 13.23

Psychology 3 11.98 10.96

Communications 11 5.05 3.37

Student perceptions Change in group work attitudes and teamwork skills There were 34 (85% response rate) usable responses to the pre-survey, and 25 (63% response rate) to the post-survey. Cronbach’s alpha for the group work attitudes and teamwork skills scales were .76 and .73 respectively, making both scales acceptable for further analysis. Independent samples t-tests found a significant difference in self-reported scores for teamwork skills at the start of the course (M = 3.44, SD = .24) and at the end (M = 4.05, SD = .68; t(57) = -4.23, p < .001). The magnitude of the difference in the means (mean difference = .60) was large (η2 = .20). There was no difference between pre- and post- scores for group work attitudes (t(57) = -.75, p = .46).

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Course perceptions Data from the 8 items in the end-of-course survey were taken for analysis. Figure 5 shows the overall mean scores (n = 24) for each item, as well as by team. The two highest rated items related to teaching staff, while the lowest rated item related to feedback. Overall the results indicate that the students had a positive experience of teamwork in this interdisciplinary project course. No significant differences due to team or discipline were found.

Figure 5: Mean ratings on course perception survey items Correlation analysis found that item 8—a measure of overall satisfaction—was most highly correlated with items 1 (r = .86, p < .01) and 4 (r = .89, p < .01), suggesting that the aspect that made this inter-disciplinary project course a valuable experience was the learning gain.

Perceived learning gain To examine what students learned from this interdisciplinary approach, the anonymous qualitative responses (n = 22) to the open-ended survey question were taken for analysis using open-coding. The most frequently occurring theme was learning about inter-disciplinary skills and knowledge; in particular students learned about different terminologies and processes such as coding or design. Students also recognised learning about working with diverse groups; a key comment from one student was “It's helped me understand that although we specialise in our own areas, perspectives from alternate disciplines can help shape the product positively”. There was also the idea of work-readiness; students could see how multidisciplinary would be evident in the workplace, how to manage a real project, and they had a better understanding of how to communicate effectively.

Discussion This study aimed to investigate the effectiveness of a new model for facilitating interdisciplinary project-based learning, in terms of the frequency and diversity of team member interactions, overall perceived impact on collaboration skills, and the student experience. To achieve this, data were gathered from self-reports, sociometric devices, and online activity in Basecamp.

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Collectively, the results indicate that the interdisciplinary approach undertaken for the course was effective in promoting group work skills. Results from this study illustrate the phenomenon of functional diversity (Bunderson, 2003), i.e., where the interaction of heterogeneous skills, knowledge, and experience of different team members creates diverse perspectives leading to a greater result (Nemeth, 1986). In this case, functional diversity of the teams facilitated a creative outcome in the form of a social media project. Important disciplinary differences in communication behaviour also emerged from this research. Communications students featured as active participants in face-to-face sessions, while Graphic Design and IT students were noticeably more active in the online space. This finding is important, because it provides evidence that students who appear to be verbally less participative in meetings may not necessarily be ‘free-riders’ in their teams. A possible implication of this finding for the implementation of interdisciplinary teams is that students’ collaboration should be monitored both online and face-to-face; this research has demonstrated how this could be done through sociometric devices and learning analytics. This will also allow for a fairer assessment of individual contributions to group work, encouraging accountability and reducing social-loafing (Tucker & Abbasi, 2016). The positive student experience with interdisciplinary teamwork in this project course resounds with earlier research (Heikkinen & Isomöttönen, 2015; Kruck & Teer, 2009). The main finding with respect to learning gain was that students had first-hand experience with the diversity of working teams and expanded their knowledge beyond their own discipline. The survey also showed that teamwork skills significantly improved at the end of the course, but not group work attitudes. The reason for the latter finding could be that the baseline attitude to group work was fairly high, meaning that this cohort came into the course with positive feelings about group work. From the course satisfaction survey, one area for improvement is the provision of feedback. Feedback is an important part of any student learning experience (Boud & Molloy, 2013); in teamwork, feedback is especially important since students rarely receive guidance on effective teamwork processes (Leshed, Hancock, Cosley, McLeod, & Gay, 2007).

Limitations One challenge of this research is the small subsamples, which made it difficult to pick up differences due to teams and disciplines. Future studies could be conducted with other cohorts to examine the consistency of results. Secondly, we limited our exploration of the sociometric badge data to the turn-taking output. Further research could embark on more in-depth analysis of additional sociometric data, e.g., speaking segments and actual speech patterns which could add further insight into team communication differences and the impact on outcomes (Pentland, 2012).

Conclusion To the best of our knowledge, no other research has employed a learning analytics approach to investigate interdisciplinary teamwork processes. Therefore, the main contribution of this research is in the form of demonstrating how learning analytics might be employed to help shed light on team processes. As interest in the use of interdisciplinary teamwork pedagogies grows, it is necessary to investigate in more depth, the dynamic process of interdisciplinary teamwork, and how to support students in their online and face-to-face collaborations, so that they can harness the benefits of interdisciplinary.

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Acknowledgements This research was supported by a grant from the Teaching Innovation Unit at the University of South Australia. Copyright © 2018 Lisa Lim, Jane Andrew, Sally Lewis and Jing Gao. The authors assign to HERDSA and educational non-profit institutions a non-exclusive license to use this document for personal use and in courses of instruction provided that the article is used in full and this copyright statement is reproduced. The authors also grant a non-exclusive license to HERDSA to publish this document in full on the World Wide Web (prime site and mirrors) and within the portable electronic format HERDSA 2018 conference proceedings. Any other usage is prohibited without the express permission of the authors.