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Andy Barfield, Collaboration, ELT Journal , Volume 70, Issue 2, April 2016, Pages 222–224, https://doi.org/10.1093/elt/ccv074

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Within the field of education, collaboration comes in many guises: teacher collaboration in the classroom (peer teaching/team teaching), collaborative learning among learners themselves, collaborative research, and collaborative curriculum development, to name some of the most common. Whatever its particular form, collaboration involves deciding goals together with others, sharing responsibilities, and working together to achieve more than could be achieved by an individual on their own. Collaborative learning can be seen to occur through dialogue, social interaction, and joint decision-making with others, and these shared processes contribute greatly to individual and collective growth, as well as to co-constructed understanding and knowledge ( Vygotsky 1978 ). Indeed, one of the major benefits of collaborative teacher development — the mode of collaboration that will be focused on in the remainder of this piece—is that it lets teachers move beyond their own individual viewpoints by working with peers, and thus at the same time lessens their dependency on outside experts ‘to a point where teachers can learn from each other, sharing and developing their expertise together’ ( Hargreaves 1994 : 186).

Collaborative teacher development is founded on dialogue, questioning, and discussion in working together towards educational change and improvement ( Medgyes and Malderez 1996 ). It is at its most effective when it emerges voluntarily and spontaneously from teachers’ own beliefs that ‘working together is productive and enjoyable’ ( Datnow 2011 : 155). To these ends, many different collaborative arrangements are possible, involving face-to-face interaction, digital mediation, or a blended combination; and within the same school or institution, inter-institutionally in the same district, or across wider, more dispersed teacher networks at a local, regional, national, or international level. Such interactions between teachers may be either informal or formal ( Hargreaves and Fullan 2012 ). On the informal side, small actions and episodes such as talking with colleagues between classes, sharing experiences and stories in breaks, and exchanging materials and activities help build open, trusting collegial relationships. This sense of trust and collegiality is fundamental to the authenticity and success of collaboration. More formalized collaborative practices may develop over time, and can include an open-ended variety of practices: collaborative peer groups and critical friendships ( Farrell 2001 ); topic-based groups, school-based groups, reading groups, writing groups, research groups, virtual groups, and teacher networks ( Richards and Farrell 2005 ); exploratory practice groups ( Allwright and Hanks 2009 ); or practitioner research for local book projects ( Barfield 2014 ). In all of these types of collaboration, varying combinations of pair, small-group, and large-group arrangements are possible.

Even in formally constituted entities (for example Special Interest Groups) within teacher associations, relative flexibility and informality of interaction may continue to work well for collaborative teacher learning ( Burns 1999 ; Lamb 2012 ; Barfield ibid.) as they allow for differentiated spontaneous participation, enable different voices to be heard and included, and can nurture involvement across a particular group or network within an association. Importantly, whatever form it takes, such joint learning can foster ‘greater readiness to experiment and take risks’ (Hargreaves op.cit.: 186). Thus, collaboration has the potential to act as a core driver of grassroots educational change.

One central challenge facing teachers interested in collaboration is to find space and time for initiatives to grow in inclusive, local arrangements voluntarily determined by those involved. It is the quality of willing interaction, the openness and honesty of dialogue, and the degree of shared decision-making among participants that open up or constrain the possibilities for collaboration to take deeper root or not (Hargreaves op.cit.). Conversely, if a group is unable to share its decision-making or negotiate its goals among its members, it is unlikely to sustain a sense of mutual and reciprocal benefit. For teachers to remain committed to collaboration, they also need to feel safe in talking openly and honestly about their own practices and concerns to do with their work (op.cit.). A readiness to understand others’ positions, interests, and views, as well as to question their own deeply held beliefs, interests, engrained views, and practices can help teachers engage in the critical reflection necessary for changing and improving their own practices ( Kelchtermans 2006 ). Such considerations are key in the development of collaboration between peers, as is the readiness to acknowledge resistance and to work through potentially divisive issues of conflict in a constructive way. Thus, compromise, articulation of diverse perspectives, and negotiation of different interests are also part and parcel of developing collaborative practices together ( Achinstein 2002 ).

Other important questions include: Who initiates the collaboration? To what degree is the collaboration determined by teachers themselves or imposed from outside? And, in a world where those working in education are being increasingly required to deliver quantifiable results and follow centrally imposed reforms, is the collaboration directed towards teachers’ own longer-term development or the short-term execution of external reforms and agendas ( Hargreaves 1991 ; Hargreaves and Fullan op.cit.)? Addressing such issues together can help teachers navigate some of the wider political tensions surrounding collaboration, as well as find ways to protect their right to develop their professional knowledge and practice for themselves in partnership with others. Finally, a further, more critical approach to teacher collaboration involves raising questions about power relations in language education, and collectively organizing to change these for the benefit of teachers and learners, as well as the local communities that they are part of ( Smyth 2011 ).

Achinstein   B . 2002 . ‘ Conflict amid community: the micropolitics of teacher collaboration ’. Teachers College Record 104 / 3 : 421 – 55 .

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Allwright   D. and Hanks J .. 2009 . The Developing Language Learner . Basingstoke : Palgrave Macmillan .

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Barfield   A . 2014 . ‘ Local engagements enhancing practitioner action and knowledge for learner development and autonomy within a collaborative teachers’ network ’ in Murray G . (ed.). Social Dimensions of Autonomy in Language Learning . Basingstoke : Palgrave Macmillan .

Burns   A . 1999 . Collaborative Action Research for English Language Teachers . Cambridge : Cambridge University Press .

Datnow   A . 2011 . ‘ Collaboration and contrived collegiality: revisiting Hargreaves in the age of accountability ’. Journal of Educational Change 12 / 2 : 147 – 58 .

Farrell   T . 2001 . ‘ Critical friendships: colleagues helping each other develop ’. ELT Journal 55 / 4 : 368 – 74 .

Hargreaves   A . 1991 . ‘ Contrived collegiality: the micropolitics of teacher collaboration ’ in Blasé J . (ed.). The Politics of Life in Schools. Power, Conflict, and Cooperation . Newbury Park, CA : Sage Publications .

Hargreaves   A . 1994 . Changing Teachers, Changing Times: Teachers’ Work and Culture in the Postmodern Age . New York, NY : Teachers College Press .

Hargreaves   A. and Fullan M .. 2012 . Professional Capital: Transforming Teaching in Every School . New York, NY : Teachers College Press .

Kelchtermans   G . 2006 . ‘ Teacher collaboration and collegiality as workplace conditions. A review ’. Zeitschrift für Pädagogik 52 / 2 : 220 – 37 .

Lamb   T . 2012 . ‘ Language associations and collaborative support: language teacher associations as empowering spaces for professional networks ’. Innovation in Language Learning and Teaching 6 / 3 : 287 – 308 .

Medgyes   P. and Malderez A . (eds.). 1996 . Changing Perspectives in Teacher Education . Oxford : Heinemann .

Richards   J. C. and Farrell T. S. C .. 2005 . Professional Development for Language Teachers . Cambridge : Cambridge University Press .

Smyth   J . 2011 . Critical Pedagogy for Social Justice . London : Continuum .

Vygotsky   L. S . 1978 . Mind in Society: The Development of Higher Psychological Processes . Cambridge, MA : Harvard University Press .

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• Research article
• Open access
• Published: 21 December 2021

Patterns of student collaborative learning in blended course designs based on their learning orientations: a student approaches to learning perspective

• Feifei Han   ORCID: orcid.org/0000-0001-8464-0854 1 &
• Robert A. Ellis 2  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  66 ( 2021 ) Cite this article

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This study combines research methods from student approaches to learning research and social network analysis (SNA) to examine patterns of students’ collaborative learning based on their learning orientations amongst 193 postgraduates enrolled in a blended course. The study identified two distinct learning orientations, namely ‘understanding’ and ‘reproducing’, which differed in approaches to learning through inquiry, approaches to using online learning technologies, perceptions of the online workload, and academic outcomes. On the basis of students’ learning orientations and their choice of whether to collaborate and with whom to collaborate, five networks representing five patterns of collaborative learning were found. From these, two did not reveal any collaboration (Understanding Alone and Reproducing Alone networks); and three revealed collaborations (Understanding Collaboration, Mixed Collaboration, Reproducing Collaboration networks). A range of SNA measures were calculated and revealed different features of the three collaboration networks. Viewed together, the combined methodologies suggest that the Understanding Collaboration network has more desirable features of collaboration, such as the intensity of collaboration, having closely knitted groups who tended to seek out and welcome peers and who tended to engage more often in both face-to-face and online modes. The study suggests that helping students adjust their learning orientations, designing some compulsory collaborative assessment tasks, and configuring the composition of collaborative groups are productive strategies likely to improve students’ experiences of collaborative learning.

Introduction

Evaluation of students’ collaborative competence has long been an essential part in higher education quality assurance agenda across countries (Indiana University Center for Postsecondary Research, 2020 ; Neves & Hewitt, 2020 ). An ability to collaborate and work in teams are not only the skills that employers require when they look for workforce ready graduates (Hill et al., 2016 ; Holland et al., 2013 ; Norton et al., 2016 ), but also what students expect to develop in order for them to work competently in teams and to collaborate effectively with their colleagues (Christensen et al., 2014 ). While collaborative competence has been continually emphasized and highlighted in the assessment of students’ learning experience in national frameworks in many countries, such as United States (Indiana University Center for Postsecondary Research, 2020 ), United Kingdom (Neves & Hewitt, 2020 ), and Australia (Department of Education, Skills & Employment, 2021 ); there is ongoing evidence that employers are dissatisfied with graduates’ collaborative skills (Harder et al., 2014 ). One of the examples of the dissatisfaction is demonstrated in the Australian national Employer Satisfaction Survey, which consistently shows that graduates’ collaborative skills are rated the second lowest of the five surveyed skills (i.e., foundation skills, adaptive skills, collaborative skills, technical skills, and employability skills) over the past five years from 2016 to 2020 (Department of Education, Skills and Employment). For instance, the 2020 survey results show that collaborative skills received a satisfaction rate of 88.1%, which was only above the rate for employability skills (86.8%), but was lower than technical skills (93.8%), foundation skills (93.7%), and adaptive skills (90.1%). To provide workforce ready graduates, higher education programs must develop strategies to address this educational need and to train this important attribute. Despite its importance, developing students’ collaborative skills and fostering desirable experience of collaborative learning remains a challenging issue in higher education sector partly because collaborative learning is a complex activity, which involves many aspects and the interplay of these aspects in learning, such as the student factor and increasingly complex blended course designs.

As a consequence of the agenda for higher education and employer interest in the collaborative ability of graduates, it is valuable, if difficult, to examine different patterns of collaborative learning in order to tease out nuanced features of different patterns, so that these can serve as an evidence-base for teaching and course design strategies that are more likely to foster desirable patterns and collaborative behaviour. To achieve this aim of research, the current study draws on two complementary research areas into university student learning: student approaches to learning (SAL) research, which provides a sound theoretical basis of student learning experience in higher education (Ellis & Goodyear, 2013 ; Ramsden, 2003 ; Trigwell & Prosser, 2020 ); and methodologies in social network research, known as social network analysis (SNA), which is a set of empirically powerful techniques that can be used to reveal nuanced features in collaboration (Grunspan et al., 2014 ; Wasserman & Faust, 1994 ). The following literature first reviews previous research on collaborative learning in university settings, followed by SAL research and educational studies using SNA.

Literature review

Research on collaborative learning in university settings.

While there are diverse definitions provided for the term ‘collaboration’, such as working constructively with others (Knight & Yorke, 2003 ); sharing unique ideas and experiences with group members (Hathorn & Ingram, 2002 ); or group members contributing to the whole to achieve a common goal (Roberts, 2004 ); these definitions share two important elements: that there is an agreed goal as well as a shared ownership of the final product (Storch, 2013 ). While collaborative learning is often used interchangeably with cooperative learning, it is possible to distinguish between the two. Cooperative learning tends to focus on each portion of the task delegated to each individual in a group, whereas collaborative learning emphasizes more on the mutual engagement and the non-separable nature of the individual contribution to the task (Kozar, 2010 ).

Collaborative learning has attracted much attention in educational research because of the importance of collaborative competence for graduates expressed by national agendas, employers, and students themselves (Robbins & Hoggan, 2019 ; Williams, 2017 ). The existing studies on collaborative learning fall into two broad themes: one theme examines benefits of collaborative learning, and the other theme investigates factors which are related to quality of collaborative learning. Regarding the first theme, research has demonstrated that collaborative learning is beneficial to develop other important learning skills, such as higher-order metacognitive abilities, critical thinking, problem solving, and decision making (e.g., Gokhale & Machina, 2018 ; Jonassen & Kwon, 2001 ); to foster positive affect, attitudes, and motivation in learning (e.g., Zheng, 2017 ); to enhance level of engagement and in-depth learning (e.g., Zhu, 2012 ), and may also lead to better academic performance (e.g., Sung et al., 2017 ).

For the second theme, which concerns the factors associated with experience in collaborative learning, three broad categories of factors have been investigated: namely (1) the setting of collaboration, including group composition (e.g., Lee & Lee, 2016 ) and group size (e.g., Schellens & Valcke, 2006 ); (2) learning activities in collaboration: including types of activities (e.g., Zheng et al., 2015 ), structure of activities (e.g., Kapur & Kinzer, 2009 ), and the availability of scaffolding (e.g., Gu et al., 2015 ); and (3) student factors, including emotion and affect (e.g., Reis et al., 2018 ), self-efficacy (e.g., Wilson & Narayan, 2016 ), regulatory behaviors in collaboration (e.g., Kwon et al., 2014 ), and metacognition (e.g., Akyol & Garrison, 2011 ). Of these student factors, however, there has been little research into students’ learning orientations, which have been systematically investigated in student approaches to learning research, showing there are distinct variations of learning orientations amongst students (Han & Ellis, 2020a , 2021 ; Lonka et al., 2004 ; Ramsden, 1988 ). The current research aims to fill this gap by investigating patterns of students’ collaborative learning based on their learning orientations.

SAL research

SAL research is a well-recognized framework in higher education to investigate variations of student learning experience and how such variations are related to qualitatively different learning outcomes (Biggs & Tang, 2011 ; Herrmann et al., 2017 ). The collective body of research using SAL framework has identified key elements that are able to distinguish between relatively more successful and less successful experiences of learning. Of the identified elements, how students’ go about learning (i.e., their approaches), how they perceive learning (i.e., their perceptions), and how the approaches and perceptions are related to learning outcomes, have been systematically researched (Entwistle, 2009 ; Trigwell & Prosser, 2020 ). Past studies have examined students’ approaches in different learning designs, such as approaches to inquiry, approaches to discussions, approaches to problem-solving, and approaches to using online technologies in blended courses. Despite the differences in the learning designs, two broad categories of approaches to learning have consistently been confirmed, namely deep and surface approaches. While the former involves strategies that are proactive, reflective, and analytical with an intent to gain meaningful and in-depth understanding of the subject matter; the latter tend to aim to satisfy learning requirements or to complete the required tasks, involving mechanistic and simplistic strategies and that are often largely fragmented from meaning (Nelson Laird et al., 2014).

Students’ approaches to learning are not a fixed personal trait, rather, they may vary depending on the learning contexts and are related to students’ perceptions of learning and teaching (Entwistle, 2009 ). When students perceive teaching being high quality, being clear about learning goals, and encouraging students’ independence in learning, they are more likely to adopt deep approaches. When students perceive the workload of study is not appropriate and the means of assessments do not match their learning goals, they tend to adopt surface approaches (Lizzio et al., 2002 ; Wilson & Fowler, 2005 ). These associations have been confirmed and extended to blended course designs. For example, positive perceptions of the online workload and an integrated learning environment, that includes both face-to-face and online learning experiences, have been found to be related to deep approaches to using online learning technologies; whereas perceptions of inappropriate online workload and fragmentation between face-to-face and online learning experiences in the same course are typically associated to surface approaches learning and to using online learning technologies.

SAL research has also shown that logical relations amongst approaches to learning and perceptions of learning and students’ learning outcomes, which jointly reflect students’ learning orientation. Students adopting deep approaches, having positive perceptions of learning and teaching, and achieving higher level of academic performance are referred to as having an ‘understanding’ learning orientation (sometimes ‘meaning’ learning orientation). On the other hand, those using surface approaches, holding negative perceptions, and attaining relatively poorer learning outcomes are known as having an ‘reproducing’ learning orientation (Ellis et al., 2016 , 2017 ; Han & Ellis, 2020a ; Han et al., 2020 ). While an individual student’s learning orientation is relatively stable as reflected in the consistency across how student’ conceive learning, approach learning, and perceive learning in one learning context or across a number of learning contexts. Nevertheless, “stability of orientations does not imply fixity”, as orientations are relational, changeable, and responsive to learning and teaching contexts, hence, contextually dependent (Ramsden, 1988 , p. 175).

While SAL research has revealed variations of students’ learning orientations, the methods used in SAL are not designed to provide detailed measures of different patterns of students’ collaborative learning. Hence, this study draws on methodologies from social network research, known as social network analysis (SNA) to complement SAL methods in order to reveal nuanced features of patterns of collaboration. The following gives a brief overview of the SNA methodology and education research using SNA.

Educational research using SNA methodology

SNA is a set of techniques that can be used to identify, detect, and interpret roles of individuals (i.e., actors) within a group and patterns of ties amongst individuals (De Nooy et al., 2011 ). In SNA, actors and ties are the two fundamental units, which can be visualised in terms of network graphs with mathematical measures to identify and analyse roles of actors and ties between them (Rulke & Galaskiewicz, 2000 ). In student learning research, for example, actors can be students and teachers, and ties can be student and teacher interaction or students’ collaboration. SNA methodology is increasingly adopted in educational research in the areas such as network connections of teaching discussions amongst university lecturers (Quardokus & Henderson, 2015 ); patterns of research collaboration amongst faculties (Shields, 2014 ); interactions between students and teaching staff in courses or study programs (Cadima et al., 2012 ); students’ social and friendship ties (Rienties et al., 2013 ); students’ knowledge sharing networks (Tomás-Miquel et al., 2016 ); students’ online discussion networks (Gašević et al., 2019 ); and networks of study partners (Stadtfeld et al., 2019 ). In this study, SNA is used to provide a set of measures about the student experience, which reveal nuanced features of the patterns of students’ collaborative learning.

Participants and the learning context

The participants were 193 students (females: 160; males: 33) aged between 19 and 61 ( M  = 25.20, SD  = 6.95). They were enrolled in a two-year Master program at one of Australian research intensive universities.

The learning context was a semester-long compulsory course, which ran 13 weeks. The course not only aimed to develop students’ in-depth understanding of disciplinary knowledge, but also to equip students with a repertoire of generic skills and attributes, such as effective inquiry, the skills of discussion and collaboration, and the capability to critically evaluate sources of information. The course was designed as a blended course, which systematically combines technologically mediated interactions between students, teachers, and resources in learning and requires students to learn across in-class and online contexts in the pursuit of learning outcomes (Bliuc et al., 2007 ). The face-to-face part of the course consisted of lectures, tutorials, and laboratory classes, each of which were two hours per week. The lectures covered the key concepts and demonstrated their links to practical issues. Tutorials offered students opportunities to work in collaboration to discuss questions related to the key concepts and their applications in real contexts. In the laboratory classes, students were also asked to work collaboratively on an assigned weekly theme. To form collaborative groups, the teaching staff did not pre-assign students, rather, it was the students’ own decisions with whom they would collaborate. The online learning component was self-paced independent study hosted in a proprietary Learning Management System (LMS), which held compulsory and supplementary readings; interactive learning activities; and assessment tasks. The course also had a compulsory 80-h work-integrated learning practicum.

Instruments

The close-ended questionnaire.

The closed-ended questionnaire was designed using the SAL literature and instruments (Biggs et al., 2001 ). The questionnaire has been used in a number of previous studies on students’ blended learning experience, demonstrating its validity and reliability (Ellis & Bliuc, 2016 ; Ellis et al., 2016 , 2017 ; Han & Ellis, 2020a , 2020b ). It consisted of five scales:

The deep approaches to learning through inquiry scale (4 items, α = 0.64) describes approaches as taking initiative, using multiple sources, and thinking deeply and critically (e.g., I often take the initiative when pursuing a line of questioning in research”).

The surface approaches to learning through inquiry scale (6 items α = 0.70) describes approaches as relying heavily on teachers and classmates, and following formulaic learning activities only to complete the task (e.g., I only use the directions my teacher gives me when researching something for a task”).

The deep approaches to using online learning technologies scale (5 items, α = 0.83) assesses using online learning technologies as a way to facilitate understanding of subject matter and to promote in-depth reflections in the course (e.g., “I find interacting with learning technologies in this course promotes deeper understanding of key ideas”).

The surface approaches to using online learning technologies scale (5 items, α = 0.68) describe approaches as limiting use of online learning technologies in learning or using them only to satisfy course requirements (e.g., “I only use the learning technologies in this course to fulfil course requirements”).

The perceptions of the online workload scale (5 items, α = 0.88) assesses levels of students’ perceptions of the appropriateness of the online workload in the course. The scale had 4 negatively worded items which were reversed when performing the analysis so that higher scale scores indicate more positive perceptions (e.g., “The online activities in this course made the workload too heavy”).

The open-ended questionnaire

The open-ended questionnaire adopted a commonly used format in SNA, which asked students to write down maximum three collaborators in the course according to the frequency of collaboration, and to specify the main mode of the collaboration, either face-to-face or both face-to-face and online.

Students’ academic performance

The course marks were used as an indicator of students’ academic performance. Students’ final scores in the course were between 13 and 85.30 ( M  = 66.34, SD  = 8.93) out of 100. The marks were made up by aggregating three formative assessment tasks (55%) and the end-of-semester summative examination (45%). The formative assessment tasks were: (1) five reflective journals of the lectures; (2) critically analysis of two selected journals from the five using teacher feedback and peer-reviewed journal articles; and (3) a poster and a report on the process of creating an online portfolio using Pebblepad.

Data collection

The study was approved by the ethics committee of the researchers’ university. The ethical procedure and requirements were strictly followed. Students were informed of the voluntary nature of participation and an essential written consent. They were ensured that the decision as to participation of the research or not would not affect their course marks and all the data would be anonymised and used only for the research purposes. Those who assigned the written consent were provided the questionnaires in one of their laboratory classes towards the end of the semester. Their final marks were obtained upon completion of the course.

Data analysis

To identify students’ learning orientations, a hierarchical cluster analysis using mean scores of the five scales in the close-ended questionnaire and the course marks were used. To examine variations of learning orientations, that is the extent to which approaches, perceptions, and course marks differed by learning orientations, one-way analysis of variance (ANOVA) was conducted. One-way ANOVAs determine if there are any statistically significant differences between the means of independent groups. The one-way ANOVAs used cluster membership as an independent variable, and the means of students’ approaches, perceptions, and course marks as dependent variables. Then based on the identified learning orientations and students’ responses to the open-ended questionnaire, the SNA was applied using Gephi software to generate different collaborative networks representing distinct patterns of students’ collaboration. To reveal features of different collaborative networks, both visualizations and SNA descriptive statistics (i.e., number of students, number of collaborations, maximum number of collaborations for a student, and biggest group size) were calculated and reported. In addition, z -tests were conducted to compare the proportions of: (1) group sizes (i.e., in pairs; in triads; and in groups of more than three); (2) types of collaborators (i.e., reciprocal—both nominated others and being nominated by others as a collaborator; active—only nominated others as a collaborator; and passive—only being nominated by others as a collaborator); and (3) collaborative modes (i.e., face-to-face mode and dual modes involving both face-to-face and online) amongst different collaborative networks. Lastly, a number of actor-level (i.e., average degree, betweenness, closeness, eccentricity, and eigenvector) and network-level (i.e., network clustering coefficient, network density, and network modularity) SNA measures were calculated and compared across different collaborative patterns. As these SNA measures were standardized, direct comparison was permitted.

Variations of students’ learning orientations as shown by the cluster analysis and one-way ANOVAs

To facilitate the interpretation, all the variables were transformed into z -scores ( M  = 0, SD  = 1) in the cluster analysis and one-way ANOVAs, and the results are displayed in Table 1 and visualized in Fig.  1 . The hierarchical cluster analysis produced a range of two-cluster to four-cluster solutions. The values of Squared Euclidean Distance revealed a relatively large increase in the value of a two-cluster solution compared to the three-cluster and four-cluster solutions, suggesting a two-cluster solution was more appropriate. The labelling of the two clusters was in accordance with the SAL literature (Trigwell & Prosser, 2020 ). Cluster 1 had 103 students and cluster 2 consisted of 90 students. The results of one-way ANOVAs suggested that there were significant differences for the five scales as well as course marks between two clusters: deep approaches to learning through inquiry: F (1, 192) = 23.95, p  < 0.01, η 2  = 0.11; surface approaches to learning through inquiry: F (1, 192) = 153.20, p  < 0.01, η 2  = 0.45; deep approaches to using online learning technologies: F (1, 192) = 54.32, p  < 0.01, η 2  = 0.22; surface approaches to using online learning technologies: F (1, 192) = 97.45, p  < 0.01, η 2  = 0.34; perceptions of the online workload: F (1, 192) = 16.59, p  < 0.01, η 2  = 0.08; and academic performance: F (1, 192) = 8.01, p  < 0.05, η 2  = 0.04. Students in cluster 1 used more deep approaches to learning through inquiry, deep approaches to using online learning technologies, had positive perceptions of the online workload, and obtained higher scores in the course; whereas students in cluster 2 reported using more surface approaches to learning through inquiry, surface approaches to using online learning technologies, negatively perceived the workload required online, and attained relatively poorer academic performance. The variations in students’ reported approaches, perceptions, and their academic performance indicated that the learning of cluster 1 students were directed towards understanding the subject matter (the ‘understanding’ learning orientation); whereas the learning of cluster 2 students were oriented towards reproducing facts and formulas (the ‘reproducing’ learning orientation).

Students’ approaches, perceptions, and academic performance by learning orientations. DAI   deep approaches to learning through inquiry, SAI   surface approaches to learning through inquiry, DAT   deep approaches to using online learning technologies, SAT   surface approaches to using online learning technologies, POW   perceptions of the online workload, CM   course mark

Patterns of collaborative learning based on students’ learning orientations and their choice of collaborations

Students’ learning orientations and their responses in the open-ended questionnaire jointly formed five networks representing five distinct patterns of collaborative learning, namely:

Understanding Alone (UA): this pattern was formed by ‘understanding’ students who worked alone

Understanding Collaboration (UC): this pattern was formed by ‘understanding’ students who collaborated with ‘understanding’ students

Mixed Collaboration (MC): this pattern was formed by both ‘understanding’ and ‘reproducing’ students, who collaborated with those having a different learning orientation

Reproducing Collaboration (RC): this pattern was formed by ‘reproducing’ students who collaborated with ‘reproducing’ students

Reproducing Alone (RA): this pattern was formed by ‘reproducing’ students who did not collaborate

The visualization and the descriptive statistics of the five patterns are displayed in Table 2 . The students in the UA and RA networks did not overlap with those in the UC, MC, and RC networks. However, those presented in the UC, MC, and RC networks were not mutually exclusive, as an individual was allowed to list more than one collaborator in the course, hence, some of them collaborated with students having the same learning orientation as well as with those having a different learning orientation. As the UA and RA networks did not have any collaboration, they were excluded in the subsequent analysis.

Comparison of the proportions of group sizes

The comparison of the proportions of different group sizes, namely collaboration in pairs, in triads, and in groups of more than three people, in the UC, MC, and RC were conducted using two-sample z -tests, whose results are presented in Table 3 . It shows that there was no significant difference in terms of the proportion of collaboration in pairs, in triads, or in groups of more than three between UC and MC. However, RC (26.79%) had significantly higher proportion of collaborations in triads than UC ( 8.33% ) and MC (13.64%), whereas RC (37.50%) had significantly lower proportion of collaborations in groups of more than three than UC (77.78%) and MC (68.18%). RC did not differ from the UC and MC in terms of the proportions of collaborations between two people.

Comparison of the proportions of types of collaborators

Two-sample z-tests were also applied for pairwise comparison of the proportions of different types of collaborators amongst UC, MC, and RC, and the results are displayed in Table 4 . Table 3 shows that there were no significant differences of the proportions of passive and active collaborators amongst UC, MC, and RC. However, the proportion of the reciprocal collaborators in RC (32.14%) was significantly lower than those in UC (52.78%) and MC (53.64%). No difference was found between UC and MC.

Comparison of the proportions of collaborative modes

The results of the two-sample z -tests for pairwise comparison of the proportions of different collaborative modes are shown in Table 5 . It shows no significant differences either between UC and MC or between RC and MC for the proportions of face-to-face collaborations or collaborations in dual modes. However, RC (43%) had a significantly higher proportion of collaborations occurred mainly in face-to-face mode than UC (28%) did, whereas UC (72%) had a significantly higher proportion of collaborations in both face-to-face and online modes than RC (57%) did.

Comparison of the standardized SNA measures

The standardized SNA measures were calculated using the undirected networks because when two students collaborated, their collaboration was the same irrespective of who was the initiator of the collaboration. The standardized SNA measures were produced in Gephi and displayed in Table 6 . Of these measures, rows 1–5 are actor-level measures, and rows 6–8 are network-level measures. The average degree shows that on average students in UC (2.361) had more collaborations than those in MC (2.218) and RC (1.607). Rows 2–4 show that the students in UC had higher betweenness (0.0025) and eccentricity (3.625) than those in MC (betweenness: 0.0006, eccentricity: 3.064) and in RC (betweenness: 0.0007, eccentricity: 1.911). However, the students in UC had lower closeness (0.545) than their counterparts in MC (0.606) and RC (0.775). Taken together, these measures suggest that the collaborative groups in the UC network were long-chained structure, hence, a student in the UC network needed to pass more students to reach the students positioned furthest in the network. The results for the eigenvector measure show that RC (0.163) was the lowest amongst the three (UC: 0.233 and MC: 0.249), suggesting that the connection strength to the neighbourhood students was the weakest in the RC network.

In rows 6 to 8, it is observed that UC had higher network clustering coefficient (0.323) and network density (0.025) than MC (clustering coefficient: 0, density: 0.015) and RC (clustering coefficient: 0, density: 0.024), but UC (0.872) had lowest network modularity among the three (MC: 0.920, and UC: 0.937). These three network measures jointly reflect that collaborations in the UC network tended to be more intense and in closely knitted groups, which were less easily to break into smaller collaborative groups.

Discussion and conclusion

The current study investigated features of different patterns of student collaborative learning based on variations of students’ learning orientations amongst a cohort of postgraduate students enrolled in a compulsory blended course using the methods in SAL research and techniques of SNA. Consistent with previous SAL research (e.g., Ellis et al., 2016 , 2017 ; Han & Ellis, 2020a ; Han et al., 2020 ), two distinct learning orientations, namely ‘understanding’ and ‘reproducing’ have also been found in this population sample. The students having an ‘understanding’ and a ‘reproducing’ learning orientation could be clearly distinguished by how they approached learning and how they perceived the workload required online in this blended course. The different learning orientations identified in this student sample and students’ choice as to whether to collaborate or not, with whom to collaborate have produced five distinct patterns of collaborations.

While group work competence and collaboration skills are essential qualities for workforce-ready graduates, students in UA and RA (together accounting for 17.1%) did not take the opportunities in this course to practise this important generic attribute. At least two possible reasons could explain such results. First, in this course, students were not pre-assigned to a collaborative group, rather, they were given freedom to choose their own collaborators. Hence, some students avoided collaborations and worked alone. The second possible reason could be a lack of compulsory assessment which required students to complete the tasks collaboratively. This also seemed to suggest a problem of the mismatch between the course objective of developing students’ collaborative skills and the assessment design in this course. To prevent students from not participating in collaborative learning, the teaching staff of the course may consider redesigning the learning and assessment tasks, which should include some mandatory collaborative activities, such as team presentations and group portfolio, which will not only signal to students the importance of collaboration in the course but also create opportunities for them to practise the skills (Barkley et al., 2014 ). Even if the assessment design required individual students to complete the tasks, modifications can be made to the instructions about the process which make it mandatory for students to work with others in the course. For instance, a reflective section in the final essay on the process of collaboration that helps complete the assessment item may be included in the instructions.

For those students who reported collaboration, on the basis of their learning orientations, UC, MC, and RC were identified depending on whether students collaborated with their peers who shared a similar or had a different learning orientation. The three networks, which represent the three patterns of student collaborative learning, exhibit different features shown by a number of SNA measures. Collaborations in the RC network were lower in quality, notably with the features of less collaborations (degree) and collaborations being less intense (network density). In addition, the RC network had a smaller proportion of reciprocal collaborators and when they occurred, they tended to be in face-to-face mode only.

Comparatively, collaborations in the UC network have more desirable features. The UC network had more collaborations (degree), which were not only more intense (network density); but also tended to be closely knitted (network clustering coefficient) and were less likely to break into smaller, disconnected groups (network modularity). Moreover, the UC network also had a higher proportion of reciprocal collaborators who both initiated collaborations and were chosen as collaborators by their peers. The collaborations in the UC network resembled the collaborative patterns of collaborations between “dominant” learners in Storch, 2002 , 2004 ), which also showed reciprocity between collaborators and a high equality in the process of collaborating. While previous studies reported that high achieving students preferred to collaborate in learning (Stadtfeld et al., 2019 ), our study further demonstrated that the collaborations between the high ability students learned were also more strategic and of higher quality.

As the study show more desirable features when students with an ‘understanding’ learning orientation collaborated with each other, how to move students’ learning orientation from ‘reproducing’ to ‘understanding’ can be thought of as a key pedagogical strategy to improve the quality of students’ collaborative learning. This could be achieved by inviting ‘understanding’ students to share their experience as to how they went about learning, their approaches to using online learning technologies to support their learning through inquiry, what they did and why they did, how they handled the course workload, and what they did in collaborations. Pairing students with different learning orientations may be another strategy for the teaching staff to use so that the group composition can be purposely reconfigured in order to foster desirable experience of collaborative (Lancellotti & Boyd, 2008 ). Teachers may also consider incorporating learning activities which are more likely to encourage deep learning, such as designing tasks involving real experiences for students to create and use new knowledge. In addition, building students’ confidence and motivation through feedback and formative evaluation cycles in the learning process may also to be effective (Fullan & Langworthy, 2014 ).

Collaborative learning in higher education remains a key strategic pedagogical goal for the sector internationally. Only by discovering evidence of features of different patterns of collaboration which can provide actionable knowledge such as that discussed in this study, can the field move forward in this important area.

Availability of data and materials

The datasets generated and analysed during the current study are not publicly available due to ethics requirement, but are available from the corresponding author on reasonable request.

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Han, F., Ellis, R.A. Patterns of student collaborative learning in blended course designs based on their learning orientations: a student approaches to learning perspective. Int J Educ Technol High Educ 18 , 66 (2021). https://doi.org/10.1186/s41239-021-00303-9

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Interprofessional collaboration in research, education, and clinical practice: working together for a better future

Interprofessional collaboration occurs when 2 or more professions work together to achieve common goals and is often used as a means for solving a variety of problems and complex issues. The benefits of collaboration allow participants to achieve together more than they can individually, serve larger groups of people, and grow on individual and organizational levels. This editorial provides an overview of interprofessional collaboration in the areas of clinical practice, education, and research; discusses barriers to collaboration; and suggests potential means to overcome them.

INTRODUCTION

Individual commitment to a group effort—that is what makes a team work, a company work, a society work, a civilization work . —Vince Lombardi

Collaboration is a term commonly used in research, clinical practice, and health professions education. There are collaborations in almost every aspect of health, such as patient advocacy and health care collaboratives, collaborative learning, interprofessional collaboration in practice and in education, health care value collaborations, business collaborations, collaborative efforts in research and funding. With the increasing use of computers, mobile devices, and social media, collaboration seems to be present more than ever before. At its core, collaboration occurs when 2 or more entities work together to produce a desired and shared outcome. The fields of research, education, and clinical practice are interrelated; research informs education, which in turn influences clinical practice and patient care. In a complementary manner, the needs of practitioners, patients, and educational systems should inform what research may be needed. If we wish to succeed in improving outcomes for students, practitioners, patients, and populations, then we need to consider working together in these environments through collaborations. Therefore, the purpose of this editorial is to discuss interprofessional collaboration in research, education, and practice, particularly with regard to problems to avoid and practices that help to achieve collaboration.

What Is Collaboration?

Collaboration may occur at virtually any level of an organizational structure. People can collaborate within an organization, between organizations, between one another, between countries, and between professions. 1 , 2 More commonly referred to as interorganizational collaboration in the business domain, 2 , 3 the principles are similar in the health professions and are often referred to as interprofessional collaboration (IPC). There are several key concepts relevant to collaboration, including sharing, partnership, interdependency, and power. 4 Mattessich and Monsey 5 (p7) nicely summarize the essence of collaboration:

• Collaboration is a mutually beneficial and well-defined relationship entered into by 2 or more organizations to achieve common goals.
• The relationship includes a commitment to a definition of mutual relationships and goals, a jointly developed structure and shared responsibility, mutual authority and accountability for success, and sharing of resources and rewards.

Creating successful collaborations is no mean task, and naysayers may scoff at the idea. However, there are several exemplars of successful large and international collaborations in research, education, and practice to show that it can be done. A sampling is presented in Table 1 .

Exemplars of Interprofessional Collaboration

Benefits of Collaboration

Collaborating usually provides a means for organizations, institutions, or professions to achieve more than they can on their own. Business has used collaboration for many years to share costs, spread risk, and reduce supply chain uncertainty while forming strategic economic alliances that also serve as fertile grounds for innovation and learning. 2 , 6 Collaboration potentially reduces self-sufficiency in environments demanding great flexibility and innovation. 7 Several benefits of collaboration are listed in Figure 1 .

Benefits of collaboration.

In health care it is generally believed that collaborative efforts yield better health services and outcomes for the populations that are served. 4 Littlechild and Smith 9 state that collaboration leads to improved efficiency, improved skills mix, greater levels of responsiveness, more holistic services, innovation and creativity, and a more user-centered practice. The World Health Organization (WHO) has linked IPC with better outcomes in family health, infectious disease, humanitarian efforts, responses to epidemics, and noncommunicable diseases. 10 Further studies have shown improvements in access to care and coordination of services, appropriate use of specialty care, chronic disease outcomes, and safety. 10 , 11 Important indicators of safety, patient care, and environment of care, such as complications and error rates, length of hospital stay, conflict among caregivers, staff turnover, and mortality rates, have all been shown to decrease in collaborative care environments. 10

Interprofessional Collaboration in Research

Science and knowledge are foundational for a health care profession to exist, and research is one means of achieving these requirements. The problems that must be solved in the modern era are complex, and solutions may not be available if one is working alone. The National Academies suggest that interprofessional or interdisciplinary collaboration may offer solutions when trying to solve multifaceted issues. 12 Interprofessional/interdisciplinary research collaboration occurs when researchers from more than 1 profession/discipline are “working together to achieve the common goal of producing new scientific knowledge.” 13

The National Academies defines research collaboration as follows 12 :

Interdisciplinary research (IDR) is a mode of research by teams or individuals that integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or field of research practice.

Collaborating parties benefit not only in accomplishing research studies, but also in other, less tangible areas. IPC among researchers can help build informational networks, encourage different ways of thinking, and stimulate new solutions to old problems.

Each profession's research community faces various obstacles, such as limited workforce, resources, and expertise. For researchers who work in the isolation of their own profession, there are limitations and risks for not collaborating. On the profession or discipline level, working in a research silo can create a false sense of security because ideas are not likely to be challenged. This can result in a small fish who thinks to be a big fish simply because he is in a small pond. When working in seclusion, work will likely go only so far and will not reach as wide an audience as might be possible as when working with others. As well, there is the possibility that a solution may have already been found elsewhere; thus, isolated research efforts may waste limited resources. Research done in segregation likely can benefit from better infrastructure. Finally, without collaboration, some research may result in poor-quality studies.

Collaboration can be a significant catalyst to positive advancement in research, as it allows access to resources so that more complex, and perhaps more meaningful, investigation may be possible. Research that may not have been possible if done by a single profession may be possible when done in collaboration with others. 14 Resnick 15 states that “while multidisciplinary research brings disciplines together, interdisciplinary research cuts across the disciplines and fosters the integration of ideas.” It has been suggested that there is a correlation between collaborative research and prestige, 16 increased success of publication, 8 and citability (ie, impact). 17 , 18 As stated by Frenken et al 19 “Research collaboration enhances the quality of research, which leads papers with more authors to be cited more often.” Results of research may have a better chance of being implemented if there are multiple professions to disseminate the findings. This is especially important if the results have direct application to clinical practice.

Breaking past professional barriers to achieve research collaboration can be challenging. A profession that has few resources and little access to funding may not be invited, or even considered, to participate in larger studies or projects. Some health professions do not have many (or any) experts in particular areas within their profession to perform certain areas of research, especially those in emerging fields. It is also difficult to initiate a collaborative effort when one has few resources to bring to the relationship. Those with few resources are often the professions and disciplines that especially need to collaborate in order to improve and may find that larger organizations are receptive to including them, especially if it helps build more collaborative infrastructure.

Trust is an important factor for collaboration in research. The culture of science has traditionally been secretive. Scientists must compete for funding; sharing novel ideas may risk that a fundable project is “stolen,” either intentionally or unintentionally. Therefore, trust and respect of others in a collaboration are necessary to prevent withholding of ideas and assistance. There may also be a funding advantage to participating in collaborative research. There has been a movement by major funding agencies to reward and value multidisciplinary collaborations. 20 So, if helping humanity is not a big enough motivator for collaboration, then better funding may be a motivating stimulus.

Value continues once the research is done. After a collaborative study is complete, the communications among researchers often will continue. Skills and ways of thinking that were shared among group members across the disciplines can have long-lasting effects. The traditional approaches of 1 discipline are expanded by working with other professions; thus, the problem or the approach will have been seen in a new light. As Lee et al 14 suggest, “There is a general consensus that interdisciplinary collaboration is important in solving large scale, complex biomedical questions.” If we wish to tackle the big problems that we are facing, then we need to consider seeking out and developing better and more interprofessional and interdisciplinary relationships in research.

Interprofessional Collaboration in Practice

We have trained, hired and rewarded people to be cowboys, but it's pit crews that we need . —Atul Gawande, MD, MPH

Interprofessional collaborative practice (IPCP) has emerged in health care over several decades, 21 , 22 but has garnered more support, particularly in the past 15 years, as a means to address medical error. 21 , 23 Also, the advent of patient-centered medical homes 21 and family home teams 24 and a global shortage of primary care providers in areas with major health disparities 10 have made IPCP an attractive model to, it is hoped, provide better care to populations of health care users.

IPCP has been defined by WHO 10 as follows:

Collaborative practice in health-care occurs when multiple health workers from different professional backgrounds provide comprehensive services by working with patients, their families, careers and communities to deliver the highest quality of care across settings.

IPCP involves more than different health care providers applying their unique skills and knowledge to the management of a patient. Collaboration occurs when individuals have mutual respect for one another and one another's professions and are willing participants in a cooperative atmosphere. 25 It has been suggested that IPCP is different from interdisciplinary, multidisciplinary, and transdisciplinary practice, all terms used within the recent past to denote care provided by more than 1 health care provider for the benefit of a patient. 26 – 28 While these practices are indeed noteworthy for their contributions to health care, the unit of study is a single patient and the model is focused on the health care provider(s), whereas the unit of study in IPCP is often a community of patients and the model is focused on improving health outcomes. Thus, when we look at IPCP, we must think of the bigger picture and health in populations.

Traditionally, an individual patient has sought care for a disease or disorder from a reputable health care provider. 29 Under such a model, it was conceived that this singular health care provider working in the “silo” of his or her own office could meet the needs of the patient. Today, with chronic and complex diseases requiring multiple specialty providers 30 and the role of primary care increasingly shifting toward the provision of services to populations of people with massive disparities, there is a great need for team-based community care more reminiscent of public health practice than health care. From a social perspective, practices that focus on the skill set and charisma of a singular health care provider are inefficient and costly. As has been noted in the Institute of Medicine's 30 landmark report The Future of the Public's Health , “smaller practices have great difficulty in organizing the array of services and support needed to efficiently manage chronic disease.”

In many countries, health care systems are fragmented and unable to meet the health care needs of the population. 10 A chief force driving the use of IPC is the shortage of primary care providers that is occurring globally. This is a critical barrier to achieving the health-related Millennium Development Goals. 10 Part of the United Nations Millennium Development Declaration, several of these 8 international goals have health-related indicators, such as reducing child mortality rates, improving maternal health, and decreasing common and endemic infectious diseases. 31 Gostin and colleagues 32 poignantly state, “Siloed models centered on temporary fixes are not sufficient to greatly alleviate the overall burden of disease in developing countries.”

Researchers at WHO have found that health care workers who are team players are the ones who succeed in austere situations dealing with extremely complex issues. 10 IPC relies upon the ready integration of people with diverse talents, including those not often associated with health care, who may assist in the improved health care outcomes of the group being served. In a collaborative environment, the input of economists, logisticians, informatics specialists, technology experts, and others may be critical to success. This perspective is something common in public health but foreign to most patient care environments. 23 Collaboration at this level requires participants to work together with open minds and to value what each team member brings to the team. Such collaboration is evident in a proposed interprofessional oath suggested by Brown and colleagues 33 : “We will work with others to provide care, recognizing the unique skills of each, and we will seek to collaborate effectively on the healthcare team.” This environment is perhaps best summarized by Hall, 34 when she says, “The milieu for collaborative practice must foster a status-equal basis between the various team members.”

Interprofessional Collaboration in Education

Interprofessional education (IPE) “occurs when students from 2 or more professions learn about, from, and with each other.” 10 It has been suggested that to be a genuinely interprofessional education experience such interaction requires purposeful integration and collaboration among the disciplines, whether in an educational or practice environment. 23

IPCP and IPE are related. Advocates state that IPE must be a part of the professional training of health care workers in order to reach the goal of IPCP. In its widely acclaimed and often-cited publication, Framework for Action on Interprofessional Education and Collaborative Practice , 10 WHO clearly outlines the necessary elements and relationships inherent to IPE and IPCP. As shown in Figure 2 , the health and education systems exist in a local context and are there to provide the health care needs of the local population. Within this environment, future health care workers ought to be trained to work together as members of a “collaborative, practice-ready workforce” 10 (ie, IPE) that provides collaborative health care to the population. A workforce that is ready for IPC therefore emerges from IPE training experiences. The goal of the IPC health care system is to deliver better health outcomes to the population. 10

The model of relating interprofessional education and collaborative practice as presented by the World Health Organization. Figure reproduced with permission from World Health Organization, Framework for Action on Interprofessional Education and Collaborative Practice ; 2010: 9, figure 1 .

Receiving formal IPE training has benefits. While some people without training in collaborative practice might be able to figure out how to function or thrive in IPCP, it has been shown that training people using IPE leads to “members who show respect and positive attitudes towards each other and work towards improving patient outcomes.” 25 While health care systems have moved toward IPC, teamwork training in health professions education has not kept pace with such changes. 21 IPE and IPCP are linked to what society needs; through IPE, the workforce is trained to work better as a team to deliver better health outcomes to the community being served.

Collaborative work in IPE and IPCP has been done across geographic and political boundaries. Much of the work that has been published related to IPE and IPCP originates from Canada, the United Kingdom, the United States, and Australia. 35 However, attention has been garnered for IPE from around the world since WHO made it an essential component of health professions education. 10 In an international survey of WHO's 193 member states, Rodger and colleagues 36 found evidence of IPE in 41 different countries, with varying levels of complexity noted in collaborative efforts. This makes sense given the shortage of primary care services occurring globally. Opportunities for involvement in various IPCP and IPE groups are widely distributed, including those in Canada ( www.cihc.ca ), Europe ( www.eipen.eu ), the United Kingdom ( http://caipe.org.uk ), United States ( www.aihc-us.org ), Australia and New Zealand ( www.aippen.net ), Japan ( www.jaipe.jp and http://jipwen.dept.showa.gunma-u.ac.jp ), Scandinavia ( www.nipnet.org ), and Eastern and African countries ( www.ecipen.org ). Furthermore, annual international conferences focus on IPCP and IPE, such as the All Together Better Health conferences that are supported by 8 different organizations with international representation ( www.atbh.org ). There are also the Collaborating Across Borders conferences, a joint IPCP/IPE effort between Canada and the United States ( http://www.aihc-us.org/collaborating-across-borders ) and the New Zealand Interprofessional Health Conference ( http://www.nziphc.co.nz ).

Potential Barriers to Collaboration

The secret is to gang up on the problem, rather than each other . —Thomas Stallkamp

Given the core elements of collaboration (participants from different cultures, high level of interaction, mutual authority, sharing of resources), it has its potential problems. What may start out as a well-meaning endeavor can lead to conflict. 3 In fact, due to the very nature of a collaborative environment, conflict should be expected; but with this comes the opportunity for better understanding. IPC is essentially a melting pot of professions, and each profession has its own unique history, culture, attitudes, values, customs, and beliefs. How professionals in collaborations come to understand and appreciate these nuances can pose several challenges. 34 For example, 1 profession may see another profession as an outsider or rival and not want to involve this profession in collaboration. There also may be professional groups that are afraid to interact with other professional groups for various reasons, not the least of which could be historical isolation or low status within the social hierarchy. There are also situations in which a culturally dominant profession may have attitudes that are prejudiced against other professions, as has been stated by Gaboury and colleagues 37 : “It is plausible that practitioners who belong to occupational groups that have obtained, or are in the process of obtaining, legitimacy through licensing, certification, or registration may be seen differently by their biomedical peers.” The aforementioned are all relatively minor obstacles compared to that of bringing together professions that historically have been at odds with one another. In summary, the ideological differences and power relations brought to collaboration from different professions can be potentially problematic. 4 , 38

Boundary disputes, status issues, language barriers, customer service orientations, and reporting structures are all potential challenges. 9 , 39 – 41 Other authors warn of problems with IPC in areas where the physical space is not adequate or appropriate in design, 42 where role overlap and confusion exist, 4 , 38 and where there is territorialism. 42 , 43 The conclusion is that collaboration 44 will lead to conflict. However, the management of any conflict to arrive at a benefit for the customer is a necessary step in building an effective collaboration. 44 , 45 It is recommended that collaborative groups have an agreed-upon method for resolving conflict, starting with resolution at the level of individual participants and working out toward the larger levels of organization. 44

There are also concerns that IPC can lead to the loss of uniqueness of a profession or professional identity. 23 When participants focus on the team goals and the people that they are serving, then differences between professions can be seen as unique opportunities to bring a valuable and different point of view or skill set to the collaboration. 23 The collaboration may result in overlap, to some degree, of the activities of any 1 profession involved in it, but the collaboration should not duplicate the efforts of any particular profession. 5 As stated by D'Amour and colleagues, 4 working together in this way requires “implementing a logic of collaboration rather than a logic of competition.”

Getting to Collaboration

If everyone is moving forward together, then success takes care of itself . —Henry Ford

Like other things that are valued but difficult to attain, such as exalted leadership, fine wine, or an enduring marriage, working collaborations are perhaps as much an art as they are a science. Experts suggest that the process of building a culture of collaboration is not exactly methodical, is somewhat organic, and requires a great deal of practice and nurturing. 2 , 46 , 47 It is suggested to start small and first learn to work collaboratively at a local level. Examples might be collaborating within an office, department, or group. Once skills in collaboration are developed (not tolerance or just working in the same building, but actually accomplishing a task together), then reaching out to larger circles of influence and other professions may be easier.

Many books and articles have been written on the topic of collaboration, and several are cited in this article; thus, we refer the reader to these sources for comprehensive discussions about attaining quality collaborations. However, there are some cogent points relevant to forging collaborations across disciplines that are expressed by several experts, and we briefly review them here and provide a checklist for assessing readiness for collaboration in Appendix A.

• Know when to use collaboration and when not to. Effective leaders can identify when it is the right time to collaborate. Essentially, collaboration is a tool to employ to achieve a desired outcome and it will not be a good tool to use in all situations. Collaboration tends to work best in diverse groups where the people participating in the collaboration have authority to make final decisions and when innovation and creativity are desired. 45 As stated by Hansen, 1(p15) “The goal of collaboration is not collaboration, but better results.”
• Know what the collaborating professions or organizations stand to gain from the alliance and what the costs are to get there. 1 , 48 One expert has suggested that collaboration should only be engaged if the net value of the partnership is more than the sum of the return on the venture, minus the costs and the opportunity costs, such as manpower and time spent on the collaboration that could be spent in other endeavors. 1 In short, some effort needs to be expended in advance of the collaboration to determine if the value/benefits outweigh the costs. 48
• Be aware of the factors that drive the strength of collaboration: alignment of mission/values, 47 , 48 personal connections, 48 value generation for each collaborating organization or profession, 48 and improved outcomes. 10
• Become familiar with the factors that lead to successful collaborations. 5 Such factors include attitudes, environmental concerns, communication, resources and trust. 2 , 7 , 46
• Recognize the intangible elements, such as tacit knowledge, social capital, ownership, disclosure, transparency, motivation, and commitment, that strongly influence peoples' decisions. 46 , 49
• Identify barriers to the collaboration that you desire to build, and find ways to remove the barriers so that collaborative relationships can evolve. 1
• Create organizational learning objectives and goals to facilitate knowledge creation. 7 , 50 Performance goals may stifle knowledge generation when they do more to encourage people to “win” than to learn. 45 If the organization has clear ideas about what it wants to learn from the collaboration, then it is easier to identify when new knowledge is generated by or obtained from the collaboration. Identify the new learning processes, outcomes, and innovations that occur during collaboration as assets and build upon these successes. 6 , 7 Inkpen 7 suggests that collaborations should consider both incremental and large gains in knowledge as successes.
• Commit to collaboration for the long haul because it is a long-term process and investment. 46 , 47 Since collaboration is experiential and based upon relationships, it takes time to develop. If an organization's culture and leadership are not invested and committed to establishing formal relationships, then collaboration is not a good fit. 7 , 23 , 25 A long-term commitment, both in principle and in resources, is a necessary element of success.
• Know when it is time to stop a collaboration. 7 Collaborations are a means to an end, and if the end is reached, it may be time to stop the collaboration or change it to meet a new goal. While ceasing a collaboration can be disappointing for some, it may be prohibitive to continue a collaboration for which the net value of the collaboration is a liability. 1

A recent expert report from the Josiah Macy Jr Foundation identified 3 important recommendations for establishing interprofessional partnerships, all of which are long-term investments for any institution 51 :

• Make changes in the content and conduct of health professions education necessary to graduate practitioners who partner with patients, families, and communities.
• Make changes in health professions education organizations and health care organizations necessary to facilitate durable partnerships, both new and existing, with patients, families, and communities.
• Build the capacity for partnerships among patients, families, and communities and health professions education and health care organizations.

Suggested Resources

For anyone interested in setting up collaborations in research, education, or clinical practice, seek out existing publications, training centers, and experts who have established collaborations. For example, those interested in research collaboration can seek out funding bodies that require collaboration, become participants in established collaborations (eg, the Cochrane Collaboration or Best Evidence Medical Education Collaboration), or seek new opportunities by identifying gaps in the literature that need interprofessional solutions. Another approach is to inventory current research at one's institution and consider if any of the projects or goals would benefit from team research efforts.

For educators, there are many excellent resources for getting started in IPE, including the WHO framework, which includes suggestions for actions, participants, and outcomes. 10 Those interested in pursuing IPE should consider this document required reading, as it serves as a primer for this topic. The WHO framework also makes the case for placing the community as the unit of concern because of global shortages in health care providers and health workers.

In clinical practice, numerous health professions are currently working on developing policies and educational competencies related to IPC, including physical therapy, 52 nursing, medicine, dentistry, public health, and pharmacy. 21 One excellent resource on IPE and IPCP is a competencies monograph by the Interprofessional Education Collaborative (IPEC) that has application across health professions. 21 In the United States, IPEC is the collaborative that has done the most work in IPE. The chiropractic profession has joined the complementary and alternative health disciplines in developing competencies for interprofessional practice through the Academic Consortium for Complementary and Alternative Health Care (ACCAHC). This consortium worked independently to develop IPE competencies and then reviewed the IPEC document, 21 which was released simultaneously; it eventually became a supporting organization of IPEC. ACCAHC also created 3 additional elements for IPE, as they relate to the professions represented by ACCAHC. These elements include a value for personal behaviors and self-care practices that reflect optimal health and wellness, a section on evidence-based care, and a section on institutional health care culture and practice. 53

Canada has been a world leader in IPC for more than 15 years, with government interest and funding of research and educational programs focused on more integrated, team-based approaches to meet the health care and service delivery needs of the Canadian population. 23 A wealth of information and wisdom is available from the Canadian Interprofessional Health Collaborative. Like IPEC, this collaborative has developed competencies for IPE. However, in their document, A National Interprofessional Competency Framework , competencies are unique in the sense that they integrate knowledge, skills, and attitudes to arrive at judgments over 6 competency domains considered to be critical to IPCP. 54

Faculty development in IPE is critical to the success of IPE programs. 23 , 39 Institutions must be willing to invest the resources necessary to develop faculties into communities of collaborators who can model the desired traits and behaviors to students. Faculty members who are legendary for their inability to work well with others are not likely to be good ambassadors for IPC, nor are they likely to appreciate training in the topic. Bridging the gap for these faculty members may be difficult, but 1 successful method is to engage them in team-oriented trainings that show obvious improvements in patient outcomes or safety. 55 Faculty development programs should be developed using the same educational principles required to teach students: team-based learning, active learning, problem solving, utilization of available resources, and feedback mechanisms. 55 Buring and colleagues 56 have published an excellent article on faculty training, including desired attributes of IPE educators, faculty development resources, and a rubric containing selected topics mapped to Institute of Medicine competencies, teaching methods, and learning objectives. Mackenzie and colleagues 57 also offer an informative account of the key elements of success of their IPE program by linking successful organizational structure and actions with the Canadian Interprofessional Health Collaborative competency domains. 57

When discussing collaboration, the key issues include putting the community or client first, the organization second, oneself last, and prejudices aside. Shortages in primary care providers and the challenges of managing chronic, complex diseases, such as musculoskeletal problems, are excellent opportunities for the health professions to bring unique skills to collaborative environments. Times are changing, silos are falling, national health burdens are being shared, and it is going to take much more than a single practitioner or paradigm to solve the serious health care issues confronting humanity today and in the future. Through collaboration, we can work together for a better future.

FUNDING AND CONFLICTS OF INTEREST

BNG and CDJ receive a stipend for services provided to the Journal of Chiropractic Education and Association of Chiropractic Colleges Research Agenda Conference (ACCRAC) meeting, respectively. However, this work was performed independent of these relationships, and the authors have no conflicts of interest to declare relevant to this work. The views expressed in this article are those of the authors and do not reflect the official policy or position of the Department of the Navy, Department of Defense, or the United States Government.

A Checklist to Assess for an Organization’s Readiness for Collaboration

Concept development: BNG, CDJ. Design: BNG. Supervision: BNG. Literature search: BNG, CDJ. Writing: BNG, CDJ. Critical review: BNG, CDJ.