Beyond grades
Brouwer, Jasperina; Jansen, Ellen
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Brouwer, J., & Jansen, E. (2019). Beyond grades: developing knowledge sharing in learning communities as a graduate attribute. Higher education research & development, 38(2), 219-234.
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Beyond grades: developing knowledge sharing in
learning communities as a graduate attribute
Jasperina Brouwer & Ellen Jansen
To cite this article: Jasperina Brouwer & Ellen Jansen (2019) Beyond grades: developing knowledge sharing in learning communities as a graduate attribute, Higher Education Research & Development, 38:2, 219-234, DOI: 10.1080/07294360.2018.1522619
To link to this article: https://doi.org/10.1080/07294360.2018.1522619
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Beyond grades: developing knowledge sharing in learning
communities as a graduate attribute
Jasperina Brouwer aand Ellen Jansen b
a
Department Educational Sciences, Faculty Behavioural and Social Sciences, University of Groningen, Groningen, the Netherlands;bDepartment Teacher Education, Faculty Behavioural and Social Sciences, University of Groningen, Groningen, the Netherlands
ABSTRACT
The ability to share knowledge is an important attribute that students develop in learning communities (LCs), enabling them to succeed in their education and careers. Insufficient research addresses the development of such knowledge sharing in LCs though, including whether it aligns with students’ success (i.e., grades). To address this gap, the current study investigates various determinants of knowledge sharing and their effects on student success. Survey data from 183 psychology students measure altruism, trust, belongingness (community identification), perceived social interaction, and attitudes toward and expectations of the benefits of knowledge sharing. A path analysis shows that trust affects the expected personal and community benefits of knowledge sharing indirectly, through students’ general attitudes toward knowledge sharing. Altruism, trust, and belongingness affect the personal benefits of knowledge sharing indirectly through social interaction. No significant relation emerges with first-year study success. Knowledge sharing as added attribute does not appear aligned with study success measured by individual course grades.
ARTICLE HISTORY
Received 31 July 2017 Accepted 12 July 2018
KEYWORDS
Knowledge sharing; study success; social capital; learning communities; higher education
Introduction
Globalization, technology, competiveness, and the importance of the knowledge economy require a well-skilled labour force (World Economic Forum,2018). These trends in the labour market have impacted views on higher education responding to the needs of the global labour market (OECD, 2012). The supply of higher education graduates should contribute to the labour market’s demands (Harvey, 2000; Marginson & Van der Wende, 2007), but how can higher education institutions help students succeed? This question immediately gives rise to a second question: What makes a twenty-first century student successful?
Prior literature highlights various correlates of study success, measured as retention, grade point average, obtained credit points, or programme completion (Richardson,
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CONTACT Jasperina Brouwer jasperina.brouwer@rug.nl https://doi.org/10.1080/07294360.2018.1522619
Abraham, & Bond,2012; Schneider & Preckel,2017). Recently, Van der Zanden, Denes-sen, CillesDenes-sen, and Meijer (2018) operationalized study success as three dimensions: GPA (grade point average), critical thinking skills, and psychosocial development. Consistent with previous reviews (Richardson et al.,2012; Schneider & Preckel,2017), they found GPA was predominantly related to student characteristics, whereas critical thinking skills were associated with the learning environment. Grades seem not only important for becoming successful as a graduate, but the acquisition of specific attributes by gradu-ates has been emphasized, beyond disciplinary knowledge, as an important element of value for employability that can be achieved through the successful completion of a study programme (Barrie,2004; Green, Hammer, & Star,2009; Hughes & Barrie,2010; Yorke,2006). Universities increasingly recognize that students need to develop graduate attributes that will equip them for professional careers (Barrie,2007).
Rather than being specific to any discipline, generic graduate attributes refer to the transferable knowledge, skills, and abilities that higher education students should develop during their studies which contribute to career competencies and professional development. The rich literature in the space of higher education and graduate employ-ability points out a variety of graduate attributes. Which knowledge, skills and qualities are most important to develop as a preparation for future career and which are generic or disciplinary-dependent remains a topic of debate though (Barrie,2012; Bridgstock & Cunningham, 2016; Hill, Walkington, & France, 2016; Jones, 2013). In the guidelines for Graduate Attributes of the Four-Year Australian Undergraduate Psychology Program (2008), besides knowledge acquisition, the key goals are understanding and application of psychology, research methods, critical thinking skills, and communication skills (Cranney et al.,2009). According to Osmani, Weerakkody, and Hindi (2017), com-munication skills, teamwork, research skills, problem solving, creativity, critical thinking, and time management are the most valued graduate attributes, because of their potential importance in workplaces. Also Wellman (2010)finds that employees value transferable communication and relationship-building skills, as well as pertinent personal traits (e.g., team player, having ideas). Holmes (2013) exemplifies graduate attributes and skills in
higher education curricula and research in nine institutions in the UK indicating that communication and teamwork are commonly represented.
Communication and teamwork relate inevitably to social interactions and knowledge sharing. For organizations, different types of knowledge shared among employees can facilitate knowledge creation and innovations (Filieri & Alguezaui, 2014). Advances in information technology and competition among organizations increase the importance of knowledge sharing and knowledge management even more (Owen-Smith & Powell,
2004; Shu, Page, Gao, & Jiang, 2012). Thus, in addition to their discipline-specific
content knowledge, graduates need to be able to use their knowledge to solve problems, acquire new knowledge and skills (life-long learning), think critically, communicate and work with others with similar or distinct knowledge, and share their knowledge with team members (teamwork). Being able to interact, collaborate, and share knowledge in a team thus is valuable in every discipline.
To ensure that students develop the generic graduate attributes of interpersonal skills, teamwork, communication, interaction, and knowledge sharing (Harvey,2000; Osmani et al., 2017; Wellman, 2010), many degree programmes embed small group learning into their curricula. Small group learning enables students to interact and
thereby learn how to work and communicate in teams and share knowledge for both personal and group benefits. A well-known example is the introduction of learning communities (LCs); broadly defined, LCs are small groups, formed intentionally out of a cohort of students, that usually include 10–15 students and are guided by a mentor, such that the members get to know one another and feel as if they belong together (Brouwer, 2017; Tinto, 2000). These students actively construct knowledge together, mostly by collaborating on assignments (Tinto, 2000; Zhao & Kuh, 2004). Important characteristics of learning communities are shared knowing, sharing knowl-edge and shared responsibility (Tinto,2000), which implies that a positive attitude to sharing knowledge is a prerequisite.
Despite the potential importance of knowledge sharing as a graduate attribute in cur-riculum-based LCs, little is known about the contributing factors of students’ knowledge sharing in small groups. Most prior research into knowledge sharing focuses on organiz-ational contexts (Majid & Wey, 2009) or, if it addresses educational contexts, relies on virtual communities (Chen & Hung, 2010; Liou, Chih, Hsu, & Huang, 2016). To expand this research stream, the current study surveys actual undergraduate psychology students, then applies a path model of the determinants that might contribute to the stu-dents’ perceptions of the benefits of sharing knowledge for themselves or their learning community. These tested determinants include individual characteristics that likely con-tribute to social interactions and knowledge sharing (altruism, attitudes toward knowledge sharing), as well as forms of social capital (trust, social interaction). Finally, this study tests whether sharing knowledge according to personal or community outcome expectations relates to study success (i.e., weighted average marks).
Knowledge sharing
Personal and community outcome expectations in LCs
Knowledge sharing is a bi-directional process, in which group or team members exchange, modify, and re-use ideas and information, based on mutual trust (Chen & Hung,2010; Garcia-Sánchez, Diaz-Diaz, & De Saá-Pérez, 2017). Trust creates and helps maintain exchange relationships, which may encourage knowledge sharing (Alstyne, 2005; Droege & Hoobler, 2003; Hsu & Lin, 2008). In contrast with an altruistic view, which suggests that people share knowledge to help others without expecting any reward, social exchange theory takes a more egocentric perspective, such that people invest in others only when they can expect a valuable return in the future or else seek to return value for resources received in the past (Blau,1964; Cook & Rise,2003; Homans,1961). Whether a relationship persists then depends on the credit or payoff balance (Katz, Lazer, Arrow, & Contractor, 2004): People continue to support others because positive value accrues from the relationship situation.
Prior literature also distinguishes proactive helping from reactive helping (Spitzmul-ler & Van Dyne, 2013). Proactive helping benefits the individual self, such as one’s reputation or positive self-evaluation. This intrinsic motivation meets personal needs. Reactive helping instead benefits others and results from a sense of compassion for others in the group or in response to prior positive experiences with team members or the organization. Knowledge also might be shared for self-interested or other-inter-ested motives.
In an educational context, students similarly engage in knowledge sharing for different reasons, namely, due to personal or community-based motivations. Knowledge sharing sparked by personal outcome expectations suggests that the student believes that sharing his or her knowledge will result in personal benefits, such as an enhanced repu-tation. Knowledge sharing due to learning community outcome expectations instead implies a belief that when the student shares knowledge, it will help the community, such as increasing group knowledge (Chiu, Hsu, & Wang,2006).
Determinants of knowledge sharing
People share their knowledge when doing so feels meaningful and beneficial, and these perceptions arise for different reasons (Majid & Wey, 2009). Personality, psychological, and contextual factors all could enhance or inhibit such behaviours. Prior research, con-ducted in business organization and virtual community settings, identifies several determi-nants that likely enhance intentions to share knowledge (Chang & Chuang,2011; Chow & Chan,2008; Wang & Hou,2015). First, attitudes toward knowledge sharing refer to the extent to which a person has a general, positive attitude toward sharing knowledge. Although it likely relates to intentions to share knowledge (Chow & Chan, 2008), the precise link remains unclear, especially with regard to the different motives for sharing knowledge in LCs. Second, altruism is voluntary helping behaviour, without expecting a return (Chang & Chuang,2011), which relates positively to the intention of knowledge sharing in online settings (Hsu & Lin,2008). Therefore, as a personality factor, altruism should contribute to a positive general attitude toward sharing knowledge and interacting with others. Third, when people interact in a group, they identify with it and perceive a role in that group, prompting perceived group membership and belonging (Hsu & Lin,
2008). This perception of belonging to the group in turn encourages them to interact more, giving them more opportunities to share their knowledge. Identification with a group thus seems important; students also are more willing to share knowledge with group members than with non-group members (Majid & Wey,2009).
Social capital in LCs
The learning community structure establishes an interactive, collaborative environment that helps students’ transition to the university setting, by encouraging social interaction and building social capital (Brouwer, Jansen, Flache, & Hofman,2016). Social capital can be defined as a person’s access to and use of valuable resources (e.g., information, knowl-edge), obtained through social relations or networks, based on trust, social norms, and values that help him or her attain personal goals (Coleman, 1990; Lin, 1999; Putnam,
1995). For students, social capital implies they can attain personal goals that could not be achieved without such valuable resources (cf. Coleman, 1990), including study success and the attainment of graduate attributes that equip students to deal with the demands of the job market after their graduation.
Mutual social trust is a crucial form of social capital that facilitates social interaction and also functions as a prerequisite for a willingness to share study-related information and knowledge, such that it also influences study success (Coleman, 1990; Putnam,
perspectives, and conceptualized variously as a personality trait, social structure, belief, or behaviour (McKnight & Chervany, 2001). Trust literally indicates a firm belief in the reliability, truth, or ability of someone or something (definition derived from the Oxford Dictionary). In risky or unknown situations, it comes into play, unlike in familiar situations with low risks (Cook,2005). For first-year students, the university context is unfamiliar, and they must develop trustworthy relationships over time. Without trust, small groups can create feelings of unease and an unwillingness to share information, opinions, or ideas (Chang, Diaz, & Hung,2015). Yet research about the impact of trust on knowledge sharing is inconclusive. Chang and Chuang (2011) find a positive link between social trust and the quality of knowledge sharing, but Chow and Chan (2008) do not uncover any significant effect of social trust on attitudes toward knowledge sharing, though attitudes toward knowledge sharing contribute to intentions to share knowledge. Previous learning community research has not explicitly linked trust to per-sonal or community outcome expectations of knowledge sharing.
In addition, the information potential inherent to social relations constitutes an impor-tant form of social capital. Relationships and interactions with fellow students provide social capital that facilitates knowledge and informational exchanges among peers (Coleman,
1990). University students must share knowledge, because only by sharing knowledge can they extend their individual and collective group social capital (Oh, Labianca, & Chung,2006). As higher education research has shown, working in small groups and knowl-edge sharing constitute important elements of the learning process that may improve per-formance (Laal & Ghodsi,2012). Tinto’s (1997) interactionalistic model emphasises the added value of social interactions, whether study- or non–study-related interaction, among fellow students for study success. Unlike lecture-centred approaches that focus on instruction, in LCs, the focus is on social interactions, collaboration, and knowledge sharing (Majid & Wey,2009). As previously mentioned, according to Tinto (2000), knowl-edge sharing is a central facet of LCs: When students mutually exchange study-related infor-mation, ideas, and expertise (Garcia-Sánchez et al., 2017), it can contribute to the competence of individual students or the learning community as a whole.
Study overview
Learning communities exist to achieve various positive outcomes related to graduate attri-butes, including collaboration, engagement, and, ultimately, study success (Butler & Dawkins, 2008; Zhao & Kuh, 2004), all of which may demand knowledge sharing. Because knowledge sharing is one of the important aspects of learning communities, this is the central theme in our study. To our best knowledge, most studies of knowledge sharing consider organizational or virtual contexts (Chiu et al.,2006; Lin, Hung, & Chen,
2009; Usoro, Sharratt, Tsui, & Shekhar,2007), with little research focused on the determi-nants of knowledge sharing among university students participating in LCs. To enhance knowledge sharing in such settings, it is critical to determine why students share their knowledge (Chen & Hung,2010). A contrast of the different motives for sharing
knowl-edge provides a nuanced view that can shed new light on this important question. The cri-ticality of social interaction and a view of knowledge sharing as a developing graduate attribute within LCs, with possible effects on student success, guide these research ques-tions. Formally:
(1) To what extent do altruism, trust, and belongingness (identification with the learning community) contribute to social interaction and general attitudes toward sharing knowledge, and how do general attitudes and social interaction relate to personal outcome and community outcome expectations of knowledge sharing?
(2) To what extent does knowledge sharing with personal and community outcome expectations relate to study success in LCs?
By leveraging previous literature, the conceptual model for the present research in turn predicts the following relations:
(1) Altruism, trust, and belongingness, through the general attitude toward knowledge sharing, relate indirectly to knowledge sharing for personal outcome expectations and community outcome expectations.
(2) Altruism, trust, and belongingness, through social interactions, relate indirectly to knowledge sharing for personal outcome expectations and community outcome expectations.
(3) Knowledge sharing for personal outcome expectations and community outcome expectations contributes directly to study success.
Methods
Participants and procedure
At a Dutch, research-intensive university,first-year psychology students were assigned to a learning community at the start of thefirst semester and received invitations to participate in an online survey, maintained by the online research management SONA-system, which allows students to obtain research participation credits. Students sign up voluntarily through the SONA-system and receive credits automatically after their participation. In the introduction to the survey, students learned about the research goals and the study procedure. The study also was approved by the university’s ethical committee, and the stu-dents provided informed consent to participate and have their results released more widely. The online survey data were collected at the end of theirfirst semester (November 2015–January 2016). Surveys were provided in English, and the amount of time needed to complete the survey was about 15 minutes. Initially 260 students enrolled for the study, but 77 (29.6%) were excluded because they did not provide their ID-number for releasing their study results, did not respond to any question or did not give serious answers (e.g., same scores for all items). Thus the sample of 183 student participants included 152 (83%) women, 31 (17%) men, with mean ages between 19 and 20 years. The response rate was 34% (183 of 534 psychology students in total).
Measurements
In addition to gathering background characteristics, such as gender, age, and nationality, the measurement items, derived from previously validated measures, either validated in the higher education context or organizational contexts. The items validated in the organ-izational context were adapted to the (Dutch) higher education context and LCs. Students
responded on 5-point Likert scales (1 =‘strongly disagree’ to 5 = ‘strongly agree’).Table 1
contains the scale items, means (M ), standard deviations (SD), and reliability (α). The Cronbach’s alpha coefficients indicated good internal consistency for the scales, ranging from .75 to .89.
Altruism
The measure for altruism was derived from compassion and solidarity scales, as applied in organizational contexts (Boom & Pennink,2012). Altruism referred to the extent to which students were willing to help other students. An example item read,‘I am willing to listen to my fellow students if they have problems’.
Trust
Trust was measured with seven items, assessing the extent to which members of the learn-ing community could rely on one another for support. These items came from studies of organizational contexts (Chow & Chan, 2008) and were adapted to the educational context. An example item was,‘Fellow students will help me whenever needed’.
Belongingness
For belongingness, the measure reflected the extent to which students identify with the group, using items such as,‘I feel a sense of belonging towards my group of fellow stu-dents’. Together, the items reveal the sense of belongingness, positive feeling toward the learning community, and group cohesion. This scale came from a measure applied in a virtual learning community context, modified to fit offline, curriculum-based LCs (Chiu et al.,2006).
General attitude toward knowledge sharing
The attitude toward knowledge sharing measure refers to favourable or positive feelings about knowledge sharing in general (Chiu et al., 2006; Chow & Chan, 2008). An example item was,‘Sharing knowledge with fellow students is always good’.
Table 1.Descriptive statistics, reliabilities, and bivariate correlations. Mean SD α (n items) 1 2 3 4 5 6 7 8 1. Altruism 4.08 0.49 .83 (5) 1.00 2. Trust 3.84 0.50 .81 (7) .44** 1.00 3. Belongingness/ identification 3.77 0.73 .89 (4) .37** .70** 1.00 4. General attitude knowledge sharing 3.86 0.61 .80 (4) .30** .48** .41** 1.00 5. Social interaction 3.93 0.48 .78 (7) .55** .74** .73** .37** 1.00 6. Knowledge sharing POE 3.79 0.48 .83 (6) .24** .33** .27** .44** .31** 1.00 7. Knowledge sharing COE 3.79 0.55 .75 (3) .26** .29** .22* .41** .21* .61** 1.00 8. Study success year 1 6.25 1.13 −.09 −.03 −.12 −.01 −.06 −.03 −.01 1.00 Note: POE = personal outcome expectations; COE = community outcome expectations.N = 183.
Social interaction
The measure of social interaction reflects perceived interactions of students with their fellow students, pertaining to non-study-related interactions (outside the LCs), discussion about study-related topics and collaboration (Severiens, Ten Dam, & Blom, 2006). An example item was,‘I work well with my fellow students’.
Knowledge sharing expectations
Knowledge sharing might result from the willingness to behave this way, in an effort to achieve individual purposes or personal outcome expectations, such as an enhanced personal reputation or more friendships (Chiu et al., 2006; Chow & Chan, 2008). Thus the survey included, for example, ‘Sharing my knowledge will help me to make friends with fellow students’. It also could stem from a willingness to share in an effort to achieve group purposes, or community outcome expectations, such as better group functioning (Chiu et al., 2006; Chow & Chan, 2008). For example, the survey included,‘Sharing my knowledge will help my group of fellow students accumu-late or enrich knowledge’.
Academic goals (study success)
Study success provides an indicator of the extent to which students achieve their academic goals, measured as the weighted average mark. That is, the students’ grades have been weighted by their obtained credit points (European credits), divided by the maximum credit points that they could have obtained during theirfirst year (60 credits).
Statistical analysis
The proportion of missing cases was 12% on study success (N = 183). The Little’s MCAR (missing completely at random) test resulted in a significant chi-square, indicating that the data were not missing completely at random (χ2(7) = 15.59, p < .05). Thus, the variables appear MAR (missing at random), because they relate to the observed data (e.g., De Leeuw, Hox, & Huisman,2003; Little & Rubin,1987). In turn, the missing values were replaced using SPSS version 23, which imputed the missing datafive times.
The conceptual model – with the hypothesized relationships of the antecedents of knowledge sharing, social interaction, knowledge sharing for personal and community outcome expectations, and study success – was tested by path analysis in Mplus Version 7.2. Maximum likelihood estimation with robust standard errors estimation (MLR) applied to handle the MAR. The overall goodness offit of the tested model was derived from several indices: the ratio between the chi-square and degrees of freedom (χ2/df ), comparative fit index (CFI), root mean square error of approximation (RMSEA), standardized root mean square (SRMR), and Tucker–Lewis index (TLI). Indi-cations of a good fit require a nonsignificant χ2/df ratio, RMSEA values less than .06, SRMR at .08 or below, and CFI and TLI close to or greater than .95 (Hu & Bentler,
1999; Kline,2011). The indirect effects instead rely on bias-corrected bootstrapped confi-dence intervals (Shrout & Bolger,2002). Students were nested in LCs, so this study con-trolled for dependency among members of the same community, using the complex option to adjust the standard errors (as in the MLR estimation too).
Results
Correlation analysis
The bivariate correlation analysis explores the relationships across the predicted determi-nants of knowledge sharing (altruism, trust, belongingness, social interaction, general atti-tudes toward knowledge sharing) and sharing knowledge with different outcome expectations, as well as with study success. The correlation analysis shows mainly positive and significant relationships, though none of the variables relate significantly to study success. Trust is highly correlated with social interaction (r = .74, p≤ .001). Sharing knowl-edge due to personal outcome expectations and due to community outcome expectations correlate relatively strongly and positively (r = .61, p≤ .001). Table 1 provides more details.
Path analysis
The path model in the conceptual model (Figure 1)fit the data well: χ2(12) = 12.832; p = .38, CFI = .998, TLI = .995, RMSEA = .020; 90% confidence interval [.000; .082], and SRMR = .033, as further detailed in Figure 2. Sharing knowledge for different reasons,
whether personal or community oriented, does not contribute to study success directly or indirectly, but a positive general attitude toward sharing knowledge contributes to sharing knowledge for personal and community benefits. Trust appears important for developing a positive general attitude about knowledge sharing and social interactions with fellow students. Altruism and belongingness or identification with the community contribute positively to social interactions and thus to sharing knowledge for personal reasons. The relatively strong relationships of trust with general attitudes toward knowl-edge sharing and, to a lesser extent, social interactions indicate that trust among learning community members helps build social capital and develop knowledge-sharing attitudes, as a graduate attribute.
As expected, several indirect effects emerge from the data. First, altruism (b∗
indirect= .04
[.01;.09]), trust (b∗indirect= .05 [.02;.13]), belongingness (b∗indirect= .07 [.02;.14]) relate indirectly to sharing knowledge for personal reasons, through social interaction. Second, trust and sharing knowledge for both personal reasons (b∗indirect= .16 [.07;.27]) and community reasons (b∗indirect= .16 [.06;.26]) relate indirectly through beneficial
general attitudes toward knowledge sharing. The model explains 67% of the variance in social interaction, 29% in general attitudes toward knowledge sharing, 25% in sharing knowledge for personal reasons, and 20% in sharing knowledge for community reasons. The standardized coefficients indicate that trust contributes most to general beneficial atti-tudes to sharing knowledge and thus to sharing knowledge due to personal and commu-nity outcome expectations.
Discussion
Nowadays, economies are driven by knowledge and creative ideas. Knowledge has a powerful influence, such that it can lead to the prosperity of not just countries but also industries, organizations, and institutions. In both organizational and economic senses, group members must be willing and able to share their knowledge with other group members (Pérez-Luño, Cabello Medina, Carmona Lavado, & Cuevas Rodríguez, 2011). Knowledge sharing as a graduate attribute in higher education in turn is an employability skill that can help students succeed in their future careers (Wei, Choy, Chew, & Yen,
2012). Most research on knowledge sharing in communities refers to virtual communities, such as online social networks (Chen & Hung,2010; Liou et al.,2016), but it also is perti-nent to make students aware of the importance of and motives for knowledge sharing in a learning environment that naturally encourages knowledge sharing, namely, curriculum-based LCs. In these LCs, students meet daily, get to know one another and can develop trusting relationships (Katz et al.,2004). As social capital, trust may facilitate social inter-actions and positive attitudes toward sharing knowledge, both of which are important for sharing knowledge to achieve personal and community outcomes (cf. Coleman, 1990; Putnam,1995). The importance of trust as a prerequisite for social interaction and knowl-edge sharing is in line with Liou et al.’s (2016) identification of a mediating role of trust in virtual communities.
The current study, conducted among curriculum-based LCs, contributes to extant lit-erature by shedding new light on the factors that affect expectations of knowledge sharing amongfirst-year students in a real-world setting. The importance of trust, belongingness or group identification, and general attitudes toward knowledge sharing are confirmed in Figure 2.Final path model.
curriculum-based LCs, going beyond the virtual environments described in prior research (e.g., Yao, Tsai, & Fang,2015). In turn, universities should realize that knowledge sharing is not a spontaneous action that students undertake automatically. Students may struggle to share their knowledge, especially if they have not yet developed trustworthy relation-ships. They also might experience competition, due to an individual grading system, or be afraid to contribute to a discussion for fear of providing incorrect information (Ardich-vili, Page, & Wentling,2003; Majid & Wey,2009). Furthermore, this research reveals that expectations of the benefits of knowledge sharing are not related to first-year study success, measured as a weighted grade point average. The individual grading system thus appears poorly aligned with the community activities, even though the LCs are implemented expli-citly to enhance student interactions, as a basic requirement for knowledge sharing. Stu-dents may not be aware of the potential benefits of sharing knowledge for themselves and the community, so they might refrain from this activity. In this real-world degree pro-gramme, knowledge sharing is not an explicit curriculum goal, so no explicit measure of this ability is included in exams. In a sense, knowledge sharing appears like an added value, beyond subject knowledge. Thus, the lack of relation between knowledge sharing and grades is not particularly surprising and consistent with the debate about the con-structive alignment and the challenges of the assessment of graduate attributes (Hughes & Barrie,2010).
This study emphasises the need to engage in constructive alignment, to integrate the development of graduate attributes into degree programmes (Treleaven & Voola,2008). In this sense, universities could use thefindings of this study as guidelines to increase knowledge sharing and learning in their LCs. First, they shouldfind ways to increase trust among learning community members, because this determinant encourages knowledge sharing to achieve different, positive outcomes. To foster knowledge sharing, as a graduate attribute that needs to be developed, universities should require specific tasks that prompt students to experience interdependence and interactions in groups (Mastin & Yoon, 2013) and build trustworthy relationships among group members. Second, assessment is important for learning and therefore it is important that students are actively involved in the evaluation process (Boud & Falchikov,
2006). Written exams provide fewer opportunities to learn actively and develop such complex skills (Treleaven & Voola, 2008). According to Hughes and Barrie (2010), graduate attributes should not be assessed according to individual course grades; rather, students should be part of the assessment, which should align with the curricu-lum programme as a whole. Reflective journals, portfolios, peer and instructor feedback, individual and team-based assignments in which attributes as teamwork, knowledge sharing and communication are explicitly addressed, provide opportunities to develop and assess graduate attributes, such as sharing knowledge, collaboration, and critical thinking.
Limitations and research directions
A strength of this study is that it investigates the perceptions of students regarding the expected benefits of knowledge sharing in real-world LCs; however, these students are voluntary participants, from one degree programme in one university. Replications in other programmes and institutions could offer a more comprehensive picture of the
value of knowledge sharing for different motives and the potentially distinct determinants in different educational settings.
This study gives educators, teachers, and other practitioners who design LCs some insights, including the need to foster altruism or pro-social behaviour, trustworthy relationships, group identification, beneficial attitudes toward knowledge sharing, and social interactions, to enhance knowledge sharing that can benefit both individuals and the community. Further research should seek additional insights and recommendations for how LCs should be designed to create social capital, including trust and knowledge sharing, to the benefit of the individual students and their learning community. The survey results offer a clearer understanding of the determinants of knowledge sharing with different outcome expectations at group and individual levels but not about with whom or what kind of knowledge people share in their interactions with fellow students. Analysis of longitudinally collected student networks can provide insight in how students share their knowledge in their networks, depending on their structural positions, personal characteristics, and factors such as trust and social norms in the learning community (cf. Brouwer, Flache, Jansen, Hofman, & Steglich,2018). Further research should investigate actual knowledge sharing, perhaps by using video observations in LCs. This could provide valuable insights about how students collaborate and share their knowledge (Leech & Onwuegbuzie, 2007), including with whom each student shares knowledge and what kind of information they share.
Conclusion
This article addresses an ongoing debate about whether student success involves more than good grades and also relates to the acquisition of graduate attributes that are necessary in career contexts. One graduate attribute, namely sharing knowledge with different outcome expectations, is the focus of the current research, which relies on social capital theory (Coleman,1990; Lin, 1999; Putnam,1995), Tinto’s (2000) inter-actionalist approach, and social exchange theory (Blau, 1964; Cook & Rise, 2003; Homans, 1961). A path model shows that an expectation that sharing knowledge will be beneficial, personally for the student and for the community, depends on the student’s general attitudes toward knowledge sharing and social interaction within the learning community. Trust contributes to a general attitude toward knowledge sharing and thus to knowledge sharing with expected personal and community out-comes, in partial support of the study predictions. Altruism, trust, and identification with the learning community (belongingness) contribute to social interaction and thus to knowledge sharing with expected personal outcomes. Trust in particular is an important incentive to encourage knowledge sharing with both personal and com-munity outcome expectations. Yet another prediction from the model was not sup-ported; study success, measured as the weighted grade point average after one year, was not significantly affected by expectations about the benefits of knowledge sharing. Student grades thus do not appear to align with a curriculum design that relies on LCs that encourage knowledge sharing development as a graduate attribute. Because assessments should better align with the curriculum goals, it seems useful to engage students to reflect on their own learning processes, as they relate to the gradu-ate attribute of knowledge sharing and interactions in LCs.
Acknowledgement
The authors thank Laura Koster for her contributions to the data collection for this research.
Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Jasperina Brouwer http://orcid.org/0000-0002-7332-9320
Ellen Jansen http://orcid.org/0000-0001-9618-1034
References
Alstyne, M. W. V. (2005). Create colleagues, not competitors. Harvard Business Review, 83(9), 24– 25.
Ardichvili, A., Page, V., & Wentling, T. (2003). Motivation and barriers to participation in virtual knowledge-sharing communities of practice. Journal of Knowledge Management, 7(1), 64–77.
doi:10.1108/13673270370310463626
Barrie, S. C. (2004). A research-based approach to generic graduate attributes policy. Higher Education Research & Development, 23(3), 261–275.doi:10.1080/0729436042000235391
Barrie, S. C. (2007). A conceptual framework for the teaching and learning of generic graduate attri-butes. Studies in Higher Education, 32(4), 439–458.
Barrie, S. C. (2012). A research-based approach to generic graduate attributes policy. Higher Education Research & Development, 31(1), 79–92.doi:10.1080/07294360.2012.642842
Blau, P. M. (1964). Exchange and power in social life. New York: Wiley.
Boom, I. H., & Pennink, B. W. (2012). The relationship between humanness and knowledge sharing in Malaysia empirical evidence from Malaysian managers. Gadjah Mada International Journal of Business, 14(2), 99–122.
Boud, D., & Falchikov, N. (2006). Aligning assessment with long-term learning. Assessment & Evaluation in Higher Education, 31(4), 399–413.doi:10.1080/0260293060067905
Bridgstock, R., & Cunningham, S. (2016). Creative labour and graduate outcomes: Implications for higher education and cultural policy. International Journal of Cultural Policy, 22(1), 10–26.
doi:10.1080/10286632.2015.1101086
Brouwer, J. (2017). Connecting, interacting and supporting. Social capital, peer network and cogni-tive perspeccogni-tives on small group teaching (Doctoral thesis). University of Groningen.
Brouwer, J., Flache, A., Jansen, E., Hofman, A., & Steglich, C. (2018). Emergent achievement seg-regation in freshmen learning community networks. Higher Education, 76(3), 483–500.doi:10. 1007/s10734-017-0221-2
Brouwer, J., Jansen, E., Flache, A., & Hofman, A. (2016). The impact of social capital on self-efficacy and study success amongfirst-year university students. Learning and Individual Differences, 52, 109–118.doi:10/1016/j.lindif.2016.09.016
Butler, K. L., & Dawkins, P. W. (2008). The impact of a“healthy youth” learning community on student learning outcome measures. Journal of Negro Education, 77(3), 264–270.
Chang, H. H., & Chuang, S.-S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & Management, 48, 9–18.
doi:10.1016/j.im.2010.11.001
Chang, W.-L., Diaz, A. N., & Hung, P. C. K. (2015). Estimating trust value: A social network per-spective. Information Systems Frontiers, 17, 1381–1400.doi:10.1007/s10796-014-9519-0
Chen, C.-J., & Hung, S.-W. (2010). To give or to receive? Factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Information & Management, 47(4), 226–236.
Chiu, C., Hsu, M., & Wang, E. T. G. (2006). Understanding knowledge sharing in virtual commu-nities: An integration of social capital and social cognitive theories. Decision Support Systems, 42, 1872–1888.
Chow, W. S., & Chan, L. S. (2008). Social network, social trust and shared goals in organizational knowledge sharing. Information & Management, 45, 458–465.doi:10.1016/j.im.2008.06.007
Coleman, J. S. (1990). Foundations of social theory. Cambridge, MA: Belknap Press.
Cook, K. S. (2005). Networks, norms, and trust: The social psychology of social capital: 2004 Cooley Mead Award address. Social Psychology Quarterly, 68(1), 4–14.
Cook, K. S., & Rise, E. (2003). Social exchange theory. In J. Delamater (Ed.), Handbook of social psychology (pp. 53–76). New York: Kluwer Academic/ Plenum Publishers.
Cranney, J., Turnbull, C., Provost, S. C., Martin, F., Katsikitis, M., White, F. A.,… Varcin, K. J.
(2009). Graduate attributes of the 4-year Australian undergraduate psychology program.
Australian Psychologist, 44(4), 253–262.doi:10.1080/00050060903037268
De Leeuw, E. D., Hox, J., & Huisman, M. (2003). Prevention and treatment of item nonresponse. Journal of Official Statistics, 19(2), 153–176.
Droege, S. B., & Hoobler, J. M. (2003). Employee turnover and tacit knowledge diffusion: A network perspective. Journal of Managerial Issues, 15(1), 50–65.
Filieri, R., & Alguezaui, A. (2014). Structural social capital and innovation. Is knowledge transfer the missing link? Journal of Knowledge Management, 18(4), 728–757.
Garcia-Sánchez, P., Diaz-Diaz, N. L., & De Saá-Pérez, P. (2017). Social capital and knowledge sharing in academic research teams. International Review of Administrative Sciences. Advance online publication.
Green, W., Hammer, S., & Star, C. (2009). Facing up to the challenge: Why is it so hard to develop graduate attributes? Higher Education Research & Development, 28(1), 17–29.
Harvey, L. (2000). New realities: The relationship between higher education and employment. Tertiary Education and Management, 6(1), 3–17.
Hill, J., Walkington, H., & France, D. (2016). Graduate attributes: Implications for higher education practice and policy. Journal of Geography in Higher Education, 40(2), 155–163. doi:10.1080/ 03098265.2016.1154932
Holmes, L. (2013). Competing perspectives on graduate employability: Possession, position, or process? Studies in Higher Education, 38(4), 538–554.
Homans, G. C. (1961). Social behavior and its elementary forms. New York: Harcourt, Brace and World.
Hsu, C.-L., & Lin, J. C.-C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45, 65–74.
doi:10.1016/j.im.2007.11.001
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural Equation Modeling: A
Multidisciplinary Journal, 6(1), 1–55.doi:10.1080/10705519909540118
Hughes, C., & Barrie, S. (2010). Influences on the assessment of graduate attributes in higher
edu-cation. Assessment & Evaluation in Higher Education, 35(3), 325–334. doi:10.1080/
02602930903221485
Jones, A. (2013). There is nothing generic about graduate attributes: Unpacking the scope of
context. Journal of Further and Higher Education, 37(5), 591–605. doi:10.1080/0309877X.
2011.645466
Katz, N., Lazer, D., Arrow, H., & Contractor, N. (2004). Network theory and small groups. Small Group Research, 35(3), 307–332.doi:10.1177/1046496404264941
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Publications.
Laal, M., & Ghodsi, S. M. (2012). Benefits of collaborative learning. Procedia - Social and Behavioral
Sciences, 31, 486–490.
Leech, N. L., & Onwuegbuzie, A. J. (2007). An array of qualitative data analysis tools: A call for data analysis triangulation. School Psychology Quarterly, 22(4), 557–584.doi:10.1037/1045-3830. 22.4.557
Lin, M. J., Hung, S. W., & Chen, C. (2009). Fostering the determinants of knowledge sharing in professional virtual communities. Computers in Human Behavior, 25, 929–939.
Lin, N. (1999). Social networks and status attainment. Annual Review of Sociology, 25, 467–487. Liou, D.-L., Chih, W.-H., Hsu, L.-C., & Huang, C.-Y. (2016). Investigating information sharing
behavior: The mediating roles of the desire to share information in virtual communities.
Information Systems and e-Business Management, 14, 187–216.
doi:10.1007/s10257-015-0279-2
Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley. Majid, S., & Wey, S. M. (2009). Perceptions and knowledge sharing practices of graduate students in
Singapore. International Journal of Knowledge Management, 5(2), 21–32. doi:10.4018/jkm.
2009040102
Marginson, S., & Van der Wende, M. (2007). Globalisation and higher education. OECD Education
Working Papers No. 8.doi:10.1787/173831738240
Mastin, T., & Yoon, K. (2013). Benefits of group knowledge sharing for student teams. College Teaching, 61(4), 153–154.doi:10.1080/87567555.2013.776005
McKnight, D. H., & Chervany, N. L. (2001). Trust and distrust definitions: One bite at a time. In R. Falcone, M. Singh, & Y.-H. Tan (Eds.), Trust in cyber-societies (pp. 27–54, LNAI 2246). Berlin: Springer.
OECD. (2012). Better skills, better jobs, better lives: A strategic approach to skills policies. OECD Publishing.doi:10.1787/9789264177338-en
Oh, H., Labianca, G., & Chung, M. (2006). A multilevel model of group social capital. Academy of Management Review, 31(3), 569–582.
Osmani, M., Weerakkody, V., & Hindi, N. (2017). Graduate attributes in higher education: Examining academics’ perception in the Middle East. Journal of Education for Business, 92(2), 53–64.doi:10.1080/08832323.2016.1274713
Owen-Smith, J., & Powell, W. W. (2004). Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15(1), 5–21. Pérez-Luño, A., Cabello Medina, C., Carmona Lavado, A., & Cuevas Rodríguez, G. (2011). How
social capital and knowledge affect innovation. Journal of Business Research, 64, 1369–1376. Putnam, R. D. (1995). Bowling alone: America’s declining social capital. Journal of Democracy, 6(1),
65–78.
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’
academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2),
353–387.doi:10.1037/a0026838
Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565–600.
Severiens, S., Ten Dam, G., & Blom, S. (2006). Comparison of Dutch ethnic minority and majority engineering students: Social and academic integration. International Journal of Inclusive Education, 10(1), 75–89.doi:10.1080/13603110500221651
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New
procedures and recommendations. Psychological Methods, 7(4), 422–445.
doi:10.1037/1082-989X.7.4.422
Shu, C., Page, A. L., Gao, S., & Jiang, X. (2012). Managerial ties andfirm innovation: Is knowledge creation a missing link? Journal of Product Innovation Management, 29(1), 125–143.
Spitzmuller, M., & Van Dyne, L. (2013). Proactive and reactive helping: Contrasting the positive consequences of different forms of helping. Journal of Organizational Behavior, 34, 560–580.
doi:10.1002/job.1848
Tinto, V. (1997). Classrooms as communities: Exploring the educational character of student per-sistence. The Journal of Higher Education, 68(6), 599–623.
Tinto, V. (2000). Exploring the role of the college classroom in student departure. In J. M. Braxton (Ed.), Reworking the student departure puzzle (pp. 81–94). Nashville, TN: Vanderbilt University Press.
Treleaven, L., & Voola, R. (2008). Integrating the development of graduate attributes through con-structive alignment. Journal of Marketing Education, 30(2), 160–173.
Usoro, A., Sharratt, M. W., Tsui, E., & Shekhar, S. (2007). Trust as an antecedent to knowledge sharing in virtual communities of practice. Knowledge Management Research & Practice, 5, 199–212.
Van der Zanden, P. J., Denessen, E., Cillessen, A. H. N., & Meijer, P. C. (2018). Domains and pre-dictors offirst-year student success: A systematic literature review. Educational Research Review.
doi:10.1016/j.edurev.2018.01.001
Wang, W., & Hou, Y. (2015). Motivations of employees’ knowledge sharing : A self-determination perspective. Information and Organization, 25, 1–26.
Wei, C. C., Choy, C. S., Chew, G. G., & Yen, Y. Y. (2012). Knowledge sharing patterns of under-graduate students. Library Review, 61(5), 327–344.doi:10.1108/00242531211280469
Wellman, N. (2010). The employability attributes required of new marketing graduates. Marketing Intelligence & Planning, 28(7), 908–930.doi:10.1108/02634501011086490
World Economic Forum. (2018). World economic forum annual meeting 2018: Creating a shared
future in a fractured world. Geneva: Author. Retrieved from https://www.weforum.org/
reports/world-economic-forum-annual-meeting-2018-creating-a-shared-future-in-a-fractured-world
Yao, C., Tsai, C., & Fang, Y. (2015). Understanding social capital, team learning, members’ e-loyalty and knowledge sharing in virtual communities. Total Quality Management & Business Excellence, 26(5-6), 619–631.
Yorke, M. (2006). Employability in higher education: What it is, what it is not. Higher Education
Academy. Retrieved from
https://www.heacademy.ac.uk/knowledge-hub/employability-higher-education-what-it-what-it-not
Zhao, C.-M., & Kuh, G. D. (2004). Adding value: Learning communities and student engagement. Research in Higher Education, 45(2), 115–138.doi:10.1023/B:RIHE.0000015692.88534.de
