Regulation of Motivation: Students’ Motivation Management in Online Collaborative Groupwork
by Jianzhong Xu & Jianxia Du - 2013
Background: Online learning is becoming a global phenomenon and has a steadily growing influence on how learning is delivered at universities worldwide. Motivation of students, however, has become one of the most serious problems in one important aspect of online learning—online collaborative groupwork or online group homework. It is surprising to note that few empirical studies have focused on how to enhance and sustain student motivation to work together in online learning environments.
Purpose: The propose of the present study is to propose and test empirical models of variables posited to predict students’ motivation management in online groupwork, with the models informed by (a) research and theorizing on regulation of motivation and (b) findings from online groupwork that alluded to a number of factors that may influence motivation management in online learning environments.
Research Design: The study reported here used cross-sectional survey data.
Participants: The participants were 150 graduate students from 46 online groups in the southeastern United States.
Results: Results from the multilevel analyses revealed that most of the variance in groupwork motivation management occurred at the student level, with online groupwork interest as the only significant predictor at the group level. At the student level, the variation in groupwork motivation management was positively related to student initiative, including arranging the environment, managing study time, and help seeking. In addition, groupwork motivation management was positively related to feedback from the instructor and peers.
Conclusion: As most of the variance in online groupwork motivation management occurred at the student level rather than at the group level, online groupwork motivation management was largely a function of individual student characteristics and experiences. The present study further suggests that feedback and student initiative (arranging the environment, managing study time, and help seeking) play an important role in online groupwork motivation management. Consequently, it would be beneficial to promote feedback among the instructor and group members in the online groupwork process. In addition, it would be beneficial to encourage students to take more initiative in online groupwork settings to better manage their motivation.
With the development of the Internet and telecommunication technologies, online learning is becoming a global phenomenon. The movement towards online learning has prompted many instructors to incorporate online collaborative groups in their courses. Online collaborative groups are typically small (e.g., two to four students), interdependent, and heterogeneous groups designed to resolve ill-structured problems (e.g., problem- or case-based learning; Jonassen, 2000) through problem exploration and group consensus (Smith, 2005), where the instructor typically serves as the facilitator. They are designed to help online learners, for example, develop communication and problem-solving skills, provide them with opportunities to learn with and from peers (e.g., to share, consider, and challenge one another's ideas), form beneficial learning relationships, and prepare them for professional careers (Brown, 1992; Smith, 2005; Smith, Sorensen, Gump, Heindel, Caris, & Martinez, 2011; Tutty & Klein, 2008).
Yet, online groupwork presents new and significant motivational challenges for students, relating to communication, scheduling, individual accountability, and increased dependence on peers (Brindley, Walti, & Blaschke, 2009; Davies, 2009; Liu, Joy, & Griffiths, 2010; Roberts & McInnerney, 2007; Piezon & Ferree, 2008; Smith et al., 2011; Thompson & McGregor, 2009). Largely due to these challenges, students' ability to influence their own motivation becomes crucial to their learning in online collaborative learning environments. It is surprising to note that online groupwork motivation management (i.e., students efforts to monitor their motivation to follow through on online group assignments) is noticeably absent from much contemporary research on online collaborative learning. Similarly, it is surprising to note that the design of collaborative online collaborative learning activities has received little attention, especially on how to help online students deal with motivational challenges. The lack of research in this area is particularly troubling in the light of increasing calls to attend to the issue of how to promote and sustain students motivation in online collaborative learning environments (Brindley et al., 2009; Davies, 2009; Roberts & McInnerney, 2007; Rovai, 2007; Thompson & McGregor, 2009; Zafeiriou, Nunes, & Ford, 2001).
Consequently, there is a critical need to propose and test models of factors that predict students motivation management while doing online groupwork. This line of research is important, as regulation of motivation has been found to have a powerful influence on engagement, persistence, performance, and achievement, within varied contexts and with diverse learners and learning tasks (Sansone, Wiebe, & Morgan, 1999; Schwinger, Steinmayr, & Spinath, 2009; Wolters, 1999, 2011). This line of research is particularly important, as students tend to have a more negative attitude toward online groupwork as compared with face-to-face groupwork (Smith et al., 2011; Tutty & Klein, 2008). This negativity is partly because online groupwork requires increased time and dependence on others, which is in direct conflict with student expectations toward online courses (Piezon & Ferree, 2008). In addition, online students often face logistical challenges and motivational obstacles in participating in group-oriented online activities (e.g., geographical distance, and varying time zones and work schedules, coupled with fewer channels available for communication; Havard, Du, & Xu, 2008; Liu et al., 2010; Piezon & Perree, 2008).
RESEARCH AND THEORIZING ON REGULATION OF MOTIVATION
One theoretical framework that bears direct relevance to online groupwork motivation management is self-regulated learning, with regulation of motivation in particular (Pintrich, 2004; Schunk, 2005; Winne, 2001; Wolters, 2003; Zimmerman, 2000, 2008). Regulation of motivation is frequently discussed under the general heading of volitional control (Boekaerts & Corno, 2005; Corno, 1994, 2001, 2004; Corno & Kanfer, 1993; Husman, McCann, & Crowson, 2000; Kuhl, 1984, 2000; Van Eerde, 2000; Winne, 2004; Wolters, 2003). The term volition refers to both the strength of will needed to complete a task and the diligence to pursuit it (Corno, 1993). Volitional control focuses on issues of implementation that occur after goals are set, characterized by self-regulatory activities involved in purposive and persistent striving. In Kuhls (1985) taxonomy of volitional strategies that an individual might use to facilitate the enactment of an intention, motivation control is designated as one important covert strategy (Corno, 1993), which involves maintaining or strengthening the motivational base of the current behavior when the intention is weak relative to other competing intentions. In the case of online groupwork, students are required to assume responsibility for managing their assignments including, for example, implementing groupwork intention, staying focused, and enhancing or sustaining motivation to follow through on the assignments in the face of an array of enticing temptations or competing personal strivings.
Regulation of motivation is a critical aspect of self-regulated learning (Pintrich, 2004; Winne, 2001; Wolters, 2011; Zimmerman, 2000). Pintrich (2000, 2004), in his model for self-regulated learning in the classroom, has classified four phases of self-regulation (forethought, monitoring, control, and reflection) and, for each phase, four possible areas for self-regulation (cognition, motivation, behavior, and context). In this model, regulation of motivation is explicitly conceptualized as an important aspect of self-regulation. It involves individuals attempts to change or control their motivation in order to complete a task that might be boring or difficult (Pintrich, 2004, p. 396), such as enhancing self-efficacy through positive self-talk or increasing intrinsic motivation for a task by making it more interesting.
Pintrichs model further suggests that regulation of motivation may be influenced by individuals attempts to control their own overt behaviors, including time regulation, study environment regulation, and help seeking. This is in line with others work that cognitive, behavioral, and contextual factors may interact to affect self-regulation (Eccles & Wigfield, 2002; Schunk, 2005; Wigfield, 1994; Wigfield & Eccles, 2002; Zimmerman, 1989) and that students initiative to optimize learning environments may influence their motivation (Xu & Corno, 2003, 2006).
In addition, research and theorizing on regulation of motivation suggests that this may be influenced by task interest (e.g., the appeal of a task or an activity). As students with a greater interest in an activity are more likely to apply adaptive self-regulatory strategies (Pintrich & Zusho, 2002), interest may influence self-regulation in general and regulation of motivation in particular (Schunk, 2005).
Finally, as self-regulated learning perspective recognizes that there are biological, sociocultural, and individual differences that can affect a student efforts at regulation (McCaslin & Hickey, 2001; Pintrich, 2004), regulation of motivation may be further influenced by student characteristics (e.g., gender and age) and others monitoring (e.g., teachers and group members). For example, females, compared with males, more frequently structure their environment for optimal learning (Ablard & Lipschultz, 1998), display more goal-setting and planning strategies (Zimmerman & Martinez-Pons, 1990), and exhibit stronger effort management (Pokay & Blumanfeld, 1990). In addition, the social environment may influence students regulation of their motivation through modeling, scaffolding, and direction instruction (Wolters, 2011)
Taken together, this body of literature suggests that regulation of motivation may be influenced by a number of variables, including background variables (e.g., gender), the influence of others (e.g., feedback from peers and the instructor), online groupwork interest, student initiative (e.g., managing time and help seeking). Therefore, it is important to incorporate these variables into models of online groupwork motivation management.
STUDIES ON ONLINE GROUPWORK
As online collaborative groupwork is becoming an increasingly popular instructional strategy, a growing number of studies have examined student experiences with this (Brindley et al., 2009; Koh, Barbour, & Hill, 2010; Minnaert, Boekaerts, de Brabander, & Opdenakker, 2011; Oliveira, Tinoca, & Pereira, 2011; Rovai, 2007; Smith et al., 2011; Zafeiriou et al., 2001). Motivation of students has become one of the most serious problems in online groupwork (Brindley et al., 2009; Davies, 2009; Jarvela, Jarvenoja, & Veermans, 2008; Liu et al., 2010; Piezon & Ferree, 2008; Roberts & McInnerney, 2007; Smith et al., 2011; Thompson & McGregor, 2009). For example, Smith et al. (2011) compared student groupwork experiences in online versus face-to-face sections of the same graduate course (n = 71); data revealed that there was a lower percentage of positive comments in the online environment than in the face-to-face environment. Along with the motivational issue associated with free riding, the students in the online sections were more concerned with the issue of communicating (e.g., in visual assignments) and organizing around the various group members schedules. The study suggests that the students in online sections felt more negative about their groupwork, and that they were less motivated to engage in online groupwork. Indeed, some students balked at the very idea of online groupwork, complaining that such should not be required in online courses.
In another study, based on data from 227 undergraduate and graduate students, Piezon and Ferree (2008) examined perceptions of social loafing within online learning groups (i.e., the tendency to reduce individual effort when working in group situations compared with the individual effort when working alone). Over one-third of the participants noted that other members of their group were loafers, although self-reported social loafing percentages were much lower. One possible explanation for this discrepancy is that individuals may be unaware that they are social loafing online or reluctant to admit it. These findings imply that motivational loss related to loafing in collaborative online groupwork may become an additional impediment to effective online learning, as the online learning environment already must deal with other challenges for group activities (e.g., geographical distance and varying work schedules).
Using survey data from 173 undergraduate and graduate students at more than 18 universities in the United Kingdom, Liu et al. (2010) examined students perceptions of factors contributing to unsuccessful online group collaboration. In addition to the lack of individual accountability and negative interdependence, poor motivation was identified as the major problem in online group collaborations. The study further revealed that the problem of poor motivation was not related to students backgrounds (e.g., age, gender, and English proficiency).
Whereas the study by Liu et al. (2010) implied that motivation in online group collaboration was not related to students backgrounds, other studies indicated that some variables may play an important role in groupwork motivation management, including feedback and time management. Based on data from 104 undergraduate students from eight German universities, Geister, Konradt, and Hertel (2006) examined the role of team process feedback on motivation in virtual teams. An Online-Feedback-System (OFS) was developed to manipulate weekly feedback for five weeks in total. Teams were randomly assigned to the OFS condition (26 teams) or the non-OFS condition (26 teams). Although there was no motivational increase on a team level, additional analyses at the individual level revealed that initial motivation served as a moderator variable. Increases in motivation occurred for the less motivated team members. When these less motivated members were compared in the OFS and non-OFS groups, data supported a beneficial effect of the OFS on valence (defined as the subjective importance of team goals for team members), self-efficacy, and interpersonal trust.
In another study, Michinov, Brunot, Bohec, Juhel, and Delaval (2011) examined the role of time management on student participation and performance in online learning environments. The participants were 83 adults, aged between 28 and 52 years, enrolled in an online learning program. The study revealed that high procrastination directly and indirectly (through low participation) predicted poor performance. High procrastinators, as compared with low procrastinators, reported that they felt less motivated to work online and were more inclined to drop out of the course. Although this study did not focus on the collaborative aspect of online work, these findings suggested that time management may play an important role in online groupwork motivation management.
Taken together, a number of studies find online groupwork presents new and significant motivational challenges for undergraduate and graduate students. However, few empirical studies have been conducted to examine a broad range of factors that may influence online groupwork motivation management.
THE CURRENT STUDY
The aim of the present study is to examine empirical models of variables posited to predict online groupwork motivation management, based on survey data from a sample of graduate students. The models differ with respect to the specific predictor variables they include and the level of these variables. Model 1 includes all student-level variables, whereas Model 2 further incorporates variables at the group level.
Specifically, Model 1 consists of nine student-level variables relating to student characteristics (gender, full-time student status, age, and previous online experiences), feedback, online groupwork interest, arranging the environment, managing time, and help seeking. In line with the perspective from self-regulation (Ablard & Lipschultz, 1998; Pajares, 2002; Zimmerman & Martinez-Pons, 1990), it is hypothesized that females are more likely to manage their online groupwork motivation than males. As students use of certain self-regulatory strategies (e.g., monitoring and organizing) leveled off after junior high school (Zimmerman & Martinez-Pons, 1990), it would be interesting to examine whether there is a difference in motivation management by age.
In addition, as familiarity with the computer and software may influence group members participation (Zafeiriou et al. 2001), it would be important to include students previous experience with online courses. Similarly, as students experience with online groupwork may be influenced by logistical difficulties associated with this (e.g., organizing around each members schedules; Smith et al., 2011), it would be also important to incorporate another variable relating to student status (full-time versus part-time).
In line with literature on self-regulation (Pintrich, 2004; Wolters, 2011) as well as findings by Geister et al. (2006), it is hypothesized that groupwork motivation management is positively associated with feedback from the instructor and peers. As students with greater interest in an activity are more likely to use adaptive self-regulatory strategies (Pintrich & Zusho, 2002; Schunk, 2005), it is further hypothesized that online groupwork motivation management is positively associated with groupwork interest. In addition, in line with Pintrichs model of self-regulated learning regarding the importance of regulating time, study environment, and help seeking, it is hypothesized that groupwork motivation management is positively associated with students effort in arranging the environment, managing time, and help seeking.
Model 2 included two group-level variables (feedback and online groupwork interest). The rationale for including these two variables is that the social and academic contexts may influence student motivation management (Corno & Mandinach, 2004), including instructor and peer influence (e.g., norm, expectation, and student engagement in online groupwork). This is further substantiated by previous research on the critical importance of interaction (e.g., feedback from peers and course instructors) in building learning communities in online courses (e.g., Ertmer at al., 2010; Swan, 2002). In addition, students shared groupwork interest in a given group may have an effect on their groupwork motivation management above and beyond the effect of groupwork interest at the student level.
The participants were 150 graduate students from 46 online groups in one public university in the Southeast. Specifically, the participants in this sample were 43.3% male and 56.7% female. Age breakdown was 73.6% for students 30 years or younger and 26.4% for students 31 years or older. The sample was 48.6% Caucasian, 45.9% African American, 3.5% Asian American, and 2.1% students from other racial and ethnic backgrounds. Among them, 78.7% were full-time students.
The participants were from the same graduate-level course over several semesters from Fall 2009 to Spring 2011, with about 20 to 25 students each semester. No noticeable difference was found among semesters relating to participants demographic characteristics (e.g., gender, age, and race/ethnicity). Overall, the survey response rate was 82.0%.
ONLINE COURSE AND ONLINE GROUP ACTIVITIES
The course focused on the design and development of multimedia applications through working with various authoring and multimedia tools in project based learning environments. The course topics included the relationship between human learning and multimedia instructional design, instructional design theories and principles, strategies for multimedia instructional design and development, application of instructional design strategies and models, and evaluation of relevant instructional software.
The course was delivered through mycourses. As one of researchers in the present study, the instructor designed instructional materials, learning activities, group projects, and assessment instruments. Online communication media was set up in several areas, including a group discussion area, a whole-class discussion area, and a student/instructor discussion area, on the Discussion Boards. In addition, the instructor interacted with group members or the entire class in a chat room in a predetermined time.
In the beginning, members were assigned to groups by the instructor. The students relied on emails, discussion boards, and chat rooms to communicate and interact with their group members and the instructor. For the final group project, group members were required to work together to develop a full instructional design portfolio project, which involved selection of a real instructional problem and the presentation of an entire evaluative design and solution for the instructional problem selected. Because of the complexity, interactivity, and collaboration involved in completing this project, students were asked to attend multiple discussion activities with group members by synchronous or asynchronous communication tools (e.g., related to general discussions, debate discussions, panel discussions, and symposium discussions).
The online groupwork survey was administrated at the end of each semester, and typically took about 40 minutes to complete. The development of the survey was informed by research and theorizing on regulation of motivation in general, with relevant studies on online groupwork in particular. In the survey, students were asked about whether they were full-time students (no = 0, yes = 1). They were also asked the number of previous online courses they had taken: including none (scored 0), one (scored 1), two (scored 2), three (scored 3), and four or more (scored 4).
Several multi-item scales were used for the present study (see Table 1). Some items were adapted from standard instruments (e.g., Xu, 2008b), whereas others were taken from related literature (e.g., Wigfield & Eccles, 2000).
Table 1. Alpha Reliability of Multi-Item Scales
Table 1. (Continued)
Table 1. (Continued)
Note. The 95% confidence intervals for coefficient alpha were calculated using a method employing the central F distribution (see Fan & Thompson, 2001).
aResponses were 1 (none), 2 (some), 3 (about half), 4 (most), and 5 (all).
bResponses were 1 (strongly disagree), 2 (disagree), 3 (neither agree nor agree), 4 (agree), and 5 (strongly agree).
cResponses were 1 (dont like it at all), 2 (dont like it some), 3 (neither like it nor dislike it), 4 (like it some), and 5 (like it very much).
dResponses were 1 (very boring), 2 (boring), 3 (neither boring nor interesting), 4 (interesting), and 5 (very interesting).
eResponses were 1 (never), 2 (rarely), 3 (sometimes), 4 (often), and 5 (routinely).
fResponses ranged from 1 (not at all true of me) to 7 (very true of me).
This scale included five items to assess the extent to which teachers and group members provide feedback (α = .82), informed by related literature (e.g., Murphy et al., 1987; Walberg, Paschal, & Weinstein, 1985; Xu, 2008a). It measures how much of the assigned online groupwork is shared, discussed, and checked.
Online Groupwork Interest
This scale incorporated five items to assess the level of online groupwork interest as perceived by students (α = .87), informed by literature on interest and intrinsic motivation (Deci, Vallerand, Pelletier, & Ryan, 1991; Isaac, Sansone, & Smith, 1999; Wigfield, 1994; Wigfield & Eccles, 2000; Xu, 2006, 2007, 2008a). It measures the extent to which students look forward to online groupwork, and to what extent they like or dislike such assignments.
Arranging the Environment
Arranging the environment refers to students' attempt to structure and manage their learning environment (Xu, 2008b, 2008c). The development of this scale was informed by previous research on self-regulation (e.g., Wolters, 2003; Zimmerman & Martinez-Pons, 1990). This scale included seven items, ranging from finding a quiet area for doing online groupwork to minimizing potential distractions (α = .85).
Managing time refers to students' attempts to plan, monitor, and regulate time use (Xu, 2008b, 2008c, 2010). It included eight items to assess student initiative in budgeting time to meet deadlines. These items range from setting priorities and planning ahead to keeping track of the remaining time (α = .91).
Informed by related items in the Motivated Strategies for Learning Questionnaire (Duncan & McKeachie, 2005; Pintrich, Smith, Garcia, & McKeachie, 1993), this scale included seven items to assess student initiative to seek social help while doing online groupwork (α = .86), such as getting help from the instructor and other students in the group.
Motivation management refers to a students efforts to enhance his or her motivation as well as that of other group members in order to complete online groupwork that might be boring or difficult. It consisted of five items to assess student initiative in managing motivation (α = .87; Xu, 2008b, 2008c), including self-consequating (Graham, Harris, & Troia, 1998; Xu & Corno, 1998; Zimmerman, Martinez-Pons, 1990), interest enhancement (Sansone et al., 1999; Wolters, 2003), and efficacy self-talk (McCann & Garcia, 1999; Wolters, 1998).
Educational researchers are often confronted with data that have multilevel structures. In the case of the present study, individual student characteristics are confounded with those of groups. This clustering effect presents several major statistical issues (e.g., aggregation bias, misestimated standard errors, and heterogeneity of regression). These issues cannot be appropriately handled with traditional regression and analysis of variance. Multilevel modeling or hierarchical linear modeling (HLM) allows for the inclusion of variables at multiple levels and takes into account the non-independence of observations by addressing the variability associated with each level of nesting (e.g., decomposing any observed relationship between variables into separate within-group and between-group components).
In the present study, multilevel analyses were conducted using the HLM 6. To enhance the interpretability of the resulting regression coefficients, we standardized all continuous variables (M = 0, SD = 1) before performing the multilevel analyses. Thus, the regression weights for all variables (except the dummy-coded variables, including gender, age, and full-time student status) are approximately comparable with the standardized weights that result from multiple-regression procedures (Trautwein, Ludtke, Schnyder, & Niggli, 2006; Xu, 2008a).
Model 1 included nine student-level variables regarding student characteristics (gender, age, full-time student status, and the number of previous online courses taken), feedback, online groupwork interest, arranging the environment, managing time, and help seeking. Model 2 included two group-level variables, including feedback and online groupwork interest. Feedback within a group was aggregated at the group level to form an index of students' shared feedback at the group level. Similarly, online groupwork interest within a group was aggregated at the group level to form an index of students' shared interest toward online groupwork.
Full maximum likelihood was used in all models. To disentangle person-level and compositional effects (Raudenbush & Bryk, 2002), feedback and online groupwork interest were centered at the group mean. The other predictor variables were introduced as uncentered variables. There were relatively few missing values, ranging from .00% to 2.00%. These missing values were imputed using the expectation-maximization algorithm, an iterative computation technique of maximum likelihood estimates for incomplete data, which yields more reliable and unbiased estimates compared with other imputation techniques (e.g., simple regression techniques, mean substitution, and the last-observation carried forward; Koszycki, Benger, Shlik, & Bradwejn, 2007; Schafer & Graham, 2002).
Table 2 presents the descriptive statistics relating to the study variables. It also includes zero-order correlations among independent variables and motivation management. Motivation management was found to correlate significantly with all of the independent variables, except full-time student status.
Table 2. Descriptive Statistics and Pearson Correlations
Note. N varies from 147 to 150. * p < .05. p < .01.
The fully unconditional (null) model was conducted to partition the variance in motivation management into between-group and within-group components, which is analogous to conducting one-way random effects ANOVA. Variance estimates produced by the unconditional model are used to calculate intraclass correlation coefficient (ICC), an index that measures the degree to which members of the same group respond in a more similar manner than do members of different groups. The results indicated that 90.4% of the variance in motivation management occurred at the student level and 9.6% of the variance occurred at the group level. The deviance statistics and number of estimated parameters for the unconditional model were 423.20 and 3, respectively.
As using multilevel modeling to control for cluster effects is justified when ICCs are as low as .02 (Kreft & de Leeuw, 1998; Von Secker, 2002), it was important to conduct multilevel analyses in the present study. Model 1 included nine student-level variables regarding student characteristics (gender, full-time student status, age, and the number of previous online courses taken), feedback, online groupwork interest, and students' initiative in arranging the environment, managing time, and help seeking. The deviance statistics and number of estimated parameters for Model 1 were 312.62 and 12, respectively. The likelihood ratio test comparing the unconditional model to Model 1 indicated that Model 1 was a significantly better fit to the data than the unconditional model, χ2 (9) = 110.58, p < .001. Model 1 explained 52.1% of the variance in motivation management at the student level, and 16.2% of the variance at the group level (see Table 3).
Table 3. Motivation Management: Results from Hierarchical Linear Modeling
Note. N = 147 from 46 online groups. b = unstandardized regression coefficient. SE = standard error of b. R2 = amount of explained variance. *p < .05. p < .01.
Model 2 included two group-level variables (feedback and online groupwork interest). The deviance statistics and number of estimated parameters for Model 2 were 292.25 and 14, respectively. The likelihood ratio test comparing Model 2 to Model 1 indicated that Model 2 was a significantly better fit to the data than Model 1, χ2 (2) = 20.37, p < .001. Model 2 accounted for an additional 1.1% of the variance in motivation management at the student level and an additional 70.5% of the variance at the group level.
Overall, the final model (Model 2) explained 53.2% of the variance in motivation management at the student level, 86.7% of the variance at the group level, and 56.4% of the total variance. As indicated in Table 3, four student-level variables were found to have a statistically significant effect on motivation management. Motivation management was positively related to feedback (b = .23, p < .01), managing time (b = .21, p < .05), arranging the environment (b = .19, p < .05), and help seeking (b = .18, p < .05).
At the group level, motivation management was positively related to online groupwork interest (b = .38, p < .01). On the other hand, the positive effect of feedback aggregated at the group level did not reach significance.
The present study examined models of students' motivation management in online collaborative groupwork. Results from the multilevel analyses revealed that most of the variance in motivation management occurred at the student level, with online groupwork interest being the only significant predictor at the group level. As most of the variance in groupwork motivation management occurred at the student level, online groupwork motivation management was largely a function of individual student characteristics and experiences. Results further revealed that four student-level variables contributed to the explanation of the variation in groupwork motivation management, including feedback, arranging the environment, managing time, and help seeking.
The finding that gender was not related to groupwork motivation management is largely in contrast to self-regulation literature that females were more likely to take initiative to regulate their learning activities than males (e.g., planning and goal-setting; Ablard & Lipschultz, 1998; Zimmerman & Martinez-Pons, 1990). One possible explanation is that gender difference in self-regulation may be moderated by the learning environment (i.e., online versus face-to-face). This is, to some extent, supported by recent findings that gender was not related to students experiences in e-learning (e.g., maintaining ones learning motivation; Paechter & Maier, 2010) and their motivational beliefs and self-regulated learning strategies in an online environment (Yukselturk & Bulut, 2009). Another possible explanation is that, in group-settings, females tend to communicate using a connected voice that emphasizes socialization, caring, and cooperation, whereas males tend to have a more independent voice that emphasizes self-sufficiency, autonomy, and competition (Rovai, 2007; Tannen, 1991). As competition is more likely to silence females than males, the gender difference on self-regulation favoring females may be therefore less evident in online groupwork settings. This is somewhat substantiated by the finding that gender was not related to motivation in online group collaboration (Liu et al., 2010).
The finding that groupwork motivation management was not related to age is in line with literature on the use of certain self-regulatory strategies after junior high school (Zimmerman & Martinez-Pons, 1990). This is also in line with the finding based on a sample of undergraduate and graduate students that age did not relate to the problem of poor motivation in online group collaboration (Liu et al., 2010). In addition, this is consistent with the finding that elementary school teachers across three age groups (≤ 30, 31-40, and ≥ 41 years) tend to have similar motivation toward web-based professional development (Kao, Wu, & Tsai, 2011).
PREVIOUS ONLINE COURSES
How do we interpret the finding that groupwork motivation management was not related to the number of previous online courses taken? Traditionally, technical limitations are viewed as a major reason that prevents online learners from communicating and learning together (Havard et al., 2008; Liu et al, 2010). However, with the development of information and communication technology, technical issues have become less of an issue affecting learner collaboration (An, Kim, & Kim, 2008; Liu et al., 2010). Another possible explanation for the lack of association is that the influence of previous online courses may be mediated by shared experiences at the group level. This was evident in Model 1, in which the number of previous online courses was positively and significantly related to groupwork motivation management.
The finding that online groupwork motivation management was positively associated with feedback from the instructor and peers is in line with existing literature on self-regulation (Pintrich, 2004; Wolters, 2011). This is also consistent with findings that the social presentence of teachers and peers in online discussion forums added motivation for continued participations in the discussions (Whipp & Chiarelli, 2004) and that less motivated team members benefited more from feedback than more motivated team members (Geister et al., 2006). Thus, it is not surprising that students are more likely to manage their motivation in online groupwork when they find out that that their progresses are monitored by the instructor and peers, and that their efforts have been acknowledged and given appropriate attention.
The influence of interest on regulation of motivation has been suggested by self-regulation literature, in the sense that students with a greater interest in an activity are more likely to use adaptive self-regulatory strategies (Pintrich, 2004; Pintrich & Zusho, 2002; Schunk, 2005). Using the multilevel perspective, the present study extends our understanding in this area by showing that groupwork interest at the group level has a positive effect on groupwork motivation management, whereas within-group differences in groupwork interest have no effect on groupwork motivation management. One possible explanation for the lack of association at the student level is that the influence of groupwork interest may be mediated by student initiative (i.e., arranging the environment, managing time, and help seeking). To test this hypothesis, we conducted additional analyses by excluding arranging the environment, managing time, and help seeking from Model 1. Indeed, groupwork interest was found to be positively associated with groupwork motivation management.
Another important contribution of the present study concerns the critical role played by student initiative (i.e., arranging the environment, managing time, and help seeking) on online groupwork motivation management. It is important to note that this is the first study that we are aware of to link student initiative to online groupwork motivation management. Our findings were in line with research and theorizing on regulation of motivation. For example, the finding that managing time was positively related to groupwork motivation management is consistent with Pintrichs model of self-regulated learning (2004) regarding the importance of regulating time. Similarly, the findings that arranging the environment and help seeking were positively associated with groupwork motivation management are congruent with Pintrichs model, which implies that individuals efforts to arrange the physical and social environment may facilitate their efforts to manage motivation. The finding relating to arranging the environment is consistent with a similar strategy labeled as environmental control within volitional literature (Corno, 1993) or environmental structuring in other empirical studies (e.g., Zimmerman & Martinez-Pons, 1990). What is noteworthy is that these effects have been demonstrated in a sample of students from diverse backgrounds, through the use of hierarchical analyses.
The present study has some limitations that should be acknowledged. First, these findings were based on self-reported data. Self-report is widely used in motivation research (Fulmer & Frijters, 2009) and is often an important source of information about student motivation (Pintrich, 2004). For example, direct observations of groupwork motivation management by trained observers are likely to be intrusive and time-consuming, thereby restricting the duration of groupwork observation and the number of students who can be examined. In addition, compared with observers, students have certain advantages as observers of their own groupwork motivation, as some aspects of their motivational responses during collaborative work are not easily observable. On the other hand, self-report may be subject to social desirability bias (Duncan & McKeachie, 2005; Fowler, 1995; Wentzel & Wigfield, 2007). Thus, our findings need to be replicated with other measures (e.g., experience sampling methods or behavioral measures; Pintrich, 2004).
Another related limitation relates to the issue of causation, a limitation facing virtually all nonexperimental research (Winship & Sobel, 2004). Although much care was taken to control for possible confounding variables (informed by research and theorizing on the regulation of motivation), other predictor variables might have had an effect on groupwork motivation management had they been included (e.g., the quality of online class or collaborative activities as perceived by students).
IMPLICATIONS FOR FUTURE RESEARCH
As this study is the first that we are aware of to link groupwork motivation management to a broad spectrum of variables at the student and group levels, further research is needed in other settings. It would also be informative to conduct longitudinal studies to examine how a range of variables such as those examined in the present study influences groupwork motivation management. Furthermore, there is a need to incorporate multiple methods (e.g., a diary study, think-aloud protocol measures, trace logs in an online environment, stimulated recall, group member interviews, and experience sampling methods) to document the nature and ongoing dynamic process of online groupwork motivation management. For example, in line with the call by Randi and Corno (2000) to examine the complexities involved when good teachers work to create experiences that fully engage their students with school, it would be important to examine the complexities involved when good online instructors work to foster student motivation in online groupwork.
Although there are multiple barriers to random assignments in applied settings in general (Shadish, Cook, & Campbell, 2002), controlled experiments are needed to better address the issue of causation. For example, it would be important to test the causal hypotheses more directly by experimentally influencing initiative (e.g., groupwork interest and time management) and by examining the effects of these influences on groupwork motivation management and academic achievement.
IMPLICATIONS FOR PRACTICE
With respect to online groupwork practices, the finding that feedback was positively related to groupwork motivation management suggests that feedback from the instructor and peers plays an important role in enhancing and sustaining students motivation in online-collaborative-group-activities. Thus, it would be beneficial to promote feedback among the instructor and group members in the online groupwork process. This may include developing ground rules to promote task-oriented interactions among group members, learning to monitor each others progress, providing directions or mid-course corrections when necessary to prevent group members from going off course, sharing effective strategies, and offering ongoing acknowledgement and encouragement.
As online groupwork interest at the group level was positively associated with groupwork motivation management, online course instructors need to pay more attention to how to make online group learning activities more purposeful, meaningful, relevant, and engaging. There is a need to conceptualize and design high-quality online group learning activities, with particular emphasis on purposes, formats, and types of collaborative activities that will engage online students and help them succeed as a group (e.g., matching the content of group activities to students interests and encouraging them to learn from each other). Similarly, it would be beneficial to provide online students with a sense of autonomy to form their own groups based on shared interests and to choose relevant topics for their groupwork.
In addition, as student initiative plays an important role in online groupwork motivation management (arranging the environment, managing time, and help seeking), online course instructors need to encourage their students to assume more responsibility in this area. For example, they may encourage online students to better manage groupwork time, by planning ahead for online groupwork from the beginning, and by learning to pace themselves along the way. They may also encourage online students to ask for assistance from multiple sources (e.g., the instructor, peers, and other online resources) through multiple channels (e.g., email, web chat, and video conferencing) when they confront difficult tasks and perceive the need for help.
Finally, it would be informative to listen to students voices about what universities can do to help them better manage online groupwork motivation, which would enable online course instructors to provide more appropriate support for their efforts at groupwork motivation management (e.g., by making online groupwork more interesting and providing more relevant feedback). For example, in his model of adaptive help seeking, Newman (1994, 2008) stated that help seeking involves expressing the need for help in the most suitable fashion given the circumstance, and that it requires that the help seeker receive and process help in a way that will optimize the probability of success in later help-seeking attempts. It would be important to better understand what help seeking means to online students in collaborative learning activities (as compared with students in face-to-face settings with individual assignments). Consequently, university instructors may be in a better position to promote help seeking in an online collaborative group-learning environment. This, in turn, will encourage online students to assume more responsibility for managing their groupwork motivation, including, for example, peer modeling on managing groupwork time, help seeking, and optimizing learning environments.
Online learning is becoming a global phenomenon and has prompted many university instructors to incorporate online collaborative groups in their courses. It is surprising to note, however, that few empirical studies have focused on how to enhance and sustain student motivation to work together in online learning environments. To address this gap in existing research, our study examines empirical models of variables posited to predict students motivation management in online groupwork.
Our analyses reveal that online groupwork motivation management was largely a function of individual student characteristics and experiences. Our analyses further reveal that feedback and student initiative (arranging the environment, managing study time, and help seeking) were positively related to online groupwork motivation management. Consequently, it would be important to promote feedback among the instructor and group members in the online groupwork process. In addition, it would be important to encourage students to take more initiative in online groupwork settings to better manage their motivation. Finally, regarding further research, it would be beneficial to conduct longitudinal, experimental, and qualitative studies to better understand factors that influence online groupwork motivation management.
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