From self-regulation to co-regulation: refining learning presence in a community of inquiry in interprofessional education

ABSTRACT

Introduction

Online interprofessional education is a collaborative process that emphasizes both individual reflection and shared discourses. A useful analytical tool for understanding the complex dynamics of online collaborative learning is the community of inquiry (CoI) framework, which originally held that there are three types of presence in such learning: teaching, cognitive, and social. However, it was later revised to include learning presence, which is characterized by self-regulated learning. Our study aims to refine the construct of learning presence through a clearer understanding of how self- and co-regulation jointly influence learning outcomes.

Methods

We surveyed 110 people involved with an online interprofessional medical-education curriculum at a university in Hong Kong. Path analysis was adopted to explore the relationships among 1) the three original presences of CoI; 2) learning presence (i.e., for this purpose, a combination of self-regulation and co-regulation); and 3) two learning outcomes: perceived progress and learner satisfaction.

Results

The results of path analysis indicated that teaching presence had a significant indirect effect, through co-regulation, on perceived progress. In terms of direct relationships, co-regulation significantly and positively influenced both self-regulation and cognitive presence; and social presence had both positive influence on learners’ satisfaction and perceived progress.

Discussion

This study’s findings suggest the important role of co-regulation in supporting self-regulation, especially in online collaborative-learning environments. Learners’ self-regulation skills are shaped by their social interactions and regulatory activities with others. This further implies that health-professions educators and instructional designers should create learning activities that facilitate the development of co-regulatory skills, as a means of improving learning outcomes. As self-regulation is an important skill for health professions learners’ lifelong learning, and because their future workplaces will be interdisciplinary in nature, it is critical to provide interactive and collaborative learning environments that will promote co-regulation and self-regulation.

KEYWORDS:

• Self-regulation
• Co-regulation
• community of inquiry
• interprofessional education
• path analysis

Introduction

In interprofessional education (IPE), health and social care learners come together to learn ‘with, from, and about each other’ [1,2]. According to the Center for the Advancement of Interprofessional Education (CAIPE), a key goal of IPE at the undergraduate level is to develop interprofessional knowledge, skills and attitudes that will promote interprofessional team behaviors and competence [3]. This approach has been credited with improving teamwork and collaboration [4,5], patient care [6], medical error rates [7], and the efficiency of services [8], among other benefits. As collaboration and community are key elements of IPE [9,10], it is important that both its theory and its practice be informed by a solid understanding of the complex dynamics of collaborative learning in it. On the other hand, the COVID-19 pandemic has accelerated the transition of teaching and learning from face-to-face to online settings. Because we were seeking a better understanding of learning processes in online IPE, we adopted the community of inquiry (CoI) [11,12] framework for our research, on the grounds it encompasses both individual reflection and shared discourse among participants in online learning environments [13].

Over the past 20 years, the CoI framework has been widely utilized to guide online collaborative learning research and design [14–16]. Rooted in social-constructivist theory, it holds that knowledge is constructed through collaboration, and that deep and meaningful online learning experiences occur at the intersection of three core elements: cognitive presence, social presence, and teaching presence [11]. Prior literature has suggested a positive relationship between the strength of the three presences and students’ learning satisfaction, perceived learning progress, sense of belonging, and actual learning outcomes [e.g [14,17,18].

More recent literature, however, has argued that the original CoI framework is lacking in learner-level determinants. As online learning relies greatly on learners’ self-direction, these researchers have recommended adding an additional learner-level construct as a means of better explaining all significant aspects of successful online learning [19,20]. For example, drawing on self-regulated learning (SRL) theory, Shea and Bidjerano [20] suggested adding a fourth presence – learning presence – to the current CoI. SRL is defined as ‘the degree to which students are metacognitively, motivationally, and behaviorally active participants in their own learning process’ [21]. Subsequently, Shea and Bidjerano [22] suggested that SRL serves as an important mediator among cognitive, social, and teaching presences. Cho et al.’s [23] findings further suggested that there was an important positive relationship between SRL and CoI presences. However, as collaboration and interaction are key to building a CoI, SRL that relies completely on learners’ own cognitive and metacognitive processes is insufficient to lead to meaningful and deep learning. Therefore, co-regulation, which emphasizes a shared learning experience that takes place between a learner and others (either a near-peer or a trained coach), has been deemed another important element in any collaborative-learning community [24–26]. Edelbring and Wahlström [27] suggested that the influence of an external-regulation strategy was more significant than that of a self-regulation strategy on students’ perceptions of the benefits of virtual-patient learning activities. However, co-regulation has not hitherto been closely examined in the context of health professions education.

In addition, research on CoI and SRL has called for more cross-disciplinary studies to identify factors that moderate or extend the relationship among the framework’s components [12]. Accordingly, the current study aims to understand the roles that self-regulation and co-regulation play in how a particular group of online IPE learners’ perceived CoI affected their learning outcomes. The two research questions addressed in this study are:

1. What are online IPE learners’ perceptions of the levels of a) CoI presences (i.e., cognitive presence, social presence, teaching presence), b) online self-regulation skills, and c) co-regulation skills in their course?

2. How do online IPE learners’ perceived online self-regulation and co-regulation skills, as well as their perceptions of cognitive presence, social presence, and teaching presence, influence their perceived learning and satisfaction?

Methods

Context and participants

This study was conducted in a cross-disciplinary and cross-faculty IPE and collaborative-practice project at a university in Hong Kong (https://www.ipe.hku.hk/). Ethical approval for conducting it was received from the University of Hong Kong’s Human Research Ethics Committee (EA210423). In Fall 2021, a total of 600 undergraduate students from five disciplines – medicine, nursing, pharmacy, social work, and law – enrolled in this two-week IPE simulation course on ‘COVID-19 pandemic infection control and management’ (for the course flow, please see Figure 1). The learners were divided into 60 groups, each consisting of 10 individuals from a mix of disciplines. Prior to the beginning of the course, a briefing session was held to acquaint learners with the learning management system (LMS), OpenEdX; the synchronous as well as asynchronous components of the course; and other logistical matters.

Figure 1. Sequence of activities for the IPE simulation (adapted from Ganotice et al., 2021).

During the course, each group participated in two synchronous online discussion sessions, each guided by a facilitator. Facilitators were either trained research assistants from the field of medical education or senior students who had previously taken the same IPE course. Three medical educators, who were also the authors, oversaw the entire facilitating process, including providing training for facilitators about key facilitating strategies, and providing briefings and debriefings before and after the facilitating sessions. An ice-breaking activity was provided at the very beginning of the first synchronous session so that the learners could get to know each other. Learners were also required and frequently reminded to turn their cameras on so that they can see ane another virtually. During these within-group discussions, learners were instructed to watch relevant videos and to answer guiding questions collaboratively in a Google document. A total of four videos including 13 tasks with relevant guiding questions were provided to help learners acquire the relevant content knowledge. In addition to their two synchronous sessions, they were asked to work within their own groups in their own time, without a facilitator, to continue video-viewing and submit their responses to the guiding questions. The frequency of interaction varied across groups, but in general they met informally (either in-person or virtually) two or three times during the two-week period of the project. A concept-mapping tool, Miro, was used to support the learners’ final assignment, which was to develop a patient-management plan. Finally, a large-group online synchronous session was held, during which selected groups presented their final group projects, and the content experts answered learners’ questions and summarized key learning points. The format and purpose of the two large-group sessions were identical, but because of the difficulty of managing the large number of learners involved, half attended a morning session, and the other half, an afternoon one. At the very end of this project, the learners were asked to fill out self-evaluations as well as single-blinded peer-evaluation in the LMS. The rubric for such peer evaluation was based on the four IPE competencies defined by the Interprofessional Collaborative Practice Board [28]: i.e., value/ethics competency, roles/responsibilities competency, communication competency, and team and teamwork competency. While self-evaluation was based on a 1–10 point scale for each competency, peer evaluation asked the learners to distribute 10 points among their fellow team members for each competency. The final average a given learner received from teammates was only visible to that learner him- or herself.

Procedures

An online survey was sent out via Qualtrics to all learners who had attended the morning large-group session (n = 300). The survey items were divided into the following categories (see Appendix for a list of sample survey questions).

Online self-regulation

To measure self-regulation in the focal online learning environment, we used the validated online self-regulated learning questionnaire (OSLQ) developed by Barnard et al. [29] The original questionnaire includes 24 items, all answered on a seven-point Likert scale. We modified 22 of these items to fit the context of IPE, and removed two that could not be made to fit it. Both the original and our modified version of the OSLQ are divided into six dimensions. These are: 1) environment structuring (e.g., ‘I choose the location where I study to avoid too much distraction’); 2) goal-setting (e.g., ‘I set goals to help me manage studying time for IPE’); 3) time management (e.g., ‘Although we don’t have to attend daily classes, I still try to distribute my studying time evenly across days’); 4) help-seeking (e.g., ‘I find someone who is knowledgeable in course content so that I can consult with him/her when I need help’); 5) task strategies (e.g., ‘I read aloud instructional materials posted online to fight against distractions’); and 6) self-evaluation (e.g., ‘I summarize my learning in online IPE to examine my understanding of what I have learned’).

Co-regulation

Co-regulation was measured using the 13 survey items proposed by Garrison and Akyol [26], which comprise two dimensions: monitoring strategies of co-regulation (e.g., ‘I reflect upon the comments of others’) and managing strategies of co-regulation (e.g., ‘I respond to the contributions that others make’).

Community of inquiry

The 22-item CoI questionnaire, adopted from Arbaugh [14], is divided into three dimensions: 1) teaching presence (e.g., ‘The instructors clearly communicated important course goals’); 2) social presence (e.g., ‘I felt comfortable interacting with other course participants’); and 3) cognitive presence (e.g., ‘I can describe ways to test and apply the knowledge created in the IPE’).

Dependent variables

It has been suggested that using course grades in multi-disciplinary studies could be subject to various limitations, including inconsistency across different disciplines and a comparatively restricted range of grades; and all these problems were true of our context [30]. In our context, the course attendees came from various departments and disciplines, each with their own set of requirements and grading standards for participating in the course. Therefore, the alternative dependent variables we used in this study were the participants’ perceived learning outcomes, i.e., perceived learning progress and satisfaction, which were also used in previous IPE studies [31]. Our four-item, one-dimension instrument for measuring satisfaction with online IPE was adopted from Kuo et al. [32], with an example item being, ‘I would recommend this online IPE to other students’. The instrument for measuring perceived learning progress, which also had four items, was adopted from Lin et al. [33] An example item from it was, ‘I understand most of the learning content in online IPE’. All of the items were answered on a seven-point Likert scale in which 1=‘strongly disagree’; 2=‘disagree’; 3=‘somewhat disagree’; 4=‘neither agree nor disagree’; 5=‘somewhat agree’; 6=‘agree’; and 7=‘strongly agree’.

Data analysis

All the instruments used for data collection had good reliability, ranging from .81 to .97. The variables were represented by the means of the aggregated scores of their corresponding survey items.

We used descriptive statistics to answer our first research question, about learners’ self-regulation, co-regulation, and perceived CoI. To answer our second research question, about the relationships among the three presences of CoI, self- and co-regulation, and perceived learning outcomes, path analysis was conducted using Stata 17.0. Given the two endogenous variables (i.e., perceived progress and satisfaction) involved in the second research question, multiple regression cannot be used, as it cannot simultaneously estimate multiple outcome variables in a single model. Path analysis, a special type of structural equation modeling [34], therefore was chosen because it allows simultaneous testing of the magnitude as well as the significance of the complex predictive relationships among a set of variables such as those embodied in this study’s research questions. To evaluate the results, goodness-of-fit chi-square and fit index values, such as CFI, RMSEA, and SRMR will be checked based on Hu and Bentler’s suggested criteria [35]. Furthermore, previous research on communities of inquiry, with a particular focus on learning presence, has extensively utilized path analysis [22,36]. This analytical approach would allow us to assess the predictive power of each presence on learning outcomes.

Findings

In general, the sampled learners reported above-midpoint perceptions of their self-regulation, co-regulation, and the three presences in the original CoI (see Table 1). Among these five variables, learners rated their co-regulation monitoring strategies the highest (M = 5.09, SD = 1.26), and their task strategies the lowest (M = 4.08, SD = 1.36). This suggests that the sampled IPE members paid attention to the others, observed others’ strategies, and considered others’ feedback. However, they were much less likely to adopt task strategies, such as preparing questions, reading aloud, and making thorough notes during their own learning of IPE. Within the category of online self-regulation, the participants rated environmental structuring the highest (M = 4.71, SD = 1.18), meaning that they were active in finding comfortable places to learn and choosing times at which distractions could be avoided. All three categories that made up the CoI construct were rated quite highly, with teaching presence scoring an average of 4.56, social presence, 4.39, and cognitive presence, 4.50. In addition, learners’ perceived progress (M = 4.56, SD = 1.40) and satisfaction (M = 4.31, SD = 1.55) were also above the midpoint.

Table 1. Descriptive statistics of the key constructs.

Variable	Obs.	Mean	SD
Online self-regulation	110	4.307	1.185
Environmental structuring	110	4.705	1.175
Goal-setting	110	4.367	1.208
Task strategies	110	4.080	1.355
Time management	110	4.306	1.387
Help-seeking	110	4.064	1.323
Self-evaluation	110	4.307	1.307
Co-regulation	110	4.864	1.175
Monitoring strategies	110	5.085	1.258
Managing strategies	110	4.675	1.190
Community of inquiry	110	4.483	1.316
Teaching presence	110	4.564	1.387
Social presence	110	4.385	1.342
Cognitive presence	110	4.496	1.407
Perceived progress	110	4.552	1.397
Satisfaction	110	4.309	1.548

Note. Obs.=observations (n); SD=standard deviation.

Pearson correlation analysis showed significant and positive correlations among these variables (see Table 2).

Table 2. Pearson correlations among key constructs.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) Online self-regulation	1.000
(2) Co-regulation	0.694***	1.000
(3) Teaching presence	0.713***	0.634***	1.000
(4) Social presence	0.796***	0.646***	0.864***	1.000
(5) Cognitive presence	0.832***	0.707***	0.852***	0.903***	1.000
(6) Perceived progress	0.764***	0.687***	0.746***	0.807***	0.837***	1.000
(7) Satisfaction	0.750***	0.597***	0.771***	0.822***	0.842***	0.887***	1.000

Note: *p < .05, **p < .01, ***p < .001.

When path analysis was conducted to test the statistical significance of the relationships among the key variables, the model results were: χ²(6) = 3.86, p=.70, SRMR = .010, CFI = 1.000, RMSEA = .000, indicating a good model fit [36]. The estimates of the path coefficients and the results of significance testing are presented in Figure 2 and Table 3.

Figure 2. Path-analysis results.

Note. CP: cognitive presence; TP: teaching presence; SP: social presence; CR: co-regulation; SR: self-regulation; PP: perceived progress; SAT: satisfaction

Table 3. Path-analysis results for the three original community of inquiry presences, self-regulation, o-regulation, and learning outcomes, with parameters.

Path	Path coefficient	SE	z	p value
CR
TP	.30	.140	2.14	.033
SP	.39	.139	2.78	.005
SR
SP	.60	.063	9.39	.000
CR	.31	.069	4.47	.000
CP
TP	.23	.069	3.29	.001
SP	.44	.078	5.71	.000
CR	.11	.050	2.16	.031
SR	.24	.061	3.92	.000
PP
CR	.19	.057	3.30	.001
SP	.27	.114	2.33	.020
CP	.46	.117	3.94	.000
SAT
CP	.54	.112	4.82	.000
SP	.33	.114	2.91	.004

Note. CP: cognitive presence; TP: teaching presence; SP: social presence; CR: co-regulation; SR: self-regulation; PP: perceived progress; SAT: satisfaction; SE: standard error.

*p < .05, **p < .01, ***p < .001.

Among the three presences of the original CoI, teaching presence (TP) and social presence (SP) were highly correlated, and both teaching (β=.23, p < .01) and social presence (β=.44, p < .001) had a significantly positive effect on cognitive presence (CP). In terms of different categories of presences’ impacts on learning outcomes, CP had the largest positive and direct effect on perceived progress (PP; β = .70, p < .001) and satisfaction (SAT; β = .84, p < .001), while SP had a small direct and positive effect on the two outcomes (PP: β = .27, p < .05; SAT: β = .33, p < .01) as well. TP, in contrast, did not have any significant and direct impact on either outcome.

Regarding the antecedent of learning presence (i.e., SR and CR), SP had a significantly positive effect on both SR (β=.60, p < .001) and CR (β=.39, p < .01), while TP only had a significant positive effect on CR (β=.30, p < .05). Both SR (β=.24, p < .01) and CR (β=.11, p < .05) had significant positive effects on CP, meaning that both self-regulation and co-regulation significantly predicted cognitive presence in online IPE learning. Moreover, CR had a significant positive effect on SR (β=.31, p < .001), suggesting that learners’ co-regulation with one another could also positively impact individuals’ self-regulation. Additionally, while TP did not have a significant and direct positive effect on PP, TP did have an indirect effect on PP through CR (total effect of TP on PP: β = .19, p < .05). In this case, co-regulation served as a mediator of the relationship between teaching presence and perceived progress.

Discussion

By exploring the relationships among the three presences of the CoI, online SRL, co-regulation, and learners’ perceived learning outcomes in an IPE program, this study directly answers previous researchers’ calls for more cross-disciplinary studies to identify factors that moderate or extend the relationship among existing CoI framework components [12]. In addition, by incorporating a learner-level presence dimension (i.e., self-regulation and co-regulation) into the original CoI framework, this study corroborates and extends previous literature’s findings that self-regulation and co-regulation both serve as important mediators among the three established CoI presences [22,36].

From self-regulation to Co-regulation

In a collaborative learning environment, shared metacognition includes both the self- and co-regulation of cognition [20]. Driven by the sociocultural perspective that learning happens through social interactions among learners, and that regulatory activities are ‘embedded in the interactions among person, context, and culture’ [35], co-regulation has been increasingly emphasized in health professions education. Co-regulation could evolve over time and influence learners’ ability to self-regulate. For example, one qualitative study reported that medical students shift from co-regulating their learning with peers to co-regulating it with clinician role models as they transition into clerkships. In addition, students develop their own SRL skills through interaction with others [37]. Specifically, the process of engaging in other learners’ regulatory processes, such as how other learners set up their learning goals, manage resources, or reflect on learning, tend to influence how the learner regulates his or her own learning processes, both cognitively and metacognitively [38]. Our work, in line with that of Bransen et al. [37] and Larsen [39], explored the relationship between self- and co-regulation, and its findings highlight that learners’ co-regulation is a significant predictor of their self-regulation. This supports and complements the conclusions of Bransen et al.’s [37] qualitative study, and implies that engaging in regulatory activities such as paying attention to others’ ideas, reflecting upon their comments, challenging their perspectives, and helping their learning develops learners’ own SRL skills.

Social presence – the creation of a learning environment for open communication and group cohesion [11] – has been shown in previous literature to be critical to online-learning outcomes. For example, learners who perceived social presence in their online learning settings as being high also believed that they had learned more in those settings than their counterparts who perceived low social presence did [40]. We found that social presence also significantly predicted both self-regulation and co-regulation, emphasizing the importance of creating an open, risk-free, and cohesive environment for online collaborative learning.

In addition, our study expanded upon the work of Shea and Bidjerano [20] by not only adding co-regulation as part of learning presence, but also linking CoI to learning outcomes. We found that co-regulation indeed helped explain how teaching presence and social presence affected learning outcomes. The indirect effect of teaching presence on perceived progress through co-regulation could probably be explained by the fact that in our IPE program, there was no single instructor; rather, the teaching role was shared among a group of content experts and discussion facilitators who did not provide any direct instruction. Instead, they facilitated discussion, helped solving learners’ problems, and provided case summaries. This weakened the usual role of the teacher in Chinese cultural contexts (i.e., as the sole authority in the classroom) and thus might have contributed to the lack of a direct effect of teaching presence on perceived progress. Likewise, the course’s instructional design and facilitation component promoted the development of co-regulation, which further influenced learning outcomes as described above.

As the focus of this study was learners’ self-regulated and co-regulated learning in IPE, their self-assessment of their own learning progress was extremely important. Self-assessment, as a fundamental skill for SRL, has been positively linked to academic achievement [41,42] and is increasingly being used as an assessment strategy, with the aim of preparing students to be lifelong learners [43]. Thus, part of our intention in using self-assessment as a measurement of learning outcomes was to prompt our participants to reflect on their own learning and to actively use it as a learning strategy to enhance their SRL in the focal IPE learning context. On the other hand, as IPE involves students from across multiple disciplines with different curricula and assessment structures, it is difficult – indeed, unrealistic – to have a standard test for all IPE learners. It has also been suggested that course grades’ restricted ranges may not be capable of reflecting what people have learned in team-based learning [30]. While we provided content experts and administrators with both process data (e.g., attendance, self-assessment, peer-assessment) and outcome data (individual-readiness assurance test, team-readiness assurance test, application exercise test), we also afforded them the autonomy to use these data as references for devising final grades based on their own respective disciplines’ assessment rubrics.

Implications for course design in online collaborative-learning environments

Co-regulation occurs when individuals’ regulatory activities are supported and guided by others in their peer group [44], and it can be triggered by questioning, prompting, or explanation [45]. Our study included some activities that we purposefully designed to facilitate the participants’ co-regulation through teamwork.

First, a concept-mapping tool, Miro, was used to support their completion of their final assignment: developing a patient-care management plan. Members of each team were instructed to work together on Miro’s electronic whiteboard using different colors of sticky notes, with each color representing one individual team member. This activity was designed to facilitate collaboration among students from different disciplines, as they would need to come up with a management plan together from a variety of different perspectives.

Second, after each group finished their management plan, they were paired with another group to present, critique and integrate their respective management plans into one. Guided by constructive-controversy theory, which emphasizes problem-solving through deliberative discussions and conflict resolution [46], this activity aimed to foster learners’ careful listening to others’ opinions, learning from the strengths of the other team’s plan, and the provision of constructive feedback.

Finally, both self-evaluation and peer evaluation were included in the final activity, in which the learners were instructed to evaluate themselves and their teammates based on the four competencies for Interprofessional Collaborative Practice: values and ethics; roles and responsibilities for collaborative practice; interprofessional communication; and teamwork and team-based care [47]. As the aim of IPE is to promote knowledge, skills, attitudes and behaviors for collaborative practice – collectively known as collaborative competencies [48,49] – having people evaluate their own competencies could be helpful for them to reflect on their IPE learning processes, and serve as an important reflective practice to facilitate their use of SRL strategies [44]. On the other hand, peer assessment provided opportunities for our participants to play the role of co-regulators of their peers’ learning, while also enhancing the peer assessors’ SRL [50]. In our study, both self- and peer assessment were only used as formative assessment, to avoid potential negative interpersonal consequences such as tension between assessors and assessees, and threats to friendship among group members [51]. In addition, prior literature has suggested that self- and peer assessment are increasingly used in workplace settings; and thus, developing such skills would likely help students prepare for their future careers and become lifelong learners [52].

We believe that, together, the designed activities helped scaffold the participants’ co-regulation monitoring and managing strategies, which helps explain our finding that co-regulation played such a significant role in supporting the CoI presences and self-regulation. These instructional design components are also in line with the co-regulated learning model for medical education, proposed by Rich [24], which emphasizes the positioning of social transactions as the central core of learning.

Limitations and future research directions

This study relied exclusively on quantitative data to explore the relationships among key variables related to self-regulation and co-regulation in an online CoI. Some items in the validated measurement instruments we adopted may not fully capture the uniqueness of this specific learning context. Additionally, scholars are increasingly calling for event measures of SRL rather than aptitude measures [53–55], because the former would be better able to capture the dynamic processes of SRL and co-regulation. Future SRL and co-regulation research in online IPE should consider using additional data sources, such as SRL microanalysis, trace analysis of learners’ written documents, or video-ethnography to illustrate specific learning behaviors [56,57]. In addition, the findings of this study are situated within our specific learning environment, which was a condensed IPE course with a hybrid learning mode in an Asian university and against the backdrop of the COVID-19 pandemic. More similar studies about different regulatory models situated in different contexts are warranted to explore the extent to which our findings about CoI in IPE are generalizable.

Conclusions

This study explored the relationships among self-regulation, co-regulation, the three original presences in the CoI framework, and learners’ perceived learning outcomes in an online IPE course focused on COVID-19 infection control. Its findings suggest the important role of co-regulation in mediating the effect of teaching presence on perceived progress, and provide statistical support for the assumption that co-regulation could significantly predict learners’ self-regulation. As health-professions students’ self-regulation and co-regulation evolve over time through interactions with other people and with their ever-changing learning environments, we recommend the careful design of learning activities that promote and encourage partnerships among such learners and established professionals and/or their more experienced peers, as well as among those from different backgrounds and perspectives, with the aim of better preparing these learners for their future careers in interprofessional collaborative workplaces.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study is funded by the Seed Fund for Basic Research for New Staff by the University of Hong Kong.

Appendix

1. I choose the location where I study to avoid too much distraction.

2. I allocate extra studying time for IPE because I know it is time-demanding.

3. I find someone who is knowledgeable in course content so that I can consult with him/her when I need help.

4. I set short-term (daily) goals as well as long-term (weekly) goals for IPE.

5. I read aloud instructional materials posted online to fight against distractions.

6. I share my problems with my classmates online so we know what we are struggling with and how to solve our problems.

7. I ask myself a lot of questions about the course material when studying for the course.

8. I don’t compromise the quality of my work because it is online.

9. I prepare my questions before joining in the chat room and discussion.

10. Although we don’t have to attend daily classes, I still try to distribute my studying time evenly across days.

Co-regulation

1. I pay attention to the ideas of others

2. I consider the feedback of others

3. I reflect upon the comments of others

4. I observe the strategies of others

5. I observe how others are doing

6. I challenge the perspectives of others

7. I monitor the learning of others

Community of inquiry

1. The instructors clearly communicated important course goals.

2. The instructors provided clear instructions on how to participate in course learning activities.

3. The instructors helped to keep students engaged and participating in productive dialogue.

4. The instructor helped to focus discussion on relevant issues in a way that helped me to learn.

5. Online IPE is an excellent medium for social interaction.

6. I feel comfortable conversing through the online medium.

7. Participant introductions enabled me to form a sense of online community.

8. I felt comfortable interacting with other course participants.

9. I have been able to apply knowledge created in IPE to subsequent class assignments.

10. I can describe ways to test and apply the knowledge created in the IPE.

Perceived progress and satisfaction

1. I am confident that I have met most of the requirements teachers made in online IPE

2. I am confident that In understand most of the learning content in the online IPE

3. I am confident that I have better performance than most of my classmates

4. Overall, I am satisfied with the online IPE

5. I am satisfied with the level of interactions that happened in the online IPE

6. The quality of this course was similar to face-to-face courses I’ve taken

7. I would recommend this online IPE to other students