Innovation Insights: Understanding the Effect of a MOOC

When examining the effectiveness of a massive open online course, or MOOC, standard metrics paint an incomplete picture.

Test scores, completion rates, and grade-point averages are often used to measure the success of a traditional academic program. These markers don’t translate as easily to MOOCs, where enrollment and completion rates diverge. Instead, research shows students enrolled in open online courses are seeking something beyond top marks.

These learners intrigued Dr. Caren Stalburg, whose popular “Instructional Methods in Health Professions Education” MOOC has continued to attract interest since its launch in 2013, with nearly 25,000 enrollees and counting. Stalburg, an associate professor of obstetrics and gynecology and learning health sciences at the University of Michigan Medical School, wanted to find out how her learners were using the course, and whether it was impacting their professional and personal goals.

Teaching the Teachers

Stalburg’s career path was shaped early on by her curiosity around teaching and learning. After completing her residency at U-M, Stalburg was a new faculty member, adjusting to her role as an educator after years of being a student. She joined a committee that included experts from the Medical School and the Center for Research on Learning and Teaching, and swiftly realized there was more to instruction than just passing along knowledge.

“I got involved in some of our curricular designs and realized that there is a science to education, and nobody teaches it to us,” Stalburg said.

She decided to learn the science. She earned a master’s degree in 2006 from what is now called the Marsal Family School of Education. Then in 2012, Stalburg again followed her curiosity, becoming one of the earliest faculty members to embrace the university’s new partnership with Coursera and launching a MOOC in collaboration with the center.

Stalburg’s course tapped into the lessons she learned early on about the importance of teaching the teachers, designing modules dedicated to informing and improving students’ instructional skills.

“I wanted the content to be broad enough so that it was applicable to any and all professionals who were teaching others about health,” she said.

Measuring the Impact

As the course’s enrollment grew, Stalburg was curious about its effectiveness. She decided to explore that question after seeing a report by the center’s senior research scientist Nate Cradit and former director Cait Hayward, which examined how students evaluated a MOOC’s quality based on the course’s affordances and attributes.

Partnering with the center, Stalburg developed a survey specific to her course to evaluate if students’ needs were being met. In addition to gathering demographic data on the learners, the survey examined how they interacted with and benefitted from the content.

“We designed this study to look at understanding the participants’ goals for the course and how completing the course has impacted their professional goals,” Stalburg said.

Surveys were sent to 278 learners, with 40 of those participants completing the survey. The respondents represented were diverse and educated, hailing from 18 different nations with 75% of them holding advanced degrees. Many were entering the middle of their careers, and they worked in a diverse range of fields, including medicine, nursing, dentistry, physical therapy, and more.

A vast majority of the learners said they took the course to increase knowledge and skill development, with some seeking professional development or meeting a requirement for their current job.

Interestingly, Stalburg and her team also discovered that the learners were using the material for their own instruction. Most of the participants downloaded the course material and used it to improve their teaching methods or create their own lessons.

It was a satisfying finding for Stalburg, affirming her pursuit of sharing the science behind the instruction with fellow educators.

“I really believe that it’s about increasing human capacity, and meeting people where they are and in the needs that they identify,” she said. “And so, to me, this is like that old parable about teaching people to fish.”

Local and Global Lessons

The impact of the Health Professions Education MOOC can be found here at the university, as well as on campuses across the globe.

In 2021, Stalburg was tapped to help design and launch the Health Infrastructures and Learning Systems Online Master of Science Degree, again in collaboration with the center. It is the first and only online degree program offered by the Medical School and Rackham Graduate School.

Stalburg’s MOOC course was selected by universities and hospitals around the world to help train their medical communities. The University of Guyana partnered with Coursera and enrolled 86 students between 2018 and 2021.

There was also a cohort of surgeons from the SSR Medical College in Mauritius who utilized the course, and praised the focus on teaching the instructors.

“For most of us MOOCs are a novelty, and the fact that the course content was so relevant to our professional activities made the experience so much more enriching,” wrote one faculty member.

More to Learn

Stalburg now wants to know more about those original respondents to the MOOC query.

She plans to contact the participants for further interviews to gain a deeper understanding of the course’s impact on their career trajectories or personal lives, how their professional roles may have changed, and how they are applying the course content with their colleagues.

Looking at the data she has collected so far, Stalburg believes the responses will reveal this MOOC’s success, and how it can’t be measured solely in numbers, but instead through its impact on learners’ lives.

“I am hoping that people will sort of say I got better at teaching, I’ve become more recognized for my teaching, or my job opportunities have increased,” Stalburg said. “You know, just a flourish, a boost in whatever direction they’re looking to go.”

References

Cradit, N., Hayward, C. (2023, October). What is a Successful MOOC? Lessons from Global Learner Narratives [Paper presentation]. IEEE Learning with MOOCS, Cambridge, MA, United States.

Finding ways to support every student is a fundamental challenge for instructors. When the learning occurs online, ensuring an equitable experience can seem daunting, especially when students are part of teams that meet outside a professor’s purview.

According to researcher Yiwen Lin, interventions aimed at boosting student engagement and experience are effective, and the strategic use of generative AI could ensure group learning benefits every team member.

Local Inspiration

As an undergraduate student at the University of Michigan, Lin got a glimpse into her future research while attending a talk on the student support tool ECoach. Developed by the Center for Academic Innovation, ECoach software provides students personalized feedback and tailored strategies for success.

Lin recalled attending the presentation given by ECoach founder Tim McKay, Arthur F. Thurnau Professor of Physics, Astronomy, Education. She was struck by McKay’s finding that while female physics students did not frequently speak in class, they did engage and contribute in other meaningful and important ways.

“What he found was that women like to back channel,” Lin said. “I thought, well women engage, but oftentimes they just engage differently, and it’s hard for an assessment that only looks at the frequency of participation.”

Lin, now a postdoctoral associate at the University of Pittsburgh, researches this deeper data with an eye on gender differences. She examines how psychological factors impact the persistence of online STEM learners, the quality of participation in team settings, and what interventions can be used to encourage more equity among students. Lin shared her research in an Innovation Insights talk titled “Charting Equity in Online Learning Teams: Opportunities and Challenges,” presented by the center.

Male vs. Female Motivation

Examining gender differences in STEM learning has traditionally evaluated how students’ psychological experiences impact outcomes. Lin’s research delves deeper into the learning process, revealing some surprising findings.

In one study, Lin and her team replicated a previous research project that looked at how a sense of belonging and STEM identity impacted female students’ desire to continue in STEM. But unlike the former study, Lin’s research used a pool of international online learners, many of them graduate students.

The results corroborated the importance of belonging and identity for women. However, when they examined the same connection for male learners, Lin’s team found that belonging and identity were also strong motivators for men. In fact, identity and belonging showed a slightly stronger link to STEM persistence for men compared to their female peers. This was the opposite of the previous findings.

Lin believes the pool of students (international and online) may have been a factor in the divergence from past research. Either way, interventions designed to increase female learners’ belonging and identity also clearly impacted male learners. Subsequent polling showed that a positive group dynamic impacted both male and female retention in STEM.

“We found that facilitating effective group dynamics can be potentially quite important for cultivating a more inclusive psychological experience,” Lin said.

Beyond Quantifying Participation

It can be challenging for instructors of online courses to incorporate those interventions, especially for small groups meeting outside the virtual classroom.

Lin outlined those challenges and the importance of diving deeper into the data in a study monitoring 88 small teams (three students per team) who were given a series of challenges to complete in a short period of time. Examining the gender differences in participation, Lin’s team confirmed that women spoke less in mixed-gender groups as well as male-majority teams, using fewer words and speaking less often compared to their peers.

The team then ran a language analysis on the transcripts of the students’ collaboration and found the female students actually provided a higher quality of participation than their male peers.

“Female students were better at responding to their teammates, building onto their contributions, and also being more cohesive with their own participation,” Lin said.

It affirmed her assertion that research can help look beyond the initial observations about frequency. Lin hopes that assessing the quality of contributions will be key to developing effective tools that encourage student participation in online courses and bring more equity to small groups.

AI for an Equitable Learning Experience

What those tools may look like is an exciting proposition to Lin, especially generative AI tools that can be applied to what she describes as the “in between,” the learning experience of students as they work through their course and team assignments.

“We sort of conceptualize that it is useful for AI to help us assess and model collaborative processes, rather than only collaborative outcomes,” Lin said.

Generative AI tools could provide personalized support for students, identifying learning patterns that may require intervention, like an intelligent tutoring system. Lin also sees potential in creating a similar generative AI program for teams, encouraging more equity in their collaboration and helping students from varied backgrounds and diverse perspectives interact in constructive and respectful ways. She referred to the center tool Tandem as an example of how well-designed support tools can reveal more about team dynamics and help instructors better support and guide students. Tandem coaches students working on team projects and allows instructors the chance to intervene when they see a group needs assistance.

Lin acknowledges that integrating generative AI with student support comes with challenges. That is why, Lin says, instructor input is key to ensuring tools are built using careful consideration of privacy and bias, and are extensively tested before launch. When done correctly, they could be powerful tools for building a more inclusive and equitable online learning environment.

“We wanted to think more deeply about how we can leverage AI as a tool for equity,” Lin said. “And this would perhaps be always a constant discussion in the community as we move forward with it.”

References

Lin, Y. & Nixon, N. (2024) STEM pathways in a global online course: Are male and female learners motivated the same?, L@S 2024: Proceedings of the Eleventh ACM Conference on Learning @ Scale, 243-249.

Lin, Y., Dowell, N., Godfrey, A., Choi, H., & Brooks, C. (2019). Modeling gender dynamics in intra and interpersonal interactions during online collaborative learning. LAK19: Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 431–435.

Nixon, N., Lin, Y., & Snow, L. (2024). Catalyzing equity in STEM teams: Harnessing generative AI for inclusion and diversity. Behavioral and Brain Sciences, 11(1), 85-92.

Lewis, N.A. , Sekaquaptewa, D. , & Meadows, L.A. (2019). Modeling gender counter-stereotypic group behavior: A brief video intervention reduces participation gender gaps on STEM teams. Social Psychology of Education, 22(3), 557–77.

Dowell, N., Lin, Y., Godfrey, A., & Brooks, C. (2019, June 25-29). Promoting inclusivity through time-dynamic discourse analysis in digitally-mediated collaborative learning. [Proceedings] In Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA. Springer International Publishing AG, Part 1(20), 207–19.

The Roundup on Research series is intended for faculty and staff who are interested in learning more about the theories, frameworks, and research in online and technology-enhanced teaching and learning.

If you have been anywhere where teaching is involved, you have probably heard mention of “learning styles.” “I’m a visual learner” vs. “I’m a hands-on learner” or “My instructor didn’t teach in my learning style” are all the types of commentary that are common when some individuals talk about their own learning. Although it is deeply appealing to be able to categorize individuals into easy methods of learning, unfortunately, it is deeply flawed, has little empirical evidence to support it, and might cause more problems than it solves.

What are Learning Styles?

To best understand why learning styles are problematic, it is important to clearly define learning styles. The idea of learning styles is that there are stable, consistent methods that individuals take in, organize, process, and remember information, and by teaching those methods, students learn better.

One popular concept in learning styles posits that the modality of information is critical – a “visual” learner learns best by seeing versus an “auditory” learner who learns best by having things spoken or described to them. Learning style theory would suggest that by using visual aids, a visual learner would organize and retain information better than say, an auditory learner. The implication is that matching modality information to the modality of learning style is critical to student success.

A note: there are additional and related concepts of “cognitive styles,” “learning strategies,” and “learner preferences” that can sometimes be used interchangeably with “learning styles.” For this article, “learning styles” refers specifically to the theory that there are ways that individuals learn best. In contrast, learning preferences suggest that there are ways people prefer to receive information, but it may not impact learning.

At face value, the concept of learning styles makes sense. Individuals learn differently. Most educational settings are trying to reach large numbers of students in personalized ways. It would be useful to have an easily applied theory that would help all students learn! As educators, we want to recognize the “uniqueness” of each student and help learners in any way we can. This desire has led educators to look for easier ways to navigate the complexities of teaching. Unfortunately, learning is not that simple.

Do Learning Styles Really Exist?

In general, most learning style theories make two presumptions:

Individuals have a measurable and consistent “style” of learning, and
Teaching to that style of learning will lead to better education outcomes, and conversely, teaching in a contradictory method would decrease achievement.

In other words, if you are a visual learner, you should learn best if you see things, regardless of the situation. If you are a kinesthetic learner, you will learn best if you can physically manipulate something, regardless of the topic. However, neither of these two assumptions shows any grounding in research. These two propositions are where we can see the concept of learning styles breaking down.

Are Learning Styles Measurable and Consistent?

Did you know that there are actually over 50 different theories of learning styles by various researchers? Researchers have been trying for years to find a correlation between individuals and how to help learning. Some theories suggest the modality of learning matters (like the common VARK theory) while others propose details like time of day and temperature of the room define a learning style. One study that suggested using a cell phone was a learning style (Pursell, 2009). Just the number of different styles makes it difficult to measure and make sense of an individual style.

In addition, most learning style inventories rely on a student’s self-report about how they perceive they learn best. These self-reports are generally not validated in any way. Generally, humans tend to be poor judges of our own learning. Therefore, these surveys are generally measuring “learner preference” rather than “learning style.” You may think you are an auditory learner but until it is validated that you objectively learn better through audio format, it is a preference, not a style.

Also, when reporting results, many studies will rely on “student satisfaction” as a measure of success, or rely on students’ reflections as a measure of success in a class. For example, many measures of learning styles will ask students how they believe they learn best. Unfortunately, satisfaction with a class or a student’s recollections of success are subjective measures, and generally not accurate (Kirschner & van Merriënboer, 2013, Kirschner, 2017). While understanding a learner’s preference is useful as is understanding student satisfaction with a lesson, it does not have the same weight as necessitating teaching to that preference.

Finally, ”styles” are unstable and unreliable. The research on learning styles has suggested that these preferences may be unstable – they be topic-specific, but they also change over time (Coffield et al., 2004). That means that although an individual may be a kinesthetic learner in history this week, that person is a visual learner in math when talking about calculus (but not about geometry), or prefers to learn how to ride a bike kinesthetically instead of reading about it in a book. This questions whether a learning style is a “trait” (or something stable and persisting for a person) or a “state” (something that is temporary and may change). Learning styles as a state of mind are not particularly useful. How can a teacher know the preference of an individual student today in a given subject?

Does Teaching a Learning Style Result in Better Learning?

Even more importantly, however, is the second assumption – does teaching to an individual’s learning style lead to achievement? Simply put, there is no evidence that supports teaching to a person’s specified learning style results in better learning (Alley, et. al., 2023; Cuevas, 2015; Kirschner & van Merriënboer, 2013; Krätzig & Arbuthnott, 2006; Pashler et al., 2008; Rogowsky et al., 2020). No study has shown that teaching to an identified learning style results in better retention, better learning outcomes or student success. Instead, we see that teaching to a self-identified learning style has no impact on learning in children or adults (Krätzig & Arbuthnott, 2006; Paschler et al., 2008; Rogowsky et al., 2015, Rogowsky et al., 2020). Some research suggests that some students performed better on tasks when taught in a different modality than their self-identified “learning style” (Krätzig & Arbuthnott, 2006, Rogowsky et al., 2020). Most studies of learning styles use a methodology that uses multiple styles to all learners – meaning that there is no way to isolate learning style to teaching method. This leads us to ultimately conclude that while the concept of learning styles is appealing, at this point, it is still a myth.

Alternate Explanations to Learning Styles

Anecdotally, there are many stories about the success of leveraging “learning styles.” If learning styles are not empirically supported, how are these successes explained? There are alternative explanations for why teaching in multiple methods increases achievement that do not prescribe students into style categories. Multi-modal learning explains how learning improves with various methods of teaching.

Learning requires sustained attention. Therefore, if an educator can capture and maintain students’ attention, students’ learning outcomes likely improve. Providing engagement with content in multiple forms – be it through hands-on activities, or different modalities – makes students pay attention to content in different ways, and requires learners to integrate knowledge in new ways. If an educator is using multiple methods and modalities, it’s just more interesting, and students pay more attention, which leads to better learning. Mayer and colleagues (2001, 2003) have extensively studied how students learn with visuals and audio, and the interaction of the two. What he and his colleagues suggest is that by providing dual streams of information in multiple methods engages learners to work harder at understanding the material, which leads to better learning. It may be that the research on learning styles is actually showing that teaching with different modalities is just more interesting to students rather than catering to a particular style of learning (Krätzig & Arbuthnott, 2006).

Why Learning Styles are Dangerous

While the intentions of learning styles are good, the implications of learning styles are more destructive than helpful. On the positive side, reflecting on how one learns is always a lesson. However, by focusing on a style suggests that learners are passive vessels at the whim of the method of teaching. Ultimately, most educators want students to actively engage in their learning. The best learning takes place when an individual can connect and incorporate information into his or her personal experiences and understanding. By focusing on a student’s learning style we reinforce a simplistic view of learning. Learning styles suggest that individuals have one way to learn best. Unfortunately, learning is complex, and not easy. This is hard and takes time! It has very little to do with the way information is handed to a learner, but rather, how the learner processes that knowledge once they have it. It is important to remember – learning is within the control of the learner.

Thinking Critically About Learning Styles

If learning styles do not impact an individual’s ability to learn, why is there so much talk about them? Articles and books are still being published about learning styles and how to tailor teaching to reach every style. Research on teaching and learning is a complicated discipline, and being able to examine theories and concepts like learning styles critically is important to anyone working in education. The challenge is to keep a skeptical eye when you hear about research supporting learning styles and ask the right questions to make sure you are getting good information.

What Should you Think About the Next Time you Encounter Learning Styles in the Wild?

What framework of learning styles are they referring to? Some are more empirically vetted than others. The most popular learning style VARK (Visual-Auditory-Read/Write-Kinesthetic) is also the least validated. Find out more about the learning style being discussed.
How are they measuring both learning style and success? Are they self-reported? Are they looking at academic results or a self-report of satisfaction with learning?
Is the study carefully controlled? Many studies fail to tailor the learning to a particular style. Rather, the lesson uses all the styles to reach all the students. There is no way to truly measure success.
Learning styles can be controversial with some people. They aren’t necessarily harmful if they encourage people to reflect on teaching and learning in different ways. They can be harmful if students believe that their learning is outside their control.

References

Alley, S., Plotnikoff, R. C., Duncan, M. J., Short, C. E., Mummery, K., To, Q. G., Schoeppe, S., Rebar, A., & Vandelanotte, C. (2023). Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement? Journal of Health Psychology, 28(10), 889–899.

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using learning styles? What research has to say to practice: Learning & Skills Research Center.

Cuevas, J. (2015). Is learning styles-based instruction effective? A comprehensive analysis of recent research on learning styles. Theory and Research in Education, 13(3), 308–333.

Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers & Education, 106, 166–171.

Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183.

Krätzig, G. P., & Arbuthnott, K. D. (2006). Perceptual learning style and learning proficiency: A test of the hypothesis. Journal of Educational Psychology, 98(1), 238–246.

Lau, W. & Yuen, A. (2009). Exploring the effects of gender and learning styles on computer programming performance: Implications for programming pedagogy. British Journal of Educational Technology. 40(4), 696-712

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43-52.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105-119.

Pursell, D. P. (2009) Adapting to student learning styles: Engaging students with cell phone technology in organic chemistry. Journal of Chemical Education. 86(10), p1219-1222.

Rogowsky, B. A., Calhoun, B. M., & Tallal, P. (2015). Matching learning style to instructional method: Effects on comprehension. Journal of Educational Psychology, 107(1), 64–78.

Rogowsky, B. A., Calhoun, B. M., & Tallal, P. (2020). Providing Instruction Based on Students’ Learning Style Preferences Does Not Improve Learning. Frontiers in Psychology, 11.

One of the first questions many educators ask when getting started teaching online is “How do you recreate the experience of a face-to-face classroom in an online environment?” While there are many facets to that question, many instructors refer to the sense of community and connection as a gap that they struggle to overcome. However, much research has been done on the impact and development of learning communities in the online classroom. In this article, we will discuss the influential framework Community of Inquiry (CoI), how it can be used to inform your own teaching, as well as how it has been used to frame online learning research in the research.

Community of Inquiry Model

One of the most used frameworks applied to the understanding of online learning environments is the community of inquiry (CoI) model (Garrison, et. al, 2000). Originally developed by observing asynchronous text-based learning environments, CoI suggests that there are three core interdependent elements to a learning experience: social presence, cognitive presence, and teaching presence. The intersection of the three presences results in what is categorized as “deep learning.” Rooted in the belief that learners construct meaning within social contexts (social constructivism), Community of Inquiry makes meaning of how learners interact online to create knowledge.

Three Presences: Cognitive, Teaching, and Social

Cognitive presence is the capacity for meaningful construction of learning. Cognitive presence is often what instructors might think of the active learning portion of a class. Indications of cognitive presence include asking questions, engaging in reflection on a topic, and scaffolding engagement with a topic. Cognitive presence can be supported by an instructor asking probing questions, modeling reflection, and encouraging active participation from learners. As the community grows together, other learners may (and should) also participate in the facilitation of cognitive presence.

Venn diagram of Community of Inquiry model with three presences (social, cognitive, and teaching) all intersecting. Common area between social presence and cognitive presence is supporting discourse. Common area between social presence and teaching presence is setting climate. Common area between teaching presence and cognitive presence is selecting content. All three areas intersect with educational experience.

Teaching presence is the design, structure, and guidance that directs the learning experience. Instructional design is one of the earliest ways to demonstrate teaching presence (course materials, assessments, activities). However, it is also important to consider how the instructor demonstrates active teaching presence throughout the time of the course. This can take the form of weekly introductory emails, specifying expectations for Zoom sessions, or providing assistance to a student struggling with a topic. Teaching presence is not isolated to the instructor alone, rather, can also be exhibited by students by providing structure and guidance to fellow students.

Social presence is the ability for participants in the community to represent themselves as whole people complete with emotions and personality. It is easy to focus on the design of a course thinking about the content that needs to be taught or the learning objectives to be met. In a face-to-face classroom, much of the social presence happens spontaneously through a shared location. In an online setting, we design our courses and spaces to encourage the development of social presence. This could involve including an introduction area for students where the instructor shares (and encourages students to share) some pieces of personal information, infusing weekly posts or announcements with personality as well as giving students space to express their own personalities.

COI in the Literature

As one of the prevailing frameworks in current online teaching and learning, the Community of Inquiry model has been in the academic spotlight frequently over the past several years. In a recent search, CoI has been cited in over 1000 articles during the last three years alone. As classrooms transitioned to emergency remote and/or online teaching during the pandemic, CoI has been used to explain students’ motivation in courses (Turk et al., 2022), how to understand the bridge between informal and formal learning (Chatterjee & Parra, 2022), and leveraging learning analytics for student feedback (Yılmaz, 2020). It is also hypothesized that different types of disciplines may have different need profiles for presence, for example, some disciplines may have greater social presence needs vs. teaching presence needs (Arbaugh, 2013).

Most critically, social presence has been associated with student satisfaction in online learning. While teaching and cognitive presence are positively correlated with students’ perceptions of learning (Akyol & Garrison, 2008; Turk et al., 2022), social presence was highlighted as faculty transitioned to emergency remote teaching during the pandemic. Studies of social presence have cited timeliness of feedback and coaching (Conklin & Dikkers, 2021), frequency of communication and feedback (D’alessio et al., 2019), and the opportunity for social interactions regardless of whether those opportunities were acted upon (Weidlich & Bastiaens, 2019) as ways to build social presence. The benefits of increased social presence suggest decreased issues with academic integrity (Eshet et al., 2021), increased student performance (D’alessio et al., 2019), and increased higher-order thinking (Stein et al., 2013).

Critiques of COI

While being one of the most popular frameworks leveraged in online teaching and learning right now, CoI is not without critique. First, it assumes that learning is inherently social. If your teaching philosophy does not align with the underlying beliefs of social-based learning (like constructivism), this may not be the best framework.

In Xin’s (2012) critique she notes the challenges of parsing out what is a “social presence” interaction (since CoI assumes all learning is social) from the other types of presences. How are cognitive presence and teaching presence different if they are also inherently social? In addition, because CoI is rooted in written communication between community members, is there a difference between what happens in written, asynchronous communication versus what may take place more spontaneously with spoken, synchronous communication? Others have suggested that CoI does not take into account interpersonal contributions to learning. Learners may also need to take responsibility for their learning, and they may not always be invested in a learning community (Shea et al., 2014; Wertz, 2022).

Finally, CoI was developed during a time when synchronous communication (like videoconferencing) was at a premium. The research has not yet determined whether CoI applies equally as well when a portion of communication is taking place synchronously.

How to Incorporate COI Into Your Online Design

One of the reasons the Community of Inquiry is so popular is that it can be used proactively as a framework for creating a more engaging learning environment. Facilitating an online course can feel like teaching to a black box. CoI provides a way to be proactive in development to make teaching online more effective. The best way to leverage CoI is to think about the three types of presences and how you are planning to address them each week.

Since CoI is rooted in active communication, one of the best things to do is to create a communication/engagement plan.

Ideas for Increasing Teaching Presence:

Write weekly introductions and weekly summaries. Consider including points that you may have found particularly interesting and/or general comments on discussions within class.
Use the Announcements feature to post timely updates.
Return emails and assignments within a set expectation. For example, “I will return short assignments within 3 days. Our longer papers will be returned within 7 days”
Create a survey for students to get feedback on organization/communication. Make adjustments based on feedback, and then communicate those changes back to students. Students need to know that you have made changes based on their feedback.

Ideas for Increasing Cognitive Presence

In videoconferencing (like Zoom), create handouts or guided notes so students can be active during lectures.
Tools like Persuall can engage students asynchronously with communications on readings.
Use case studies, application, and reflection assignments to encourage students to consider content topics and make meaningful connections

Ideas for Increasing Social Presence

Social presence is facilitated by the instructor. Demonstrate commitment to connection with students. Create a communication plan. Students frequently cite feedback from instructors as a critical aspect of feeling connected in a class. Give students expectations for timeliness of feedback and provide enough detail to build an academic relationship.
Create space for social interactions during Zoom sessions. Take the first 3 minutes for small talk, have a question of the day, or use a poll to encourage students to share about themselves if they feel comfortable.
Use a discussion board for informal conversations. Consider a theme – favorite meme, favorite place to travel, food that reminds you of home. Make sure that as the instructor, you participate as well.

If you are interested in learning more about Community of Inquiry, visit the COI website.

References

Arbaugh, J. B. (2013). Does academic discipline moderate CoI-course outcomes relationships in online MBA courses? The Internet and Higher Education, 17, 16–28.

Chatterjee, S., & Parra, J. (2022). Undergraduate Students Engagement in Formal and Informal Learning: Applying the Community of Inquiry Framework. Journal of Educational Technology Systems, 50(3), 327–355.

Conklin, S., & Dikkers, A. G. (2021). Instructor Social Presence and Connectedness in a Quick Shift from Face-to-Face to Online Instruction. Online Learning, 25(1).

D’alessio, M. A., Lundquist, L. L., Schwartz, J. J., Pedone, V., Pavia, J., & Fleck, J. (2019). Social presence enhances student performance in an online geology course but depends on instructor facilitation. Journal of Geoscience Education, 67(3), 222–236.

Eshet, Y., Steinberger, P., & Grinautsky, K. (2021). Relationship between statistics anxiety and academic dishonesty: A comparison between learning environments in social sciences. Sustainability (Switzerland), 13(3), 1–18.

Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education. The Internet and Higher Education, 2(2), 87–105.

Shea, P., Hayes, S., Uzuner-Smith, S., Gozza-Cohen, M., Vickers, J., & Bidjerano, T. (2014). Reconceptualizing the community of inquiry framework: An exploratory analysis. The Internet and Higher Education, 23, 9–17.

Stein, D. S., Wanstreet, C. E., Slagle, P., Trinko, L. A., & Lutz, M. (2013). From “hello” to higher-order thinking: The effect of coaching and feedback on online chats. Internet and Higher Education, 16(2013), 78–84.

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