by Greta Underhill

Are you interested in qualitative research? Are you currently working on a qualitative project? Some researchers find it helpful to use a computer-assisted qualitative data analysis software (CAQDAS) program to help them organize their data through the analysis process. Although some programs can perform basic categorization for researchers, most software programs simply help researchers to stay organized while they conduct the deep analysis needed to produce scientific work. You may find a good CAQDAS program especially helpful when multiple researchers work with the same data set at different times and in different ways. Choosing the right CAQDAS for your project or team can take some time and research but is well worth the investment. You may need to consider multiple factors before determining a software program such as cost, operating system requirements, data security, and more.

For the Ecampus Research Unit, issues with our existing CAQDAS prompted our team to search for another program that would fit our specific needs: Here’s what we were looking for:

Needs	Reasoning
General qualitative analysis	We needed a program for general analysis for multiple types of projects; Other programs are designed for specific forms of analysis such as Leximancer for content analysis
Compatibility across computer operating systems (OS)	Our team used both Macs and PCs
Adherence to our institution’s IRB security requirements	Like many others, our institution and our team adhere to strict data security and privacy requirements, necessitating a close look at how a program would manage our data
Basic coding capabilities	Although many programs offer robust coding capabilities, our team needed basic options such as coding one passage multiple times and visually representing coding through highlights
Export of codes into tables or Excel books	This function is helpful for advanced analysis and reporting themes in multiple file formats for various audiences
A low learning-curve	We regularly bring in temporary team members on various projects for mentorship and research experience, making this a helpful function
A one-time purchase	A one-time purchase was the best fit for managing multiple and temporary team members on various projects

Testing a CAQDAS

I began systematically researching different CAQDAS options for the team. I searched “computer-assisted qualitative data analysis software” and “qualitative data analysis” in Google and Google Scholar. I also consulted various qualitative research textbooks and articles, as well as blogs, personal websites, and social media handles of qualitative researchers to identify software programs. Over the course of several months, I generated a list of programs to examine and test. Several programs were immediately removed from consideration as they are designed for different types of analysis: DiscoverText, Leximancer, MAXQDA, QDA Miner. These programs are powerful, but best suited for specific analysis, such as text mining. With the remaining programs, I signed up for software trials, attended several product demonstrations, participated in training sessions, borrowed training manuals from the library, studied how-to videos online, and contacted other scholars to gather information about the programs. Additionally, I tested whether programs would work across different operating systems. I kept recorded details about each of the programs tested, including how they handled data, the learning curve for each, their data security, whether they worked across operating system, how they would manage the export of codes, and whether they required a one-time or subscription-based payment. I started with three of the most popular programs, NVivo, Dedoose, and ATLAS.ti. The table below summarizes which of these programs fit our criteria.

	NVivo	Dedoose	ATLAS.ti
General Qualitative Analysis	✓	✓	✓
Cross-OS Collaboration		✓	✓
Data security			✓
Basic coding capabilities	✓	✓	✓
Export codes	✓		✓
Low learning curve	✓	✓
One-time purchase	✓		✓

A table demonstrating whether three programs (NVivo, Dedoose, and ATLAS.ti) meet the team’s requirements. Details of requirements will be discussed in the text of the blog below.

NVivo

I began by evaluating NVivo, a program I had used previously. NVivo is a powerful program that adeptly handled large projects and is relatively easy to learn. The individual license was available for one-time purchase and allowed the user to maintain their data on their own machine or institutional servers. However, it had no capabilities for cross-OS collaboration, even when clients purchased a cloud-based subscription. Our team members could download and begin using the program, but we would not be able to collaborate across operating systems.

Dedoose

I had no prior experience with Dedoose, so I signed up for a trial of the software. I was impressed with the product demonstration, which significantly helped in figuring out how to use the program. This program excelled at data visualization and allowed a research team to blind code the same files for interrater reliability if that suited the project. Additionally, I appreciated the options to view code density (how much of the text was coded) as well as what codes were present across transcripts. I was hopeful this cloud-based program would solve our cross-OS collaboration problem, but it did not pass the test for our institution’s IRB data security requirements because it housed our data on Dedoose servers.

ATLAS.ti

ATLAS.ti was also a new program for me, so I signed up for a trial of this software. It is a well-established program with powerful analysis functions such as helpful hierarchical coding capabilities and institutive links among codes, quotations, and comments. But the cross-OS collaboration, while possible via the web, proved to be cumbersome and this too did not meet the data security threshold for our institution’s IRB. Furthermore, the price point meant we would need to rethink our potential collaborations with other organizational members.

Data Security

Many programs are now cloud-based, which offer powerful analysis options, but unfortunately did not meet our IRB data security requirements. Ultimately, we had to cut Delve, MAXQDA, Taguette, Transana, and webQDA. All of these programs would have been low-learning curve options with basic coding functionality and cross-OS collaboration; however, for our team to collaborate, we would need to purchase a cloud-based subscription, which can quickly become prohibitively expensive, and house our data on company servers, which would not pass our institutional threshold for data security.

Note-taking programs

After testing multiple programs, I started looking beyond just qualitative software programs and into note-taking programs such as DevonThink, Obsidian, Roam Research, and Scrintal. I had hoped these might provide a work around by organizing data on collaborative teams in ways that would facilitate analysis. However, most of them did not have functionalities that could be used for coding or had high learning curves that precluded our team using them.

It seemed like I had exhausted all options and I still did not have a program to bring back to the Research Unit. I had no idea that a low-cost option was just a YouTube video away. Stay tuned for the follow-up post where we dive into the solution that worked best for our team.

For the first part of this post, please see Media Literacy in the Age of AI, Part I: “You Will Need to Check It All.”

Just how, exactly, we’re supposed to follow Ethan Mollick’s caution to “check it all” happens to be the subject of a lively, forthcoming collaboration from two education researchers who have been following the intersection of new media and misinformation for decades.

In Verified: How to Think Straight, Get Duped Less, and Make Better Decisions about What to Believe Online (University of Chicago Press, November 2023), Mike Caulfield and Sam Wineburg provide a kind of user’s manual to the modern internet. The authors’ central concern is that students—and, by extension, their teachers—have been going about the process of verifying online claims and sources all wrong—usually by applying the same rhetorical skills activated in reading a deep-dive on Elon Musk or Yevgeny Prigozhin, to borrow from last month’s headlines. Academic readers, that is, traditionally keep their attention fixed on the text—applying comprehension strategies such as prior knowledge, persisting through moments of confusion, and analyzing the narrative and its various claims about technological innovation or armed rebellion in discipline-specific ways.

The Problem with Checklists

Now, anyone who has tried to hold a dialogue on more than a few pages of assigned reading at the college level knows that sustained focus and critical thinking can be challenging, even for experienced readers. (A majority of high school seniors are not prepared for reading in college, according to 2019 data.) And so instructors, partnering with librarians, have long championed checklists as one antidote to passive consumption, first among them the CRAAP test, which stands for currency, relevance, authority, accuracy, and purpose. (Flashbacks to English 101, anyone?) The problem with checklists, argue Caulfield and Wineburg, is that in today’s media landscape—awash in questionable sources—they’re a waste of time. Such routines might easily keep a reader focused on critically evaluating “gameable signals of credibility” such as functional hyperlinks, a well-designed homepage, airtight prose, digital badges, and other supposedly telling markers of authority that can be manufactured with minimal effort or purchased at little expense, right down to the blue checkmark made infamous by Musk’s platform-formerly-known-as-Twitter.

Three Contexts for Lateral Reading

One of the delights in reading Verified is drawing back the curtains on a parade of little-known hoaxes, rumors, actors, and half-truths at work in the shadows of the information age—ranging from a sugar industry front group posing as a scientific think tank to headlines in mid-2022 warning that clouds of “palm-sized flying spiders” were about to descend on the East Coast. In the face of such wild ideas, Caulfield and Wineburg offer a helpful, three-point heuristic for navigating the web—and a sharp rejoinder to the source-specific checklists of the early aughts. (You will have to read the book to fact-check the spider story, or as the authors encourage, you can do it yourself after reading, say, the first chapter!) “The first task when confronted with the unfamiliar is not analysis. It is the gathering of context” (p. 10). More specifically:

The context of the source — What’s the reputation of the source of information that you arrive at, whether through a social feed, a shared link, or a Google search result?

The context of the claim — What have others said about the claim? If it’s a story, what’s the larger story? If a statistic, what’s the larger context?

Finally, the context of you — What is your level of expertise in the area? What is your interest in the claim? What makes such a claim or source compelling to you, and what could change that?

“The Three Contexts” from Verified (2023)

At a regional conference of librarians in May, Wineburg shared video clips from his scenario-based research, juxtaposing student sleuths with professional fact checkers. His conclusion? By simply trying to gather the necessary context, learners with supposedly low media literacy can be quickly transformed into “strong critical thinkers, without any additional training in logic or analysis” (Caulfield and Wineburg, p. 10). What does this look like in practice? Wineburg describes a shift from “vertical” to “lateral reading” or “using the web to read the web” (p. 81). To investigate a source like a pro, readers must first leave the source, often by opening new browser tabs, running nuanced searches about its contents, and pausing to reflect on the results. Again, such findings hold significant implications for how we train students in verification and, more broadly, in media literacy. Successful information gathering, in other words, depends not only on keywords and critical perspective but also on the ability to engage in metacognitive conversations with the web and its architecture. Or, channeling our eight-legged friends again: “If you wanted to understand how spiders catch their prey, you wouldn’t just look at a single strand” (p. 87).

SIFT graphic by Mike Caulfield with icons for stop, investigate the source, find better coverage, and trace claims, quotes, and media to the original context.

Image 2: Mike Caulfield’s “four moves”

Reconstructing Context

Much of Verified is devoted to unpacking how to gain such perspective while also building self-awareness of our relationships with the information we seek. As a companion to Wineburg’s research on lateral reading, Caulfield has refined a series of higher-order tasks for vetting sources called SIFT, or “The Four Moves” (see Image 2). By (1) Stopping to take a breath and get a look around, (2) Investigating the source and its reputation, (3) Finding better sources of journalism or research, and (4) Tracing surprising claims or other rhetorical artifacts back to their origins, readers can more quickly make decisions about how to manage their time online. You can learn more about the why behind “reconstructing context” at Caulfield’s blog, Hapgood, and as part of the OSU Libraries’ guide to media literacy. (Full disclosure: Mike is a former colleague from Washington State University Vancouver.)

If I have one complaint about Caulfield and Wineburg’s book, it’s that it dwells at length on the particulars of analyzing Google search results, which fill pages of accompanying figures and a whole chapter on the search engine as “the bestie you thought you knew” (p. 49). To be sure, Google still occupies a large share of the time students and faculty spend online. But as in my quest for learning norms protocols, readers are already turning to large language model tools for help in deciding what to believe online. In that respect, I find other chapters in Verified (on scholarly sources, the rise of Wikipedia, deceptive videos, and so-called native advertising) more useful. And if you go there, don’t miss the author’s final take on the power of emotion in finding the truth—a line that sounds counterintuitive, but in context adds another, rather moving dimension to the case against checklists.

Given the acceleration of machine learning, will lateral reading and SIFTing hold up in the age of AI? Caulfield and Wineburg certainly think so. Building out context becomes all the more necessary, they write in a postscript on the future of verification, “when the prose on the other side is crafted by a convincing machine” (p. 221). On that note, I invite you and your students to try out some of these moves on your favorite chatbot.

Another Postscript

The other day, I gave Microsoft’s AI-powered search engine a few versions of the same prompt I had put to ChatGPT. In “balanced” mode, Bing dutifully recommended resources from Stanford, Cornell, and Harvard on introducing norms for learning in online college classes. Over in “creative” mode, Bing’s synthesis was slightly more offbeat—including an early-pandemic blog post on setting norms for middle school faculty meetings in rural Vermont. More importantly, the bot wasn’t hallucinating. Most of the sources it suggested seemed worth investigating. Pausing before each rabbit hole, I took a deep breath.

Related Resource

Oregon State Ecampus recently rolled out its own AI toolkit for faculty, based on an emerging consensus that developing capacities for using this technology will be necessary in many areas of life. Of particular relevance to this post is a section on AI literacy, conceptualized as “a broad set of skills that is not confined to technical disciplines.” As with Verified, I find the toolkit’s frameworks and recommendations on teaching AI literacy particularly helpful. For instance, if students are allowed to use ChatGPT or Bing to brainstorm and evaluate possible topics for a writing assignment, “faculty might provide an effective example of how to ask an AI tool to help, ideally situating explanation in the context of what would be appropriate and ethical in that discipline or profession.”

References

Caulfield, M., & Wineburg, S. (2023). Verified: How to think straight, get duped less, and make better decisions about what to believe online. University of Chicago Press.

Mollick, E. (2023, July 15). How to use AI to do stuff: An opinionated guide. One Useful Thing.

Oregon State Ecampus. (2023). Artificial Intelligence Tools.

Have you found yourself worried or overwhelmed in thinking about the implications of artificial intelligence for your discipline? Whether, for example, your department’s approaches to teaching basic skills such as library research and source evaluation still hold up? You’re not alone. As we enter another school year, many educators continue to think deeply about questions of truth and misinformation, creativity, and how large language model (LLM) tools such as chatbots are reshaping higher education. Along with our students, faculty (oh, and instructional designers) must consider new paradigms for our collective media literacy.

Here’s a quick backstory for this two-part post. In late spring, shortly after the “stable release” of ChatGPT to iOS, I started chatting with bot model GPT-3.5, which innovator Ethan Mollick describes as “very fast and pretty solid at writing and coding tasks,” if a bit lacking in personality. Other, internet-connected models, such as Bing, have made headlines for their resourcefulness and darker, erratic tendencies. But so far, access to GPT-4 remains limited, and I wanted to better understand the more popular engine’s capabilities. At the time, I was preparing a workshop for a creative writing conference. So, I asked ChatGPT to write a short story in the modern style of George Saunders, based in part on historical events. The chatbot’s response, a brief burst of prose it titled “Language Unleashed,” read almost nothing like Saunders. Still, it got my participants talking about questions of authorship, originality, representation, etc. Check, check, check.

The next time I sat down with the GPT-3.5, things went a little more off-script.

One faculty developer working with Ecampus had asked our team about establishing learning norms in a 200-level course dealing with sensitive subject matter. As a writing instructor, I had bookmarked a few resources in this vein, including strategies from the University of Colorado Boulder. So, I asked ChatGPT to create a bibliographic citation of Creating Collaborative Classroom Norms, which it did with the usual lightning speed. Then I got curious about what else this AI model could do, as my colleagues Philip Chambers and Nadia Jaramillo Cherrez have been exploring. Could ChatGPT point me to some good resources for faculty on setting norms for learning in online college classes?

“Certainly!” came the cheery reply, along with a summary of five sources that would provide me with “valuable information and guidance” (see Image 1). Noting OpenAI’s fine-print caveat (“ChatGPT may produce inaccurate information about people, places, or facts”), I began opening each link, expecting to be teleported to university teaching centers across the country. Except none of the tabs would load properly.

“Sorry we can’t find what you’re looking for,” reported Inside Higher Ed. “Try these resources instead,” suggested Stanford’s Teaching Commons. A closer look with Internet Archive’s Wayback Machine confirmed that the five sources in question were, like “Language Unleashed,” entirely fictitious.

An early chat with ChatGPT-3.5, asking whether the chatbot can point the author to some good resources for faculty on setting classroom norms for learning in online college classes. "Certainly," replies ChatGPT, in recommending five sources that "should provide you with valuable information and guidance."

Image 1: An early, hallucinatory chat with ChatGPT-3.5

As Mollick would explain months later: “it is very easy for the AI to ‘hallucinate’ and generate plausible facts. It can generate entirely false content that is utterly convincing. Let me emphasize that: AI lies continuously and well. Every fact or piece of information it tells you may be incorrect. You will need to check it all.”

The fabrications and limitations of chatbots lacking real-time access to the ever-expanding web have by now been well-documented. But as an early adopter, the speed and confidence ChatGPT brought to the task of inventing and describing fake sources felt unnerving. And without better guideposts for verification, I expect students less familiar with the evolution of AI will continue to experience confusion, or worse. As the Post recently reported, chatbots can easily say offensive things and act in culturally-biased ways—”a reminder that they’ve ingested some of the ugliest material the internet has to offer, and they lack the independent judgment to filter that out.”

Just how, exactly, we’re supposed to “check it all” happens to be the subject of a lively, forthcoming collaboration from two education researchers who have been following the intersection of new media and misinformation for decades.

Stay tuned for an upcoming post with the second installment of “Media Literacy in the Age of AI,” a review of Verified: How to Think Straight, Get Duped Less, and Make Better Decisions about What to Believe Online by Mike Caulfield and Sam Wineburg (University of Chicago Press, November 2023).

References

Mollick, E. (2023, July 15). How to use AI to do stuff: An opinionated guide. One Useful Thing.

Wroe, T., & Volckens, J. (2022, January). Creating collaborative classroom norms. Office of Faculty Affairs, University of Colorado Boulder.

Yu Chen, S., Tenjarla, R., Oremus , W., & Harris, T. (2023, August 31). How to talk to an AI chatbot. The Washington Post.

By: Julie Jacobs, Jana King, Dana Simionescu, Tianhong Shi

Overview

A recent scenario with our course development team challenged our existing practices with lecture media. Formerly, we had encouraged faculty to include only slides with narration in their lecture videos due to concerns about increasing learners’ cognitive load. Students voiced their hope for more instructor presence in courses, and some instructors started asking about including video of themselves inserted into their lectures. This prompted us to begin thinking about instructor presence in lecture videos more deeply: why were we discouraging faculty from including their faces in lecture videos? While our practices were informed by research-based media theory, we also recognized those theories might be outdated.

We began to explore the latest research with the following question in mind: does visual instructor presence in lectures increase extraneous cognitive load in learners? We use the phrase “visual instructor presence” to refer to lecture videos where an instructor’s moving image is seen giving the lecture, composited together with their slides. This technique is also commonly referred to as “picture-in-picture”, as seen in the image below.

Image 1: Adam Vester, instructor in College of Business, in his lecture design for BA 375 Applied Quantitative Methods.

A task force was created to review recent research on visual instructor presence and cognitive load, specifically in lecture-type videos. Our literature review included a look at leading multimedia learning scholar Richard E. Mayer’s newest group of principles. We also reviewed more than 20 other scholarly articles, many of which were focused on learner perception, motivation & engagement, and emotion.

Findings

According to recent work in multimedia learning, research in this area should focus on three areas, namely learning outcomes (“what works/ what does not work?”), learning characteristics (“when does it work?”), and learning process (“how does it work?”) (Mayer, 2020). Below are our conclusions from the 23 research articles we reviewed regarding instructional videos, attempting to answer the above questions of “what works”, “when does it work”, and “how does it work”.

This review of recent literature shows no evidence that visual instructor presence increases extraneous cognitive load.
Students tend to prefer lectures with visual instructor presence – they report increased satisfaction and better perceived learning, which can boost motivation and engagement.
While some studies find no difference in performance outcomes when visual instructor presence is utilized, others found increased performance outcomes with visual instructor presence. Proposed explanations: embodiment techniques such as gestures, eye contact, and body movement which fosters generative processing (the cognitive processes required for making sense of the material); social cues can help direct the learners’ attention; increased motivation (as per point 2 above) contributes to better learning.
The effects may depend on the specific type of visual instructor presence (e.g., small picture-in-picture, green-screen, or lightboard) and the characteristics of the content (complex/difficult vs simpler/easier).

Recommendations

Based on these findings, our team has decided to remove the default discouragement of instructors wishing to use picture-in-picture in lectures. If an instructor is interested in having their visual presence in the lectures, we encourage them to discuss this option with their Instructional Designer and Lecture Media Coordinator to determine if this style is a good fit for them and their content.

Image 2: Bryony DuPont, associate professor of Mechanical Engineering, utilizing visual instructor presence in her lecture design for ME 382 Introduction to Design.

We recommend considering the following points:

What is their presentation style? Do they tend to spend a lot of time talking over a slide or is there a lot of text or other action (e.g. software demo) happening in the video? If there’s a lot happening on the screen, perhaps it’s better to not put their video on top of it (the instructor video could be placed only at the beginning and/or end instead).
What type of content? Is it simple or more complex? For more visually complex content, a lightboard or digital notation without picture-in-picture may work better, to take advantage of the dynamic drawing principle and the gaze guidance principle.
Is it a foreign language course? If so, it’s likely helpful for the learners to see the instructor’s mouth and body language.
Is the instructor comfortable with being on video? If they’re not comfortable with it, it may not add value. This being said, our multimedia professionals can help make instructors more comfortable in front of the camera and coach them on a high-embodied style of lecturing.

Since implementing these guidelines and working with an increased number of lectures with visual instructor presence, we also noticed that it works best when the instructor does not look and sound like they’re reading. Therefore, for people who like working with a script, we recommend practicing in advance so they can sound more natural and are able to enhance their presentation with embodiment techniques.

We would love to hear about your opinions or experiences with this type of video. Share them in the comments!

For a detailed summary of our findings and full citation list, please see the full Literature Review.

Some form of group work is a common activity that I help design with faculty every term. Oftentimes, faculty ask how to consider the different levels of engagement from individual group members and how to assess group work, often in the form of a group grade. Improving group work in asynchronous courses and group contracts to promote accountability are some of many ways to guide students into collaborative work. However, collaborative work may require offering equitable opportunities to all students to succeed. Based on the work by Feldman (2019), I’d like to outline some suggestions for assessment design through an equity lens.

Before jumping into assessing group work, Feldman outlines three pillars of equitable grades:

“They are mathematically accurate, validly reflecting a student’s academic performance.
They are bias-resistant, preventing biased subjectivity from infecting our grades.
They motivate students to strive for academic success, persevere, accept struggles and setbacks, and to gain critical lifelong skills” (Feldman, p. 71).

With these three pillars in mind, let’s examine some potential issues with a group receiving one grade for their work.

Accuracy: a collective group grade does not necessarily reflect an individual’s contribution to the group work or assess an individual student’s learning in terms of outcomes. For example, if a group splits up sections of a project into individual responsibilities, a student who did their assigned section very well may not have had an opportunity to gain new knowledge or build on their learning for aspects where they were struggling. And a group grade does not accurately capture their individual work or learning.
Bias: Many times peer evaluations of group work come with some kind of group contract or accountability measure. However, there is a possibility for bias in how students evaluate their peers, especially if that evaluation is based on behaviors like turning things in on time and having strong social skills instead of learning. For example, maybe one of the group members had a job with a variable schedule from week to week, making it difficult to join regular group discussions and complete work at the same pace every week for the duration of the project. Other group members may perceive them as difficult to work with or inconsistent in their commitment and award them fewer points in a peer evaluation, especially if other group members did not have outside factors noticeably impacting their performance.
Motivation: Group contracts and using evaluation as a way to promote productivity is an external motivator and does not instill a sense of internal relevance for students participating in group work. Instead, students may feel resentful that their peers may evaluate them harshly for things outside of their control, which can quickly snowball into a student disengaging from group work entirely.

“The purpose of group work is not to create some product in which all members participate, but for each student to learn specific skills or content through the group’s work together.”
Feldman, p. 104

So how do we assess this learning? Individually. If we can reimagine group work as a journey toward an individual reaching a learning outcome, then instead of assessing a behavior (working well and timeliness in a group) or what a group produces, we can instead create an assessment that captures the individual impact of the group work instead. Feldman outlines some tips for encouraging group work without a group grade:

Have a clear purpose statement and overview for the group work that outlines the rationale and benefit of learning that content in a group context.
Have clear evaluation criteria that shows the alignment of the group work with a follow-up individual assessment.
If possible, include students in the process by having a brainstorm or pre-work discussion ahead of time about what makes groups productive, how to ensure students learn material when working in groups, and what kinds of collaborative expectations can be set for a particular cohort of students.
Be patient with students navigating a new assessment strategy for the first time and offer ample feedback throughout the process so students are set up for success on their assessments.
Ensure the follow-up individual assessment is in alignment with learning outcomes and is focused on the content or skills students are expected to gain through group work.

As an added bonus, assessing group work individually in this way is often simpler than elaborate group work rubrics with separate peer evaluations factored in, making it both easier for the instructor and easier for the student to understand how their grade is calculated. Additionally, it will be important to design this group work with intention—if an individual could learn the material on their own, then what is the purpose of the group interaction? Think about a group project you may have assigned or designed in the past. What was the intention for that journey as a group? And how might you reimagine it if there was an individual assessment after its completion? I hope these questions are great starting points for reflecting on group work assessments and redesigning with equity in mind!

References

Feldman, J. (2019). Grading for equity: What it is, why it matters, and how it can transform schools and classrooms. Thousand Oaks, CA: Corwin.

By Cat Turk and Mary Ellen Dello Stritto

In this time of rapid change in online education, we can benefit from leveraging the expertise of faculty who have experienced the evolution of online education. At the Oregon State University (OSU) Ecampus Research Unit, we have been learning from a group of instructors who have taught online for ten years or more. A review of recent research uncovered that these instructors are an untapped resource. Their insights can provide valuable guidance for instructors who are just beginning their careers or instructors who may be preparing to teach online for the first time. Further, their perspectives can also be enlightening for online students.

In 2018-2019 we conducted interviews with 33 OSU faculty who had been teaching online for 10 years or more as a part of a larger study. Two of the questions we asked them were the following:

What skills do you think are most valuable for online instructors to have?
What skills do you think are most valuable for online students to have?

We will share some of the results of a qualitative analysis of these questions and highlight the similarities and differences.

When asked about the most valuable skills for online instructors, three key skills emerged: communication, organization, and time management. When asked about the most valuable skills for online students to have, the same skills were among the most frequently mentioned by these instructors.

As the table below shows, in the responses about skills for online instructors, communication emerged as the most prominent skill, with 85% of instructors in the study emphasizing its importance, while time management and organization were split evenly at 45%. In their response about skills for students, 64% of the instructors emphasized both communication and time management, while 42% discussed organization. When discussing communication for instructors, they indicated that effective communication is essential for building rapport with students, providing clear instructions, and facilitating meaningful interactions in the online environment. Organization (such as structuring course materials or their weekly work process) and time management skills (such as scheduling availability to connect with students) were also highly valued by these instructors. Read more about the analysis of instructor skills here.

	Skills for Instructors	Skills for Students
Communication	28 responses (85%)	21 responses (64%)
Time Management	15 responses (45%)	21 responses (64%)
Organization	15 responses (45%)	14 responses (42%)
Self-Motivation	—	21 responses (64%)

Frequency of responses of skills for instructors and students.

The responses to both questions emphasized the significance of communication skills in written assignments and in proactive connections within the scope of the online learning environment. Instructors articulated that online students needed to be proactive communicators themselves. Examples of this include contacting their instructors about questions and clarification in a timely way, interacting with their peers in a respectful manner, and turning in quality written assignments that demonstrate comprehension of their learning material. For students, clear and effective communication ensures understanding and engagement, while organization facilitates seamless navigation through course materials, and time management ensures that students are able to make the most of the asynchronous environment.

While time management and organization were both considered by instructors to be just as crucial for students, their responses demonstrated that these skills were needed for different reasons than would be the case for instructors. Instructors personally valued time management and organization due to the nature of facilitating courses online. When the online classroom can travel from place to place, setting blocks of intentional time and structuring hours accordingly were considered essential to instructors for maintaining a work-life balance and so tasks would not be missed.

On the other hand, according to these instructors, students need time management and organization due to the asynchronous and sometimes isolating nature of online courses. One instructor stressed that:

“[Students] do need to be more organized than on-ground students, because there’s not that weekly meeting to keep students on track.”

These instructors indicated some online students may need to structure their study time to accommodate a different time zone, while others may need to structure their academic pursuits around careers or children. Another instructor emphasized that:

“A lot of our [online students] actually work full-time, so they have families and kids and have to be much more organized too.”

While there were overlaps with the responses to the two questions, a notable difference was the emergence of another skill for students: self-motivation. This concept of self-motivation emerged from the instructor responses about students’ capacity to persevere in online courses. This included their level of motivation, capacity to learn on their own, and comfort with self-paced learning.

One instructor said the following about students’ self-motivation,

“Some people would say it’s self-discipline, but I think it’s more of they have to have a purpose for that class.”

Self-motivation was not mentioned by the instructors as a skill for online instructors, suggesting that these instructors perceive this as more pertinent to students for success in managing their own learning process. It is worth noting that proactive communication was highlighted as an essential aspect of self-motivation, with instructors emphasizing that students who take the initiative in reaching out to them tend to be more successful. This observation suggests that self-motivated individuals are more likely to actively seek support and clarification, which can enhance their learning experience and overall success.

Another noteworthy aspect was the need for students to be comfortable with learning in physical isolation. Instructors acknowledged that online learners must navigate the challenges of studying independently without the immediate presence of peers and instructors. For online students specifically,

“They need to be motivated because they’re not going to have peers sitting in a classroom with them, and they don’t have a place that they have to physically go every week.”

This finding underscores the importance of maintaining motivation and engagement, as students ideally possess an intrinsic drive to succeed despite the absence of a physical connection to the university and their classmates.

The findings from this study highlight the importance of certain similar skills for both online instructors and students. Effective communication, organization, and time management are vital for success in the online learning environment for both instructors and students. We found this to be an interesting connection that online students might benefit from understanding: these are key skills that students and instructors have in common.

Our findings about self-motivation may be useful for online instructors. Consider incorporating strategies that foster student self-motivation, such as goal-setting exercises, regular check-ins, and providing opportunities for self-reflection. By empowering students to take ownership of their learning, instructors might enhance student engagement and success in the online environment.

Further, students can learn from the instructors’ emphasis on communication, organization, and time management skills. They can intentionally work on improving their communication skills, seeking clarification when needed, and actively participating in online discussions. Developing effective organization and time management strategies, such as creating schedules, prioritizing tasks, and breaking them down into manageable chunks, may significantly enhance their online learning experience.

The field of online education is evolving rapidly, and here we can see how educators and students alike are adapting to these changes. The experiences of long-term online instructors provide valuable insights into the skills necessary for success in the online learning environment. In the future, what answers would we find if we asked students the same question: what do online students think are the skills needed to succeed in the online classroom? By understanding the shared and distinct perspectives of instructors and students, educators can design effective online courses and support systems that foster meaningful learning experiences and empower students to succeed.

An illustration of a person kneeling and question marks around

Have you ever been assigned a task but found yourself asking: “What’s the point of this task? Why do I need to do this?” Very likely, no one has informed you of the purpose of this task! Well, it likely was because that activity was missing to show a critical element: the purpose. Just like the purpose of a task can be easily left out, in the context of course design, a purpose statement for an assignment is often missing too.

Creating a purpose statement for assignments is an activity that I enjoy very much. I encourage instructors and course developers to be intentional about that statement which serves as a declaration of the underlying reasons, directions, and focus of what comes next in an assignment. But most importantly, the statement responds to the question I mentioned at the beginning of this blog…why…?

Just as a purpose statement should be powerful to guide, shape, and undergird a business (Yohn, 2022), a purpose statement for an assignment can guide students in making decisions about using strategies and resources, shape students’ motivation and engagement in the process of completing the assignment, and undergird their knowledge and skills. Let’s look closer at the power of a purpose statement.

What does “purpose” mean?

Merriam-Webster defines purpose as “something set up as an object or end to be”, while Cambridge Dictionary defines it as “why you do something or why something exists”. These definitions show us that the purpose is the reason and the intention behind an action.

Why a purpose is important in an assignment?

The purpose statement in an assignment serves important roles for students, instructors, and instructional designers (believe it or not!).

For students

The purpose will:

answer the question “why will I need to complete this assignment?”
give the reason to spend time and resources working out math problems, outlining a paper, answering quiz questions, posting their ideas in a discussion, and many other learning activities.
highlight its significance and value within the context of the course.
guide them in understanding the requirements and expectations of the assignment from the start.

For instructors

The purpose will:

guide the scope, depth, and significance of the assignment.
help to craft a clear and concise declaration of the assignment’s objective or central argument.
maintain the focus on and alignment with the outcome(s) throughout the assignment.
help identify the prior knowledge and skills students will be required to complete the assignment.
guide the selection of support resources.

For instructional designers

The purpose will:

guide building the structure of the assignment components.
help identify additional support resources when needed.
facilitate an understanding of the alignment of outcome(s).
help test the assignment from the student’s perspective and experience.

Is there a wrong purpose?

No, not really. But it may be lacking or it may be phrased as a task. Let’s see an example (adapted from a variety of real-life examples) below:

Project Assignment:

“The purpose of this assignment is to work in your group to create a PowerPoint presentation about the team project developed in the course. Include the following in the presentation:

Title
Context
Purpose of project
Target audience
Application of methods
Results
Recommendations
Sources (at least 10)
Images and pictures

The presentation should be a minimum of 6 slides and must include a short reflection on your experience conducting the project as a team.”

What is unclear in this purpose? Well, unless the objective of the assignment is to refine students’ presentation-building skills, it is unclear why students will be creating a presentation for a project that they have already developed. In this example, creating a presentation and providing specific details about its content and format looks more like instructions instead of a clear reason for this assignment to be.

A better description of the purpose could be:

“The purpose of this assignment is to help you convey complex information and concepts in visual and graphic formats. This will help you practice your skills in summarizing and synthesizing your research as well as in effective data visualization.”

The purpose statement particularly underscores transparency, value, and meaning. When students know why, they may be more compelled to engage in the what and how of the assignment. A specific purpose statement can promote appreciation for learning through the assignment (Christopher, 2018).

Examples of purpose statements

Below you will find a few examples of purpose statements from different subject areas.

Example 1: Application and Dialogue (Discussion assignment)

Courtesy of Prof. Courtney Campbell – PHL /REL 344

Example 2: An annotated bibliography (Written assignment)

Courtesy of Prof. Emily Elbom – WR 227Z

Example 3: Reflect and Share (Discussion assignment)

Courtesy of Profs. Nordica MacCarty and Shaozeng Zhang – ANTH / HEST 201

With the increased availability of language learning models (LLMs) and artificial intelligence (AI) tools (e.g., ChatGPT, Claude2), many instructors worry that students would resort to these tools to complete the assignments. While a clear and explicit purpose statement won’t deter the use of these highly sophisticated tools, transparency in the assignment description could be a good motivator to complete the assignments with no or little AI tools assistance.

Conclusion

“Knowing why you do what you do is crucial” in life says Christina Tiplea. The same applies to learning, when “why” is clear, the purpose of an activity or assignment can become a more meaningful and crucial activity that motivates and engages students. And students may feel less motiavted to use AI tools (Trust, 2023).

Note: This blog was written entirely by me without the aid of any artificial intelligence tool. It was peer-reviewed by a human colleague.

Resources:

Christopher, K. (02018). What are we doing and why? Transparent assignment design benefits students and faculty alike. The Flourishing Academic.

Sinek, S. (2011). Start with why. Penguin Publishing Group.

Trust, T. (2023). Addressing the Possibility of AI-Driven Cheating, Part 2. Faculty Focus.

Yohn, D.L. (2022). Making purpose statements matter. SHR Executive Network.

Introduction

We’ve all heard by now of ChatGPT, the large language model-based chat bot that can seemingly answer most any question you present it. What if there were a way to provide this functionality to students on their learning management system, and it could answer questions they had about course content? Sure, this would not completely replace the instructor, nor would it be intended to. Instead, for quick course content questions, a chatbot with access to all course materials could provide students with speedy feedback and clarifications in far less time than the standard turnaround required through the usual channels. Of course, more involved questions about assignments and course content questions outside of the scope of course materials would be more suited to the instructor, and the exact usage of a tool like this would need to be explained, as with anything.

Such a tool could be a useful addition to an online course because not only could it potentially save a lot of time, but it could also keep students on the learning platform instead of using a 3rd-party solution to answer questions as is the suspected case right now with currently available chatbots.

To find out what this would look like, I researched a bit on potential LLM chatbot candidates, and came up with a plan to integrate one into a Canvas page.

Disclaimer!
This is simply a proof of concept, and is not in production due to certain unknowns such as origin of the initial training data, CPU-bound performance, and pedagogical implications. See the Limitations and Considerations section for more details.

How it works

The main powerhouse behind this is an open source, Large Language Model (LLM) called privateGPT. privateGPT is designed to let you “ask questions to your documents” offline, with privacy as the goal. It therefore seemed like the best way to test this concept out. The owner of the privateGPT repository, Iván Martínez, notes that privacy is prioritized over accuracy. To quote the ReadMe file from GitHub:

100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection!
privateGPT, GitHub Site

privateGPT, at the time of writing, was licensed under the Apache-2.0 license, but during this test, no modifications were made to the privateGPT code. Initially, when you run privateGPT, train it on your documents, and ask it questions, you are doing all of this locally through a command line interface in a terminal window. This obviously will not do if we want to integrate it into something like Canvas, so additional tools needed to be built to bridge the gap.

I therefore set about making two additional pieces of software: a web-interface chat box that would later be embedded into a Canvas page, and a small application to connect what the student would type in the chat box to privateGPT, then strip irrelevant data from its response (such as redundant words like “answer” or listing the source documents for the answer) and push that back to the chat box.

*A diagram showing how the front-end of the system (what the user sees) interacts with the back-end of the system (what the user does not see). Self-creation.*

Once created, the web interface portion, running locally, allows us to plug it into a Canvas page, like so:

*A screenshot showing regular Canvas text on the left, and the chat box interface on the right, connected to the LLM.*

Testing how it works

To begin, I let the LLM ‘ingest’ the Ecampus Essentials document provided to course developers on the Ecampus website. Then I asked some questions to test it out, one of which was: “What are the Ecampus Essentials?”

I am not sure what I expected here, as it is quite an open ended question, only that it would scan its trained model data and the ingested files looking for an answer. After a while (edited for time) the bot responded:

A video showing the result of asking the bot “What are the Ecampus Essentials?”

A successful result! It has indeed pulled text from the Ecampus Essentials document, but interestingly has also paraphrased certain parts of it as well. Perhaps this is down to the amount of text it is capable of generating, along with the model that was initially selected.

A longer text example

So what happens if you give it a longer text, such as an OpenStax textbook? Would it be able to answer questions students might have about course content inside the book?

To find out, I gave the chatbot the OpenStax textbook Calculus 1, which you can download for free at the OpenStax website. No modifications were made to this text.

Then I asked the chatbot some calculus questions to see what it came up with:

*Asking two questions about certain topics in the OpenStax Calculus 1 book.*

It would appear that if students had any questions about mathematical theory, they could get a nice (and potentially accurate) summary from a chatbot such as this. Though this brings up some pedagogical considerations such as: would this make students less likely to read textbooks? Would this be able to search for answers to quiz questions and/or assignment problems? It is already common to ask ChatGPT to provide summaries and discussion board replies, would this bot function in much the same way?

Asking the chatbot to calculate things, however, is where one would run into the current limitations of the program, as it is not designed for that. Simple sums such as “1 + 1” return the correct answer, as this is part of the training data or otherwise common knowledge. Asking it to do something like calculate the hypotenuse of a triangle using Pythagorus’ theorem will not be successful (even using a textbook example of 3² + 4² = c²). The bot will attempt to give an answer, but its accuracy will vary wildly based on the data given to it. I could not get it to give me the correct response, but that was expected as this was not in the ingested documentation.

Limitations and Considerations

OK, so it’s not all perfect – far from it, in fact! The version of privateGPT I was using, while impressive, had some interesting quirks in certain responses. Responses were never identical either, but perhaps that is to be expected from a generative LLM. Still, this would require further investigation and testing in a production-ready model.

How regular and substantive interaction (RSI) might be affected is an important point to consider, as a more capable chatbot could impact the student-instructor Q&A discussion board side of things without prior planning on intended usage.

A major technical issue was that I was limited to using the central processing unit (CPU) instead of the much faster graphics processing unit (GPU) used in other LLMs and generative AI tools. This meant that the time between the question being sent and the answer being generated was far higher than desired. As of writing, there appears to be a way to switch privateGPT to GPU instead, which would greatly increase performance on systems with a modern GPU. The processing power required for a chatbot that more than one user would interact with simultaneously would be substantial.

Additionally, the incorporation of a chatbot like this has some other pedagogical implications, such as how the program would respond to questions related to assignment answers, which would need to be researched.

We also need to consider the technical skill required to create and upkeep a chatbot. Despite going through all of this, I am no Artificial Intelligence or Machine Learning expert; a dedicated team would be required to maintain the chatbot’s functionality to a high-enough standard.

Conclusion

In the end, the purpose of this little project was to test if this could be a tool students might find useful and could help them with content questions faster than contacting the instructor. From the small number of tests I conducted, it is very promising, and perhaps a properly built version could be used as a private alternative to ChatGPT, which is already being used by students for this very purpose. A major limitation was running the program from a single computer with consumer components made 3 years ago. With modern hardware and software – perhaps a first-party integrated version built directly into a learning management system like Canvas – students could be provided with their own course- or platform-specific chatbot for course documents and texts.

If you can see any additional uses, or potential benefits or downsides to something like this, leave a comment!

Notes

Martínez Toro, I., Gallego Vico, D., & Orgaz, P. (2023). PrivateGPT [Computer software]. https://github.com/imartinez/privateGPT.
“Calculus 1” is copyrighted by Rice University and licensed under an Attribution-NonCommercial-Sharealike 4.0 International License (CC BY-NC-SA).

I have always struggled with test anxiety. As a student, from first-grade spelling tests through timed essay questions while earning my Masters of Science in Education, I started exams feeling nauseous and underprepared. (My MSEd GPA was 4.0). I blame my parents. Both were college professors and had high expectations for my academic performance. I am in my 50s, and I still shutter remembering bringing home a low B on a history test in eighth grade. My father looked disappointed and told me, “Debbie, I only expect you to do the best you can do. But I do not think this is the best you can do.”

I am very glad my parents instilled in me a high value of education and a strong work ethic. This guidance heavily influenced my own desire to work in Higher Ed. Reflecting on my own journey and the lingering test anxiety that continues to haunt me, it has become evident that equipping students with comprehensive information to prepare for and navigate quizzes or exams holds the potential to alleviate the anxiety I once struggled with.

Overlooking the instructions section for an exam, assignment, or quiz is common among instructors during online course development. This might seem inconsequential, but it can significantly impact students’ performance and overall learning experience. Crafting comprehensive quiz instructions can transform your course delivery, fostering a more supportive and successful student learning environment.

The Role of Quizzes in Your Course

Quizzes serve as diagnostic and evaluative tools. They assess students’ comprehension and application of course materials, helping identify knowledge gaps and areas for additional study. The feedback instructors receive through student quiz scores enables instructors to evaluate the effectiveness of the course learning materials and activities and understand how well students are mastering the skills necessary to achieve the course learning outcomes. This enables instructors to identify aspects of the course design needing improvement and modify and adjust their teaching strategies and course content accordingly. By writing thorough and clear quiz instructions, you support students’ academic growth and improve the overall quality of your course.

Explain the Reason

Explain how the quiz will help students master specific skills to motivate them to study. The skills and knowledge students are expected to develop should be clearly defined and communicated. Connect it to course learning outcomes and encourage students to track their progress against them (Align Assessments, Objectives, Instructional Strategies – Eberly Center – Carnegie Mellon University, n.d.).

Why did you assign the quiz? Would you like your students to receive frequent feedback, engage with learning materials, prepare for high-stakes exams, or improve their study habits?

Equipping Students for Successful Quiz Preparation

Preparing for a quiz can be daunting for students. To help them navigate this process, provide a structured guide for preparation. Leading up to the quiz, you may want to encourage your students to:

Review the lectures: Highlight the importance of understanding key concepts discussed.
Review the readings: Encourage students to reinforce their understanding by revisiting assigned readings and additional materials.
Engage in review activities: Suggest using review materials, practice questions, or study guides to cement knowledge.
Participate in discussions: Reflecting on class discussions can offer unique insights and deepen understanding.
Seek clarification: Remind students to contact their instructor or teaching assistant for any questions or clarifications. You add a Q&A discussion forum for students to post questions leading up to the quiz.

Crafting Clear and Detailed Quiz Instructions

When taking the quiz, clear instructions are vital to ensure students understand what is expected of them. Here’s a checklist of details to include in your quiz instructions:

Time Limit: Explicitly mention the duration of the quiz, the amount of time students have to complete the quiz once they have started it, or if it’s untimed. Suggest how they may want to pace the quiz to ensure they have time to complete all the questions.
Availability Window: You should specify an availability window for asynchronous online students. It refers to the time frame during which the quiz can be accessed and started. By giving an extended window, you allow students to take the quiz at a time that suits them. Once they begin, the quiz duration will apply.
Number of Attempts: Indicate whether students have multiple attempts or just a single opportunity to take the quiz.
Question Format: Provide information about the types of questions included and any specific formatting requirements.
Quiz Navigation: Have you enforced navigational restrictions on the quiz, such as preventing students from returning to a question or only showing questions one at a time? Share this information in the instructions and explain the reasoning.
Point Allocation: Break down how points are distributed, including details for varying point values and partial credit.
Resources: Specify whether students can use external resources, textbooks, or notes during the quiz.
Academic Integrity Reminders: Reinforce the importance of academic integrity, detailing expectations for honest conduct during the quiz.
Feedback and Grading: Clarify how and when students will receive feedback and their grades.
Showing Work: If relevant, provide clear guidelines on how students present their work (solving equations, pre-writing activities, etc.) or reasoning for particular question types.

End with a supportive “Good Luck!” to ease students’ nerves and inspire confidence.

Crafting comprehensive quiz instructions is a vital step in ensuring successful course delivery. Providing students with clear expectations, guidelines, and support enhances their quiz experience and contributes to a positive and productive learning environment (Detterman & Andrist, 1990). As course developers and designers, we are responsible for fostering these optimal conditions for student success. Plus, as my father would say, it is satisfying to know you have “done the best you can do.”

References

Align Assessments, Objectives, Instructional Strategies—Eberly Center—Carnegie Mellon University. (n.d.). Eberly Center: Carnegie Mellon University. Retrieved June 28, 2023, from https://www.cmu.edu/teaching/assessment/basics/alignment.html

Detterman, D. K., & Andrist, C. G. (1990). Effect of Instructions on Elementary Cognitive Tasks Sensitive to Individual Differences. The American Journal of Psychology, 103(3), 367–390. https://doi.org/10.2307/1423216

Footnote: My son called as I was wrapping up this post. I told him I was finishing up a blog post for Ecampus. “I kind of threw Grandpa under the bus,” I said. After I shared the history test example, he said, “you didn’t learn much.” He and his sister felt similar academic pressure; I may have even used the same line about the best you can do. In my defense, he is now. Ph.D. candidate in Medicinal Chemistry and his sister just completed a Masters in Marine Bio.

Image by: pingebat, licensed from Adobe Stock

As higher-ed professionals involved in course design, we have the honor, privilege, and responsibility of shaping the learning experiences for countless students. Among the many tools at our disposal, course mapping stands out as a fundamental technique that deserves a spotlight. Couse mapping fosters clarity, and showcases alignment between the learning outcomes/objectives and course materials, assessments and activities. In this blog post, we will explore the importance of course mapping in online higher-ed courses, highlighting its role in meeting the new requirements in the recently updated Quality Matters (QM) rubric 7th edition. Join us as we delve into the transformative power of course mapping, benefiting course developers, instructors, instructional designers, and learners alike.

The Big-Picture:

The updated QM rubric (7th edition) recognizes the strength of course maps as a design tool, and has now made them a required element for course review. To quote the QM rubric update workshop (2023), “the course map must include all of the following components mapped to one another so the connection between them is apparent: course learning [outcomes/] objectives, module learning outcomes/objectives, assessments, materials, activities, and tools.” At its core, course mapping involves creating a visual representation of the entire course curriculum, breaking it down into manageable units, and illustrating the relationships between various components. This visual often takes the form of a table, but many variations exist. Course mapping is a holistic approach, which provides a roadmap for instructors, course developers, and designers to create a comprehensive, cohesive and well-structured learning experience; and for students to easily navigate and find the content and assignments. By explicitly relating the aforementioned course components, course maps simply demonstrate alignment and make clear the purpose of each element as part of the larger picture.

Orchestrating a Symphony of Learning & Student Success:

With the implementation of the new QM rubric (7th edition), course mapping has gained significant prominence as a means of ensuring alignment and coherence across the curriculum. By mapping out the weekly outcomes/objecives, learning activities, materials, tools, and assessments, instructors can ensure that each component of the course aligns with the overall outcomes/objetcives. This process can highlight pathways for students to progress logically through the content. Additionally, course mapping facilitates coordination among multiple instructors or instructional designers involved in a course, enabling a consistent design and a more harmonic learning experience for students. Much like a conductor of an orchestra, a course map provides the nuanced direction to each section. Harmony in a design means that elements are unified. Learners benefit from this because they more clearly connect their learning activities with a specific purpose.

By imbuing the many learning activities with clear purpose (alignment to the outcomes/objectives), learners understand the work they are being asked to complete. Mapping out course activities also provides instructors with a high-level view of their course, which helps ensure a balanced distribution of learning strategies, which can help accommodate a variety of learning needs. As a result, students are more likely to be engaged, motivated, and empowered to take ownership of their learning, which can lead to improved learning. Course maps act as a first step towards transparent course design, which empowers learners to take initiative and work through problems independently. If we give them all the pieces and help them make connections, they can forge their own pathway to success.

Efficiency and Continuous Improvement:

Course mapping also acts as a vehicle for efficiency and continuous improvement in higher education courses. By visualizing the entire course, instructors and instructional designers can identify potential gaps, redundancies, or misalignments, leading to more effective course revisions. Moreover, the iterative nature of course mapping promotes reflection and collaboration among course developers, instructors, instructional designers, and course reviewers, fostering a culture of continuous improvement.

Additionally, for instructors the course map then acts as a blue print for the course, which can enhance the connection between the course elements, which can also be helpful if course outcomes/objectives need to change. For instance, courses with detailed maps might be more efficiently adapted, as instructors can easily identify parts of their courses that will need to change and know where to focus their energy.

Assessment and Accreditation – Meeting Quality Standards:

Accreditation bodies and quality assurance agencies like QM place a strong emphasis on clearly defined learning outcomes/objectives and assessment strategies. Course mapping provides a comprehensive framework for demonstrating alignment with quality standards or accreditation competencies. By mapping learning outcomes/objectives to assessments, instructors can provide evidence of student achievement and ensure that all necessary areas are adequately covered. This not only satisfies accreditation requirements but also enhances transparency and accountability within the course, program, and even the institution. At OSU Ecampus, we use the Ecampus Essentials list to ensure we are creating high-quality online and hybrid learning experiences. All Ecampus courses are expected to meet the essential standards and are strongly encouraged to meet the exemplary standards.

Conclusion:

As higher education professionals, we have a shared responsibility to provide transformative courses and programs that prepare learners for the challenges of the future. Course mapping stands as a crucial tool in achieving this goal by fostering alignment, engagement, and continuous improvement. As the new Quality Matters (QM) rubric (7th edition) recognizes, course mapping is an essential practice in creating intentional and effective courses. By investing time and effort in course mapping, instructors and instructional designers can craft coherent and purposeful learning experiences that empower students and maximize their potential for success.

Let’s embrace course mapping as a tool for success in online higher education, ensuring that our courses are meticulously crafted, intentional, and impactful.

Course Mapping Tools:

Course Map Samples Shared in the QM Rubric Update:

References:

Beckham, R., Riedford, K., & Hall, M. (2017). Course Mapping: Expectations Visualized. Journal for Nurse Practitioners, 13(10), e471–e476. https://doi.org/10.1016/j.nurpra.2017.07.021

Digital Learning Hub in the Teaching + Learning Commons at UC San Diego. (n.d.). What is a Course Map? The Online Course Mapping Guide. Retrieved July 5, 2023, from https://www.coursemapguide.com/what-is-a-course-map

Quality Matters. (2023, May 22). QM Course Worksheet, HE Seventh Edition. Retrieved July 5, 2023, from https://docs.google.com/document/d/16d1mDaII_kgXvyjeT_brn-TKqACnr_OY_D_r5SnJlC0/edit

Testing a CAQDAS

NVivo

Dedoose

ATLAS.ti

Data Security

Note-taking programs

The Problem with Checklists

Three Contexts for Lateral Reading

Reconstructing Context

Another Postscript

Related Resource

References

References

Overview

Findings

Recommendations

References

What does “purpose” mean?

Why a purpose is important in an assignment?

For students

For instructors

For instructional designers

Is there a wrong purpose?

Examples of purpose statements

Example 1: Application and Dialogue (Discussion assignment)

Example 2: An annotated bibliography (Written assignment)

Example 3: Reflect and Share (Discussion assignment)

Conclusion

Resources:

Introduction

How it works

Testing how it works

A longer text example

Limitations and Considerations

Conclusion

Notes

The Role of Quizzes in Your Course

Explain the Reason

Equipping Students for Successful Quiz Preparation

Crafting Clear and Detailed Quiz Instructions

References

The Big-Picture:

Orchestrating a Symphony of Learning & Student Success:

Efficiency and Continuous Improvement:

Assessment and Accreditation – Meeting Quality Standards:

Conclusion:

Course Mapping Tools:

Course Map Samples Shared in the QM Rubric Update:

References:

Contact Info