In my last post, I outlined my search for a computer-assisted qualitative data analysis software (CAQDAS) program that would fit our Research Unit’s needs. We needed a program that would enable our team to collaborate across operating systems, easily adding in new team members as needed, while providing a user-friendly experience without a high learning curve. We also needed something that would adhere to our institution’s IRB requirements for data security and preferred a program that didn’t require a subscription. However, the programs I examined were either subscription-based, too cumbersome, or did not meet our institution’s IRB requirements for data security. It seemed that there just wasn’t a program out there to suit our team’s needs.
However, after weeks of continued searching, I found a YouTube video entitled “Coding Text Using Microsoft Word” (Harold Peach, 2014). At first, I assumed this would show me how to use Word comments to highlight certain text in a transcript, which is a handy function, but what about collating those codes into a table or Excel file? What about tracking which member of the team codes certain text? I assumed this would be an explanation of manual coding using Word, which works fine for some projects, but not for our team.
Fortunately, my assumption was wrong. Dr. Harold Peach, Associate Professor of Education at Georgetown College, had developed a Word Macro to identify and pull all comments from the word document into a table (Peach, n.d.). A macro is “a series of commands and instructions that you group together as a single command to accomplish a task automatically” (Create or Run a Macro – Microsoft Support, n.d.). Once downloaded, the “Extract Comments to New Document” macro opens a template and produces a table of the coded information as shown in the image below. The macro identifies the following properties:
Page: the page on which the text can be found
Comment scope: the text that was coded
Comment text: the text contained in the comment; for the purpose of our projects, the code title
Author: which member of the team coded the information
Date: the date on which the text was coded
You can move the data from the Word table into an Excel sheet where you can sort codes for patterns or frequencies, a function that our team was looking for in a program as shown below:
This Word Macro was a good fit for our team for many reasons. First, our members could create comments on a Word document, regardless of their operating system. Second, we could continue to house our data on our institution’s servers, ensuring our projects meet strict IRB data security measures. Third, the Word macro allowed for basic coding features (coding multiple passages multiple times, highlighting coded text, etc.) and had a very low learning curve: teaching someone how to use Word Comments. Lastly, our institution provides access to the complete Microsoft Suite so all team members including students that would be working on projects already had access to the Word program. We contacted our IT department to have them verify that the macro was safe and for help downloading the macro.
Testing the Word Macro
Once installed, I tested out the macro with our undergraduate research assistant on a qualitative project and found it to be intuitive and helpful. We coded independently and met multiple times to discuss our work. Eventually we ran the macro, pulled all comments from our data, and moved the macro tables into Excel where we manually merged our work. Through this process, we found some potential drawbacks that could impact certain teams.
First, researchers can view all previous comments made which might impact how teammates code or how second-cycle coding is performed; other programs let you hide previous codes so researcher can come at the text fresh.
Second, coding across paragraphs can create issues with the resulting table; cells merge in ways that make it difficult to sort and filter if moved to Excel, but a quick cleaning of the data took care of this issue.
Lastly, we manually merged our work, negotiating codes and content, as our codes were inductively generated; researchers working on deductive projects may bypass this negotiation and find the process of merging much faster.
Despite these potential drawbacks, we found this macro sufficient for our project as it was free to use, easy to learn, and a helpful way to organize our data. The following table summarizes the pro and cons of this macro.
Pros and Cons of the “Extract Comments to New Document” Word Macro
Pros
Easy to learn and use: simply providing comments in a Word document and running the macro
Program tracks team member codes which can be helpful in discussions of analysis
Team members can code separately by generating separate Word documents, then merge the documents to consensus code
Copying Word table to Excel provides a more nuanced look at the data
Program works across operating systems
Members can house their data in existing structures, not on cloud infrastructures
Macro is free to download
Cons
Previous comments are visible through the coding process which might impact other members’ coding or second round coding
Coding across paragraph breaks creates cell breaks in the resulting table that can make it hard to sort
Team members must manually merge their codes and negotiate code labels, overlapping data, etc.
Scientific work can be enhanced and advanced by the right tools; however, it can be difficult to distinguish which computer-assisted qualitative data analysis software program is right for a team or a project. Any of the programs mentioned in this paper would be good options for individuals who do not need to collaborate or for those who are working with publicly available data that require different data security protocols. However, the Word macro highlighted here is a great option for many research teams. In all, although there are many powerful computer-assisted qualitative data analysis software programs out there, our team found the simplest option was the best option for our projects and our needs.
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.
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).
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.
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.
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).
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.
Learning outcomes (LOs) are used in instructional design to describe the skills and knowledge that students should have at the end of a course or learning unit, and to design assessments and activities that support these goals. It is widely agreed that specific, measurable outcomes are essential for planning instruction; however, some educators question the benefits of explicitly presenting them to students. I have been asked (and wondered myself): “What is the point of listing learning outcomes in the course?” “How do they help learning? “Do students even read them?”
So, I went on a quest for research that attempted to answer such questions. I was particularly interested in unit/module-level outcomes, as those are the ones that directly steer the content, and students see them throughout the course. Here’s a brief summary of what I found.
Note: the studies use the terms “learning outcome”, “learning objective”, or “learning goal” – they all refer to the same concept: a specific and measurable description of the skills and knowledge that students are expected to have at the end of a learning unit/period of study. At OSU we use the term “outcomes”.
What Does the Research Say?
Armbruster et al. (2009) redesigned an Introductory Biology course at Georgetown University, Washington, DC, using active learning and student-centered pedagogies, leading to increased student performance and satisfaction. One of the strategies used was to include explicit learning goals in the lecture slides, and labeling exam and quiz questions with the related goals. Students’ attitudes towards the course were assessed via a questionnaire and comparison of university-administered student evaluations. Students were asked to rank lecture components in terms of helpfulness to learning, and the authors found that one of the highest-ranking elements was the inclusion of explicit learning goals.
Simon and Taylor (2009) surveyed 597 students from computer science and microbiology and immunology courses at the University of British Columbia, where instructors presented learning goals at the beginning of each lecture or topic area. The questions were open and the answers coded into a number of categories, which helped them identify several values of goals. The main value was “knowing what I need to know”: students reported that the goals showed them how to focus their efforts and felt that the goals “allowed them to organize the information more effectively and be more expertlike in their approach to the class” (Simon & Taylor, 2009, p.55). The authors did not find any difference between presenting the goals before each lecture versus at the beginning of the unit/topic area.
Brooks et al. (2014) examined students’ views of learning outcomes at the University of Leicester, UK. First, they surveyed 918 students taking Biological Sciences, English and Medicine courses. They found that 81% of participants agreed or strongly agreed that learning outcomes are useful learning aids. Additionally, 46% found LOs more useful as their courses progressed, and 49% reported that they engaged more with the LOs as the course progressed. The authors also investigated when LOs are most useful, and found that the most common answer (46%) was when reviewing the material. Moreover, 49% of students reported that LOs can only be fully understood at the end of a module. The researchers followed up on these results with a focus group, which confirmed that students use LOs in various ways and at various points during the course.
Osueke et al. (2018) looked into students’ use and perceptions of learning objectives at University of Georgia. 185 students in an undergraduate Introduction to Biochemistry and Molecular Biology course took part in the study. The instructors included instructions in the syllabus, which they also stated on the first day of class: “Focus on the learning objectives. The exams will assess your accomplishment of the learning objectives. Use the learning objectives as a guide for what to focus on when you are completing assignments and studying for exams.” Students completed two assignments requiring them to explain their use of the LOs. The researchers found that many students (33.8%) reported they had been instructed on how to use LOs to study – these instructions ranged from passively “look over” to using them as a study guide. The ways students used the LOs were: as questions to answer (47.4%), as a resource for studying (24.1%), as a self-assessment tool (14.3%), and passive use (13.5%). When asked why they find the LOs helpful, students said that they help them: narrow down the information (57.1%); organize their studying (23.3%); communicate information (5.3%); monitor their understanding (4.5%); forced them to study (1.5%).
Sana et al. (2020) conducted three experiments aiming to find to what extent presenting the LOs improve retention of information. Participants were asked to read five passages on a neuroscience topic, and then they were tested on comprehension and retention. The experiments took place at McMaster University, Ontario and employed different participants, methods, materials, and procedures. They found that: interpolating LOs throughout the lesson (as opposed to all LOs presented at the beginning) improved learning compared to not including LOs, especially when students’ attention was explicitly directed to them; converting LOs into pretest questions (that students attempted to answer) further enhanced performance; multiple-choice and short answer questions were equally effective; and withholding feedback on pretests was more effective than providing feedback – the explanation proposed by the authors for this last finding was that students may be more motivated to seek the correct answers themselves, which causes further processing of the material.
Barnard et al. (2021) investigated students’ and academics’ perspectives on the purpose of learning objectives and approaches to assessment preparation. They conducted focus groups with participants from an undergraduate Psychology course at the University of Nottingham, UK. The students reported that LOs are useful for guidance, as they “use them to create direction for some of the learning and revision strategies” (Barnard et al., 2021, p. 679).
Conclusions and Recommendations
Good news! The findings of these studies suggest that many students do appreciate clear LOs and use them to guide their learning. The LOs help them understand what they are expected to know – thus, students use them to focus their study, to review for an exam, and to self-check their knowledge.
As instructors and instructional designers, what can we do to help students take full advantage of LOs? Apart from having specific and measurable LOs, make sure that the LOs are well aligned with the activities, and make this alignment explicit. It may also be helpful to offer some guidance on how to use the LOs, for instance by prompting students to recap their learning at the end of a unit based on the LOs. Finally, we could turn the LOs into questions and use them as a pretest.
For more on creating and using LOs, check out the CBE—Life Sciences Education website, which has an informative guide, including a section on student use.
Do you have any other ideas or resources on how to use learning outcomes to improve students’ experience and study habits? If so, we’d love to hear from you!
References
Armbruster, P., Patel, M., Johnson, E., & Weiss, M. (2009). Active learning and student-centered pedagogy improve student attitudes and performance in Introductory Biology. CBE Life Sciences Education, 8(3), 203–213. https://doi.org/10.1187/cbe.09-03-0025
Barnard, M., Whitt, E., & McDonald, S. (2021). Learning objectives and their effects on learning and assessment preparation: Insights from an undergraduate psychology course. Assessment and Evaluation in Higher Education, 46(5), 673–684. https://doi.org/10.1080/02602938.2020.1822281
Brooks, S., Dobbins, K., Scott, J. J. A., Rawlinson, M., & Norman, R. I. (2014). Learning about learning outcomes: The student perspective. Teaching in Higher Education, 19(6), 721–733. https://doi.org/10.1080/13562517.2014.901964
Osueke, B., Mekonnen, B., & Stanton, J. D. (2018). How undergraduate science students use learning objectives to study. Journal of Microbiology & Biology Education, 19(2). https://doi.org/10.1128/jmbe.v19i2.1510
Sana, F., Forrin, N. D., Sharma, M., Dubljevic, T., Ho, P., Jalil, E., & Kim, J. A. (2020). Optimizing the efficacy of learning objectives through pretests. CBE Life Sciences Education, 19(3), ar43–ar43. https://doi.org/10.1187/cbe.19-11-0257
I was recently assigned to be the Instructional Designer for an introductory programming course here at OSU. While working with the instructor, I was happy to see his inventiveness in assessment design. As one example, the instructor created an assignment to introduce loops, a block of code in a computer program that repeats while a condition is true. Here’s how he described the assignment to the students:
Your assignment is to simulate the progression of a zombie epidemic as it spreads through Portland, Oregon, beginning in the year 2001 (which was about the time that zombies became unnervingly popular). This assignment will test whether you can use loops when translating from a problem to a computational solution.
(Scaffidi, 2019)
I was excited about the design possibilities this introduced to a usually dry topic. Zombies! I built the page in our LMS, Canvas, and was excited to review it with him.
“Isn’t this fun?” I asked, showing him the assignment page I had created:
“I guess so,” he said, “is there any research to indicate that decorative graphics support learning?” he asked me. I guess that’s fair to ask, even if it was a bit of a buzzkill.
I had no idea if including cool pictures was a research-based best practice in online course design. While I really wanted it to be true and felt like it should be true, I could not immediately cite peer-reviewed studies that supported the use of zombie images to improve learner engagement; I had never seen such research. But, I was determined to look before our next meeting.
The instructor’s research challenge led me to discover Research Rabbit. Research Rabbit is a relatively new online platform that helps users find academic research. Research Rabbit has users organize found research into collections. As articles are added to a collection, Research Rabbit helps identify related research.
Without realizing how much time I was exploring, four hours quickly passed in which I was wholly engrossed in the search to justify including a zombie picture in one assignment for one instructor. Below, I will share a few of the features that enamored me with Research Rabbit and why I continue to use it regularly.
Why I love Research Rabbit
Visualization of Search Results
Rather than combing through reference lists at the bottom of a paper, you can quickly view any works cited by a paper you have selected or change views and get a list of articles that have cited the selected document. Those results are presented in a list view, a network view, or on a timeline.
A Tool for Discovery
Research Rabbit starts generating suggested additions as soon as you add a paper to a collection. The more papers you add, the more accurate these recommendations become. It works somewhat like personalized Netflix or Spotify recommendations (ResearchRabbit, n.d.), helping you discover research you may not have been aware of in this same area of study.
Using their discovery functionality, you can identify clusters of researchers (those that have published together or frequently cite each other’s work). You can also use the “Earlier Work” option to see when research on a particular topic may have started and identify foundational papers in the field. Looking for “Later Work” helps you find the latest research and stay current on your research topic.
Free Forever
The Research Rabbit founders explain their reasoning for keeping their tool Free Forever as follows:
Why? It’s simple, really.
Researchers commit years of time, energy, and more to advance human knowledge. Our job is to help you discover work that is relevant, not to sell your work back to you.
(Research Rabbit FAQ)
Research Rabbit Syncs Collections to Zotero
I would have lost a lot of enthusiasm for Research Rabbit if I had to manually add each new paper to my Zotero collection. But Research Rabbit integrates with Zotero, and automatically syncs any designated collections. If you use a different reference tool, you can also export Research Rabbit collections in common bibliographic formats.
A Tool for Sharing and Collaboration
Once you have created a collection, you can invite other researchers to view or edit a collection based on the permissions you set. Collaborators can also add comments to individual items. Research Rabbit also gives you an opportunity to create public collections that can be shared with a custom link.
How to Explore Research Rabbit on Your Own
The feature set of Research Rabbit is beautifully demoed on the Research Rabbit website. From there, you can explore how to visualize papers, discover author networks, and start building collections. There is also a growing list of introductory and instructional videos by the academic community online.
So What Happened with the Zombies?
You can review some of the research yourself by checking out my Research Rabbit Collection of Articles on Visual Design in Online Learning. Much to my delight, after conducting my (4-hour) search, I did find some research-based evidence that aesthetics improved engagement and recall (Deanna Grant-Smith et al., 2019). Many of the studies, however, also suggested that visuals in online courses should also have some instructional function and help communicate ideas to avoid cognitive overload (Rademacher, 2019).
Deanna Grant-Smith, Timothy Donnet, James Macaulay, Renee Chapman, & Renee Anne Chapman. (2019). Principles and practices for enhanced visual design in virtual learning environments: Do looks matter in student engagement?https://doi.org/10.4018/978-1-5225-5769-2.ch005
Over the last couple of terms, I joined a series of reading sessions with instructional design colleagues to read Alfie Khon and Susan Blum’s book Ungrading. Why Rating Students Undermines Learning (and What to Do Instead) and discuss the practices and implications of this approach to reconceptualize assessment design and the place of grading. This two-part blog aims to capture the takeaways from those discussions including the main concepts, approaches, types of activities, implications, and challenges of adopting ungrading practices. This first part of the blog covers a brief overview of the concept of ungrading, its major benefits, and design considerations; and the second blog will include a summary of the types of ungrading practices and challenges to implementation ––all derived from the authors’ extensive arguments and examples. For a detailed review and summary of the book chapters, you can also check the blog Assessment Design: Ideas from Ungrading Book.
Overview of Ungrading
The concept of ungrading is sparking widespread interest only recently even though educators have been studying and using ungrading approaches for quite some time. The foundational premise of ungrading is to move away from a focus on grades that judge, rank, sort, and quantify student learning to adopting an approach that focuses on using alternative and authentic means to assess learning such as self-evaluation, reflection, student-generated questions, peer feedback, to name a few. Along with that premise is the questionable ranking that comes with grading which makes students compete with one another in an artificial way. Sorensen-Unruh (chapter 9) sees ungrading as a conversational method that facilitates the communication between instructors and students about how students perform in the class. If, as underscored by the authors, grading and the fact of assigning point values to students’ performance makes more harm than good, then, why use grading? Considering that grading is rooted in our educational systems, many of these authors conclude that it becomes inevitable to grade student learning as it is currently done today.
Several scholars and instructors consider grading to be problematic. First, grades are not good indicators of learning. Blum (chapter 3) argues that grading assumes all students are the same, does not provide accurate information about student learning gains, is consequential, adds fear and avoidance of negative consequences, and is arbitrary and instructor-led. Second, the overemphasis on grades can lead to a decrease in intrinsic motivation, students’ excessive anxiety, and the complexity of quantifying how learning happens (Stommel, chapter 1). Third, it can also decrease interest in learning, students may feel inclined for easier tasks, and critical thinking is lacking (Alfie Khon, foreword). Fourth, grading makes students be fixated more on their grades than on the process of learning, leading them to believe that grades are all that matters in school (Khon & Blum; Talbert, 2020). And finally, too much focus on grades can be detrimental to students’ mental health (Eyler, 2022). However, ungrading does not mean dismissing grades altogether. Instead, Stommel proposes creating a learning space that fosters critical thinking, reflection, and metacognition– all skills that are valuable for 21st-century education. Likewise, Alfie Khon contends that grading can be participatory since it does not require a unilateral decision, and thus, students can also propose their own grades (with the instructor’s reservation to accept them).
“Ultimately ungrading— eliminating the control-based function of grades, with all its attendant harms— means that, as long as the noxious institutional requirement to submit a final grade remains in place, whatever grade each student decides on is the grade we turn in, period.”
(Khon, 2020, p. xv)
While ungrading may be an innovative approach to assessments, it should be thought of carefully and adopted with a clear objective. Ungrading, as pointed out by Katopodis and Davison (chapter 7), needs a structure to be effective, allowing students to envision themselves as authoritative, creative, confident, and active, thus achieving a high impactful goal. As ungrading requires instructors to evolve in their approach to assessment, it does too for students who are expected to engage in a process of self-evaluation, self-assessment, and reflection. This requires engagement in metacognitive practices that many students might not be ready to embark on or don’t know how to do it. In addition, while ungrading is believed to be student-centered, it can deepen equity gaps if guideposts are entirely removed. Sorensen-Unruh (chapter 9) believes that ungrading is a matter of social justice –going beyond the expected student agency and aiming at having students exercise their voice and participate in assessment decisions.
As a whole, Blum (introduction chapter) provokes us all to rethink the nature of grading considering that students’ learning conditions vary, with many enduring inequities at many levels. Blum wants us to keep focused on how “varying assessment and feedback methods contribute to the real learning of real individual learners, rather than imposing an arbitrary method of sorting.” (p. xxii); all for the sake of healthy learning.
These are a few key points about the arguments for ungrading. While this assessment practice is taking force in higher education, there are also many critics and skeptics. The purpose of this blog is not to enter into the discussion and controversy of ungrading, but to share a few perspectives and takeaways after an intense and well-structured book club discussion. In the following section of this part-one blog, I will share considerations for designing for ungrading.
Assessment Design Considerations
The educational system requires all instructors to submit grades at the end of every term. There is dissatisfaction with the current grading practices among many instructors and students as explained at large in the book. Here is where the ungrading movement takes force to provide alternative ways to account for evidence of student learning. Riesbeck (chapter 8) argued that by implementing ungrading practices, students can focus more on the content and feedback than on the grades. The use of critique-driven learning allows for more easily quantifiable efforts, progress, and accomplishment. Each ungrading consideration is dependent on a myriad of factors that may or not apply to each instructor’s context. The bottom line in ungrading is re-envisioning the teaching and learning process, engaging students in active learning, and active self-assessment through feedback. The following are design considerations:
Decenter grading and communicate (un)grading practices
Instructors can encourage students to focus on the process of learning, instead of talking about grades, Blums says, we should talk about the purpose and goals of the activities with students. These conversations can help develop relationships with students to encourage them to own their learning and have a voice in that process. Decentering grades also involves having an ongoing conversation with students, colleagues, and administrators about assessment decisions. Although each instructor exercises their academic freedom, it is also essential to share assessment practices that work and possible changes to implement. In these conversations, it is also important to carefully use language that conveys a clear understanding of the concept and practice of ungrading to avoid confusion, anxiety, misunderstandings, and reactions that prevent its implementation. Having these kinds of conversations can help shift the mind from a grade-focused to a learning-focused approach. A key element in these conversations is to ensure that the pedagogical reason behind the adoption of ungrading practices is not only clear but well understood (and this may take time).
Set a structure for ungrading
As with other elements of exemplary course design considerations, the structure of assessment practices is necessary. Adding a structure for ungrading assignments gives students a clear objective, steps, and flow that allow them to be consistent and accountable to their own learning goals and strategies.
Reflect on pedagogical and assessment practices
Instructors are invited to examine more in-depth their grading policies, why they grade in the way they do, what they are grading, and how they grade. In many cases, the path to ungrading is a response to dissatisfaction with grading policies. Aaron Blackwelder (chapter 2) says that, over time, he turned into a gatekeeper; he lost focus and was more interested in meeting institutional and “rigor” requirements than building relationships with students. His students had turned into competitive grade seekers. He questioned what a grade really suggests and posits that grades fail to communicate learning. The fact that grading allocates a specific number or letter that can bring some negative feelings to students, can also negatively affect the potential for learning. Sackstein (chapter 4) calls for a change in mindset to identify the way in which learning can be communicated and understood beyond the traditional use of numbers and letters. While Blum also argues for assessing the entire learning experience (with portfolios, for example), Sacksatein suggests considering changes in the language of grading which can provide students with an opportunity to shift the way they feel and think about their own learning.
Teach students to view mistakes as a necessary step in the learning process
Instructors are invited to reflect on how traditional grading practices are punitive, dehumanizing, and demotivating. Gibbs (chapter 6) points out that a system that punishes students for making mistakes reinforces the notion that all learning is flawless and therefore mistakes need to be avoided. Ungrading, on the other hand, aims to implement and cement the idea that learning is a process that needs constant feedback for that learning to be consolidated. Therefore, students need to be given opportunities not only to learn from their mistakes but to act on them in an interactive way. This requires instructors to plan for assessments that include steps for review (e.g., self, peer) to help the student build their skills, and knowledge over time.
Care for students and their learning
Instructors are also invited to demonstrate more explicitly that they care and validate students’ work. Further, Gibbs (Chapter 6) argues that her teaching philosophy is better summarized by the word “freedom”, the freedom that learners have to learn and grow at their rhythm and the freedom to make mistakes and learn from them. The role of the instructor is then to be supportive in that process through feedback and empathy. Ungrading, as it is overall discussed throughout the book, does not mean that there are no assessments or grading at all. On the contrary, the assessments should focus on helping students build their knowledge and understanding in less stressful ways, allowing students to build learning habits, develop creativity, become better communicators, and connect to their lived experiences and contexts. Caring for students also involves valuing their identity as learners and what they bring into the learning environment.
Be aware that ungrading can increase student anxiety and uncertainty
It is critical that instructors who are considering ungrading be cognizant that it involves a high level of anxiety and uncertainty on the part of students. Let’s recognize that students are so used and “conditioned” to grades that they will find it confusing not to have a grade associated with each assignment in the course. Many students consider being successful if they score a perfect grade which can be overwhelming and obscure the value of learning. Instructors who adopt ungrading should explain why and how ungrading will be done in certain classes. This will add transparency to the expectations and assumptions that instructors have about students.
Implement student voice and choice supported with personalized feedback
Instructors can help students take ownership of their learning through hands-on, real-life activities that allow students to use the content they are learning in projects of their interest, conduct research, and solve problems. Students can choose their topics and projects and the instructors can guide them to narrow topics and ensure the projects are feasible within the course timeframe. Consider feedback as a formative assessment approach that enables students to make choices about their learning strategies and needs to improve their learning tasks. Sackstein (chapter 4 ) suggests teaching students to collect feedback and identify the strategies that work for different kinds of assignment revisions. This way, students can develop better strategies that move them from lower-thinking to higher-thinking processes.
Since ungrading promotes the use of student-self assessment and reflection practices, it implies that instructors will need to personalize and tailor feedback to meet students where they are. In addition, instructors can consider setting a culture of feedback (Gibbs, chapter 6) where instructors teach students to use feedback to improve their work, provide peer feedback effectively, and see the value of learning from their mistakes.
Promote peer support
Authors of several chapters in this book have posited that students are more likely to give each other better feedback in the absence of grades. This kind of feedback can allow students to help each other, learn from one another, expand their awareness of their own understanding, and develop skills for life. Peer support will also help students build their confidence and autonomy to learn from each other.
Trust students
One critical aspect of assessment is trust –trust that students do the work they are expected to do by themselves. Instructors have legitimate reasons to express their concerns and create course policies about academic integrity that lead them to adopt plagiarism systems and surveillance tools to monitor and proctor students’ work. In adopting ungrading, trust is fundamental to change the way learning and performances are assessed. It involves helping students think differently about what it means to learn. Instructors can help students evolve in their approach to learning to move away from grades to focus on their learning by including in assessments strategies for building capacity for metacognition, confidence in their skills, life-long learning goals, and owning their learning.
Ungrading does not mean instructors don’t grade and students don’t receive grades on their work. Ungrading, as posited by the authors in the Ungrading book, is a mindset to approach student learning differently. In the second part of this blog, I will share the types of ungrading practices, implications, and challenges as presented in the book.
References
Coghlan, S., Miller, T. & Paterson, J. (2021). Good Proctor or “Big Brother”? Ethics of Online Exam Supervision Technologies. Philos. Technol. 34, 1581–1606. https://doi.org/10.1007/s13347-021-00476-1
The changes in higher education precipitated by the COVID-19 pandemic have reignited questions and misconceptions about online education. This is a time that we should draw on the insights and experience of online faculty. At Oregon State University (OSU) we have a significant number of faculty who have been teaching online for over a decade. In the 2018-2019 academic year, the Ecampus Research Unit interviewed 33 OSU instructors who had taught online for 10 years or more. In a series of interviews, the instructors were asked to reflect on their experiences as an online educator and how their perspectives have changed over time. More information about the broader study can be found on the study website. The final question asked of the instructors was, “What do you think is the future of online learning?” We conducted a qualitative analysis of their responses to this question. The findings were recently published in the Online Journal of Distance Learning Administration. Below, we discuss some of the key findings from this analysis.
Key finding #1: Online and blended learning will continue to grow
Two-thirds (22) of the instructors expected online learning to expand as higher education moves toward increased access and accessibility, and as employers show increased expectations of continuing education. They acknowledged that online learning would continue to be the choice of adult learners as they balance work and life responsibilities.
Key finding #2: Online learning will increase access and accessibility
More than half (18) of the instructors predicted that online learning would increase access to education. These instructors discussed how online learning increases accessibility because online courses can be taken anywhere (location flexibility) and online courses can be accessed anytime (time flexibility). While these instructors were interviewed before the COVID-19 pandemic, their responses are now particularly timely and relevant, as the pandemic shifted higher education’s focus to remote and online teaching.
Key finding #3: Will online learning replace brick and mortar institutions?
One third (11) of the instructors discussed the possibility that online learning may grow to become the primary modality used in higher education, replacing face-to-face learning. However, 13 instructors indicated that they did not think the face-to-face learning should be eliminated in the future. Many of these instructors hoped that online education could provide more options for students rather than replacing brick and mortar institutions.
Key finding #4: Technology development will increase
Nearly 40% of the instructors (13) discussed the role of technology development in the future of online education. Acknowledging that the development of technology has already made teaching online easier and more effective, many optimistically predicted this would continue to improve the teaching and learning experience. Others were more pessimistic about technology replacing elements like face-to-face communication.
Overall, instructors’ ideas of the future aligned with some themes in the broader field of higher education, such as diversity, opportunity, and access. These key findings have implications for the professional development of online instructors. As more faculty transition to online teaching, it is important that they be well prepared for the online learning landscape. As the population of students in online education continues to evolve, it is also important that instructors understand the diversity of their students and the needs of adult learners. As technology is rapidly changing, timely and accessible training that can be used across multiple modalities is needed for future faculty development. Enhancing instructors’ pedagogy and technology skills across a range of modalities will enhance the educational experience for online learners around the globe.
A group of instructional designers at Ecampus participated in a book club reading “Ungrading” (Kohn & Blum, 2020). We learned many creative ways of designing assessments through participation in this book club. If you happen to be searching for ideas on designing or re-designing assessments in your teaching, we would highly recommend this book!
The idea of “Ungrading” may sound radical to many of us. Yet instructors at all types of educational institutions have tried ungrading in many different courses, ranging from humanity courses, to STEM courses, and from primary education to higher education. Starr Sackstein (author of Chapter 4 “Shifting the Grading Mindset” of the book) encourages educators to consider “ways to adjust small things in the classroom that will lead to important growth for students”. And this suggestion of starting small is coherent with what James Lang proposes in his book “Small Teaching” (Lang, 2016) and Thomas Tobin’s +1 strategy for implementing new teaching and learning strategies (Tobin & Behling, 2018). Sackstein provides a table comparing the grades vocabulary that focuses on judgement or criticism, with the non-grade vocabulary focusing on assessing and opportunity for improvement.
In chapter 5, Arthur Chiaravalli proposed a way for teaching without grades: Descriptive Grading Criteria, such as A for outstanding, B for Good, C for Satisfactory and I for Incomplete. Do you remember elementary school report cards that use E for Excellent, S for Satisfactory, and NI for Need Improvement type of categories? I think that is exactly what descriptive grading criteria represent.
In chapter 7, Christina Katopodis and Cathy Davidson offer a new approach to start a new term/semester by asking students:” What is Success in this class for you? And How can I help you achieve it?” (p. 107) Katopodis and Davidson also remind us the importance of explaining why when you challenge your students to take their own learning seriously and give students opportunities for metacognitive reflections about the learning activities themselves. Katopodis and Davidson also offer a model of contract grading for Twenty-First Century Literacies and a model of collaborative peer evaluation. Students’ grades in the course come from self-and-peer evaluations using detailed evaluation forms.
In chapter 8, Christopher Riesbeck described his critique-driven learning and assessment design of do-review-redo submission process for his intermediate-level programming course. I have used similar approach in my own teaching before and it works very well for any course with manageable number of students. The advantage for this approach is every one of your students can improve their first submissions based on feedback they receive from the instructor. The disadvantage for this approach is the potentially extended time instructors may spend on providing the feedback and reviewing the submissions and re-submissions. The key to this assessment method is making sure that the workload of providing feedback and reviewing revisions is manageable. In chapter 9, Clarissa Sorensen-Unruh provided her experience of using ungrading in her organic chemistry II course, giving students opportunities to practice evaluating their own work.
And that is only snippets of what I took away from a few chapters from this book. Many resources about ungrading outside the book were shared during our book club meetings, such as two-stage exams, group exams and public exams. To answer a common question that ungrading practices may fit humanity courses more easily, Cyndie McCarley shared “Grading for Growth” blog written and maintained by two math instructors Robert Talbert and David Clark. To learn about all the creative assessment design methods introduced in this book, read it yourself either through library ebook or get a hard copy and enjoy reading, designing and experimenting!
References
Kohn, A. and Blum, S. (2020). Ungrading. West Virginia University Press.
Lang, J. (2016). Small Teaching. Jossey-Bass.
Tobin, T.J. and Behling, K.T. (2018). Reach Everyone, Teach Everyone. West Virginia University Press.
