Author: Hannah Lee Otto

Empowering Learners to Work in Community: Designing for Collaborative Learning

Empowering Learners to Work in Community: Designing for Collaborative Learning

Collaboration, teamwork, community: these terms are familiar across disciplines and industries, and often, they reflect organizational values and goals. Collaboration is supposed to be a worthwhile practice for the benefit of the stakeholders involved.  

And yet, why do students dread group projects? As a lifelong student and instructor of adult learners, let’s together consider the dynamics of a typical group: one or two students do most of the work, one disappears from group communications until the day before a deadline due to unforeseen circumstances, and the less dominant members offer contributions that are either dismissed or less prioritized by the self-appointed group leaders.  

As an instructor or one possessing instructional design responsibilities for learning, there are ways to facilitate collaboration for students that might avoid common pitfalls to meaningful and equitable peer exchange. This includes student-to-instructor exchange, as a common approach to online learning via prerecorded lectures and auto-graded feedback leaves students without a feeling of human connection or presence—hardly collaborative.  

Collaborative learning and learning design  

Continuing a keywords-inspired approach of unpacking a learning design referent to extract pedagogical and practical applications, let’s take on the subject of collaborative learning design.  

The way I refer to collaborative learning is inspired by my time in writing center work and composition studies, namely Andrea Lunsford’s (1991) article “Collaboration, Control, and the Idea of a Writing Center.” Lunsford’s work on collaboration and learning has found collaboration to engage students and encourage active learning; lead to higher academic achievement; support deeper critical thinking; and lead to deeper understanding of others (p. 5). Such collaboration is not synonymous with lack of direction, support, or inclusion for its members. 

Research-based keys to collaborative learning  

Both the Center for Applied Special Technology (CAST) and the Online Learning Consortium (OLC) offer research-based support for collaboration. Below are some synthesized findings between a learning design perspective, student perspectives for collaborative learning, and a renewed approach to inclusive teaching. Insights are lifted from the CAST Universal Design for Learning Guidelines, the 2024 OLC Report, “Empowering Change Together: Student Perspectives on Quality Online, Digital, and Blended Learning,” and insights from the Inclusive Teaching in STEM course faculty edX.  

Sustain engagement through careful learning community. According to CAST (2018), learners in the 21st century “must be able to communicate and collaborate within a community,” as such mindfully structured peer work can “significantly increase the available support for sustained engagement.” Student feedback highlighted the desire for community in online learning environments, as well as faculty responsibility for fostering class participation in such a way that acknowledged social challenges from not being in a physical classroom (OLC, 2024, p. 12). For instructors stuck with a lack of engagement, defining peer roles, expectations, and means for providing one another with feedback instills a sense of responsibility in one another’s learning and success.  

Create a culture of collaboration by enabling learners to be active agents in designing their learning. UDL Checkpoint 8.3: Foster collaboration and community specifies a strategy to “Create cooperative learning groups with clear goals, roles, and responsibilities.” The OLC finds that students also “want to be consulted as co-creators of community and DEI strategy,” moving beyond buzzwords to adaptable, actionable frameworks for practice (p. 13). A course lends itself as a space to facilitate a community of practice that rises out of a body of theory or aligned with learning goals. Allowing each member of a course community to co-design their individual roles and recognize their own commitments to the greater whole helps to build rapport while learning.  

Collaborative learning tool spotlight: VoiceThread 

Learning tools designed to facilitate feedback and collaboration can help instructors save time on designing technical logistics for student activities. Some tools also offer multiple modes of engaging dialogue and feedback between members. 

Though several learning tools may overlap in learning activity type, such as written discussions or conversations, few offer specifically collaborative engagement adaptable for a variety of activities as much as VoiceThread. With the new user interface to be fully implemented by this June, VoiceThread also offers a more accessible tool for learners to engage in collaborative learning. VoiceThread facilitates multimodal means for members to give one another feedback, including written, audio, and video commenting.  

Learning design for collaboration 

Let us also not forget Lunsford’s (1991) warning of collaboration misconstrued in pedagogical application, where such can “masquerade as democracy when it in fact practices the same old authoritarian control” (p. 3-4). Collaborative learning design must be careful and clear in its aim to empower students to take part in constructing their learning contexts and sense of community.  

 

CAST (2018). Universal Design for Learning Guidelines version 2.2.
Retrieved from http://udlguidelines.cast.org 

Lunsford, A. (1991). Collaboration, Control, and the Idea of a Writing Center. The Writing Center Journal, 12(1), 3–10. http://www.jstor.org/stable/43441887  

Weber, N.L. & Gay, K. (2024). Empowering change together: Student perspectives on quality
online, digital, and blended learning. Online Learning Consortium.  

Image credit: Photo by Brooke Cagle on Unsplash

Keywords in Higher Ed: AI Authoring Tools

Keywords in Higher Ed: AI Authoring Tools

During my graduate degree coursework in composition and rhetoric, I came across a book titled Keywords in Writing Studies, edited by Paul Heiker and my professor himself, Peter Vandenberg.

The book’s concept is given in its title: Keywords provides a fresh and concise array of essay entries, each packed with heavy research dedicated to unpacking an operative referent in the realm according to its related studies, theories, and applications.

As a student that has kept nearly every required textbook, I can reflect on the utility of such a cogent textbook concept, and now would like to transfer its reader-friendly approach to the great wide realm of instructional technologies—to start, within in the smaller realm of AI authoring tools for teaching and learning.

I anticipate my keywords approach will be much messier and less formal in scholarship, as the body of published works, studies, and opinions on AI authoring is sprawling and immense. However, the goal is to offer an ongoing collection of resources that facilitate your own research and dialogue around important questions about technology in teaching and learning.

With this keywords approach in mind, let’s begin!

AI authoring tools & learning

AI authoring tools such as ChatGPT, Bard, DALL-E3, and the like, pose immediate questions for rethinking how to teach core learning tasks and skills, particularly those assigning students to compose original work.

Though there is no direct teaching solution to safeguard against cheating, and worse, whether a student is actually demonstrating their learning, many conversations in higher education circle back to how assessments are designed for students to think critically about information and acquire digital literacy. Such classroom-rooted strategies and conversations about AI authoring are also recommended by the leading product developing company in AI writing detection, Turnitin.

Difficulties in regulating AI use & ethical concerns

Studies have noted areas of AI use that pose challenges for demarcating its ethical scope and regulation. Key questions implicated by AI machine learning and data science include responsibility for use, bias and discrimination within development, transparency in development, and responsibility for stakeholder action or policy.

From a corporate stance, the move towards regulation is difficult, if not impossible, as implementation of restrictions cannot be imposed on a scale that corresponds with its users. Though statements and calls to pause development have been made, much AI development is within the private sector, and those that might be in the position to draft such regulations do not necessarily understand the nature and scope of the technological developments to impose effective boundaries.

Ethical considerations with AI authoring tools that more directly relate to teaching and learning include biases against non-English speakers and replications that bypass creative attribution, such as the popular query of Greg Rutkowski styled outputs that mimic his aesthetic without his consent.

Academic integrity & teaching with AI

Because of its dominance in the assessment tools arena and Loyola’s adoptions of several products, Turnitin resources on academic integrity and AI writing are within the purview of technology-based assessment in higher education. Their latest webinar offering on how to include AI in institutional policy offers a puzzle map for approaching the complex issue of AI.

An Exigence for Faculty Development

A silver lining that AI authoring brings to our attention is the prompt for enriching faculty development through dialogue and creative learning design.

Though some find AI authoring tools a cause for panic, many specialized faculty in the fields of medicine and sciences are excited about the opportunities AI provides for teaching and learning.

Reflections in faculty panels, such as this one at Ole Miss University of Mississippi or professional higher ed groups, such as the AI in Education Google group.

While Loyola Instructional Technology and Research Support does not decide on the adoption of learning tools for the institution, we do invite ideas for teaching strategies, further research, and learning designs.

Teaching Strategies for “the ChatGPT wave”: Transferable Lessons from Proctoring Tools

Teaching Strategies for “the ChatGPT wave”: Transferable Lessons from Proctoring Tools

Read time: 5 minutes

In my popular culture research, a cultural movement often carries the referent of a “wave.” Example: The Hallyu movement of the 1980s to 2000s (debatable depending on the scholar you consult) refers to a “wave” of Korean popular culture beyond the nation’s borders.

In my day-to-day work, I might use the referent “wave” to refer to the conversation en vogue in the fields of teaching, learning, and academic integrity: in this instance, let’s use the referent “the ChatGPT wave.”

But first, a quick blast from the past [three years] for context:

Higher education conversations about assessment in digital learning environments rarely avoid a debate on academic integrity. From my experience—and likely yours—this specific debate maps itself on a spectrum ranging somewhere from “enforcing academic integrity with the latest and most stringent means available” to “recognizing no perfect enforcement is possible and does not seem productive to ensure student learning”.

My emphasis here is on two points, to be revisited very soon: (1) that no flawless enforcement of academic honesty is possible with a tool; and (2) that a fixation on enforcement of not cheating rather than a focus on fostering student learning leads to costly outcomes for all.

Perhaps this diversity of positions on assessment with academic integrity emerged rather sharply during the emergency move to online learning per the COVID-19 pandemic. The immediate legacy might be summed up in some phases: faculty unrest for a technology-based solution to prevent students from cheating, a hasty adoption of an inadequate solution, uncomfortable and stressful assessments for both its administrating faculty and its examinee students using said inadequate solution, then a quick abandonment of said inadequate solution due to privacy violations (some of which are undergoing legal disputes, well within our region).

As we embark on the amazing frontier of AI (artificial intelligence) authoring tools, let us brace ourselves for the ChatGPT wave by remembering to prioritize student learning rather than hunting for cheaters. Here are some teaching strategies for AI authoring tools like ChatGPT, very much informed by our recent misadventures with proctoring tools:

Remember that a tool is not a human. Just like the highly touted and speedily adopted proctoring tools of yesteryear cannot guarantee or completely safeguard cheating by a human student, ChatGPT and AI tools share an obvious quality: ChatGPT is not a human student. A human demonstrates learning for a specific learning outcome, whether by sharing a sentiment or committing an error that is irrevocably human. Looking for signs of life might mean creating space for students to show their human selves, perhaps by engaging conversation about something fun to them, or posing a writing prompt that is more specific to their periphery of being, or assigning something creative or audio recorded. If you assign work that is general and without connection to your students, expect machine-like responses.

Revise your learning objectives and corresponding activities for someone who wants to learn. As an instructor, I find my essential job description, whether I am teaching professional business writing or instructional design, is to facilitate meaningful learning experiences for my students. Many times, essential charge prompts reflection and revision of my coursework and assessment designs. Rising to the occasion of facilitating meaningful learning is an easy move when students want to learn. National enrollment in higher education has seen better days, so being interesting seems like a project of mutual interest for faculty.

Find help for the things you don’t know. Since my start in the field of teaching and learning support, I have seen resources and services grow rapidly in the name of faculty teaching online and with instructional tools. It is highly likely that your place of teaching extends such resources and services to you, if only you seek them out. “Closed mouths don’t get fed,” as the saying goes, and in my experience, if you don’t ask for help, you will only fall more behind. Technologies are always updating and departments may shift in structure, but you can control your own course (pun intended) by looking for those that literally have in their job descriptions to help you.

Learn about the tool’s development and limitations, and share this with your students. OpenAI, the developers behind ChatGPT, are very transparent about its testing process and limitations as an AI authoring tool. Some key and critical limitations to note so far include a proclivity to outputs that are “toxic or biased” with made-up facts; and an English-speaking, and therefore cultural bias “towards the cultural values of English-speaking people.” Having a conversation with your students about such limitations makes for transparency in your class while addressing the serious possibilities for mis-presentations of self. Who wants to be seen as toxic or treacherous?

If we have learned anything from the Test Cheating Scare of 2020, let us brace for this ChatGPT wave with clarity of purpose as instructors, and aim for human exchanges with our students.

What’s New: Zoom 5.0 + Zoom in Sakai

What’s New: Zoom 5.0 + Zoom in Sakai

The Zoom tool integration in Sakai and the Zoom Desktop Client have both been updated to improve your Zoom experience. Please take a moment to note and use the updated features that will make for smoother class scheduling and meetings.

Zoom Desktop Client: Zoom 5.0

Download the Latest Version of Zoom

The Zoom Desktop Client is the application that runs your Zoom meetings once you’ve started or joined a meeting. Because the Zoom Desktop Client is therefore critical for your Zoom meetings, it is important to have the latest version installed on your computer.

Download the latest Zoom Desktop Client at https://luc.zoom.us/download. The same download link will provide you with an update to your existing Zoom Desktop Client.

New in Zoom 5.0

The following features are highlighted from Zoom’s complete release notes at https://support.zoom.us/hc/en-us/articles/201361963 and https://support.zoom.us/hc/en-us/articles/360042599192-New-updates-for-May-3-2020

Prevent private chatting with external users.

Users will no longer be able to privately chat with other members of the same channel if they are not on the same Zoom account or organization. To continue chatting with contacts outside of their Zoom account, they can add them as external contacts.

If the host has the web setting Embed password in meeting link for one-click join disabled in the Zoom web portal, the Zoom client will no longer include the password in the URL when inviting new participants.

  • Re-enable clickable links in meeting chat
    Users will be able to send clickable links through the in-meeting chat. The link must include http or https to be clickable.

Meeting features

  • Report a user during a meeting
    The meeting host can now report a user during a meeting by clicking on the Security icon, then Report. This feature will generate a report which will be sent to the Zoom Trust and Safety team to evaluate any misuse of the platform and block a user if necessary.
security-report-feature
New security feature to report a participant.
  • Enhancements to meeting end/leave flow
    The host will now be required to assign a new host when leaving the meeting. Additionally, the pop-up message asking if the host would like to leave or end the meeting will now be displayed by the Leave button.
assign-new-host
Assign a New Host when Host leaves meeting.
End-Meeting-for-All
Host option to End Meeting for All.

Zoom in Sakai: The New Zoom Pro Integration

To resolve many bugs and ambiguity with scheduling Zoom meetings through your Sakai site, the new Zoom Pro integration in Sakai now displays a detailed dashboard of your course meetings and recordings.

Schedule a Meeting.

Instead of one single Join Meeting button, course participants will now see a list of upcoming meetings. To start or join a meeting, the host or participants must select Start for the scheduled meeting.

View Previous Meetings and Upcoming Meetings.

The new Zoom integration allows you to view and access upcoming meetings and previous meetings through selecting the respective tabs at the top of the Zoom tool page.

upcoming-and-previous-meetings
View Upcoming Meetings and Previous Meetings with tabs.

Access Cloud Recordings.

Another convenient feature of the new integration allows for accessing cloud recordings without having to distribute a link. Course participants can go to the Cloud Recordings tab and play the recording in their browser.

cloud-recordings-tab
Select the Cloud Recordings tab to access course recordings.

For added protection, a password will appear to copy in order to play the recording.

play-cloud-recording
Select play to play recording in browser. Copy and paste the provided password to access playback.

Next Steps for You

Download Zoom 5.0 to update your Zoom Desktop client. The latest set of updates is included in Zoom 5.0, which we strongly recommend you do by going to luc.zoom.us/download. Select the blue Download button for Zoom Client for Meetings, and this version will be the latest provided by Zoom. Follow the installation cues to complete the update.

zoom5.0-download
Select Download to update Zoom Desktop Client.

Zoom Resources from Loyola ITRS

Thanks for becoming refreshed with the latest from Zoom. For more Zoom resources and assistance, check out the new Zoom Guide from ITRS (Instructional Technology & Research Support) or report an issue with ITS Service Desk at ITSServiceDesk@luc.edu.