Category: Instructional Technologies

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

Introducing Learning Analytics at Loyola: The ‘How’ and ‘Why’ of Data-Informed Instruction

Introducing Learning Analytics at Loyola: The ‘How’ and ‘Why’ of Data-Informed Instruction

Data-driven decision-making is becoming increasingly important across many sectors, including education. As an instructor, you might have come across the term “learning analytics.” But what exactly does it entail, and how can it benefit faculty and students? Read on to gain a better understanding of what learning analytics encompasses and how it may boost your instructional efficacy.   

What is Learning Analytics? 

Learning analytics can be defined as the “collection, analysis, and interpretation of data related to students’ learning and the contexts in which it occurs”. This data is typically derived from various sources, including learning management systems (LMS), online course platforms, and student information systems. At Loyola, learning analytics data is currently drawn from LOCUS and Sakai—including several third-party tools integrated with Sakai such as Zoom, Panopto, VoiceThread, and Turnitin. 

How Does it Work? 

Learning analytics involves the use of advanced technologies and statistical techniques to extract meaningful insights from educational data. These insights can range from understanding student engagement and performance to identifying patterns and trends in learning behavior. At Loyola, faculty can glean insights from the Sakai Statistics tool and the analytics offered by specific teaching and learning tools (e.g., Panopto). They may also use their personalized Learning Analytics Reports to view aggregated instructional data in one location. 

Why is it Useful for Faculty?

1. Personalized Learning Experiences:

By leveraging learning analytics, faculty members can gain valuable insights into individual students’ learning needs, preferences, and progress. This allows them to tailor their teaching strategies and interventions to better meet the diverse needs of their students, ultimately fostering a more accessible and personalized learning experience.

2. Early Intervention:

Learning analytics can help faculty identify students who may be at risk of falling behind or struggling academically. By detecting these warning signs early on, instructors can intervene promptly, providing additional support and resources to help students succeed.

3. Data-Informed Decision-Making:

Learning analytics data, in conversation with other metrics such as teaching evaluations, test scores, and final grades, can help faculty make informed decisions about curriculum (re)design, instructional methods, and assessment strategies. This data-driven approach empowers instructors to refine their teaching practices to enhance student learning outcomes. 

4. Continuous Improvement:

By analyzing trends and patterns in student data over time, faculty can identify areas for change and improvement in their teaching practices and course design. This iterative cycle of reflection and refinement enables instructors to adapt to the evolving needs of their students and enhance their overall quality of instruction. 

Getting Started with Learning Analytics 

Learning analytics holds immense potential for faculty in higher education to enhance teaching and learning experiences. Embracing learning analytics can empower faculty and instructional support staff to create more effective and engaging learning environments that support the success of all students. 

To learn more about learning analytics at Loyola, visit our website. Faculty are invited to schedule a consultation with an ITRS Learning Design Engineer, who will provide access to the Learning Analytics Report as well as guidance in interpreting the report data and deriving actionable insights.

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.