LMS Experience, Teaching Practices, and IT Support in MBA Education
Context and Teaching Philosophy
The conversation centers on evaluating entrepreneurial opportunities and a lecturer who reformats his teaching approach around a customer-centric framework rather than loving a product.
Core teaching method described: establish a customer segment, identify the paying or gain commitments those customers have, interview those customers to confirm or deny your assumptions, to refine the market understanding and uncover the actual problems to solve.
Emphasis on falling in love with the problem, not just the solution; the method supports pivoting to address market reality instead of pursuing a fixed future solution.
The approach aims to help students discover real market needs before committing to a specific product concept.
Lean Startup / Customer Discovery Framework Emphasized
Build from customer problems and gains: start with who the customers are, what pains they experience, and what they would pay for.
Validate assumptions through interviews with potential customers or users.
Use feedback to refine or pivot the idea toward a viable market.
The instructor contrasts this with a common entrepreneur pitfall: loving the product too early and pushing it as a solution without market validation.
The Instructor's Background and Credibility
The instructor is described as a professor on a tenure-track/research-track with extensive classroom experience.
The instructor values being around professors and classroom settings and is attentive to real-world applicability of class materials.
LMS Landscape: Canvas vs Blackboard; SMU vs Chicago
The discussion focuses on different Learning Management Systems (LMS):
Chicago used Blackboard.
SMU uses Canvas (recently established; formerly Blackboard or other systems).
The interviewees compare experiences across institutions to understand LMS usability and support.
There is a question about whether SMU had always used Canvas or switched from Blackboard; the response indicates a switch to Canvas, with Blackboard used previously.
The campus supports LMS learning through seminars and recorded sessions, but the learner notes that watching recordings can be time-consuming and begs for more guided assistance.
Course Design Workflow: Templates, Duplicating Courses, Due Dates, and Assignments
The instructor describes a recurring workflow when starting a new course:
Copy an old course to create a template so the skeleton (structure) remains the same across terms.
Arrange content by weeks or modules (e.g., Module 1, etc.); some instructors might organize differently, but he prefers week-based modules.
Templates are reused; dates need to be updated when duplicating.
What does not automatically carry over: due dates, group vs. individual assignments, and some other course-specific settings.
He often checks for and updates these non-carryover items to ensure accuracy.
Panopto and Zoom integration:
He uses Panopto for recordings and likes to provide access to recordings for students who cannot attend live.
Zoom is part of the ecosystem; Panopto provides an alternative for access to content.
He notes a particular issue about microphone setup and the importance of audio quality for recordings.
Common setup steps:
When creating or updating a course, he configures modules, assigns tasks, and embeds materials (PDFs, readings, etc.).
He emphasizes the importance of proper due dates and clear assignment settings to avoid confusion.
Recording, AV, and Technology Stack
The technology stack includes:
LMS (Canvas) for course structure and content delivery.
Panopto for video recording and hosting.
Zoom for live sessions, with integration considerations.
Audio and video reliability issues:
The instructor notes a ceiling microphone setup that can pick up room audio well when enabled; if the audio option is turned off, recordings may have poor sound quality due to ambient noise.
A recurring problem involves forgetting to enable or configure audio input correctly, which degrades the usefulness of recordings for students who watch later.
On-site testing and preparation:
The instructor arrived a week before classes to test in three different classrooms to ensure compatibility of lighting, AV, and classroom tech.
He personally tests the room’s AV setup to minimize in-class disruptions.
Sign-in and backup strategies:
He has a backup laptop ready in his office in case the desktop fails.
He has become efficient at switching between apps and tabs during class, typically having around four to five tabs open and ready.
Troubleshooting and contingency planning:
If issues arise, the typical response is to call IT support (example number: 768-8888) and wait for on-site assistance.
In one case, a student who happened to be in SMU helped resolve an issue after IT was slow to respond.
Common Issues and Troubleshooting
Recurrent setup issues include:
Duplicating a course and releasing all content at once can cause misalignment of due dates and missing weekly pacing.
Ensuring that solutions (e.g., answer keys) are unpublished when content is released to students to avoid premature exposure.
The need to cross-check settings to prevent accidental publication of solutions.
There is a perception of limited quality control within Canvas:
There is no built-in reviewer for a professor’s Canvas setup; the process is largely self-managed.
An analogy is drawn to Amazon shipping: releasing content to all students without proper gating can lead to bad customer (student) experience if the item is not relevant or arrives early.
The practical implication: heavy reliance on the instructor to manage all LMS aspects, with minimal automated review or support.
A recurring theme is the tension between efficiency and thorough onboarding: better onboarding could save time and reduce mistakes, but it requires time and the right people.
IT Support and Resource Constraints
Institutional resources vary significantly between schools:
The interviewee notes SMU’s endowment and resources are substantial but not necessarily aligned with the needs of the business school.
In comparison with a larger institution (e.g., Chicago or UT Arlington), there can be substantial differences in IT staffing and support availability.
IT department scaling issues:
A university with a larger budget might have more IT staff (e.g., dozens of IT pros dedicated to business school operations) than SMU, leading to slower response times and higher burden on individual faculty.
The assistant (Jennifer) in I/O IT is helpful but insufficient as a systemic solution:
Jennifer provides one-on-one guidance and reminders, but there is no formal, scalable process to automate recurring setup tasks and cross-term checks.
Real-world constraints illustrate a broader problem: technology adoption outpaces available human support, creating bottlenecks for faculty.
The Role of TAs and Human-AI Support
TAs (e.g., Hannah) play a critical role in course support and grading:
Hannah is a former SMU MBA student who understands students well and brings appropriate empathy.
She works full-time and is paid from the research budget in the speaker’s setup; this contrasts with Chicago where TAs may be funded differently.
The TA has the right mix of experience and sensitivity to students’ needs; this is seen as a value add beyond purely technical support.
Challenges with TA allocation and funding:
The instructor’s ability to pay Hannah is constrained by the university’s budgeting, leading to variability in TA support across institutions.
The idea of a dedicated, AI-assisted support role:
The discussion explores the possibility of a trained TA plus AI-based guidance to map classrooms, configure systems, and troubleshoot in real time.
The concept of an “agentic AI” that can remind and guide faculty through LMS setup and course planning is proposed as a future enhancement.
The question of value add vs time investment:
Faculty want guided support that identifies high-impact features and walks them through implementation rather than generic video tutorials.
A one-time cost for expert guidance could yield long-term efficiency if a dedicated collaborator helps implement and optimize features.
Agentic AI and Future Enhancements
Proposed features include:
An AI assistant that provides step-by-step, context-aware guidance for LMS tasks (e.g., duplicating courses, setting due dates, publishing content).
“Mapping” classrooms to ensure instructors know how to operate a given space and its tech, reducing setup time and errors.
A dedicated, non-technical TA or support specialist who can act as a consistent point of contact for instructors across terms.
Rationale:
Faculty experience inertia: even experienced instructors need guided help to adopt new tools efficiently.
One-to-one support is highly valued, far more than generic training videos, because it reduces trial-and-error time and aligns with individual teaching workflows.
Practical benefits:
Reduces wasted classroom time due to avoidable tech glitches.
Improves consistency across courses and terms.
Enables instructors to focus more on teaching and content rather than configuration tasks.
Real-World Contexts and Personal Anecdotes
The broader theme is resource disparity and its impact on teaching quality and student experience:
Even if the campus has state-of-the-art facilities, disparities in who has access to training, support, and time to learn new tools can hamper effective teaching.
The discussion also touches on the value of experiential and practical knowledge in teaching:
The speaker emphasizes the importance of hands-on understanding of classroom dynamics, including lighting, acoustics, and the practical use of tech in live settings.
Reflections on Teaching, Efficiency, and Change Management
The conversation centers on how to balance efficiency with thoroughness in LMS administration and course design.
The speaker argues for a support system that reduces repetitive, low-value tasks and provides tailored advice for instructors.
Key tension points include:
Time vs value of onboarding: instructors need guidance, but there may not be enough institutional resources to provide consistent, personalized support.
The need for one-to-one support vs self-service training: personalized coaching is preferred but more costly and logistically complex.
The desire for future solutions that combine AI with human guidance to achieve scalable, high-quality support.
Practical Takeaways and Open-Ended Questions
Practical takeaways:
When designing courses in Canvas, copy a proven template, then adjust dates, group vs individual settings, and due dates carefully.
Use Panopto for recorded content to accommodate students who cannot attend live sessions, and ensure audio is enabled to avoid silent or low-quality recordings.
Test classroom AV in advance to minimize live-class disruptions; keep a backup laptop ready.
Maintain a clear process for publishing content and unpublishing solutions to prevent accidental disclosure of answers.
Recognize the limitations of institutional support and plan for self-reliance with targeted, expert-guided help when possible.
Open-ended questions to consider for future study or class prep:
How can an agentic AI be integrated into LMS workflows to reduce friction without increasing cognitive load on professors?
What would a dedicated TA-plus-AI support model look like in practice across different universities with varying levels of resources?
How can we design automated checks within Canvas to flag common misconfigurations (e.g., incorrect due dates, unpublished solutions, missing Panopto links) before the term starts?
In MBA programs where attendance is not strictly monitored, what alternative structures or assessments can ensure accountability without undermining adult learner autonomy?
What trade-offs exist between providing high-quality, live IT support and modern, scalable AI-assisted guidance in higher ed?