Possible ways to improve attendance

One of the most frequent concerns I hear is, “My students just aren’t coming to class.” With so much content available online, recorded lectures at their fingertips, and the sense of distance that can come with large classes, this challenge is becoming more common and more complex. In this post, I will look at some of the more popular reasons reported for students not attending class and share practical, evidence-based ways to re-engage students in the classroom.

The Anonymity Epidemic: When Students Feel Like Just Another Face

Many students, particularly in large enrollment courses, feel anonymous. They don’t believe their individual presence makes a difference, leading to a disengagement from the classroom community. This isn’t just a large-class problem; it arises when students lack meaningful connections with instructors, TAs, or even their peers. Overcoming this anonymity is key to fostering a sense of responsibility and belonging.

Strategies to Combat Anonymity:

  • Be Present Before Class: Arriving early to chat informally with students is a simple yet powerful way to build rapport. Ask about their weekend, recent movies, or even their experience with the last assignment. These small gestures humanize you and create a connection.

  • Active Engagement is Key: Design activities that actively involve students with the material. Pose intriguing questions, facilitate brief peer discussions, or utilize classroom response systems like TopHat https://uits.iu.edu/tophat/index.html to “vote” on responses. This transforms passive listening into active participation, fostering an intellectual community.

  • Learn Their Names (or Try): Even the attempt to learn student names is deeply appreciated. Ask for names when students speak and use them in your response. Consider using a photo roster from Canvas to help you put names to faceshttps://toolfinder.iu.edu/tools/iu-photo-roster. A study in a high-enrollment biology course found that students’ perception of their instructor knowing their name was highly correlated with a sense of belonging, even though the instructors didn’t know every student’s name https://www.lifescied.org/doi/full/10.1187/cbe.16-08-0265 This suggests that the effort and intention behind using a student’s name are just as important as the memorization itself. For more strategies see: https://teachinginhighered.com/podcast/how-to-learn-students-names/

  • Cultivate Peer Connections: Encourage students to get to know each other. In in Relationship-Rich Education: How Human Connections Drive Success in College(Felten & Lambert, 2020) https://iucat.iu.edu/catalog/19430355mention that students benefit when they are guided in how to connect, not just told to “work together.” On the first day, have them introduce themselves to those around them. Additional strategies might include teaching collaboration skills, establishing norms for group work, or prompting reflection on what makes a partnership effective. If you use group work, rotate group members throughout the semester. Periodically have students shift seating to broaden their peer interactions.

  • Personalized Feedback (Even in Large Classes): While challenging, finding ways to provide even small amounts of personalized feedback on assignments can significantly reduce feelings of anonymity. This could be through targeted comments on a rubric or brief, individualized responses to discussion forum posts. In large classes, it’s impossible to give every student a paragraph of detailed feedback each week, but you can make feedback feelpersonal by thinking in layers. I like to frame it as macro, meso, and micro feedback. At the macro level, I share short announcements summarizing class-wide trends; what students are doing well, what’s tripping them up, and a few standout examples. At the meso level, I provide targeted feedback to lab sections, project teams, or discussion groups that speaks directly to their shared progress. Then at the micro level, I use rubrics and comment banks to individualize comments just enough to sound human…adding a student’s name or referencing something specific from their work. It’s not about writing more; it’s about being intentional with how students experience the feedback they receive.

The “Why Bother?” Dilemma: Lack of Incentive, Relevance, and Engagement

Students often skip lectures if they perceive the content as readily available elsewhere, not directly relevant to their goals, or simply boring.

 

Strategies to Create Incentive and Relevance:

  • Incentivize Attendance: Leverage students’ natural focus on grades. Make attendance a component of the grade, or administer short, low-stakes quizzes at the beginning of class using tools like Canvas or TopHat.

  • Design Slides to Drive Presence:Explicitly state that your posted slides are incomplete. Design them as skeletal frameworks, requiring students to annotate and fill in critical explanations and examples during lecture. This creates a clear value proposition for attending.

  • Debunk the “Notes from a Peer” Myth:Directly address the inadequacy of relying solely on peer notes or even AI-generated summaries. Emphasize that context, instructor insights, and the organic flow of a live lecture cannot be fully replicated.

  • Connect to Their World: Embed examples, applications, and topics that resonate with students’ fields of study and current cultural interests. Utilize Canvas Course Analytics, Reports and Dashboardsand/or  pre-course surveys to understand your student demographics and tailor examples accordingly.

  • Pique Interest from the Start: Begin lectures with a challenging question, an intriguing anecdote, or a real-world problem that immediately grabs attention and motivates sustained engagement.

  • Convey Your Enthusiasm: Your passion for the subject is contagious! Share personal stories, recent discoveries, and your excitement for the discipline. Voice and body language naturally convey this enthusiasm.

Overcoming Information Overload and Misaligned Expectations

Sometimes, students skip because they feel overwhelmed, confused by lecture goals, or perceive the lecture as redundant to textbook material.

Strategies for Clarity and Complementary Learning:

  • Chunk Your Lectures & Re-engage:Recognize that typical attention spans are 10-20 minutes. Plan your lectures in shorter chunks, incorporating varied activities every 15-20 minutes to re-engage attention (e.g., questions, visuals, demonstrations, group work, videos). Consider attending the upcoming Active Learning Block Party for Large Classrooms sponsored by CITL for engagement ideas.

  • Complement, Don’t Reiterate, the Textbook: Use class time to expand on readings, provide alternative perspectives, facilitate problem-solving, or have students generate their own examples. The lecture should offer something the textbook doesn’t.

  • Provide Unique Experiences: Bring in guest speakers, conduct live demonstrations of code or hardware, or share cutting-edge research and innovations that students wouldn’t encounter elsewhere that connect with course content.

  • State Your Goals Clearly: Explicitly articulate the learning objectives for each lecture. Use these goals as “mileposts” to help students track their progress and understand the desired outcomes.

  • Share the Organization: Provide an outline, agenda, or visual representation of the lecture’s structure. Don’t assume novices will automatically see the logical connections among concepts.

  • Encourage Support Services: If you identify students struggling with academic or non-academic demands, refer them to appropriate support services like Academic Development, the Counseling Center, or Student Health.  Student Resource Slideshow.pptx

  • Support Language Learners: For students whose first language is not English, refer them to resources like the Office of International Services which offers drop-in English tutorials for second language students https://ois.iu.edu/get-involved/english-tutorials/index.html

  • Provide Recordings (Strategically):While recordings can reduce attendance, they are a valuable accessibility tool. If you record, emphasize that the recording is a supplement for review or for those with legitimate absences, not a substitute for live engagement. Consider how you might make the live session distinctly more valuable than the recording (e.g., interactive elements through PlayPosithttps://uits.iu.edu/services/technology-for-teaching/instruction-and-assessment-tools/playposit/index.html, Q&A).

The Power of Visuals and Storytelling

In fields like Computer Science and Engineering, abstract concepts can be difficult to grasp. Visuals and real-world narratives can significantly enhance comprehension and engagement.

Additional Tips:

  • Integrate Visualizations: When explaining complex algorithms, data structures, or system architectures, use diagrams, flowcharts https://miro.com/, and animations  Show, don’t just tell. Consider generating some of these visualizations on the fly with your students!

  • Tell Stories of Impact: Frame technical concepts within the context of real-world problems they solve or innovative applications. How did this algorithm enable a new technology? What societal problem does this data science technique address?

  • Live Coding Demonstrations: For programming or data manipulation courses, live coding is incredibly effective. It allows students to see the process, observe debugging strategies, and ask questions in real-time. Make sure to slow down and explain your thought process.

  • Guest Speakers from Industry: Invite professionals from relevant industries to share how the concepts taught in class are applied in their day-to-day work. This provides tangible career relevance.

By adopting these evidence-based strategies, faculty can transform their lectures from passive information dissemination into vibrant, engaging learning experiences that students genuinely want to attend. The goal isn’t just to fill seats, but to foster deeper learning and a stronger connection to the academic community.

 

Building a Framework for Academia-Industry Partnerships and AI Teaching and Learning Podcasts

In March, I shared the an overview of the  “Practitioner to Professor (P2P)‘ survey that the CRA-Education / CRA-Industry working group analyzed. They recently released a report titled Breadth of Practices in Academia-Industry Relationships which explores a range of engagement models from research partnerships and personnel exchanges to master agreements and regional innovation ecosystems.

Key Findings and Observations

The report organizes its findings from the workshop into three categories: observations, barriers, and common solutions:

  • Observations A major theme was the critical need to embed ethical training into AI and computing curricula through both standalone courses and integrated assignments. It was noted that while academia is best suited to drive curriculum development, input from industry is essential to ensure the content remains relevant to real-world applications.

  • Barriers Key barriers to successful collaboration were identified, including cultural differences and misconceptions between academic and industry partners. For instance, industry’s focus on near-term goals can clash with academia’s long-term vision. A significant practical barrier is the prohibitive cost of cloud and GPU hardware, which limits students’ experience with cloud and AI development tools.

  • Common Solutions Effective solutions include the fluid movement of personnel between organizations through internships, co-ops, sabbaticals, and dual appointments. Streamlined master agreements at the institutional level also help facilitate research collaborations by reducing administrative friction.

Strategies for Research Collaboration

The report outlines a multi-level approach to enhancing research partnerships:

  • Individuals Faculty and industry researchers can initiate relationships through internal seed grants, sabbaticals in industry, dual appointments, and by serving on industry advisory boards.

  • Departments Departmental leaders can foster collaboration by strategically matching faculty expertise with industry needs, offering administrative support, and building a strong departmental brand with local industry.

  • University Leadership Senior leaders can address systemic barriers by creating a unified, institution-wide strategy, developing flexible funding models, and implementing master agreements to streamline partnerships.

  • Regional Ecosystems The report emphasizes the importance of universities partnering with local industries and startups to build thriving regional innovation ecosystems, which can drive economic development and secure government support.

Education and Workforce Development 

With the rise of generative AI, the report highlights an urgent need for universities and industry to partner on education.

  • Curriculum Adaptation Computing curricula need to be updated to include foundational concepts in DevOps and scalable systems, which are often not part of the core curriculum. While AI literacy is essential, the report suggests a balance, with 80% of instruction remaining focused on core computer science skills. Ethical reasoning should be integrated throughout the curriculum, not just in a single course.

  • Workforce Programs To meet industry demands for job-ready graduates, the report advocates for university-industry partnerships in co-op programs, internships, and capstone projects. It also points to the need for universities to offer flexible programs like certificates and online courses to help upskill and reskill the existing workforce.

Recommendations

The report concludes with five main recommendations for universities, industry, and government:

  1. Enhance research impact by combining academia’s long-term vision with real-world problems from industry. This can be achieved by embedding faculty in industry and industry researchers in universities.

  2. Leverage the convening power of universities to build partnerships that benefit the wider community, using mechanisms like industrial advisory boards and research institutes.

  3. Accelerate workforce development by aligning university programs with regional innovation ecosystems and having industry invest in talent through fellowships and internships.

  4. Deliver industry-relevant curricula grounded in core computing principles, and collaborate with industry experts to co-design courses in high-demand areas like AI and cloud computing.

  5. Establish new incentives and metrics to recognize and reward faculty for their contributions to industry partnerships in promotion and tenure evaluations.

AI Teaching and Learning Podcasts:What If College Teaching Was Redesigned With AI In Mind?

https://learningcurve.fm/episodes/what-if-college-teaching-was-redesigned-with-ai-in-mind

A former university president is trying to reimagine college teaching with AI in mind, and this year he released an unusual video that provides a kind of artist’s sketch of what that could look like. For this episode, I talk through the video with that leader, Paul LeBlanc, and get some reaction to the model from longtime teaching expert Maha Bali, a professor of practice at the Center for Learning and Teaching at the American University in Cairo.

The Opposite of Cheating Podcast

https://open.spotify.com/show/5fhrnwUIWgFqZYBJWGIYml

(Produced by the authors of the book with the same name) the podcast shares the real life experiences, thoughts, and talents of educators and professionals who are working to teach for integrity in the age of AI. The series features engaging conversations with brilliant innovators, teachers, leaders, and practitioners who are both resisting and integrating GenAI into their lives. The central value undergirding everything is, of course, integrity!

Teaching in Higher Ed podcast, “Cultivating Critical AI Literacies with Maha Bali”.

https://teachinginhighered.com/podcast/cultivating-critical-ai-literacies/

In the episode, host Bonni Stachowiak and guest Maha Bali, a Professor of Practice at the American University in Cairo, explore the complexities of integrating artificial intelligence into higher education.

Bali advocates for a critical pedagogical approach, rooted in the work of Paulo Freire, urging educators to actively experiment with AI to understand its limitations and biases. The discussion highlights significant issues of cultural and implicit bias within AI systems. Bali provides concrete examples, such as AI generating historically inaccurate information about Egyptian culture, misrepresenting cultural symbols, and defaulting to stereotypes when prompted for examples of terrorism.

The Actual Intelligence podcast

speakswith Dr. Robert Neibuhr from ASU regarding his recent article in Insider Higher Ed: “A.I and Higher Ed: An Impending Collapse.” Full Podcast: https://podcasts.apple.com/us/podcast/is-higher-ed-to-collapse-from-a-i/id1274615583?i=1000725770519

with Bill Gates having just said that A.I. will replace most teachers within ten years, it seems essential that professional educators attune to the growing presence of A.I. in education, particularly its negative gravitational forces.

The Guide on the Side: Coaching STEM Students in Problem-Solving

From Manager to Mentor: A Practical Strategy for AI Development

As faculty, we know that working effectively with our Assistant Instructors (AIs) is key to a successful course. In last week’s post on Best Practices for Working with Assistant Instructors,” I highlight the importance of mentorship and creating professional development opportunities. But what does that mentorship look like in practice?

One of the most impactful ways to mentor our AIs is to equip them with high-leverage teaching strategies. Instead of just managing their grading, we can teach them how to teach. A powerful approach for this is the Guide on the Side philosophy, which shifts the AI’s role from a simple answer-key to a learning coach.

The Guide on the Side: Coaching STEM Students in Problem-Solving

It’s a familiar scene in any STEM lab or office hour: a student, staring at a screen, is utterly stuck. For new teaching assistants (Associate Instructors, or AIs), the temptation is strong to take the shortcut; to grab the keyboard, write the line of code, or simply provide the answer. But while this solves the immediate problem, it bypasses a crucial learning opportunity.

This is where the Guide on the Side approach comes in. It’s a teaching philosophy that equips new AIs with practical strategies to coach students through the problem-solving process rather than solving problems for them. For faculty in STEM, empowering your AIs with these skills can transform your students’ learning experience. 

Why This Shift in Pedagogy Matters

Across STEM disciplines, students frequently encounter “sticking points” moments of cognitive friction where the path forward isn’t obvious. If an instructor or AI simply hands over the solution, the student leaves with a single answer but no transferable skill. They learn to be dependent on an external expert.

By contrast, an instructor who guides the process models resilience, inquiry, and expert reasoning. The student leaves not only with a solution but with strategies they can apply to the next problem, and the one after that. They learn how to think.

Putting Theory into Practice: Activities for Your AIs

Faculty can use these activities in their own training sessions to help AIs develop a coaching mindset:

  • “Sticking Point” Brainstorm: In a think-pair-share format, AIs identify the most common places their students struggle. This builds a shared awareness of teaching challenges and normalizes the experience.

  • Scenario Analysis: AIs compare two contrasting dialogues: one where the AI gives the answer directly, and another where the AI uses Socratic questioning to lead the student to their own solution.

  • Questioning Roleplay: In pairs, AIs practice how to respond with guiding questions when students make common statements like, “I’m totally lost,” or “Can you just tell me if this is right?”

A Simple Framework for Modeling Expertise

A core strategy of this approach is teaching AIs to make their thinking visible. Experienced problem-solvers naturally follow steps that are often invisible to novices. Encourage your AIs to narrate their own problem-solving process explicitly using a simple four-step framework:

  1. Understand: Restate the problem in your own words. What are the inputs, the desired outputs, and the constraints?

  2. Plan: Outline possible approaches. What tools, algorithms, or libraries might be useful? What are the potential pitfalls of each approach?

  3. Do: Execute the plan step by step, narrating the reasoning behind each action. (“First, I’m going to create a variable to hold the total because I know I’ll need to update it in a loop.”)

  4. Reflect: Test the solution. Does it work for edge cases? Could it be more efficient? Are there alternative ways to solve it? 

This explicit modeling teaches students how to think, not just what to do.

The Power of a Good Question: Building a Question Bank

Guiding questions are the primary tool of a “Guide on the Side.” They skillfully shift the cognitive work back to the student. Encourage your AIs to build a bank of go-to questions, such as:

  • To start a conversation: “What have you tried so far?” or “Can you walk me through your current approach?”

  • To prompt a next step: “What does that error message suggest?” or “What’s the very next small step you could take?”

  • To encourage deeper thinking: “Why did you choose that particular method?” or “What are the trade-offs of doing it that way?”

  • To promote reflection and independence: “How could you check your answer?” or “What would you do if you encountered a similar problem next week?” 

Navigating Common Classroom Challenges

This approach provides concrete strategies for these common moments:

  • When a student is silent: Allow for sufficient wait time. If the silence persists, break the problem down and ask a simpler, first-step question.

  • When a student is frustrated: Acknowledge their feelings (“I can see this is frustrating; these problems are tough.”) and normalize the struggle before gently re-engaging with the task.

  • When a student just wants confirmation: Instead of giving a simple “yes” or “no,” redirect with a metacognitive prompt like, “What makes you confident in that answer?” or “How could you design a test to verify that?”

Resources for a Deeper Dive 

For faculty and AIs who want to explore this pedagogical approach further, these resources are short, impactful, and highly relevant:

  • Book: Small Teaching: Everyday Lessons from the Science of Learning by James M. Lang

  • Article: Asking Questions to Improve Learning – Washington University in St. Louis Center for Teaching and Learning

  • Video: Eric Mazur’s video on Peer Instruction is a great resource for understanding how to shift from traditional lecturing to more active, student-centered learning. He effectively demonstrates the curse of knowledge and how students learning from each other can be more effective than an expert trying to explain something they’ve long ago mastered.
    His approach, where students first think individually, then discuss with peers, and finally re-evaluate their understanding, directly aligns with the principles of guiding students through problem-solving rather than just showing them the answer. It emphasizes active processing and peer teaching, which are crucial for deeper learning and developing independent problem-solvers.

The Takeaway for Faculty

The “Guide on the Side” approach aligns perfectly with evidence-based teaching practices. By encouraging your AIs to slow down, model your thinking, and use questions effectively, you help them grow from being answer keys into becoming true teaching coaches. The result is a more engaged and resilient cohort of students who leave your courses not only with solutions, but with the confidence and strategies to tackle the next challenge independently.

Best Practices for Working with Assistant Instructors

Assistant instructors (AIs) can play an essential role in supporting your course.  They support student learning, enhance faculty efficiency, and gain valuable professional development experience along the way. When managed thoughtfully, the faculty-assistant instructor partnership creates a stronger, more engaging learning environment for students and a meaningful growth opportunity for graduate students.

This following are recommendations collected from the resources mentioned below in the reference section.

Core Principles of a Strong Partnership

The faculty–assistant instructor relationship is most successful when approached as a collaborative teaching partnership. Here are some guiding principles:

  • Clear Expectations and Roles
    Both faculty and assistant instructors need a shared understanding of their responsibilities. Clarity reduces confusion and sets everyone up for success.

  • Faculty as the Ultimate Authority
    While assistant instructors play an active role in teaching and assessment, faculty ultimately carry the responsibility for the course administration duties, including grading and alignment with institutional policies.

  • Professional Development Opportunity
    Serving as an assistant instructor should be a learning experience. Faculty should connect assigned tasks to professional growth, teaching skills, and career preparation whenever possible.

  • Consistent Communication
    Regular check-ins, open conversations, and transparency help prevent misunderstandings and make problem-solving much easier when issues arise.

Setting Up for Success

Before the Semester Begins

Early connection is key. Meet with your assistant instructor before classes start to set expectations, share goals, and establish communication methods. Some items to cover:

  • Course goals and learning outcomes

  • Roles, tasks, and boundaries

  • Meeting schedules and communication channels

  • Workload expectations (respecting weekly hour limits)

  • Familiarity with technology tools

  • Academic integrity policies

  • An introduction plan so students understand the assistant instructor’s role. 

Please see https://blogs.iu.edu/luddyteach/2023/08/16/quick-tip-working-with-ais/for a checklist developed by Dr. Angela Jenks and Katie Cox , in the Department of Anthropology at the University of California, Irvine.

Having these conversations upfront helps everyone enter the semester with confidence.

During the Semester

  • Regular Meetings
    Weekly or biweekly meetings provide a chance to prepare for upcoming lessons, review grading approaches, and troubleshoot challenges.

  • Grading Consistency
    Provide rubrics and sample feedback. Calibration or grade norming activities where everyone grades the same sample are especially effective for ensuring fairness.

  • Office Hours
    Encourage assistant instructors to hold consistent and accessible office hours at different times of day to accommodate students.

  • Mid-Semester Check-In
    Use this time to gather feedback, review workloads, and adjust if necessary.

End of the Semester

Wrap up with a reflective meeting. Discuss what worked well, identify challenges, and preserve useful materials for future iterations of the course. These conversations also strengthen the mentoring relationship.

Supporting Assistant Instructor Development

Faculty aren’t just supervisors, they’re mentors. Assistant instructors benefit when faculty take the time to:

  • Coach them on teaching strategies and classroom management

  • Encourage them to set professional development goals and build a teaching portfolio if they are interested in pursuing a faculty position

  • Provide opportunities for peer observation and self-reflection

  • Direct them to school and university-wide teaching resources

By positioning the role as both service and growth opportunity, faculty help assistant instructors build skills that last well beyond a single course.

References

Teaching Tip: What Are You Really Trying to Assess?

As you design quizzes, projects, and exams, it’s worth pausing to ask: What am I really trying to assess? Too often, assessments measure peripheral skills like memorization, rather than the intended learning outcomes. For example, a timed coding exam may end up evaluating typing speed and syntax recall more than algorithmic thinking or problem-solving strategy. Similarly, a multiple-choice exam on HCI principles may privilege memorization over the ability to apply design heuristics to new contexts.

Evidence-based practices to align assessments with your goals:

  1. Backwards Design (Wiggins & McTighe, 2005)

  2. Constructive Alignment (Biggs, 1996)

    • Ensure that learning activities, assessments, and outcomes are in sync. For instance, if collaboration is a stated goal, include a group design critique, not just individual tests.

    • Example: Reflections on applying constructive alignment with formative feedback for teaching introductory programming and software architecture (2016): https://dl-acm-org.proxyiub.uits.iu.edu/doi/pdf/10.1145/2889160.2889185

  3. Authentic Assessment (Herrington & Herrington, 2007; )

  4. Reduce Construct-Irrelevant Barriers

    • If the skill being assessed is debugging, for example, provide starter code so students aren’t penalized for setup. If the goal is conceptual understanding, consider allowing open-book resources so recall doesn’t overshadow reasoning.

Students also struggle not because the concepts are beyond their ability, but because the expectations of the assessment are unclear.

For example:

  • A programming assignment asks students to “optimize” code, but it’s unclear whether grading is based on correctness, runtime efficiency, readability, or documentation.

  • A human–computer interaction (HCI) project requires a prototype, but is the emphasis on creativity, usability testing, or fidelity of the mockup?

  • An informatics paper asks for “analysis,” but it’s unclear whether success depends on critical thinking, proper use of data, or following citation conventions.

When assessments lack clarity, students must guess what matters. This shifts the focus from demonstrating learning to playing a hidden “what does the professor want?” game.

Why It Matters (Evidence-Based):

  • Cognitive Load: Ambiguous assessments create unnecessary cognitive load—students waste energy interpreting instructions instead of applying knowledge (Sweller, 2011).

  • Equity Impact: Lack of clarity disproportionately disadvantages first-generation and other structurally disadvantaged students, who may not have tacit knowledge about faculty expectations (Winkelmes et al., 2016).

  • Misalignment: As mentioned above, vague assessments often misalign with course outcomes, undermining constructive alignment (Biggs, 1996).

What Faculty Can Do:

  1. State the Core Construct: Ask yourself: Am I assessing correctness, creativity, reasoning, or communication? Then state it explicitly.

  2. Communicate Priorities: If multiple criteria matter, indicate their relative weight (e.g., correctness 50%, efficiency 30%, documentation 20%).

  3. Provide a Sample Response: A brief example—annotated to show what “counts”—helps students see what you value.

  4. Check for Hidden Criteria: If you penalize for style, clarity, or teamwork, ensure that’s written down. Otherwise, students perceive grading as arbitrary.

Faculty Reflection Prompt:
Pick one upcoming assignment and ask yourself: If I gave this to a colleague in my field, would they immediately know what I was assessing? Or would they have to guess? If the latter, refine the task or rubric until the answer is obvious.

Takeaway: Unclear assessments don’t just frustrate students, they distort what is being measured. By clarifying exactly what skill or knowledge is under the microscope, faculty ensure assessments are fair, transparent, and aligned with learning outcomes. Before finalizing any assignment or test, ask yourself: Am I measuring the skill that truly matters, or something adjacent? That small moment of reflection can make assessments more equitable, meaningful, and aligned with the professional practices of your discipline.