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.

 

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.

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.

Evidence-Based Classroom Assessment Techniques (CATS) for STEM Courses

Teaching a large lecture course in a STEM course can feel like steering a cargo ship; you’re moving a lot of people in the same direction, but small adjustments can be hard to see and manage in real time. Traditional assessments (midterms, finals, projects) may measure end-point achievement, but they don’t always help faculty understand how students are learning along the way. This is where classroom assessment techniques (CATs) https://vcsacl.ucsd.edu/_files/assessment/resources/50_cats.pdf come in: quick, research-backed methods that provide timely insights into student understanding, enabling instructors to adapt instruction while the course is still in motion.

Why CATs Matter in STEM Large-Enrollment Courses

Evidence from STEM education research underscores that formative assessment and feedback loops significantly improve student learning outcomes, especially in large courses where anonymity and disengagement can take hold. Studies show that structured opportunities for feedback (e.g., one-minute papers, peer assessments, low-stakes quizzes) can reduce achievement gaps and support retention in challenging majors.

At the same time, as Northwestern’s Principles of Inclusive Teaching https://searle.northwestern.edu/resources/principles-of-inclusive-teaching/note, students often struggle not only with course content but also with the “hidden curriculum” or unspoken rules about what “counts” as good work or participation https://cra.org/crn/2024/02/expanding-career-pipelines-by-unhiding-the-hidden-curriculum-of-university-computing-majors/ . Transparent communication about assessment criteria and expectations helps level the playing field.

High-Impact CATs for CS, Engineering, and Informatics

  • Algorithm Walkthroughs (Think-Alouds)
    Students articulate their reasoning step-by-step. Helps faculty identify gaps in procedural knowledge.

  • Debugging Minute Paper
    Prompt: “What was the most confusing bug/issue we discussed today, and why?” Surfaces common misconceptions in programming logic.

  • Concept Maps for Systems Thinking
    Students draw connections between components (e.g., CPU, memory, OS). Research shows concept mapping fosters transfer across domains.

  • Peer Review of HCI Prototypes
    Students exchange usability sketches with rubrics. Builds critique skills and awareness of user-centered design.

  • Low-Stakes Quizzing with Digital Dashboards
    LMS quizzes or polling tools provide immediate data on misconceptions while also scaffolding students’ goal monitoring.

Making CATs Inclusive in Large Lecture Halls

To avoid reinforcing inequities, instructors should:

  • Clarify criteria with rubrics for coding projects, design critiques, or participation.

  • Co-create ground rules for collaboration in labs and online forums, ensuring respectful and equitable engagement.

  • Balance rigor and empathy: challenge students while providing structures that acknowledge different starting points and prior knowledge.

Putting It into Practice

  • In a 250-student programming class, use a digital Muddiest Point poll after each lecture, then address top confusions in the next class.

  • In an HCI course, scaffold peer review CATs for wireframes inside the LMS, combining digital rubrics with analog small-group feedback.

  • In a systems engineering class, embed progress dashboards with reflective CAT prompts (“Where are you stuck? What resource might help?”). This makes metacognition visible and actionable.

Final Thought

Large-enrollment CS, engineering, informatics, and HCI courses don’t have to feel impersonal or assessment-heavy. By integrating classroom assessment techniques faculty can design courses that are responsive, transparent, and inclusive. The result: students who not only master disciplinary knowledge but also learn how to manage their own learning, a skill set essential for both the classroom and the future of work.

Further Reading:

  1. Angelo & Cross’s Classroom Assessment Techniques https://iucat.iu.edu/catalog/20750208
    50+ adaptable CATs. For large STEM courses, techniques like the “Muddiest Point” or “Background Knowledge Probe” are especially powerful.

  2. Nilson’s Teaching at Its Best https://iucat.iu.edu/catalog/16660002
    Offers frameworks for aligning CATs with learning objectives—critical in CS/engineering courses where problem-solving, debugging, and design thinking are central.

  3. Northwestern University, Principles of Inclusive Teaching https://searle.northwestern.edu/resources/principles-of-inclusive-teaching/; and Making Large Classrooms feel Smaller: https://searle.northwestern.edu/resources/our-tools-guides/learning-teaching-guides/making-large-classes-feel-smaller.html