Piloting a Well-Being Lab in the Public Health Studies Curriculum

Posted on April 2, 2026 by abrusini

[Guest post by Leslie Bauman, Junior Lecturer in the Department of Psychological and Brain Sciences, Johns Hopkins University]

Introduction
The academic public health community has long recognized the value in experiential learning when it comes to public health practice. Like programs at many other universities, PHS majors at JHU must complete at least 80 hours of field work in a professional public health setting and engage in reflection on how those experiences bring their coursework to life. At the same time, a gap exists in that public health curricula generally do not reflect the importance of experiential learning when it comes to the very concept of health and well-being itself. Enter the Well-Being Lab.

In 2023, we launched an ambitious 2-year pilot of an experiential Well-Being Lab designed to address this curriculum gap. The idea was simple but powerful—what if students not only learned about population health and well-being but experienced it firsthand?

Our goals for the pilot were to test feasibility, gather student input, and explore models for integrating the Lab into the curriculum. During the pilot, students selected from a “menu” of activities and workshops that were available on campus through the PHS program or one of our campus partners (Health Promotion and Well-Being, Life Design Lab, Counseling Center, etc.), participate in the activity, and then reflect on how those experiences connected to their personal health and to broader public health concepts. The activities are categorized into eight aspects of health and well-being established by JHU’s Health Promotion and Well-Being office:

Emotional/mental health
Physical health
Spiritual health
Social health

Sexual health
Environmental health
Financial health
Professional health

Students were required to complete at least two well-being labs, each addressing a different dimension of well-being.

Results of the Pilot
The results were encouraging. Students engaged deeply with well-being concepts and were able to clearly articulate how their activities connected to population health themes. Many participants reported meaningful gains in stress management skills and increased awareness of campus well-being resources.

Reflection activities—while in need of refinement—proved valuable. Students described them as a bridge between personal growth and public health theory, helping them see how well-being practices relate to professional practice.

Student Feedback
If there was one message that came through loud and clear, it was this: students want the Well-Being Lab to be a real course.

Across the board, participants endorsed:

Making the Lab required
Offering 1.0 academic credit
Structuring it as pass/fail
Keeping it in-person and experiential, not fully asynchronous

Students emphasized that without credit, the Lab would feel undervalued and less likely to be taken seriously. They also expressed a strong preference for interactive elements—guest speakers, group activities, and opportunities to build social connection. While students supported flexibility through hybrid formats, they were far less enthusiastic about fully asynchronous options.

Importantly, this preference was not just about format—it was about connection. Across the pilot and follow-up focus groups, students consistently shared that they do not want another set of online modules, resource lists, or one-off workshops. They want small, peer-supported spaces where they can talk, connect, and grow.

Students were particularly drawn to peer-driven models, such as small groups led by trained peer mentors (e.g., upperclassmen or graduate students). They felt peer leadership would reduce formality, increase relatability, and create the psychological safety needed for honest conversation, accountability, and skill-building. These spaces, students emphasized, should feel social—but still structured enough to foster inclusion and follow-through.

Results from the activities and reflections in the pilot further reinforced this theme: students most valued well-being experiences that felt practical, low-barrier, and embedded into everyday life.

Students also asked for:

Shorter, completion-based reflections
Options for anonymity in sensitive topics
“Looser,” everyday activities like walks, gratitude practices, or financial planning
Practical life-skills content—time management, cooking, budgeting, navigating campus resources

In other words, students want well-being education that feels relevant, doable, and grounded in real life.

Lessons Learned:

Provide more structure and built-in educational content
Build in small-group, peer-to-peer connection through structured check-ins and trained peer mentors to increase belonging, accountability, and engagement.
Expand the menu of qualifying activities
Add asynchronous modules (in moderation)
Integrate the Lab into the PHS curriculum as a required component
Start Lab participation earlier in the semester
Build in biweekly check-ins instead of only start and end reflections
Strengthen partnerships with campus offices

These changes would make the Lab more coherent, more flexible, and more aligned with students’ needs.

“…students most valued well-being experiences that felt practical, low-barrier, and embedded into everyday life.”

If you are considering launching a well-being or experiential learning initiative, here are a few takeaways from our pilot:

Build in flexibility, especially in recruitment and participation.
Credit matters—students engage more deeply when the work “counts.”
Balance rigor with feasibility: shorter reflections and a manageable credit load help.
Partner with existing campus resources to improve feasibility and avoid duplication.
Connect reflection activities to disciplinary content to enhance both learning and well-being.

Final Thoughts
The pilot confirmed what many educators already suspect: students are eager for learning experiences that integrate personal well-being with academic and professional growth. The Well-Being Lab not only helped students understand public health principles more deeply—it supported them in living those principles. With thoughtful refinement and continued collaboration, this Lab has real potential to become a signature element of the Public Health Studies experience.

Leslie Bauman
Junior Lecturer, Psychological and Brain Sciences
Johns Hopkins University

Leslie Bauman is a junior lecturer in the Department of Psychological and Brain Sciences. Her research interests include clinical psychology, research methods, resilience, well-being, and mental health in university settings.

Image source: BalanceFormCreative – stock.adobe.com, JHU Vice Provost website, peopleimages.com – stock.adobe.com

Lunch and Learn: Practical AI Pedagogies

Posted on March 19, 2026 by abrusini

On Thursday, February 19th, the Center for Teaching Excellence and Innovation (CTEI) hosted a Lunch and Learn featuring two faculty members discussing Practical AI Pedagogies. Emily Fisher, Director of Undergraduate Studies and Associate Teaching Professor of Biology, and Illysa Izenberg, Associate Teaching Professor in JHU’s Center for Leadership Education, presented two approaches to integrating AI into their courses. Caroline Egan, Teaching Academy Program Manager, moderated the discussion.

Fisher began the presentation by describing how she and co-instructors Nichole Broderick and Chiara De Luca redesigned an exam preparation assignment to intentionally incorporate generative AI, also known as the “AI Study Buddy,” in their Molecular Biology course. Their goal was not to outsource thinking to AI, but to foster metacognition; they wanted students to appreciate the role of retrieval in learning, view exam prep as a learning opportunity, and practice self-directed learning and studying.

In previous iterations of this assignment (2020 – 2024), students were asked to write and answer their own exam questions (without AI). While this exercise was useful, it was unclear how many students had improperly used AI to complete their work. Also, Fisher noted that many of the questions produced were generally of low quality.

In 2025, the instructors reframed the activity: instead of avoiding the use of AI, they integrated it deliberately. Students were asked to use HopGPT (GPT-4o mini) to complete the following steps:

Prompt AI to generate a challenging exam question about molecular biology (without providing the answer).
Answer the AI-generated question themselves.
Ask AI to answer the same question.
Evaluate the AI’s response for:
-Accuracy
-Similarity to their own answer
-Alignment with the style of course exams
-Overall usefulness as a study tool

The assignment was worth 2 points out of a total of 106 for the course. A non-AI alternative was available for students who preferred not to use generative AI. (Two students selected this option.)

Overall, students rated the AI answers as highly accurate – but many times the students’ answers were different than the AI answers. Questions and answers were often very broad; the course content is much more detailed and includes experimental techniques that the AI answers did not include. Still, student feedback was mostly positive, rating it about “medium” in usefulness in helping them study. Fisher added that it was a valuable exercise, moving the focus away from writing exam questions and instead towards answering, analyzing, and critically evaluating content.

There were a few questions from the audience about the AI Study Buddy:

Q: Are there any data or future plans to compare these results with having students study together, asking each other questions?
EF: Not at the moment. This would be a challenge. I would have to trust that they weren’t using AI.

Q: Did students do better on the exam after doing this exercise?
EF: Students usually do well on exams in this class. I did not look to compare.

Q: Have you thought about asking HopGPT for more topics for more study? For example, telling students, “Here is what we think you should focus on, how does that compare with what AI recommends?”
EF: This is a good idea. Students are telling us they use AI to help them study. I guess I just wanted them to wonder how well it was helping.

Q: What about student equity to AI platforms?
EF: That’s why I used HopGPT, since all students have access to that one. I would have selected Claude if I did it again.

Q: Next time, would you keep it the same or expand it? Also, next time would you continue to focus on the answers?
EF: I thought it was useful the way it went. I would keep it. I love that there are ways to go deeper, but this is the right number of points for this class.

Illysa Izenberg continued the presentation by sharing her approach to using AI in her Engineering Management and Leadership course. Like many instructors, she wants to help students use AI tools productively without becoming overly dependent on them. Izenberg believes in total AI transparency between instructors and students and provides very clear guidance for students about its use. She uses color-coded symbols to show when and how students may use AI, and requires them to cite all sources (including AI tools) while providing detailed instructions on proper citation.

Izenberg has developed a unique framework that encourages students to view AI as “teammates” they can rely on for different types of support, depending on the task. Each teammate has a clearly defined role:

The Tasker handles repetitive work like formatting citations, organizing notes, or cleaning data — tasks that are routine but still require students to verify accuracy.

The Draftsmith can help refine writing, generate study materials, or suggest improvements. Students need discipline when using this role, as allowing AI to draft too much early on can lead to missed learning opportunities. For example, Izenberg notes that students need to develop their own writer’s voice which will be severely impacted if they rely on AI to draft everything for them.

The Facilitator acts as a thinking partner, asking questions that help the student consider alternatives, evaluate plans, and expand and sharpen their analyses. This role also requires discipline so that learning is not undermined. When used thoughtfully, this role can promote reflection and deeper understanding.

In addition to the AI teammates framework, Izenberg also introduced students to an “AI Gatekeeper,” a tool similar to a rubric that guides students in regulating their use of AI. The AI Gatekeeper asks students to first define their own criteria for using AI and then rate each task on a scale from 1 to 5 to decide whether to proceed (1 = no AI use, 5 = AI use is appropriate). Izenberg recommends a rating of at least “3” for students to consider using AI for assistance. Example student criteria and ratings:

Practice and expertise:
- 1 if this task gives me essential practice in a skill I’ll need later.
- 5 if I’ve done it many times and already have the needed expertise.

Creativity vs. rote work:
- 1 if the task requires creativity or original thought.
- 5 if it is mostly rote.

Students were not required to use AI or these tools in class, but they were there if they chose to use them. According to Izenberg, students who used the tools realized that they knew less about AI than they initially thought and learned a great deal in the process: to push themselves when articulating their reasoning, to avoid the temptation of allowing AI to make decisions for them, and to resist using it too early in the process so their creativity would not be compromised. Several students found the tools extremely helpful and suggested that they be made available and integrated across all of their courses.

The presentation concluded with additional questions from the audience and facilitator Caroline Egan:

Q: Given that the paid-for LLMs seem like they do a better job, what are the equity issues around giving them GPTs and prompts to use for coursework?
II: This is something we need to acknowledge. First, no assignment required AI use and students could succeed in the course without it. For those who chose to use it, many didn’t realize that HopGPT can access various ChatGPT models as well as Claude and others. I tried to mitigate the equity issues by either giving them prompts that they could edit and use in HopGPT or giving them access to GPTs I created for my engineering students working on senior design. Every time I create a GPT for students, I hire 2-3 people to try out the GPT at least 3 times. At least one of those times has to be using the free version of ChatGPT. I made up a rubric to evaluate GPTs and so I can see where the GPT might not work well for someone with a free account before I launch it so I can edit it. For now, that’s the best I can do I think. I’m going to be working on porting over my GPTs to another tool during the next few months in the hopes of resolving this and other issues.

Q: Maybe [the AI Gatekeeper] could be addressed as part of the introductions for all classes.
II: I love that idea.  Students are harming themselves [with their unregulated approach to using AI] and they don’t know it.

CE: A question for Emily and Illysa: both of you have had students critically reflect on the use of AI. What would you recommend for faculty who haven’t done that yet?
II: It is important to decide for yourself and your course where the lines are. Ask yourself, what do students need to be able to do? From there, draw the lines. Then talk to the students about what happens if they go outside of the lines.
EF: I agree. In another course, I showed students data from a paper about what happens when you use AI summaries vs. a web search. Participants in the experiment learned less overall with the AI summaries.
II: Also, in my opinion, students are overloaded with classes, credits, and extracurriculars. It is costing them the ability to reflect and learn. So we have to help them and ourselves by planning ahead on what we want to turn to AI for and what we want to do on our own. Without clear guidance from faculty, students are never sure if they are doing something they should hide. This harms trust. Make the lines clear, relate them to your learning outcomes, and then teach them Total AI Transparency. You don’t have to hide what you’re allowed to do!

CE: Emily, the case study you brought up was on metacognition. It was an opportunity for students to practice metacognition. Illysa, did you also do this?
II: Yes – anytime students used AI, they had to explain why they chose to use it, why they made those choices, and why they used or didn’t use the output. They had to evaluate the output compared to their original goal. They weren’t just doing it, they were explaining why and how. The point is to teach them discernment.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Lunch and Learn logo, Unsplash

Active Learning Cards and Online Resource

Posted on November 4, 2025 by abrusini

On Monday, October 6, 2025, the Center for Teaching Excellence and Integration (CTEI) hosted a Brown Bag Lunch and Learn featuring the release of the Center’s Active Learning Cards and Online Resource. Caroline Egan, Teaching Academy Program Manager and Lecturer in the University Writing Program, developed the content and authored the cards. Beth Hals, Senior Instructional Technologist in the CTEI, created the online resource as well as the extension elements for each of the strategies. Both Caroline Egan and Beth Hals facilitated the session, which was hybrid, with both in-person and online participants.

Egan and Hals began the presentation by asking audience members why they might consider using active learning, which at a basic level is an evidence-based instructional approach that engages students directly in the learning process (Cavanagh, 2023). Audience responses included: engaging students, making classes more fun, and encouraging students to drive their own learning. Egan shared additional research about active learning, including how it improves learning and grades, reduces equity gaps, and lowers drop, fail, and withdraw (DFW) rates in STEM classes. She also noted that active learning can be used in any class modality (online, hybrid, in-person, etc.) and with any class size.

The session was very interactive, with Egan and Hals demonstrating the use of several active learning strategies throughout the workshop, pointing out the strategies used and explaining how they had been modified for a hybrid delivery format. For example, at one point, audience members were asked to think of a favorite active learning activity from a course, workshop, or training and post their responses to Padlet, an online collaboration tool used to creatively display visual content. This led to a lively share-out among audience members who were interested in hearing about their peers’ experiences with the strategies they shared, as well as interest in Padlet. Audience responses included: a gallery walk, think-pair-share, group work, brainstorming, case studies, peer instruction, stations, and a jigsaw activity.

Despite evidence-based support for active learning, Egan and Hals acknowledged that faculty sometimes face barriers during implementation. Using an online survey in Microsoft Forms, Hals asked audience members to select the top three barriers to using active learning. Survey results and suggestions for overcoming those barriers were then shared:

Time constraints: Depending on the activity, active learning can sometimes take more time to plan and implement than a traditional lecture. A suggestion was made to start small; try incorporating one strategy into one lesson and build from there.
Lecture/content heavy course: Faculty often feel pressured to cover vast amounts of content and feel that they won’t be able to cover everything if they implement active learning. Suggestions include using strategies to encourage reflection, rehearsal, or application of concepts and/or short knowledge checks throughout the semester to check for understanding.
Predeveloped course: Faculty sometimes inherit a predeveloped course and don’t always feel comfortable making changes. A suggestion was made to have a conversation with others in the department or program, including their teaching and learning center, about how active learning might be integrated into the course and where there might be room for customization.

The second half of the session was dedicated to introducing the active learning cards and the corresponding online resource. The set of cards includes sixteen strategies, each with a description of an activity that can be used immediately or planned over a longer period of time. Each card also includes information about preparation time, student engagement time, debrief time, and whether the activity is best suited for individuals, pairs, or groups. The interactive online resource includes the information from each of the cards, as well as additional information about how to implement these strategies in different modalities, helpful tips, potential extensions or modifications, and recommended technology tools to support these strategies. Audience members were given plenty of time to review the cards and explore the online component before taking part in a few activities including a think-pair-share exercise and a small group activity.

The workshop concluded with the presenters sharing information on how to access the cards and online resource which are both available online from the CTEI website. The cards can be downloaded as a PDF or printed professionally in bulk by an external printing service (for a fee). If you are a JHU instructor or graduate student with teaching responsibilities, please feel free to drop by our center for a free set of cards or contact us at ctei@jhu.edu if you would like them mailed to you.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Lunch and Learn logo, Padlet, MS Forms, CTEI website

References:

Cavanagh, S. R. (2023, September 18). How to make your teaching more engaging. The Chronicle of Higher Education. https://www.chronicle.com/article/how-to-make-your-teaching-more-engaging/

Faculty Sharing Session: Best Practices in Course Design

Posted on April 29, 2025 by abrusini

Four Johns Hopkins faculty recently attended the National Effective Teaching Institute workshop. At a Center for Teaching Excellence and Innovation discussion this spring, each faculty member shared lessons learned as they presented a quick overview of a main topic in the workshop.

Student Motivation

Liz Walker, JHU Liz Walker, a lecturer in the Center for Leadership Education, shared that the workshop began with discussion of student motivation as a spectrum based on the article, Investigating the motivational behavior of pupils during outdoor science teaching within self-determination theory, in Frontiers in Psychology.

The workshop discussion used Self-Determination Theory as a framework to understand motivation from several angles.

intrinsic motivation: learning for enjoyment and personal satisfaction
identified regulation: learning because it aligns with personal goals
introjected regulation: learning due to internal pressures, such as guilt or social approval
external regulation: learning controlled by external rewards or punishments

The discussion also described how motivation contains three elements: relatedness, autonomy, and competence.

Relatedness happens on multiple levels: making connections to others, learning from and teaching others, and knowing how the work affects others. Consider these questions as you motivate students through relatedness:

How do your students connect with each other?
How do you connect with your students?
How do you help your students make connections to engineering practice?
How do you connect what you are teaching to society?
How do some professors discourage relatedness?

Autonomy is both implicit and explicit. You can engender autonomy by helping students feel like they are allowed to do something or feeling like it is OK to make choices. Discussion questions on autonomy included

In what ways do you let your students make choices?
In what ways do you let your students control what happens in the classroom?
Are there ways that professors communicate that the students aren’t in control?

Competence is having the knowledge, skills, and abilities to succeed. Students must have the confidence that they are competent. As you plan your lessons,

How do you make sure students are able to do what you ask of them?
How do you develop their confidence?
Are there things you do that make students lose confidence?

The main takeaway of the NETI section on motivation is that motivation is important for learning. To increase student motivation, instructors should ask themselves

Are my students able to do what I’m asking them to do?
How can I give them choices?
How can I help them make connections to what I’m teaching?

Active Learning

Marina Choy, a lecturer in the Center for Leadership Education, discussed active learning, another theme explored in the workshop. The workshop facilitators defined active learning as “students doing anything in class to learn material, other than Marina Choy, JHU listening to the instructor and taking notes.” Research shows active learning is more effective than lecturing. It helps students learn better through activity and engagement. It falls on a continuum ranging from instructor-focused to student-focused learning. Instructor-focused means high instructor control and low student autonomy. An example of that is an active lecture, where traditional lecturing is interspersed with engaging activities. Student-focused means high student autonomy and low instructor control. Examples include problem-based and project-based learning, which require critical thinking, collaboration, and problem-solving. Shared responsibility is in the middle of the continuum. Examples include structured discussions, guided problem-solving, etc.

Other examples of active-learning discussed include

One-minute paper
Think-Pair-Shares
Start of class recap
Polling or low-stake quizzes
Asking students to generate questions
Muddiest Point
Peer Reviews

The presenters also shared common mistakes in implementing active-learning along with how to avoid them.

Always calling on volunteers. This creates a participation gap; the same students will participate while others observe. As an alternative, use cold-calling, warm-calling, use think-pair-share, or use a randomized system.
Waiting for everyone to finish. Some students will finish quickly and lose engagement, others will feel pressure or rushed. As an alternative, use a reasonable time limit and let students know how much time they have, and/or consider pairing up students to work on the task together.
Using trivial activities. Activities that lack depth are often perceived as busywork and may reduce student motivation. Active learning is not about entertaining students but engaging them meaningfully. Ensure activities have a clear purpose and are aligned with your learning objectives. Ask: does this activity challenge students to think, analyze, or apply knowledge?

Learning Objectives

Ali Madooei, an associate teaching professor in computer science, discussed learning objectives. He developed an AI-based application (OPENAI API Key required to use) to help instructors write learning objectives that are S.M.A.R.T. and motivated by Bloom’s Taxonomy. Ali Madooei, JHU The purpose of learning objectives is to

provide students with clear expectations about what they should learn and help them track their own academic progress.
guide instructors in aligning course design, assessments, and activities with learning objectives.
enable programs to map learning goals across courses and identify curriculum gaps or overlaps.
allow institutions to measure program effectiveness and make evidence-based improvements to student learning.
help accrediting bodies assess program quality and educational standards.

Assessment

Sara More, an associate teaching professor in computer science, talked about Sara More, JHU assessment. Assessment is gathering data about the learning process. It is more than just evaluation, where instructors collect data for the purpose of making evaluative and pass/fail judgments. Assessment helps the faculty member facilitate the learning process for students which includes providing feedback to help them improve.

More discussed three categories used to classify course-based assessments.

Diagnostic – Diagnostic assessments help instructors determine what students already know, and what misconceptions they are starting with so instructional plans can be tailored to meet student needs. Methods to support diagnostic assessments include the following.
- Consider giving an early quiz on prerequisite knowledge
- Announce in advance that the quiz will be counted in the course grade so it is taken seriously
- Provide a study guide with learning objectives and practice problems
- Link to resources for students who feel underprepared
- Hold office hours where students can come to discuss practice problems they are not able to solve
Formative – Formative assessments help instructors measure student progress during the learning process. Effective learning takes place when students engage in a cycle involving practice, feedback, more practice, and more feedback. Examples of formative assessment include the following.
- In-class formative assessment (e.g., clicker questions – think/pair/share, minute papers, evaluate a sample solution using provided rubric, active learning involving discussion)
- Out-of-class formative assessment (e.g., homework, draft outlines of solutions, online quizzes)
- Formative assessment to improve instruction (e.g., mid-semester evaluation, classroom observation)
Summative – Summative assessments are used when instructors are evaluating (e.g., assigning grades) student work. A summative assessment can also be considered formative if it is low stakes and helps students prepare for more significant assessments. Examples of summative assessment include the following.
- In-class exams
- Take-home exams
- Essays
- Case studies
- Projects

If you are interested in learning more about these topics, consider attending the Johns Hopkins Best Practices in University Teaching workshop or the NETI workshop.

Mike Reese
Associate Dean of the Center for Teaching Excellence and Innovation and Associate Teaching Professor in Sociology, Johns Hopkins University

Image source: JHU Whiting School of Engineering website, Pixabay

Quick Tips: Low to No Prep Classroom Activities

Posted on August 30, 2024 by abrusini

Student engagement is a critical component of higher education and a frequent topic of interest among instructors. Actively engaging students in the learning process helps increase motivation, supports collaboration, and deepens understanding of course material. Finding activities that instructors can implement quickly while also proving worthwhile to students can be a challenge. I recently attended a conference with a session titled, “Low to No Prep Classroom Activities.” Jennifer Merrill, psychology professor from San Mateo County Community College, shared some simple classroom activities that require very little or no preparation ahead of time that I thought were worth sharing:

Music:
Playing music as students enter the classroom creates a shared experience which can encourage social interaction, inspire creative thinking, and lead to positive classroom dynamics. It can be used as an icebreaker, to set a particular mood, or specifically relate to the course in some way. Research shows that music stimulates activity in the brain that is tied to improved focus, attention, and memory.

Incorporate music as part of a regular classroom routine to indicate that it’s time to focus on the upcoming lesson.
Use it to introduce a new topic or review a current or past topic. Ask students to articulate how they think the music/artist/song relates to the course material and then share with the class.
Allow students to suggest/select what type of music they would like to hear.

Academic Speed Dating:
Like traditional speed dating, academic speed dating consists of short, timed conversations with a series of partners around a particular topic. In this case, students are given a prompt from the instructor, briefly discuss their response with a partner, and then rotate to a new partner when the time is up. Partners face each other in two lines, with one line of students continuously shifting through the other line until they return to their original partner. This can also be done by having students form inner and outer circles, instead of lines. A few of the benefits of academic speed dating include:

Sharing and questioning students’ own knowledge while gaining different perspectives on a topic.
Enhancing communication skills as students learn to express their ideas quickly and efficiently.
Providing a safe space to share ideas as students interact with others, which can lead to a positive classroom climate.

Memory:
The classic “Memory Game” consists of a set of cards with matching pairs of text or images. Cards are shuffled and placed face down; players take turns turning over 2 cards at a time, trying to find matching pairs. In this version, students take part in creating the cards themselves, using index cards, before playing the game. Memory can be used to reinforce learning and enhance the retention of course material.

Suggested steps for implementation:
1. On the board, the instructor lists 10 terms or concepts related to the course in some way.
2. Students are divided into groups of no more than 5 people. Each student in the group selects 2 terms/concepts from the list.
3. Using index cards, students write the name of the term/concept on one card, and an example of the term/concept on another card (e.g., “supply and demand” and “gasoline prices rising in the summer with more people driving”). Examples could also include images, instead of text.
4. When the groups are finished creating their sets of cards, they exchange their cards with another group and play the game, trying to match as many pairs as they can.

Use Memory to review definitions, formulas, or other test material in a fun, collaborative environment.
Enhance cognitive skills, such as concentration, short-term memory, and pattern recognition.
Facilitate team building skills as students work in groups to create and play the game.

Pictionary:
In this version of classroom Pictionary, students are divided into groups that are each assigned a particular topic. Each group is tasked with drawing an image representation of their topic, e.g., “Create images that represent the function of two glial cells assigned to your group.” Ideally, it works best if drawings are large enough to be displayed around the classroom, such as on an easel, whiteboard, or large Post-it note paper. When each group is finished with their drawings, all students participate in a gallery walk, offering feedback to the other groups. Facilitate a small or whole group discussion to reflect on the feedback each group received.

Enhance problem solving skills and creativity by asking students to think critically about how to represent information visually.
Use Pictionary to get students up and moving around the classroom, which will help keep them actively engaged with course content.
Help students develop constructive feedback skills as they participate in the gallery walk part of the activity.

Hawks and Eagles:
This activity is a version of “think-pair-share” that gets students up and moving around the classroom.

Suggested steps for implementation:
1. Students pair with someone nearby and decide who will be the Hawk and who will be the Eagle.
2. Give all students a prompt or topic to discuss and allow them time to think about their response (1-3 minutes).
3. Students share their responses with their paired partner (1-3 minutes).
4. Ask Hawks to raise their hands. Ask the Eagles to get up and go find a different Hawk.
5. Students share their responses with their new partner.
6. Repeat steps 4 and 5, if desired, to allow students to pair with multiple partners.
7. Debrief topic with the whole class.

Use Hawks and Eagles as an icebreaker activity for students to introduce and get to know one another.
Use this activity as a formative assessment to gauge student comprehension of a particular topic.
Expose students to multiple perspectives or viewpoints on a particular topic by having them engage with multiple partners.

IQ Cards:
IQ cards (“Insight/Question Cards”) is an exit ticket activity that acts as a formative assessment strategy. At the end of a class or unit, ask students to write down on an index card any takeaways or new information they have learned. On the other side of the card, ask them to write down any remaining questions they have about the lesson or unit. Collect student responses and share their “insights” and “questions” with the class at the next meeting.

Gather instant feedback from students and quickly assess their grasp of the material, noting where any changes or adjustments might be needed.
Reinforce knowledge by asking students to recall key concepts of the lesson or unit.
Use IQ Cards as a self-assessment activity for students to reflect on their own learning.

Do you have any additional low or no prep activities you use in the classroom? Please feel free to share them in the comments. If you have any questions about any of the activities described above or other questions about student engagement, please contact the CTEI – we are here to help!

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

References:
Baker, M. (2007). Music moves brain to pay attention, Stanford study finds. Stanford Medicine: News Center. Retrieved August 26, 2024, from https://med.stanford.edu/news/all-news/2007/07/music-moves-brain-to-pay-attention-stanford-study-finds.html

Image source: Jennifer Merrill, Pixabay

The Teaching Fellows Project: Community-based Learning in Baltimore City Schools

Posted on July 15, 2024 by abrusini

[Guest post by Katharine Noel, Associate Teaching Professor, Writing Seminars, Johns Hopkins University]

As a grad student, I was given three days of teacher training before being thrown into a classroom. This was a composition class with fifty first-year students. My pedagogical training – if you can call it that – included how to grade on a curve and how to confront cheating and plagiarism without using the words “cheating” or “plagiarism,” a dodge that would supposedly protect the university from lawsuits. The focus on school protocols implied that teaching itself would be straightforward. After all, as grad students, we were working at advanced levels; shouldn’t imparting a simplified version of our subject be easy?

Without any real idea of how to convey my “expertise,” I thought back to my own professors. I wanted, of course, to emulate the best of them, teachers who’d connected with their students, creating excitement while at the same time expressing warmth and engagement. And yet I’d learned just as many lessons – possibly more – from the ineffective teaching I’d observed: the professor who was charismatic and funny but seemed more concerned with making the students adore him than helping them engage with the subject. Or the one who started discussions with questions so long and complicated – filled with backtracking, amendments, and counterarguments – that by the time she was finished, students had no idea what they were meant to discuss. Teachers who seemed dismissive or defensive. Teachers too rigid to pivot; teachers too lax to have an overarching plan.

The Teaching Fellows and WBS mentors outside of a building in Baltimore.

The Teaching Fellows and WBS mentors on a teaching field trip to Baltimore Youth Arts, a creative entrepreneurship and job training program focusing on young people involved in the justice system

Recently, I’ve been reminded of this early teaching experience. At Hopkins, I teach in The Writing Seminars department. In addition to standard creative writing courses, for the last five years I’ve taught a community-based learning class in partnership with the nonprofit organization Writers in Baltimore Schools (WBS), founded by Hopkins alum Patrice Hutton. Thanks to a grant from CTEI, this past year we expanded the class to become the year-long Teaching Fellows Project.

The Teaching Fellows – twelve undergraduates chosen by application from across KSAS – lead weekly creative writing groups in under-resourced elementary and middle schools across the city. The class they take with me provides training and support, as well as the opportunity to work closely with WBS mentors. We study topics like student-centered pedagogy, educational equity, and the social and political context in which Baltimore schools operate. Writers in Baltimore Schools provides curriculum for teaching elementary and middle-school writing groups, but near the end of the year, each Hopkins student creates, teaches, and revises an original lesson plan based on the interests and needs of their students.

A guest lecture by Ms. Araba Maze, founder of Storybook Maze, which aims to increase book access in Baltimore’s book deserts

The CUE-2 final report stresses the importance of community-based and applied learning, stating that “we should aspire to transform the college experience from one composed solely of traditional elements – lectures, papers, problem sets, and exams – to one in which these elements sit amid a much broader range of learning activities.” The Teaching Fellows Project is designed to provide this kind of community-based and applied learning opportunity, connecting academic theory to real-world impact. Students bring what they learn in our classroom to their worksites, and bring what they learn in their worksites back to our classroom. Knowledge gained from meaningful engagement in the community is deepened by the knowledge gained through reading, discussions, and guest lecturers, and vice-versa. At times, it all feels very meta: as I teach students to teach, I also point out the techniques and tactics I’m using as a teacher; as students encounter challenges at their worksites, I adjust my curriculum to address those topics that feel most relevant and vital.

One of the first exercises the Teaching Fellows do, based on my own early groping for models, is to think back to their best teachers and try to isolate what made them so effective. Some of these “teachers” are the ones they had in classrooms; others turn out to be coaches, bosses, ministers, co-workers, or relatives. The next week, we repeat the same exercise by discussing frustrating learning experiences. The complicated interwoven skills of teaching can be most visible when they work together imperfectly, since excellent teachers achieve a kind of magic that means – as with any magician – their moves can be too deft to make out.

My scant training as a grad student seemed predicated on the idea that teaching could and should be easy. What the WBS mentors and I try to convey in The Teaching Fellows Project is very different: that teaching is often hard, confounding, overwhelming, and awkward – as well as thrilling, meaningful, revealing, and joyous. It can even – at rare moments – feel nearly effortless.

Katharine Noel
Associate Teaching Professor, Writing Seminars
Johns Hopkins University

Prior to coming to Johns Hopkins, Katharine Noel was the Writer in Residence at Claremont McKenna College (2009 to 2013). From 2002-2009, she was a Jones Lecturer at Stanford University, where she held Wallace Stegner and Truman Capote fellowships in 2000–2002. Before teaching at Stanford, she worked for two years at Gould Farm, a program in the Berkshire Mountains for adults with mental illnesses, and for four years at an Oakland, California shelter for women and children experiencing homelessness. At Hopkins, in addition to teaching fiction writing, she directs The Teaching Fellows Project.

Image Source: Writers in Baltimore Schools

Lunch and Learn: Generative AI Uses in the Classroom

Posted on May 15, 2024 by abrusini

On Tuesday, April 23rd, the Center for Teaching Excellence and Innovation (CTEI) hosted a Lunch and Learn on Generative AI Uses in the Classroom. Faculty panelists included Louis Hyman, Dorothy Ross Professor of Political Economy in History and Professor at the SNF Agora Institute, Jeffrey Gray, Professor of Chemical and Biomolecular Engineering in the Whiting School, and Brian Klaas, Assistant Director for Technology and instructor at the Bloomberg School of Public Health. Caroline Egan, Teaching Academy Program Manager, moderated the discussion.

Louis Hyman began the presentation by reminding the audience what large language models (LLMs) like ChatGPT can and cannot do. For example, ChatGPT does not “know” anything and is incapable of reasoning. It generates text that it predicts will best answer the prompt it was given, based on how it was trained. In addition to his course work, Hyman mentioned several tasks he uses ChatGPT to assist with, including text summarization, writing complicated Excel formulas, writing and editing drafts, making PowerPoint tables, and turning image files in the right direction.

In Hyman’s course, AI and Data Methods in History, students are introduced to a variety of tools (e.g., Google Sheets, ChatGPT, Python) that help them analyze and think critically about historical data. Hyman described how students used primers from LinkedIn Learning as well as Generative AI prompts to increase their technical skills which enabled them to take a deeper dive into data analysis. For example, while it would have been too complicated for most students to write code on their own, they learned how to prompt ChatGPT to write code for them. By the end of the semester, students used application programming interface (API) calls to send data to Google, used OpenAI to clean up historical documents and images presented using optical character recognition (OCR), and used ChatGPT and Python to plot and map historical data.

Hyman noted that one of the most challenging parts of the course was convincing students that it was OK to use ChatGPT, that they were not cheating. Another challenge was that many students lacked basic computer literacy skills, therefore, getting everyone up to speed took some time. There was also not one shared computer structure/platform. The successes of the course include students’ ability to use libraries and APIs to make arguments in their data analysis, apply statistical analysis of the data, and ask historical questions about the results they were seeing in the data.

Jeff Gray continued by describing his Computational Protein Structure Prediction and Design course that he has taught for over 18 years. In this course, students use molecular visualization and prediction tools like PyRosetta, an interactive Python-based interface that allows them to design custom molecular modeling algorithms. Recently, Gray has introduced open-sourced AI tools into the curriculum (AlphaFold and RoseTTAFold), which predict 3D models of protein structures.

One of the challenges Gray mentioned was the diversity of student academic backgrounds. There were students from engineering, biology, bioinformatics, computer science, and applied math, among others. To accommodate this challenge, Gray used specifications grading, a grading method in which students are graded pass/fail on individual assessments that align directly with learning goals. In Gray’s class, students were presented with a bundle of problem sets categorized at various difficulty levels. Students selected which ones they wanted to complete and had the option of resubmitting them a second time for full credit. Gray is undecided about using this method going forward, noting that half of the students ended up dropping the course when they tried to complete all of the problems instead of just a few, and found the workload too heavy. Another challenge was how to balance the fundamental depth of the subject matter versus application. To address this, Gray structured the twice weekly class with a lecture on one day and a hands-on workshop the other day, which seemed to work well.

Brian Klaas teaches a one credit pass/fail course called Using Generative AI to Improve Public Health. The goal of this course is to allow students to explore AI tools, gain a basic understanding of how they work, and then apply them to their academic work and research. In addition to using the tools, students discussed the possible harms in Generative AI, such as confabulations, biases, etc., the impact of these tools in Public Health research, and future concerns such as the impact on the environment and copyright law. Klaas shared his syllabus statement regarding the usage of AI tools in class, something he strongly recommends all faculty share with their students.

Hands-on assignments included various ways of using Generative AI. In one assignment, students were asked to write a summary of a journal article and then have GenAI write a summary of the same article geared towards different audiences (academics vs. high school students). Students were then asked to analyze the differences between the summaries. For another assignment, students were asked to pick from a set of topics and use Generative AI to teach them about the selected topic, noting any confabulations or biases present. They then asked GenAI to create a five-question quiz on the topic and take the quiz. A final assignment was to create an Instagram post on the same topic including a single image and a few sentences explaining the topic to a lay audience. All assignments included a reflection piece which often required peer review.

Lessons learned: Students loved the interdisciplinary approach to the course, confabulations reinforce core data research skills, and learning from each other is key.

The discussion continued with questions from the audience:

Q: What would you recommend to an instructor who is considering implementing GenAI in the classroom? How do they start thinking about GenAI?
JG: Jupyter notebooks are pretty easy to use. I think students should just give it a try.
LH: I recommend showing students what ”bad” examples look like. The truth is, we can still write better than computers. Use AI to draft papers and then use it as an editing tool – it’s very good as an editing tool. Students can learn a lot from that.
BK : I recommend having students experiment and see where the strengths lie, get an overall awareness of it. Reflect on that process, see what went well, not so well. Feed in an assignment and see what happens. Use a rubric to evaluate the assignment. Put a transcript in and ask it to create a quiz on that information. It can save you some time.

Q for Brian Klaas: What version of GPT were you using?
BK: Any of them – I didn’t prescribe specific tools or versions. We have students all over the world, so they used whatever they had. ChatGPT, Claude, MidJourney, etc. I let the students decide and allowed them to compare differences.

Q for Jeff Gray: Regrading the number of students who dropped, is the aim of the course to have as many students as possible, or a group who is wholly into it?
JG: I don’t know, I’m struggling with this. I want to invite all students but also need to be able to dig into the math and material. It feels like we just scratched the surface. Maybe offering an intersession course to learn the tools before they take this class would be helpful. There is no standard curriculum yet for AI. Where to begin…we’re all over the map as far as what should be included in the curriculum.
LH: I guess it depends on what your goals are. Students are good at “plug and chug,” but bad at asking questions like, “what does this mean?”
BK: We didn’t get to cover everything, either – there is not enough time in a one credit class. There are just so many things to cover.

Q: What advice do you have for faculty who are not computer scientists? Where should we start learning? What should we teach students?
LH: You can ask it to teach you Python, or how to do an API call. It’s amazing at this. I don’t know coding as well as others, but it helps. Just start asking it [GenAI]. Trust it for teaching something like getting Pytorch running on your PC. Encourage students to be curious and just start prompting it.
BK: If you’re not interested in Jupyter notebooks, or some of the more complicated functions, you can use these tools without dealing in data science. It can do other things. It’s about figuring out how to use it to save time, for ideation, for brainstorming.
JG: I have to push back – what if I want to know about what’s going on in Palestine and Israel? I don’t know what I don’t know. How do I know what it’s telling me is correct?
LH: I don’t use it for history – but where is the line of what it’s good and not good at?
BK: I would use it for task lists, areas to explore further, but remember that it has no concept of truth. If you are someone who knows something about the topic, it does get you over the hurdles.
JG: You have to be an expert in the area to rely on it.
LH: Students at the end of my course made so much progress in coding. It depends on what you ask it to do – protein folding is very different than history that already happened.

Q: How can we address concerns with fairness and bias with these tools in teaching?
BK: Give students foundational knowledge about how the tools work. Understand that these are prediction machines that make stuff up. There have been studies done that show how biased they are, with simple prompts. Tell students to experiment – they will learn from this. I suggest working this in as a discussion or some practice for themselves.

Q: Students have learned to ask questions better – would you rather be living now with these tools, or without them?
JG: Students are brainstorming better. They are using more data and more statistics.
BK: AI requires exploration and play to get good responses. It really takes time to learn how to prompt well. You have to keep trying. Culturally, our students are optimized for finding the “right answer;” AI programs us to think that there are multiple answers. There is no one right answer for how to get there.
LH: Using AI is just a different process to get there. It’s different than what we had to do in college. It was hard to use computers because many of us had to play with them to get things to work. Now it all works beautifully with smart phones. Students today aren’t comfortable experimenting. How do we move from memorization to asking questions? It’s very important to me that students have this experience. It’s uncomfortable to be free and questioning, and then go back to the data. How do we reconcile this?

JG: What age is appropriate to introduce AI to kids?
LH: Students don’t read and write as much as they used to. I’m not sure about the balance.
Guest: I work with middle and high school teachers. Middle school is a great time to introduce AI. Middle school kids are already good at taking information in and figuring out what it means. Teachers need time to learn the tools before introducing it to students, including how the tools can be biased, etc.

Q: How can we encourage creative uses of AI?
BK: Ethan Mollick is a good person to follow regarding creative uses of AI in education and what frameworks are out there. To encourage creativity, the more we expose AI to students, the better. They need to play and experiment. We need to teach them to push through and figure things out.
LH: AI enables all of us to do things now that weren’t possible. We need to remember it’s an augment to what we do, not a substitute for our work.

Resources:
Hyman slides
Gray slides
Klaas slides

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Lunch and Learn logo, Hyman, Gray, and Klaas presentation slides, Unsplash

Lunch and Learn: Active Learning Techniques

Posted on March 20, 2024 by abrusini

On Tuesday, March 12th, the Center for Teaching Excellence and Innovation (CTEI) hosted a Lunch and Learn on Active Learning Techniques: Advice and Guidance from Experienced Faculty. Faculty panelists included Nate Brown, Senior Lecturer, University Writing Program (KSAS); Robert Leheny, Professor and Department Chair, Department of Physics and Astronomy (KSAS); and Michael Falk, Vice Dean of Undergraduate Education and Professor, Department of Materials Science and Engineering (WSE). Caroline Egan, Teaching Academy Program Manager, moderated the discussion.

Caroline began the session by asking panelists how they got started with active learning and what they would recommend to those who were thinking about implementing it.

Nate Brown described how he heavily relied on his lecture notes to deliver content to students when he first started teaching. As he’s gained more experience, he’s moved away from using lecture notes and actively involves students in their learning. Brown structures his classes now in such a way that the students drive the conversation and solve problems collectively, leading to greater retention of information and increased engagement. He makes a point of having students think about “why they are doing what they are doing.”

Robert Leheny recalled how the Provost’s Gateway Sciences Initiative from several years ago provided funding to support the redesign of gateway science courses, such as the Introduction to Physics course, which he teaches. The goal of the Gateway Sciences Initiative was to evolve the pedagogy in large introductory science courses to produce better student outcomes.

The Intro to Physics redesign, which was modeled after similar large lecture style courses at NC State and MIT, moved away from a traditional lecture style to a much more interactive experience. Students are divided into groups and sit at round tables instead of in rows, and they solve problem sets together during class rather than at home. This in-class work is partly enabled by a flipped classroom approach which enables students to review the content before coming to class. Leheny said the department now offers two versions of Introduction to Physics that students can select from: an active learning style and a more traditional auditorium/lecture style.

Michael Falk first started implementing active learning by using clickers in a 200-student Introduction to Computer Programming course at the University of Michigan several years ago. Since he’s been at Hopkins, his classes have been smaller, allowing him to approach active learning in different ways. Falk gave an example of how he flipped an upper-level materials science course (which is now also part of the Gateway Computing program) so that students work in an online textbook outside of class and do more collaborative work during class. Another example is a First-Year Seminar class taught by Falk, Turing’s Shadow: Uncovering What’s Hidden in STEM. This discussion-based course covers a range of topics, some of which are sensitive, and students are often afraid to speak up. To address this, Falk created a series of discussion cards to help ease students’ discomfort. The cards provide discussion prompts for students, such as “Clarification: Ask for further explanation about something,” and they also inject some fun elements into the conversation by asking respondents to present their response “in the form of a song,” or “while walking around the room very quickly,” for example. It turns the exercise into a game and helps students to feel more comfortable participating in class.

Caroline continued by asking the panelists what their definition of active learning is and to provide a counter-example of it, which would bring its definition into better relief.

NB: I think it involves giving students a stake in what we’re doing. For example, helping to define the parameters of a paper we’re going to write. I see the professorial role as one of support, like “air traffic control.” With active learning, students are involved in the creation of their own learning.

RL: The primary component of active learning in physics is peer instruction. Students need to be able to solve problems. We don’t use class time to introduce students to concepts, but instead give students an opportunity to practice solving problems where there are resources to help facilitate these skills. For example, students are divided into groups of three and explain to each other how they would go about solving a problem. The act of explaining the problem to someone else helps to solidify their own understanding. A counter example would be the old way of the instructor speaking from the blackboard, talking uninterrupted for most of the class period.

MF: Active learning is learning by doing. Students are engaging with content in a supportive environment. We are teaching a different group of students at Hopkins now – there are many more first- generation, limited-income, and/or underrepresented students with very different backgrounds. We need to think proactively about leveling the playing field for students. This is evident in the data around class outcomes: classes taught using active learning techniques have lower levels of students failing or dropping out. This is even more true for students in underrepresented groups.

RL: We also see this in Intro to Physics. We have the two versions of the course: one in the auditorium (which may have some active learning elements in it), and one designed specifically as an active learning course. The homework and exams are the same in both courses. The outcomes show that failing grades are much less likely to occur in the active learning course.

MF: I used to think my job as an instructor was to deliver content and material. Now, with active learning, I think my job is to deliver an experience.

Caroline continued by asking panelists for a simple active learning technique that instructors can implement right away.

NB: This may sound crazy – it comes from a writer colleague of mine. I was having students read out loud in class and noticed they were struggling: they didn’t feel comfortable, they were shy, or were experiencing language barriers, etc. I then asked them all to read in chorus (at the same time). No one understood what they were hearing, but it shocked them into it being ok to share. It really helped them get over their nerves.

RL: Think-Pair-Share. This technique works very well in a large lecture environment. We give students a multiple choice question and have everyone vote on the answer. Next, they have to find someone who voted differently and try to convince them to change their answer. We then ask students to vote again. The results are that there are usually more correct answers the second time. You do need good questions for this to be effective.

MF: An idea for STEM classes, figure out a way to shorten your lecture and hand out the problem set at the end of class. Allow students to work on them with each other. Ask students to write down the steps on how they would solve the problem, but not actually solve it. Allow time for a report out at the end. This gives them a chance to support each other while organizing information.

CE: A complimentary Humanities example: In my first-year writing class, I hand out labeled strips of paper to students with our class readings on them and ask them to organize the strips in a way that would help the students use the readings in their papers. Also, I give students writing prompts, break them into groups, and ask them to find out where they would find the answers to the prompts. This helps to get them in the right mindset of locating good sources.

Two other examples of active learning were mentioned by faculty guests. One instructor explained how she has students use Legos to construct the analysis of an argument. They connect more and more Legos to build supportive elements of their argument and take away those that they disagree with. Another instructor mentioned that she has students act out responses in class.

The session continued with questions from the audience for the panelists:

Q: In reference to Think-Pair-Share, have you observed any competitiveness among students or reluctance to participate in these activities?
MF: We tell students it has nothing to do with their grade.
RL: We do the same. We also tell them there is no curve and it is possible for everyone to get an A, which reduces overall competitiveness.
NB: One of the great things about this exercise, where students are engaging with each other, is that they get to hear from peers that are from all over the world. We turn it into a social space where they can feel comfortable sharing.

Q: (From a librarian) I recently had about 30 minutes to work with students in a research class. I received feedback from a student that I didn’t do enough active learning in the class, despite doing a brainstorming exercise with them. What do you do when you need more active learning in such a short amount of time?
RL: Explain to students why you structured the class like you did. It will help if you get their buy-in. Maybe the answer is to announce at the beginning that what you’re doing is in fact active leaning.
MF: Students like playing – it makes for a positive learning experience. Perhaps turn part of it into a game/play. And then explain what and why you’re doing it this way.

Q: Are there any active learning experiences to share when you’re guest lecturing? Do you use the same or different strategies?
MF: It needs to be a different strategy. As a guest, you don’t have the advantage of repetition or control of the environment. Explain to students what you’re doing and do the best you can with the constraints that you’re under.
CE: Be very intentional about your choices. At the end, ask them one thing they will remember from the class. This is a good recall exercise.
NB: As a guest speaker, you already are a bit novel since your presence is different than their regular day. Maybe use a novel activity that they will remember.

Q: Could you each share how you put groups together intentionally instead of having students self-form?
RL: Students are put into groups of three. Groups are engineered this way – we switch a few times during the semester. The students don’t know it, but we add them to groups according to their performance on the midterm. In each group, there is one person that scored at the top, one from the middle, and one from a low level. The top level person gets more practice articulating ideas. The lower level person gets the benefit of working with someone who has command of the material. We also group according to gender: we avoid placing two men and one woman in a group to avoid women being excluded. There is research that supports this.
NB: We also do a lot of group work. Halfway through the semester, I ask students to work with someone they haven’t worked with before. I also ask them to sit next to someone different. It results in a richer peer review experience.
MF: I have students do a self-assessment at the very beginning of the course and use the results of the assessment to group students.

For more information about the active learning topics discussed at the event, please see this Active Learning For Distribution folder of materials developed by Caroline Egan.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image source: Lunch and Learn logo, Unsplash, Pixabay

Preparing to Teach: Lessons from a Gamemaster

Posted on March 6, 2024 by abrusini

Once upon a time, just before the age of COVID-19, I was asked to teach a course for an undergraduate minor program through JHU’s Center for Leadership Education. I began my journey to teaching my first course by meeting the program director for lunch. After the proposition, it became clear to me that there was sparse content for this new course on data visualization, and it would be up to me to develop and deliver it the following semester. With only a few months to prepare, I quickly realized that my quest to create something from nothing would be full of challenges that might result in a perilous journey, but the bounties were rumored to be plentiful.

When I started to delve into the instructional design of my course including developing content, setting learning goals, and creating a syllabus, I was delighted by the discovery that there are many parallels between designing a course and creating an adventure for Dungeons and Dragons (D&D). D&D is a type of role-playing game where players take on roles of fictional characters and attempt to complete a fantasy adventure designed and delivered by the Dungeon Master (I will use the term “gamemaster” for its broader applicability). In a D&D adventure, the gamemaster introduces the fantasy world to the players and presents challenges for them to overcome by performing actions as their characters. As a gamemaster, I realized that I had resources that would help me structure the course, plan activities, and engage my students.

Learning Goals vs Plotline

One of the first parallels I found was that the learning goals provided a structure much like plot elements would provide a structure to a D&D adventure. The gamemaster tells stories and sets the stage for the players to interact, and, with the stories, they build on each other to an eventual climactic event. In both cases, you have to sequence the elements in a logical way that builds up to the desired result such as a learning goal or a successful adventure. For instance, one of my learning goals was to have students apply visual design principles to different types of presentations of data. I invested time up front in my course structure to ensure students knew a variety of data visualization types, could identify design principles that work for each, and had practice applying those design techniques. Ensuring your participants are adequately prepared for their true test is important, whether it be acing their final project or slaying an evil dragon.

Activities vs Encounters

Planning activities for each class felt closely related to the gamemaster’s balancing act of creating encounters for their players. In D&D, each player controls a character with specific abilities set by their current level. As a character gains experience, they unlock more abilities as they reach the next level. The gamemaster has to make sure that at each level, the challenge of each encounter is commensurate with the players’ levels to keep them engaged.

For many weeks in the course, I highlighted a topic that would span two class periods that bookended the weekend. Before the first day, there would be a reading to introduce the topic, followed by a lecture at the beginning of the first day to expand on the topic (the background). Then, the students would work on an activity in class, most times in groups, that utilized the concepts presented in the introduction (an encounter). At the end of the activity, we would chat about the results and the related assignment that would be due the following week (rest and reflect). During that weekend, the students would have a short reading that was relevant to the topic and would complete their assignment (continued journey). On the second day for that topic, we would begin the class with a zero-stakes quiz that was based on the readings and mini lecture (another encounter). We then discussed the assignment submissions in a class critique, offering feedback and best practices in a safe setting (the aftermath). The last portion of the class would expand on the topic with one last activity on the topic (gain experience).

Being a Good Host

Just like sitting down for a game of D&D, when teaching your class, you are welcoming students into your space. It’s not a space you own, but it is one in which you have control over the tone and the proceedings. As a good host, whether for a dinner party, a classroom activity, or a D&D adventure to clear out a cave of kobolds, you must be aware of how your guests are responding to the experience. In the case of D&D, that means being aware of how each player is interacting and contributing to the story you are building together. From the classroom perspective you should be similarly mindful of student engagement and progress. You can achieve this not only with summative assessments (the results from quizzes, assignments, etc.) but also formative assessments (ungraded quizzes, surveys, etc.). For example, a mid-semester survey can help inform you of what the students are enjoying about the class, what could make it better, and any issues with the content that they are having trouble with.

Side Quests

The concept of a Side Quest in gaming refers to an optional task to achieve a supplemental benefit for your character. I used this concept to offer extra-credit assignments that would allow the students to gain bonus points towards assignments, participation, or the final project. The Side Quests provided the opportunity for the students to reengage with the content, give them more data visualization practice, or reflect deeper on topics. The following are examples of a few of my favorite Side Quest assignments:

“Find the Gestalt!”: Students find a data visualization and describe what gestalt technics were used and where. This provided more practice identifying technics in the wild.
“You be the Instructor!”: Students develop up to five challenging quiz questions from the course content that had accurate answers. This allowed them to think deeper about a topic.
“Journal of the Journey!”: Students submit pages from their class notes/sketchbook. This incentivized them to record tidbits from class that they found interesting, which gave me feedback on the parts of the course that resonated with the students.

Final Thoughts

D&D helped me to pull from years of experience as a gamemaster. In the end, as long as you are thoughtfully guiding your participants/students/adventurists to new heights through balanced challenges, they will all surely level up to be ready for their next adventure.

Reid Sczerba, Digital Solutions Designer
Center for Teaching Excellence and Innovation

Image Source: Reid Sczerba, Pixabay

This blog post was adapted from the full article, “Lessons from a Gamemaster,” which is part of our printed Innovative Instructor series.

Lunch and Learn: Generative AI – Teaching Uses, Learning Curves, and Classroom Guidelines

Posted on November 9, 2023 by abrusini

On Tuesday, October 3rd, the Center for Teaching Excellence and Innovation (CTEI) hosted its first Lunch and Learn of the academic year, a panel discussion titled, “Generative AI: Teaching Uses, Learning Curves, and Classroom Guidelines.” The three panelists included Jun Fang, Assistant Director of the Instructional Design and Technology Team in the Carey Business School, Carly Schnitzler, KSAS instructor in the University Writing Program, and Sean Tackett, Associate Professor in the School of Medicine. The discussion was moderated by Caroline Egan, project manager in the CTEI. Mike Reese, director of the CTEI, also helped to facilitate the event.

The panelists began by introducing themselves and then describing their experiences with generative AI. Jun Fang loves new technology and has been experimenting with AI since its inception. He noticed the faculty that he works with generally fall into two categories when it comes to using AI: some are quite concerned about students using it to cheat and are not ready to use it, while others see a great deal of potential and are very excited to use it in the classroom. In speaking with colleagues from across the institution, Fang quickly realized these are common sentiments expressed by faculty in all JHU divisions. This motivated him to lead an effort to create a set of AI guidelines specifically geared toward faculty. The document contains a number of strategies for using AI including: designing engaging course activities, providing feedback for students on their assignments, and redesigning course assessments. The section on redesigning course assessments uses two approaches: the “avoidance approach,” which involves deliberately designing assessments without AI, and the “activation approach,” which intentionally integrates AI tools into the curriculum. The document includes specific examples of many of the strategies mentioned as well as links to widely used generative AI tools.

Fang described a recent scenario in which a faculty member was concerned that students were using ChatGPT to generate answers to online discussion board questions. To mitigate this situation, Fang suggested the faculty member revise the questions so that they were tied to a specific reading or perhaps to a topic generated in one of his online synchronous class sessions. Another suggestion was to have students submit two answers for each question – one original answer and one generated by ChatGPT – and then have the students compare the two answers. The faculty member was not comfortable with either of these suggestions and ended up making the discussion more of a synchronous activity, rather than asynchronous. Fang acknowledged that everyone has a different comfort level with using AI and that one approach is not necessarily better than another.

Carly Schnitzler currently teaches two introductory writing courses to undergraduates and is very open to using generative AI in her classroom. At the start of the semester, she asked students to fill out an intake survey which included questions about previous writing experiences and any technologies used, including generative AI. She found that students were reluctant to admit that they had used these technologies, such as ChatGPT, for anything other than ‘novelty’ purposes because they associated these tools with cheating. After seeing the results of the survey, Schnitzler thought it would be beneficial for students to explore the potential use of generative AI in class. She asked students to do an assignment where they had to create standards of conduct in a first year writing class, which included discussing their expectations of the course, the instructor, their peers, and how AI would fit in among these expectations. The class came up with three standards:

AI tools should support (and not distract from) the goals of the class, such as critical thinking, analytical skills, developing a personal voice, etc.
AI tools can be used for certain parts of the writing process, such as brainstorming, revising, or editing, but students must disclose that AI tools were used.
If there appears to be an over-use or over-reliance on AI tools, a discussion will take place to address the situation rather than disciplinary action. (Schnitzler wants students to feel safe exploring the tools without fear of repercussion.)

This assignment comes from an open collection of cross-disciplinary assignments that use text generation technologies, mostly in a writing context. TextGenEd: Teaching with Text Generation Technologies, co-edited by Schnitzler, consists of freely accessible assignments submitted by scholars from across the nation. Assignments are divided into categories, such as AI literacy, rhetorical engagements, professional writing, creative explorations, and ethical considerations. Most are designed so that the technologies used are explored by students and instructors together, requiring very little ‘expert’ technological skills. Schnitzler noted that there is a call for new submissions twice each year and encouraged instructors to consider submitting their own assignments that use text generation AI.

Sean Tackett was initially fearful of ChatGPT when it was released last year. Reading article after article stating how generative AI was going to “take over” pushed him to learn as much as he could about this new technology. He began experimenting with it and initially did not find it easy to use or even necessarily useful in his work with medical school faculty. However, he and some colleagues recognized potential in these tools and ended up applying for and receiving a JHU DELTA grant to find ways they could apply generative AI to faculty development in the medical school. Tackett described how they are experimenting with generative AI in a curriculum development course that he teaches to the med school faculty. For example, one of the tasks is for faculty to learn to write learning objectives, so they’ve been developing prompts that can be used to specifically critique learning objectives. Another example is developing prompts to critique writing. Most of Tackett’s students are medical professionals who do not have a lot of time to learn new technologies, so his team is continually trying to refine prompts in these systems to make them as useful and efficient as possible. Despite being so busy, Tackett noted the faculty are generally enthusiastic about having the opportunity to use these tools.

The discussion continued with a question and answer session with audience members:

Q: How do we transfer and integrate this knowledge with teaching assistants who help manage the larger sized classes? What about grading?
ST: I would advocate for the potential of AI to replace a TA in terms of grading, but not in terms of a TA having a meaningful dialogue with a student.
JF: Generative AI tools can be used to provide valuable feedback on assessments. There are a lot of tools out there to help make grading easier for your TAs, but AI can be used for the feedback piece.

Q: How might professors provide guidelines to students to use generative AI to help them study better for difficult and complex topics?
MR: One possibility is to generate quiz questions – and then have students follow up by checking the work of these quizzes that have been generated.
CS: Using a ChatGPT or other text generation tool as a reading comprehension aid is something that has been useful for non-native English speakers. For example, adding a paragraph from an academic article into ChatGPT and asking what this means in plain language can be helpful.

CE: This gets to what I call ‘prompt literacy,’ which is designing better prompts to give you better answers. There is a very good series about this on Youtube from the University of Pennsylvania.
Sean, what have you experienced with prompting right now, in terms of challenges and opportunities?
ST: We’re trying to put together advice on how to better prompt the system to get more refined and accurate answers. After a few iterations of prompting the system, we refine the prompt and put it into a template for our faculty, leaving a few ‘blanks’ for them to fill in with their specific variables. The faculty are experts in their subject areas, so they can tell if the output is accurate or not. We’re in the process of collecting their output, to put together best practices about what works, what does not work.

CE: What would you all like to see in terms of guidelines and best practices for AI on a web page geared towards using AI in the classroom?
Guest: And along those lines, how to we move forward with assigning research projects, knowing that these tools are available for students?
ST: I think it could be useful for students to learn research skills. They could use the tools to research something, then critique the results and explain how they verified those results. It can also be useful for generating ideas and brainstorming. Another thought is that there are a number of domain specific generative AI databases, such as Open Evidence which is useful in the medical field.
CS: To Sean’s point, I think a comparative approach is useful with these tools. The tools are very good at pattern matching genre conventions, so doing comparative work within a genre could be useful.
JF: I think ChatGPT and other generative AI tools can be useful for different parts of the research process, such as brainstorming, structure, and editing. But not for something like providing or validating evidence.

Q: As a grad student, I’m wondering how the presence of AI might force us to refine the types of questions and evaluations that we give our students. Are there ways to engineer our own questions so that the shift of the question is changed to avoid the problem [of having to refine and update the question] in the first place?
CS: There is an assignment in our collection that talks about bringing an assignment from past to present. Again, thinking in terms of a comparative approach, ask ChatGPT the question, and then ask your students the same question and see how they compare, if there are any patterns. I think it can be helpful to think of ChatGPT as adding another voice to the room.
JF: We have a section in the guidelines on how to redesign assessment to cope with generative AI related issues. We suggest two approaches: the avoidance approach and the activation approach. The avoidance approach is for faculty who are not yet comfortable using this technology and want to avoid having students use it. One example of this approach is for faculty to rework their assignments to focus on a higher level of learning, such as creativity or analysis, which will hopefully reduce or eliminate the opportunity for students to use AI tools. The activation approach encourages faculty to proactively integrate AI tools into the assessment process. One example of this approach I mentioned earlier is when I suggested to a faculty member to rework their discussion board questions to allow students to submit two versions of the answers, one created by them and the other by ChatGPT, and then analyze the results.

Q: What is the ultimate goal of education? We may have different goals for different schools. Also, AI may bridge people from different social backgrounds. In China, where I grew up, the ability to read or write strongly depends on the social status of the family you come from. So there is some discomfort using it in the classroom.
CS: I feel some discomfort also, and that’s what led to the development of the guidelines in my classroom. I posed a similar question to my students: if we have these tools that can allegedly write for us, what is the point of taking a writing class? They responded by saying things like, “writing helps to develop critical thinking and analytical skills,” to which I added, “being here is an investment in yourself as a student, a scholar, and a thinker.” I think asking students to articulate the value of the education that they want to get is really helpful in determining guidelines for AI.
ST: Going to school and getting an education is an investment of your time. You pay now so you can be paid later. But it’s not as transactional as that. AI is already in the work environment and will become more prevalent. If we’re not preparing students to succeed in the work environment, we are doing them a disservice. We teach students to apply generative AI in their classes so they are prepared to use it in the workforce.
JF: In the business school, everything is market driven. I think education can fit into that framework as well. We’re trying to provide graduates with the confidence they need to finish the work and meet the market’s need. We know that generative AI tools have really changed the world and they’re starting to emerge in every part of our life. We need to train students to realize that ChatGPT might be part of their education, part of life in the future, and part of the work in the future as well. There are things AI can help us do, but there are still fundamentals that students need to learn. One example is calculators: we still need to learn from the beginning that 1 + 1 = 2.
CE: This question also reminded me of asking your students, what is the ultimate purpose of a research paper? Where do they think ChatGPT should fit into the research process?

Q: I work at the library and we’re getting lots of questions about how to detect if students are using AI. And also, how do you determine if students are relying too heavily on AI?
JF: We also get this question from our faculty. The most used detection tool right now is Turnitin, which is embedded in Canvas. But the level of accuracy is not reliable. We encourage faculty to always validate before accepting the results. For faculty who are actively using AI in the classroom, we also encourage them to provide clear guidance and expectations to students on how they are allowed to use it. This may make it a little easier to determine if they are using it correctly or not.
MR: There are some other tools out there, such a GPTZero, ZeroGPT, but to Jun’s point, the difficult thing is that it’s different than plagiarism detection which says this is copied, and here’s the source. These tools say there’s a probability that part of this was taken, but you can’t point to a direct source. It’s up to instructors whether or not to use these tools, but consider using them to facilitate a conversation with students. In my own classes if I suspect academic misconduct, I usually start by asking them to explain, talk to me about what is happening before I make accusations. With these tools, there tends to be no hard evidence, just probabilities that something may have happened. This is definitely an area we’re all still learning about.
Guest: I was just thinking that having a conversation with students about why they are turning to the tool in the first place might prevent misconduct. Instead of sending them to an academic misconduct committee, we could have these conversations, like Carly mentioned. Making students aware of the limitations of the tool could also be helpful.
CS: Yes, I say that in our guidelines that I’m prioritizing conferences with students over immediate disciplinary action. I try to pre-empt anxiety students might feel around using these tools. Designing your assignments in a way that reduces anxiety is also helpful. For example, I tend to design assignments that build on one another throughout the semester in smaller bits, rather than one giant chunk all at once.

Q: Is there any discussion around combining AI with teaching, such as generating personalized explanations of a topic? Students will have different levels of expertise and comfort with different topics.
ST: We’re trying to do this, to create a teaching aid for the future. We’re planning to use it to create assessment items.

Amy Brusini, Senior Instructional Designer
Center for Teaching Excellence and Innovation

Image Source: Pixabay, Unsplash

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Lunch and Learn: Generative AI – Teaching Uses, Learning Curves, and Classroom Guidelines