Learning Outcomes that Lead to Student Success 

What are learning outcomes and why do you need them?

There’s a famous misquote from Lewis Carroll, “If you don’t know where you’re going, any road will get you there.” The same is true in our courses: if you don’t know what you want your students to learn, it doesn’t really matter how or what you teach them. Every instructor wants to ensure student success, but if we as instructors don’t have accurate and well-thought-out learning outcomes, what does success mean in our classes? Creating learning outcomes should be a collaborative process where instructors responsible for teaching a course come together to craft these statements based on the most important learning in a course, taking care to maintain a balance between critical thinking and base knowledge while keeping an eye toward what makes a learning outcome an achievable learning goal.

Learning outcome creation

Before you create course learning outcomes

  • If your course is part of a program, you should ensure that the learning outcomes mesh with the rest of the program to meet all program learning outcomes.
  • Plan collaboratively with colleagues teaching the same course. All learning outcomes for sections taught of the same course should have the same learning outcomes according to the HLC (Higher Learning Commission) criteria 3a.
  • With colleagues, determine and list the most important learning or skills that will take place in this course.
  • Whittle down the list if it is too large. Consider what you and your colleagues can reasonably accomplish during the semester.
  • Pay attention to the conversation around Generative AI. What your students need to know and do may change because of the rapid development of AI.

Considerations as you create your learning outcomes

  1. Keep assessment and, therefore, your verb choices in the forefront of your mind. As you write learning outcomes, you want to ensure that the learning outcomes contain actions that can be demonstrated. When you ask students to “understand” something, this is difficult to demonstrate. If they “explain” it instead, that is an action that can be done and measured in various ways.
  2. Keep Bloom’s Taxonomy next to you as you create. It makes sense to use a taxonomy when writing outcomes. In Bloom’s model, skills and verbs on the bottom of the pyramid are less complex or intellectually demanding than those at the top of the pyramid; keep in mind they may still be totally appropriate, especially for lower-level courses. More critical thinking skills are required for those skills at the top of the pyramid, but it is useful and acceptable to use verbs and abilities from all levels of the pyramid. If you are teaching an upper-level course, you don’t want to draw all your verbs and skills from Bloom’s Taxonomy’s knowledge level. You should be using some higher levels in Bloom’s system.  The chart below can be a guide as you create those learning outcomes and note that generative AI developments may make the original chart problematic in different ways. There are alternatives to Blooms, as well.

    Alternatives to Blooms Taxonomy levels and verbs.
    Newtonsneurosci, CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0>, v Wikimedia Commons
  3. Use SMART Goals also. In addition to including Bloom’s Taxonomy as part of your learning outcomes, we encourage you to make sure that your learning outcomes are created using the SMART goals model.   SMART goals were developed in 1981 by George Duran, who noticed that most business goals were not created in a way that could be implemented effectively.

SMART is an acronym we can use to describe the attributes of effective learning outcomes for your students. Please note that you will find different versions of the acronyms in the SMART goal model, but these are the ones CATL uses to discuss learning outcomes:

    • Specific – target a specific area, skill, or knowledge
    • Measurable – progress is quantifiable
    • Attainable – able to be achieved or realistic
    • Relevant – applicable to the students in the class
    • Time-based – achieved in a specific timeframe, such as a semester

Example: By the end of the semester (T), students will be able to diagram (M) the process of photosynthesis (S, A) in this biology class (R).

Learning outcomes are more likely to be meaningful if they can meet all of the qualifiers in the SMART acronym. Think specifics as you create your learning outcome. If you can’t tell if your learning outcome meets one of the qualifiers, you should rework it until it does.

Review your learning outcomes

Your next step as a team should be to review your learning outcomes. Compare them to the SMART model and Bloom’s Taxonomy or any other relevant model you might be using. If it helps, consider these examples. First, “Students will improve their understanding of passive voice.” On the surface, it might look like a reasonable goal, but then as you ask, “What does it mean to improve? Where did the student start from? When does this need to be done by?” This goal offers no answers to those questions.

How about this one? “By the end of the semester, all students will receive a 100% score on their math notation quiz.” For context, this is a Writing Foundations course. That begs the question, is this outcome relevant to this group of students? Is 100% a reasonable and attainable goal?

Consider these questions as a guide when creating SMART goals. A more reasonable goal for this group of writing students is that by the end of the semester, students will be able to identify and accurately and effectively use scholarly research in their writing projects 80% of the time. One part of the review process is ensuring your outcomes are SMART, but there are additional elements to consider, including the questions below.

  • Can you identify the verb in your learning outcome?
  • If your students master the skills in your learning outcomes, will they be satisfactorily prepared to go to another course that teaches the next level of this material?
  • If this is a course in a series, have you checked to be sure that your outcomes make sense with the previous and next courses?
  • Has your unit done curriculum mapping for its goals, and do your course outcomes align with that mapping?

Put it all together

Creating learning outcomes that reflect the learning necessary to achieve mastery in a course can be an arduous process. It should be a collaborative process as well. We encourage you to reach out to the CATL team if you would like guidance or help walking through Bloom’s Taxonomy and the SMART goal model. We are always available to help!

Resources on creating learning outcomes

A colorful, geometric, and somewhat abstract illustration featuring buildings and streets covered with arrows, numbers, and the text "AI"

Generative AI and Assessments Workshop (June 28, July 18, Aug. 8, & Aug. 30, 2023)

Please join CATL for a virtual summer workshop focused on creating assessments in the age of generative AI (e.g., ChatGPT)! CATL facilitators will work with instructors to review their learning objectives, discuss the implications of emerging AI products, and brainstorm creative, high-quality, aligned, and feasible strategies for adapting course materials and assessments.

To participate in this virtual workshop, CATL asks that instructors bring a course syllabus with learning outcomes, ideas for at least two assessments for that course, and a willingness to engage in a reflective process that includes thinking about how generative AI technologies might impact those course materials. This workshop, “Generative AI and Assessments,” will occur three times throughout the summer months with more offerings to come in the fall. While registration is not required to attend, we encourage you to register today to receive a calendar reminder for the timeslot that works best for you!

Workshop Dates and Times:

All sessions are fully virtual and will meet via Microsoft Teams. Each workshop will be the same so please only sign up for one timeslot.

If you need accommodation for this virtual event, please contact CATL at CATL@uwgb.edu.

Register

 

How Will Generative AI Change My Course (GenAI Checklist)?

With the growing prevalence of generative AI applications like ChatGPT and the ongoing discussions surrounding their integration in higher education, it can be overwhelming to contemplate their impact on your courses, learning materials, and field. As we navigate these new technologies, it is crucial to reflect on how generative AI can either hinder or enhance your teaching methods. CATL has created a checklist designed to help instructors consider how generative artificial intelligence (GAI) products like Copilot, ChatGPT, and more may affect your courses and learning materials (syllabi, learning outcomes, and assessment).

Each step provides guidance on how to make strategic course adaptations and set course expectations that address these tools. As you go through the checklist, you may find yourself revisiting previous steps as you reconsider your course specifics and understanding of GAI.

Checklist for Assessing the Impact of Generative AI on your Course

View the 2024 Checklist for Assessing the Impact of Generative AI on your Course as a PDF.

Step One: Experiment with Generative AI

  • Experiment with GAI tools like Copilot (available to UWGB faculty, staff, and students), ChatGPT, or a similar application by inputting your own assignment prompts and assessing their performance in completing your assignments.
  • Research the potential benefits, concerns, and use cases regarding generative AI to gain a sense of the potential applications and misuses of this technology.

Step Two: Review Your Learning Outcomes

  • Reflect on your course learning outcomes. A good place to start is by reviewing this resource on AI and Bloom’s Taxonomy which considers AI capabilities for each learning level. Which outcomes lend themselves well to the use of generative AI and which outcomes emphasize your students’ distinctive human skills? Keep this in mind as you move on to steps three and four, as the way students demonstrate achieved learning outcomes may need to be revised.

Step Three: Assess the Extent of GAI Use in Class

  • Assess to what extent your course or discipline will be influenced by AI advancements. Are experts in your discipline already collaborating with GAI tools? Will current or future careers in your field work closely with these technologies? If so, consider what that means about your responsibility to prepare students for using generative AI effectively and ethically.
  • Determine the extent of usage appropriate for your course. Will you allow students to use GAI all the time or not at all? If students can use it, is it appropriate only for certain assignments/activities with guidance and permission from the instructor? If students can use GAI, how and when should they cite their use of these technologies? Be specific and clear with your students.
  • Revisit your learning outcomes (step two). After assessing the impact of advancements in generative AI on your discipline and determining how the technology will be used (or not used) in your course, return to your learning outcomes and reassess if they align with course changes/additions you may have identified in this step.

Step Four: Review Your Assignments/Assessments

  • Evaluate your assignments to determine how AI can be integrated to support learning outcomes. The previous steps asked you to consider the relevance of AI to your field and its potential impact on students’ future careers. How are professionals in your discipline using AI, and how might you include AI-related skills in your course? What types of skills will students need to develop independently of AI, such as creativity, interpersonal skills, judgement, metacognitive reflection, and contextual reasoning? Can using AI for some parts of an assignment free up students’ time to focus more on the parts that develop these skills?
  • View, again, this resource on AI capabilities versus distinctive human skills as they relate to the levels of Bloom’s Taxonomy.
  • Define AI’s role in your course assignments and activities. Like step three, you’ll want to be clear with your students on how AI may be used for specific course activities. Articulate which parts of an assignment students can use AI assistance for and which parts students need to complete without AI. If AI use doesn’t benefit an assignment, explain to your students why it’s excluded and how the assignment work will develop relevant skills that AI can’t assist with. If you find AI is beneficial, consider how you will support your students’ usage for tasks like editing, organizing information, brainstorming, and formatting. In your assignment instructions, explain how students should cite or otherwise disclose their use of AI.
  • Apply the TILT framework to your assignments to help students understand the value of the work and the criteria for success.

Step Five: Update Your Syllabus

  • Add a syllabus statement outlining the guidelines you’ve determined pertaining to generative AI in your course. You can refer to our syllabus snippets for examples of generative AI-related syllabi statements.
  • Include your revised or new learning outcomes in your syllabus and consider how you will emphasize the importance of those course outcomes for students’ career/skill development.
  • Address and discuss your guidelines and expectations for generative AI usage with students on day one of class and put them in your syllabus. Inviting your students to provide feedback on course AI guidelines can help increase their understanding and buy-in.

Step Six: Seek Support and Resources

  • Engage with your colleagues to exchange experiences and practices for incorporating or navigating generative AI.
  • Stay informed about advancements and applications of generative AI technology.

Checklist for Assessing the Impact of Generative AI on Your Course © 2024 by Center for the Advancement of Teaching and Learning is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International

Want More Resources?

Visit the CATL blog, The Cowbell, for more resources related to generative AI in higher education.

Need Help?

CATL is available to offer assistance and support at every step of the checklist presented above. Contact CATL for a consultation or by email at CATL@uwgb.edu if you have questions, concerns, or perhaps are apprehensive to go through this checklist.