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The Center for Teaching and Learning Innovation aims to support educators across MSU's ecosystem with a "library" of student-facing surveys for collecting formative feedback, checking in with learners, and setting the tone for your learning experience. Google Forms can be used to design anonymous surveys, providing long-form or multiple-choice questions, basic Likert-scale answer keys, and basic statistical data. CTLI Student Feedback (google drive folder) offers pre-made forms to reduce barriers to educator use. Additional information about putting these forms into practice can be found on iteach.msu.edu. Existing forms for duplication currently include:

mid-semester feedback
post-crisis check-in 
pre-semester technology and access
group agreements (for in-class group work)*
pre-semester accessibility needs/preferences
weekly student check-ins (example 1 and 2)

*not anonymous  
CTLI’s survey templates require some edits and modification.
Users of the Google Form are asked to Copy the Google Form to their own Google Drive (while logged in with their MSU credentials) so that the form and collected data is owned by the user. 
Instructions for copying a Google Form from the "survey library":

Right click on the CTLI Google Form you’d like to send to your students. Click Make a Copy.
Open your copy of the Google Form and review the instructions embedded in the Google Form itself. Please review the survey headers and descriptions in their entirety. The Google survey templates are built in a way that the instructor can quickly and easily update the information to individualize it to their course.
When you have completed your edits, click the Send button in the right-hand corner of the Google Form. To maintain anonymity, please ensure that the “Automatically collect respondent's Michigan State University email address” checkbox is unchecked.

For the Group Contract Form, anonymity is unnecessary. 


Select the link or the HTML embed link as options to send your survey. Please note that sending the survey via email will deanonymize the survey. 


Please direct questions on process or access to Makena Neal. 
Photo by Philip Strong on Unsplash

Want to improve your teaching? Participating in a peer observation process is a great way to create a space for you to reflect upon your own teaching and open up a dialogue related to best practices in teaching. It is very important to note that peer observations are NOT evaluative and are NOT tied to performance review. They are a training and development tool to facilitate reflection and personal growth.A peer observation process can:

create a culture that values best practices in teaching and facilitation; 
provide learning opportunities for employees to reflect upon their own teaching and facilitative leadership skills and learn from their peers; and 
build capacity in teacher training, observation feedback, and general pedagogy within the organization. 

The MSU Extension Peer Observation Process is based on the following premises.
Premise #1: Peer observation is helpful for teachers, especially for the one observing.

Faculty in higher education report that peer observation is useful (83%) and a majority (74%) feel it should be required (Divall, M. et al. 2019).
In peer observation, the true learner is the one who is observing (Richardson, 2000; Hendry & Oliver, 2012). Watching another teach is useful and instructive and allows teachers to discover new resources and ways of teaching, supports career-long learning in teaching, and provides a forum for teachers to discuss what good teaching is (Richardson, 2000).

Premise #2: Evaluative observation can be invalid and potentially destructive.

In evaluative observation, staff doing the observing may lack the motivation or knowledge to make good recommendations. It is also possible that that observer’s critique may damage the self-efficacy of the teacher being observed as a result of feedback that is not delivered in an appropriate way (Hendry & Oliver, 2012).
The validity of evaluative observations for measuring teacher efficacy is troublesome. Strong et al. (2011) looked at observations of teachers who were classified as “effective” or “ineffective” based on student achievement data, and then had observers with different levels of expertise watch recordings of those teachers teach and classify the teachers as “effective” or “ineffective.” Although judges were in high agreement (rater reliability), they demonstrated a low ability to identify effective teachers. Administrators and teacher educators were accurate only about one-third of the time. In other words, observers are unable to identify effective teachers from ineffective teachers.
To explore the conundrum of why evaluative observation isn’t accurate, I recommend reading Dr. Robert Coe’s blog post “Classroom observation: It’s hard than you think” (2014), published by the Centre for Evaluation & Monitoring at Durham University.

Premise 3#: Peer observation processes align to adult learning theory.

Theories of experiential learning, the teaching model used in 4-H, align to our proposed peer observation process. Experiential learning includes doing, reflecting, and applying. In the proposed peer observation process, the educators involved “do” by teaching or observing, “reflect” through post-observation reflection forms and structured conversations, and then “apply” by integrating new ideas and concepts into their own teaching.
The peer observation process aligns with social cognitive theory (Bandura, 1997) which posits that personal, behavioral, and environmental influences interact in learning. Concepts of self-efficacy, the belief that we can take actions to improve performance, is supported through the peer observation process.

Learn more about the MSU Extension Peer Observation Process.
References:
Bandura, A. (1997). Self-efficacy: The exercise of control.  London: W.H. Freeman & Co Ltd.
Coe, R. (2014, January 9). Classroom observation: it’s harder than you think. [Blog post]. Retrieved from https://www.cem.org/blog/414/.
DiVall, M., PharmD., Barr, Judith,M.Ed, ScD., Gonyeau, M., PharmD., Matthews, S. J., Van Amburgh, J., PharmD, Qualters, D., PhD., & Trujillo, J., PharmD. (2012). Follow-up assessment of a faculty peer observation and evaluation program. American Journal of Pharmaceutical Education, 76(4), 1-61. Retrieved from http://ezproxy.msu.edu.proxy1.cl.msu.edu/login?url=https://search-proquest-com.proxy1.cl.msu.edu/docview/1160465084?accountid=12598
J., Van Amburgh, J., PharmD, Qualters, D., PhD., & Trujillo, J., PharmD. (2012). Follow-up assessment of a faculty peer observation and evaluation program. American Journal of Pharmaceutical Education, 76(4), 1-61. Retrieved from http://ezproxy.msu.edu.proxy2.cl.msu.edu/login?url=https://search-proquest-com.proxy2.cl.msu.edu/docview/1160465084?accountid=12598
Hendry, G. D., & Oliver, G. R. (2012). Seeing is believing: The benefits of peer observation. Journal of University Teaching and Learning Practice, 9(1), 1-11. Retrieved from http://ezproxy.msu.edu.proxy2.cl.msu.edu/login?url=https://search-proquest-com.proxy2.cl.msu.edu/docview/1037909669?accountid=12598
Richardson, M. O. (2000). Peer observation: Learning from one another. Thought & Action, 16(1), 9-20. Retrieved from http://ezproxy.msu.edu.proxy2.cl.msu.edu/login?url=https://search-proquest-com.proxy2.cl.msu.edu/docview/62336021?accountid=12598
Strong, M., Gargani, J., & Hacifazlioğlu, Ö. (2011). Do We Know a Successful Teacher When We See One? Experiments in the Identification of Effective Teachers. Journal of Teacher Education, 62(4), 367–382. https://doi.org/10.1177/0022487110390221
Weller, S. (2009). What does "peer" mean in teaching observation for the professional development of higher education lecturers? International Journal of Teaching and Learning in Higher Education, 21(1), 25-35. Retrieved from http://ezproxy.msu.edu.proxy2.cl.msu.edu/login?url=https://search-proquest-com.proxy2.cl.msu.edu/docview/757171496?accountid=12598

Want your course content to look more polished and consistent, while also being accessible to all students? D2L’s content templates are a great way to achieve both clarity and visual consistency without needing advanced design skills.
Whether you're already using D2L webpages or you're looking to move away from uploading Word Docs and PDFs, content templates are an excellent way to create clean, accessible, and mobile-friendly course materials.
Why use content templates?

Ensure consistent formatting across all course pages
Improve accessibility and readability for students
Save time by using pre-built layouts instead of designing from scratch
Create content that’s easier to view on all devices, including mobile

Templates are especially helpful for courses with lots of custom text-based instructions, resources, or media links.
How do you use content templates in D2L?
When you create a new webpage (HTML file) in D2L, you have the option to apply a content template. These templates are already built into MSU’s version of D2L and follow accessibility best practices.

Go to the Content section of your course.
Click “Upload/Create” → “Create a File.”
You’ll see a “Select a Document Template” panel on the right.
Choose a layout that fits your needs (e.g., text with image, objectives, etc.).
Edit the content directly in the page editor, then click “Save and Close.”

Watch video tutorials on how to edit content:

For general assistance regarding D2L content, watch this 5-minute tutorial on adding content to D2L courses.
Watch this 5-minute "how-to" video from MSU Instructional Technology and Development. Scroll below the video for links to the templates.
Watch this 3-minute tutorial from Brightspace Tutorials: Edit HTML Templates. This is a helpful walkthrough from D2L's official documentation.

Learn how to modify content templates:
See this #iteachmsu article to learn how to create your own templates.

(Photo by Steve Johnson on Unsplash )
You can also access the Generative AI Syllabus Guide Playlist with this content broken down into the following sections. Table of Contents:

MSU Guidance and [Non]Permitted Uses
Developing and Communicating a Course-level Generative AI Use policy
Example Syllabus Statements for the Use of AI Tools in Your Course

Design For Generative AI (restrict, permit, require)
Design Around Generative AI (ban)


Example Statements from Current USA, Higher Education Educators
Developing your Scholarly and Ethical Approaches to Generative AI
Beyond Syllabi Language
Additional considerations to help you develop your generative AI philosophy (Watkins, 2022)
References

The following MSU-specifics should be used to inform your decisions...
Overall guidance: We collectively share the responsibility to uphold intellectual honesty and scholarly integrity. These are core principles that may be compromised by the misuse of GenAI tools, particularly when GenAI-generated content is presented as original, human-created work.  
Permitted uses in Teaching & Learning: Instructors are expected to establish a course-specific guidance that defines the appropriate and inappropriate use of GenAI tools.

Students may only use GenAI tools to support their coursework in ways explicitly permitted by the instructor.  

Non-permissible uses: 

Do not Use GenAI to deliberately fabricate, falsify, impersonate, or mislead, unless explicitly approved for instruction or research in a controlled environment.
Do not Record or process sensitive, confidential, or regulated information withnon-MSU GenAI tools.
Do not Enter FERPA-protected student records, PII, PHI, financial, or HR data into unapproved tools; comply with MSU’s data policy and all regulations.
Do not Use export-controlled data or CUI with GenAI tools unless approved for MSU’s Regulated Research Enclave (RRE).

Developing and Communicating a Course-level Generative AI Use policy 
A well-prepared course should be designed for ("restrict", "permit" or "require") or designed around ("ban") generative AI. Courses designed for AI should detail the ways and degrees to which generative AI use will be incorporated into activities and assessments. Courses designed for AI may incorporate AI for some activities and not others and depending on course AI may be explicitly excluded or included at different stages. Courses designed around AI may discuss impacts of generative AI as a topic but expectations are that students will not use these types of tools, and the course should be intentionally designed such that the use of generative AI would either not be conducive to the completion of assessments and activities, or such that the attempt to do so would prove overly cumbersome. 
Regardless of your approach, communicating your expectations and rationale to learners is imperative.
Set clear expectations. Be clear in your syllabus about your policies for when, where, and how students should be using generative AI tools, and how to appropriately acknowledge (e.g., cite, reference) when they do use generative AI tools. If you are requiring students to use generative AI tools, these expectations should also be communicated in the syllabus and if students are incurring costs, these should be detailed in the course description on the Registrar’s website. 
Regardless of your approach, you might include time for ethics discussions. Add time into your course to discuss the ethical implications of chatGPT and forthcoming AI systems. Talk with students about the ethics of using generative AI tools in your course, at your university, and within your discipline or profession. Don’t be afraid to discuss the gray areas where we do not yet have clear guidance or answers; gray areas are often the places where learning becomes most engaging.
Example Syllabus Statements for the Use of AI Tools in Your Course 
There is no “one size fits all policy” for AI uses in higher education. Much like attendance/participation policies, GenAI course-level rules and statements will be determined by individual instructors, departments, and programs. The following resource is provided to assist you in developing coherent policies on the use of generative AI tools in your course, within MSU's guideline. Please adjust these examples to fit your particular context. Remember communication of your course generative AI policies should not only be listed in your syllabus, but also explicitly included  in assignment descriptions where AI use is allowed or disallowed. 
It is your responsibility as instructor to note and explain your individual course-level rule. A conversation with your department is highly recommended so that generative AI use in the classroom reflects broader use in the unit and  discipline. If you have specific questions about writing your course rules, please reach out to the Center for Teaching and Learning Innovation. 
Design For Generative AI
Restrict [This syllabus statement is useful when you are allowing the use of AI tools for certain purposes, but not for others. Adjust this statement to reflect your particular parameters of acceptable use. The following is an example.] 
Example1:  

The use of generative AI tools (e.g. ChatGPT, Dall-e, etc.) is permitted in this course for the following activities:

[insert permitted your course activities here*]


The use of generative AI tools is not permitted in this course for the following activities:

[insert not permitted your course activities here*]


You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge.

Example2: Taken, with slight modification, from Temple University’s Center for the Advancement of Teaching  to demonstrate the kinds of permitted/restricted activity an instructor could denote.
The use of generative AI tools (e.g. ChatGPT, Dall-e, etc.) is permitted in this course for the following activities: 



Brainstorming and refining your ideas; 
Fine tuning your research questions; 
Finding information on your topic; 
Drafting an outline to organize your thoughts; and 
Checking grammar and style. 



The use of generative AI tools is not permitted in this course for the following activities: 



Impersonating you in classroom contexts, such as by using the tool to compose discussion board prompts assigned to you or content that you put into a Zoom chat. 
Completing group work that your group has assigned to you, unless it is mutually agreed within your group and in alignment with course policy that you may utilize the tool. 
Writing a draft of a writing assignment. 
Writing entire sentences, paragraphs or papers to complete class assignments. 



You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge. For example, [Insert citation style for your discipline. See these resources for APA guidance, and for other citation formats.]. Any assignment that is found to have used generative AI tools in unauthorized ways [insert the penalty here*]. When in doubt about permitted usage, please ask for clarification. 
 
Use permitted [This syllabus statement is useful when you are allowing, and perhaps encouraging, broad use of generative AI tools. Adjust this statement to reflect your particular parameters of acceptable use in your course. The following is an example.] 
Example:
You are welcome to use generative AI tools (e.g. ChatGPT, Dall-e, etc.) in this class as doing so aligns with the course learning goal [insert the course learning goal use of AI aligns with here*]. You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge.
Use required [This syllabus statement is useful when you have certain assignments that will require that students use generative AI tools. Adjust this statement to reflect your particular parameters of acceptable use. The following is an example.] 
Example:
You will be expected to use generative AI tools (e.g. ChatGPT, Dall-e, etc.) in this class as doing so aligns with the course learning goal [insert the course learning goal use of AI aligns with]. Our class will make use of the [insert name of tool(s) here*] tool, and you can gain access to it by [insert instructions for accessing tool(s) here*]. You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge.
 
Design Around Generative AI
Ban [This syllabus statement is useful when you are forbidding all use of generative AI tools for any purpose in your class. Adjust this statement to reflect your particular parameters of acceptable use. The following is an example.] 
The use of generative AI tools (such as ChatGPT, DALL-E, etc.) is not permitted in this class; therefore, any use of AI tools for work in this class may be considered a violation of Michigan State University’s policy on academic integrity, the Spartan Code of Honor Academic Pledge andStudent Rights and Responsibilities, since the work is not your own. The use of unauthorized AI tools will result in [insert the penalty here*].

CONCERN: The ubiquity of generative AI tools, including their integration into Google search results and MS Office products, means that an outright generative AI ban is implausible for any activity that makes use of the Internet or MS Office Suite.

* It is highly recommended that you have conversations in your department about the appropriate penalties for unauthorized use of an AI. It is important to think about the appropriate level of penalty for first-time offenders and those who repeatedly violate your policies on the use of AI. 



Example Statements from Current USA, Higher Education Educators
This collection of example statements are a compilation from a variety of sources including Faculty Learning Community (FLC) at Cleveland State University, Ohio University’s AI, ChatGPT and Teaching and Learning, and some of Michigan State University’s own educators! (If you have an example generative AI policy from your course that you’d be willing to share, please add it to the comments below or e-mail it to MSU Center for Teaching and Learning Innovation at teaching@msu.edu) NOTE: making your own course-level determination of "ban", "restrict", "permit", or "require" and using the sample language is the best, first place to start!
 “AI (artificial intelligence) resources such as ChatGPT can be useful in a number of ways. Because it can also be abused, however, you are required to acknowledge use of AI in any work you submit for class. Text directly copied from AI sites must be treated as any other direct quote and properly cited. Other uses of AI must be clearly described at the end of your assignment.” -Claire Hughes-Lynch
 “While AI tools can be useful for completing assignments and detecting plagiarism, it is important to use them responsibly and ethically. Practice based on these guidelines as a future or current K-12 teacher. The following are some guidelines for what not to do when using AI in your assignments and for plagiarism detection:

Do not rely solely on AI tools to complete assignments. It is important to understand the material and complete assignments on your own, using AI tools as a supplement rather than a replacement for your own work.
Do not use AI tools to plagiarize*. Using AI to generate or modify content to evade plagiarism detection is unethical and violates academic integrity.
Do not assume that AI responses are always correct. It has been noted that AI can generate fake results.* Please see the plagiarism/academic integrity policy in the course syllabus.” -Selma Koc

“Intellectual honesty is vital to an academic community and for my fair evaluation of your work. All work submitted in this course must be your own, completed in accordance with the University’s academic regulations. Use of AI tools, including ChatGPT, is permitted in this course. Nevertheless, you are only encouraged to use AI tools to help brainstorm assignments or projects or to revise existing work you have written. It is solely your responsibility to make all submitted work your own, maintain academic integrity, and avoid any type of plagiarism. Be aware that the accuracy or quality of AI generated content may not meet the standards of this course, even if you only incorporate such content partially and after substantial paraphrasing, modification and/or editing. Also keep in mind that AI generated content may not provide appropriate or clear attribution to the author(s) of the original sources, while most written assignments in this course require you to find and incorporate highly relevant peer-reviewed scholarly publications following guidelines in the latest publication manual of the APA. Lastly, as your instructor, I reserve the right to use various plagiarism checking tools in evaluating your work, including those screening for AI-generated content, and impose consequences accordingly.” -Xiongyi Liu
“If you are ever unsure about whether collaboration with others, including using artificial intelligence, is allowed or not, please ask me right away. For the labs, although you may discuss them in groups (and try using AI), you must all create your own code, output and answers. Quizzes will be done in class and must be solely your own work. You alone are always responsible for the correctness of the final answers and assignments you submit.” - Emily Rauschert on AI as collaboration partner
“Chat GPT: The use of Chat GTP is neither encouraged nor prohibited from use on assignments for GAD 250. Chat GPT is quickly becoming a communication tool in most business settings. Therefore, if you choose to use Chat GPT for assignments, please be sure to revise the content for clarity, conciseness, and audience awareness. Chat GPT is simply a tool and should not be used as a way to produce first and only drafts. Every assignment submission will be graded using the rubric provided in the syllabus. Be aware that Chat GPT may not develop high-quality work that earns a passing grade. It is your responsibility to review and revise all work before submitting to the instructor.” -Leah Schell-Barber for a Business Communications Course
“Use of Generative AI, such as ChatGPT and Microsoft Bing-Chat, must maintain the highest standards of academic integrity and adhere to the OU Code of Student Conduct.  The use of Generative AI should be seen as a tool to enhance academic research, not as a replacement for critical thinking and originality in assignments. Students are not permitted to submit assignments that have been fully or partially generated by AI unless explicitly stated in the assignment instructions. All work submitted must be the original work of the student. Any ideas garnered from Generative AI research must be acknowledged with proper in-text citation and reference. Students may be asked to save the AI chat as a PDF file for verification.” -Ohio University College of Business Generative AI Use for Academic Work Policy
“‘The policy of this class is that you must be the creator of all work you submit for a grade. The use of others’ work, or the use of intelligent agents, chat bots, or a.i. engines to create your work is a violation of this policy and will be addressed as per MSU and Broad College codes of conduct.’ - Jeremy Van Hof… Or, you might consider this, which I asked ChatGPT to write for me: ‘Sample Policy Language: Students should not use ChatGPT to complete course assignments or for any other academic activities. ChatGPT should be used as a supplemental resource and should not replace traditional academic activities.’ (ChatGPT per Jeremy Van Hof’s prompting) 
Or this much longer version, also written by ChatGPT: ‘The following course policy statement prohibits the use of Artificial Intelligence (AI) for the’ completion of assignments and activities during the duration of the course. At the Broad College, we strive to create an academic environment where learning is the foremost priority. We strongly believe that learning is best achieved through the hard work and dedication of our students. As such, we prohibit the use of Artificial Intelligence (AI) for the completion of assignments and activities during the course.  Our policy is in line with our commitment to providing a fair and equitable learning environment for all students. We believe that AI should not be used to substitute human effort, as it defeats the purpose of our educational goals, which are to encourage critical thinking and problem-solving.  We understand that AI can be a useful tool in many contexts, and we do not discourage its use in other courses. However, in this course, we will not accept assignments or activities that have been completed through the use of AI. We expect our students to be honest and to complete their work independently.  We will be monitoring student work closely to ensure compliance with this policy. Violations of this policy will be met with disciplinary sanctions. All students are expected to adhere to this policy and to abide by the standards of the University.’ (ChatGPT per Jeremy Van Hof’s prompting)” -Jeremy Van Hof, Broad College of Business
“I study AI. I research it in my role as faculty in the Experience Architecture and Professional & Public Writing majors. And I don’t think it’s inherently bad or scary, in the same way that a calculator isn’t bad/scary for math. Artificial intelligence technologies such as ChatGPT can be an excellent starting point and a place to begin inquiry. But they are not a replacement for human thinking and learning. Robots lack empathy and nuance. As such, here is my policy:
You may use AI as a tool, but you may not use AI to replace your own beautiful brain. That means that you may ask ChatGPT, for example, to give you a list of bands similar to one that you hear and appreciate in this course. You may ask ChatGPT to give you an overview of a punk scene in a geographic location at a particular time. You may ask it for the history of punk rock and punk cultures. You may ask it what happened to Sid Vicious. 
But you may not ask it to write on your behalf, and you must not turn in anything that has been written by ChatGPT and pass it off as your own for any assignment in this class, including discussion responses, papers, and exams. If you do so, I will know, and that will lead to an uncomfortable moment–and to you failing the assignment.
This is not meant to be punitive. It’s meant to reinforce how much I value you and your ideas and your intellect. In a face-to-face environment, we would have a lengthy conversation about AI, ethics, and human learning. If you want to have that conversation, I’m happy to do so via Zoom–email me!” -Kate Birdsall, asynchronous US23 course on punk-rock politics
Developing your Scholarly and Ethical Approaches to Generative AI 
Taken, with slight modification, from “Update Your Course Syllabus for chatGPT” by Ryan Watkins, Professor of Educational Technology Leadership, and Human-Technology Collaboration at George Washington University in Washington DC (2022), via Medium. 
Beyond Syllabi Language 
Communicate your perspective about AI use. In addition to syllabus statements, consider talking with your students about AI tools like ChatGPT. Regardless of your orientation to generative AI use, it is important that you clearly communicate your expectations with the introduction of each assignment/assessment.   
Different levels of familiarity: As an emerging technology, students will have differing levels of familiarity with these tools. For instance, while ChatGPT can write a grammatically correct paper or appear to solve a math problem, it may be unreliable and limited in scope. Discuss with students the uses and limitations of AI tools more broadly in addition to your perspective on their use in your class. 
Connect to critical thinking skills: AI tools have many implications beyond the classroom. Consider talking with students about how to be engaged-consumers of AI content (e.g., how to identify trusted sources, reading critically, privacy concerns). Discuss how you and colleagues use AI in your own work.
Adapt assessments. AI tools are emerging and it can be incredibly difficult to make any assessment completely free from AI interference. Beyond a syllabus statement, you may also consider adapting your assessments to help reduce the usefulness of AI products. However before revising any assignment, it’s helpful to reflect on what exactly you want students to get out of the experience and share your expectations with your students. Is it just the end product, or does the process of creating the product play a significant role? 

Create assessments that allow students to develop ideas over time. Depending on your class size, consider scaffolding assessments to be completed in small components (e.g., proposal, annotated bibliography, outline, first draft, revised drafts). 
Ask students to connect their writing to specific course materials or current events. Students can draw from the course textbook, additional readings on Moodle or Blackboard, and even class discussion boards or in-class discussions.  
Incorporate personal experiences and reflections. Provide students with opportunities to connect what they are learning to their own lives and experiences—stories unique to each individual. 
Incorporate Multimedia Assessments. Consider developing or adapting assessments to include multimedia submissions (e.g., audio or video components). Also, consider peer-review and social annotation tools like Eli Review or Google Docs for students to use when responding to assigned readings or other materials.  
Use class time. Ask students to complete writing assignments during class time (e.g. complete reading reflections at the beginning of class, or use exit tickets). Asking students to organize their ideas by writing during class may also support student engagement in other class activities such as discussions and group work.  

Get Creative With Your Assignments: Visit “Update Your Course Syllabus for chatGPT” by Ryan Watkins (Medium article) for 10 ideas for creative assignments adapted for a classroom with chatGPT. You can mitigate the risk of students using chatGPT to cheat, and at the same time improve their knowledge and skills for appropriately using new AI technologies inside and outside the classroom.
Additional considerations to help you develop your generative AI philosophy (Watkins, 2022)
Expand your options. Consider your repertoire of instructional strategies. Atsusi Hirumi offers a guide to research-grounded strategies for any classroom. These are not, however, “a la carte” menus; you must use all of the steps of any strategy to gain the evidence-based benefits.
Reflect on your values. As Tyler Cowen pointed out, there will be those who gain and those that lose with the emergence of chatGPT and other generative AI tools. This is as true for students as it is for faculty and instructors. Be ready to openly discuss the ethical implications of generative AI tools with your students, along with the value of what you are teaching and why learning these are important to their futures.
Consider time. As discussed during Bryan Alexander’s webinar, chatGPT and other generative AI tools offer a short-cut to individuals who are short on time. Examine your course schedule to determine if you are unknowingly pushing students to take short-cuts. Some instructors try to cover too much content in their courses already.
Remember, AI is not human. Be careful not to anthropomorphize chatGPT and other generative AI tools. ChatGPT is a language model, and if we anthropomorphize these technologies, then it will be much harder to understand their promise and perils. Murray Shanahan suggests that we avoid statements such as, “chatGPT knows…”, or “ChatGPT thinks…”; instead, use “According to chatGPT…” or “ChatGPT’s output…”.
Again, AI is likely to be a part of your students’ life to some extent this semester, so plan accordingly. Critically considering your course design in the context of generative AI is an important educator practice. Following the Provost’s call, MSU instructors are encouraged to 1) develop a course-level generative AI use policy and actively discuss with students about expectations for generative AI use in the work for your class, 2) promote equitable and inclusive use of the technology, and 3) work with colleagues across campus to determine ethical and scholarly applications of generative AI for preparing students to succeed in an evolving digital landscape. MSU does not currently have a university-wide policy on AI in the classroom, so  it is your responsibility as instructor to note and explain your individual course policy. A conversation with your department is highly recommended so that generative AI use in the classroom reflects that in the discipline. 
References
This resource is collated from multiple sites, publications, and authors with some modification for MSU context and links to MSU specific resources. Educators should always defer to University policy and guidelines. 

MSU Office of Student Support & Accountability Faculty Resources, including Academic Dishonesty Report form. 
Watkins, R. (2022) Update Your Course Syllabus for chatGPT. Educational Technology Leadership, The George Washington University via Medium: https://medium.com/@rwatkins_7167/updating-your-course-syllabus-for-chatgpt-965f4b57b003 
Center for the Advancement of Teaching (2023). Sample Syllabus Statements for the Use of AI Tools in Your Course. Temple University 
Center for Teaching & Learning (2023) How Do I Consider the Impact of AI Tools like ChatGPT in My Courses?. University of Massachusetts Amherst. https://www.umass.edu/ctl/how-do-i-consider-impact-ai-tools-chatgpt-my-courses 
Center for Teaching, Learning and Assessment (2023). AI, ChatGPT and Teaching and Learning. Ohio University. https://www.ohio.edu/center-teaching-learning/instructor-resources/chat-gpt
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Teaching and Learning at Cleveland State University by Center for Faculty Excellence is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
 

Instructional Guidance Is Key to Promoting Active Learning in Online and Blended Courses Written by: Jay Loftus Ed.D. (MSU / CTLI) & Michele Jacobsen, Ph.D. (Werklund School of Education - University of Calgary)
Abstract - Active learning strategies tend to originate from one of two dominant philosophical perspectives. The first position is active learning as an instructional philosophy, whereby inquiry-based and discovery learning are primary modalities for acquiring new information. The second perspective considers active learning a strategy to supplement the use of more structured forms of instruction, such as direct instruction. From the latter perspective, active learning is employed to reinforce conceptual learning following the presentation of factual or foundational knowledge. This review focuses on the second perspective and uses of active learning as a strategy. We highlight the need and often overlooked requirement for including instructional guidance to ensure active learning, which can be effective and efficient for learning and learners.
Keywords - Active learning, instructional guidance, design strategy, cognitive load, efficiency, online and blended courses
 
Introduction
Learner engagement in online courses has been a central theme in educational research for several years (Martin, Sun and Westing, 2020). As we consider the academic experiences during the COVID-19 pandemic, which began in 2020 and started to subside in 2022, it is essential to reflect on the importance of course quality (Cavanaugh, Jacquemin and Junker, 2023) and learner experience in online courses (Gherghel, Yasuda and Kita, 2023). Rebounding from our collected experience, learner engagement continues to be an important element of course design and delivery. This fact was highlighted in 2021, when the United States Department of Education (DOE) set forth new standards for institutions offering online courses. To be eligible for Title IV funding, new standards require non-correspondence courses to ensure regular and substantive interactions (RSI) between instructors and students (Downs, 2021). This requirement necessitates the need to find ways to engage students allowing instructors the ability to maximize their interactions. One possible solution is to use active learning techniques that have been shown to increase student engagement and learning outcomes (Ashiabi & O’ Neal, 2008; Cavanaugh et al., 2023).
Active learning is an important instructional strategy and pedagogical philosophy used to design quality learning experiences and foster engaging and interactive learning environments. However, this is not a novel perspective. Many years ago in their seminal work, Chickering and Gamson (1987) discussed the issue of interaction between instructors and students, suggesting that this was an essential practice for quality undergraduate education. The newfound focus on active learning strategies has become more pronounced following an examination of instructional practices from 2020 to 2022. For example, Tan, Chng, Chonardo, Ng  and Fung (2020) examined how chemistry instructors incorporated active learning into their instruction to achieve equivalent learning experiences in pre-pandemic classrooms. Similarly, Misra and Mazelfi (2021) described the need to incorporate group work or active learning activities into remote courses to: ‘increase students’ learning motivation, enforce mutual respect for friends’ opinions, foster excitement’ (p. 228). Rincon-Flores & Santos-Guevara (2021) found that gamification as a form of active learning, ‘helped to motivate students to participate actively and improved their academic performance, in a setting where the mode of instruction was remote, synchronous, and online’ (p.43). Further, the implementation of active learning, particularly gamification, was found to be helpful for promoting a more humanizing learning experience (Rincon-Flores & Santos-Guevara, 2021).
This review examines the use of active learning and presents instructional guidance as an often-overlooked element that must be included to make active learning useful and effective. The omission of explicit and direct instructional guidance when using active learning can be inefficient, resulting in an extraneous cognitive burden on learners (Lange, Gorbunova, Shcheglova and Costley, 2022). We hope to outline our justification through a review of active learning and offer strategies to ensure that the implementation of active learning is effective.
Active Learning as an Instructional Philosophy
Active learning is inherently a ‘student-centered’ instructional paradigm that is derived from a constructivist epistemological perspective (Krahenbuhl, 2016; Schunk, 2012). Constructivism theorizes that individuals construct their understanding through interactions and engagements, whereby the refinement of skills and knowledge results over time (Cobb & Bowers, 1999). Through inquiry, students produce experiences and make connections that lead to logical and conceptual growth (Bada & Olusegun, 2015). Engaging learners in activities, tasks, and planned experiences is an overarching premise of active learning as an instructional philosophy. As an overarching instructional philosophy, the role of instructional guidance can be minimized. As Hammer (1997) pointed out many years ago, the role of the instructor in these environments is to provide content and materials, and students are left make ‘discoveries’ through inquiry.
Inquiry-based learning (IBL) is an instructional practice that falls under the general category of ‘active learning’. The tenets of IBL adhere to a constructivist learning philosophy (de Jong et al., 2023) and can be characterized by the following six elements (Duncan & Chinn, 2021). Students will:

Generate knowledge through investigation of a novel issue or problem.
Work ‘actively’ to discover new findings.
Use of evidence to derive conclusions.
Take responsibility for their own learning through ‘epistemological agency’ (Chinn & Iordanou, 2023) and share their learning with a community of learners.
Use problem-solving and reasoning for complex tasks.
Collaborate, share ideas, and derive solutions with peers.

Historically, inquiry-based learning as a form of active learning was adopted as an overall instructional paradigm in disciplines such as medicine and was closely aligned with problem-based learning (PBL) (Barrows, 1996). Proponents of PBL advocate its use because of its emphasis on the development of skills such as communication, collaboration, and critical thinking (Dring, 2019). Critics of these constructivist approaches to instruction highlight the absence of a structure and any form of instructional guidance (Zhang & Cobern, 2021). Instead, they advocate a more explicit form of instruction such as direct instruction (Zhang, Kirschner, Corben and Sweller, 2022).
The view that a hybrid of IBL coupled with direct instruction is the optimal approach to implementing active learning has been highlighted in the recent academic literature (de Jong et al., 2023). The authors suggest that the selection of direct instruction or active learning strategies, such as IBL, should be guided by the desired outcomes of instruction. If the goal of instruction is the acquisition of more foundational or factual information, direct instruction is the preferred strategy. Conversely, IBL strategies are more appropriate ‘for the promotion of deep understanding and transferrable conceptual understanding of topics that are open-ended or susceptible to misconceptions’ (de Jong et al., 2023 p. 7).
The recommendation to use both direct instruction and approaches like IBL has reframed active learning as an instructional strategy rather than an overarching pedagogical philosophy. Active learning should be viewed as a technique or strategy coupled with direct instructional approaches (de Jong et al., 2023).
Active Learning as an Instructional Strategy
Approaching active learning as an instructional strategy rather than an overarching instructional philosophy helps clarify and address the varying perspectives found in the literature. Zhang et al. (2022) suggested that there is a push to emphasize exploration-based pedagogy. This includes instructional approaches deemed to be predicated on inquiry, discovery, or problem-based approaches. This emphasis has resulted in changes to curricular policies that mandate the incorporation of these instructional philosophies. Zhang et al. (2022) discussed how active learning approaches can be incorporated into science education policy to emphasize ‘inquiry’ approaches, despite adequate evidence for effectiveness.  Zhang et al. (2022) stated that the ‘disjoint between policy documents and research evidence is exacerbated by the tendency to ignore categories of research that do not provide the favored research outcomes that support teaching science through inquiry and investigations’ (p. 1162). Instead, Zhang et al. (2022) advocate for direct instruction as the primary mode of instruction in science education with active learning or ‘inquiry’ learning incorporated as a strategy, arguing that conceptual or foundational understanding ‘should not be ‘traded off’ by prioritizing other learning outcomes’ (p. 1172).
In response to Zhang et al. ’s (2022) critique, de Jong et al. (2023) argued that research evidence supports the use of inquiry-based instruction for the acquisition of conceptual understanding in science education. They asserted that both inquiry-based (or active learning approaches) and direct instruction serve specific learning needs. Direct instruction may be superior for foundational or factual learning, while inquiry-based or active learning may be better for conceptual understanding and reinforcement. The conclusion of de Jong et al. ’s (2023) argument suggests the use of a hybrid of direct instruction and active learning techniques, such as inquiry-based designs, depending on the stated learning objectives of the course or the desired outcomes.
This hybrid approach to instructional practice can help ensure that intended learning outcomes are matched with effective instructional strategies. Furthermore, a hybrid approach can help maintain efficiency in learning rather than leaving the acquisition of stated learning outcomes to discovery or happenstance (Slocum & Rolf, 2021).  This notion was supported by Nerantzi's (2020) suggestion that ‘students learn best when they are active and immersed in the learning process, when their curiosity is stimulated, when they can ask questions and debate in and outside the classroom, when they are supported in this process and feel part of a learning community’ (p. 187). Emphasis on learner engagement may support the belief that active learning strategies combined with direct instruction may provide an optimal environment for learning. Active learning strategies can be used to reinforce the direct or explicit presentation of concepts and principles (Lapitan Jr, Tiangco, Sumalinog, Sabarillo and  Diaz, 2021).
Recently, Zhang (2022) examined the importance of integrating direct instruction with hands-on investigation as an instructional model in high school physics classes. Zhang (2022) determined that ‘students benefit more when they develop a thorough theoretical foundation about science ideas before hands-on investigations’ (p. 111). This supports the earlier research in post-secondary STEM disciplines as reported by Freeman, Eddy, McDonough and Wenderoth (2014), where the authors suggested that active learning strategies help to improve student performance. The authors further predicted that active learning interventions would show more significant learning gains when combined with ‘required exercises that are completed outside of formal class sessions’ (p. 8413).
Active Learning Strategies
Active learning is characterized by activities, tasks, and learner interactions. Several characteristics of active learning have been identified, including interaction, peer learning, and instructor presence (Nerantzi, 2020). Technology affords students learning opportunities to connect pre-, during-, and post-formal learning sessions (Zou & Xie, 2019; Nerantzi, 2020). The interactions or techniques that instructors use help determine the types of interactions and outcomes that will result. Instructors may be ‘present’ or active in the process but may not provide adequate instructional guidance for techniques to be efficient or effective (Cooper, Schinske and Tanner, 2021; Kalyuga, Chandler and Sweller. 2001). To highlight this gap, we first consider the widely used technique of think-pair-share, an active learning strategy first introduced by Lyman (1981). This active learning strategy was introduced to provide all students equitable opportunities to think and discuss ideas with their peers. The steps involved in this technique were recently summarized (Cooper et al., 2021): i) provide a prompt or question to students, (ii) give students a chance to think about the question or prompt independently, (iii) have students share their initial answers/responses with a neighbor in a pair or a small group, and (iv) invite a few groups a chance to share their responses with the whole class.
Instructional guidance outlines the structure and actions associated with a task. This includes identifying the goals and subgoals, and suggesting strategies or algorithms to complete the task (Kalyuga et al., 2001). Employing the strategy of think-pair-sharing requires more instructional guidance than instructors may consider. The title of the strategy foreshadows what students will ‘do’ to complete the activity. However, instructional guidance is essential to help students focus on the outcome, rather than merely enacting the process of the activity. Furthermore, instructional guidance or instructions given to students when employing think-pair-sharing can help make this activity more equitable. Cooper et al. (2021) point out that equity is an important consideration when employing think-pair-share. Often, think-pair-share activities are not equitable during the pair or share portion of the exercise, and can be dominated by more vocal or boisterous students. Instructional guidance can help ensure that the activity is more equitable by providing more explicit instructions on expectations for sharing. For example, the instructions for a think-pair-share activity may include those that require each student to compose and then share ideas on a digital whiteboard or on a slide within a larger shared slide deck. The opportunity for equitable learning must be built into the instructions given to students. Otherwise, the learning experience could be meaningless or lack the contribution of students who are timid or find comfort in a passive role during group learning.
Further considerations for instructional guidance are necessary since we now use various forms of Information and Communications Technology (ICT) to promote active learning strategies. Web conferencing tools, such as Zoom, Microsoft Teams, and Google Meet, were used frequently during the height of required remote or hybrid teaching (Ahshan, 2021). Activities that separated students into smaller work groups via breakout rooms or unique discussion threads often included instructions on what students were to accomplish in these smaller collaborative groups. However, the communication of expectations or explicit guidance to help direct students in these groups were often not explicit or were not accessible once the students had been arranged into their isolated workspaces. These active learning exercises would have benefited from clear guidance and instructions on how to ‘call for help’ once separated from the larger group meetings. For example, Li, Xu, He, He, Pribesh, Watson and Major, (2021) described an activity for pair programming that uses zoom breakout rooms. In their description, the authors outlined the steps learners were expected to follow to successfully complete the active learning activity, as well as the mechanisms students used to ask for assistance once isolated from the larger Zoom session that contained the entire class. The description by Li et al. (2021) provided an effective approach to instructional guidance for active learning using Zoom.  Often, instructions are verbalized or difficult to refer to once individuals are removed from the general or common room. The lack of explicit instructional guidance in these activities can result in inefficiency (Kalyuga et al., 2001) and often inequity (Cooper et al., 2021).
The final active learning approach considered here was a case study analysis of asynchronous discussion forums. To extend engagement with course content, students were assigned a case study to discuss in a group discussion forum. The group is invited to apply course concepts and respond to questions as they analyze the case and prepare recommendations and a solution (Hartwell et al., 2021). Findings indicate that case study analysis in discussion forums as an active learning strategy “encouraged collaborative learning and contributed to improvement in cognitive learning” (Seethamraju, 2014, p. 9). While this active learning strategy can engage students with course materials to apply these concepts in new situations, it can also result in a high-volume-low-yield set of responses and posts without sufficient instructional guidance and clear expectations for engagement and deliverables. Hartwell, Anderson, Hanlon, and Brown (2021) offer guidance on the effective use of online discussion forums for case study analysis, such as clear expectations for student work in teams (e.g., a team contract), ongoing teamwork support through regular check-ins and assessment criteria, clear timelines and tasks for individual analysis, combined group discussion and cross-case comparison, review of posted solutions, and requirements for clear connections between case analysis and course concepts.
Active Learning & Cognitive Load Theory
In a recent review of current policy and educational standards within STEM disciplines, Zhang et al. (2022) argued that structured instructional approaches such as direct instruction align more closely with cognitive-based learning theories. These theories are better at predicting learning gains and identifying how learning occurs. Cognitive load theory is one such theory based on three main assumptions. First, humans have the capacity to obtain novel information through problem-solving or from other people. Obtaining information from other individuals is more efficient than generating solutions themselves. Second, acquired information is confronted by an individual’s limited capacity to first store information in working memory and then transfer it to unlimited long-term memory for later use. Problem-solving imposes a heavy burden on limited working memory. Thus, learners often rely on the information obtained from others. Finally, information stored in long-term memory can be transferred back to working memory to deal with familiar situations (Sweller, 2020). The recall of information from long-term memory to working memory is not bound by the limits of the initial acquisition of information in working memory (Zhang et al., 2022).
Zhang et al. (2022) state that ‘there never is a justification for engaging in inquiry-based learning or any other pedagogically identical approaches when students need to acquire complex, novel information’ (p. 1170). This is clearly a one-sided argument that focuses on the acquisition of information rather than the application of acquired information. This also presents an obvious issue related to the efficiency of acquiring novel information. However, Zhang et al. (2022) did not argue against the use of active learning or inquiry learning strategies to help reinforce concepts, or the use of the same to support direct instruction.
The combination of active learning strategies with direct instruction can be modified using assumptions of cognitive load, which highlights the need to include instructional guidance with active learning strategies. The inclusion of clear and precise instructions or instructional guidance is critical for effective active learning strategies (Murphy, 2023). As de Jong et al. (2023) suggest, ‘guidance is (initially) needed to make inquiry learning successful' (p.9). We cannot assume that instructional guidance is implied through the name of the activity or can be determined from the previous learning experiences of students. Assumptions lead to ambiguous learning environments that lack instructional guidance, force learners to infer expectations, and rely on prior and/or potentially limited active learning experiences. In the following section, we offer suggestions for improving the use of active learning strategies in online and blended learning environments by adding instructional guidance.
Suggestions for Improving the Use of Active Learning in Online and Blended Courses
The successful implementation of active learning depends on several factors. One of the most critical barriers to the adoption of active learning is student participation. As Finelli et al. (2018) highlighted, students may be reluctant to participate demonstrating behaviors such as, ‘not participating when asked to engage in an in-class activity, distracting other students, performing the required task with minimal effort, complaining, or giving lower course evaluations’ (p. 81). These behaviors are reminiscent of petulant adolescents, often discouraging instructors from implementing active learning in the future. To overcome this, the authors suggested that providing a clear explanation of the purpose of the active learning exercise would help curb resistance to participation. More recently, de Jong et al. (2023) stated a similar perspective that ‘a key issue in interpreting the impact of inquiry-based instruction is the role of guidance’ (p. 5). The inclusion of clear and explicit steps for completing an active learning exercise is a necessary design strategy. This aspect of instructional guidance is relatively easy to achieve with the arrival of generative artificial intelligence (AI) tools used to support instructors. As Crompton and Burke (2024) pointed out in their recent review, ‘ChatGPT can assist teachers in the creation of content, lesson plans, and learning activities’ (p.384). More specifically, Crompton and Burke (2024) suggested that generative AI could be used to provide step-by-step instructions for students. To illustrate this point, we entered the following prompt into the generative AI tool, goblin.tools (https://goblin.tools/) ‘Provide instructions given to students for a carousel activity in a college class.’ The output is shown in Fig. 1. This tool is used to break down tasks into steps, and if needed, it can further break down each step into a more discrete sequence of steps.

Figure 1 . Goblin.tools instructions for carousel active learning exercises.
The omission of explicit steps or direct instructional guidance in an active learning exercise can potentially increase extraneous cognitive load (Klepsch & Seufert, 2020; Sweller, 2020). This pernicious impact on cognitive load is the result of the diversion of one’s limited capacity to reconcile problems (Zhang, 2022). Furthermore, the complexity of active learning within an online or blended course is exacerbated by the inclusion of technologies used for instructional purposes. Instructional guidance should include requisite guidance for tools used in active learning. Again, generative AI tools, such as goblin.tools, may help mitigate the potential burden on cognitive load. For example, the use of webconferencing tools, such as Zoom or Microsoft Teams, has been pervasive in higher education. Anyone who uses these tools can relate to situations in which larger groups are segmented into smaller groups in isolated breakout rooms. Once participant relocation has occurred, there is often confusion regarding the intended purpose or goals of the breakout room. Newer features, such as collaborative whiteboards, exacerbate confusion and the potential for excessive extraneous load. Generative AI instructions (see Figure 2) could be created and offered to mitigate confusion and cognitive load burden.

Figure 2. Zoom collaborative whiteboard instructions produced by goblin.tools
 
Generative AI has the potential to help outline the steps in active learning exercises. This can be used to minimize confusion and serve as a reference for students. However, instruction alone is often insufficient to make active learning effective. As Finelli et al. (2018) suggest, the inclusion of a rationale for implementing active learning is an effective mechanism to encourage student participation. To this end, we suggest the adoption of what  Bereiter (2014) called Principled Practical Knowledge (PPK) which consists of the combination of ‘know-how’ with ‘know why’ (Bereiter, 2014). This perspective develops out of learners’ efforts to solve practical problems. It is a combination of knowledge that extends beyond simply addressing the task at hand. There is an investment of effort to provide a rationale or justification to address the ‘know why’ portion of PPK (Bereiter, 2014). Creating conditions for learners to develop ‘know-how’ is critical when incorporating active learning strategies in online and blended courses. Instructional guidance can reduce ambiguity and extraneous load and can also increase efficiency and potentially equity.
What is typically not included in the instructional guidance offered to students is comprehensive knowledge that outlines the requirements for technology that is often employed in active learning strategies. Ahshan (2021) suggests that technology skill competency is essential for the instructors and learners to implement the activities smoothly. Therefore, knowledge should include the tools employed in active learning. Instructors cannot assume that learners have a universal baseline of technological competency and thus need to be aware of this diversity when providing instructional guidance.
An often-overlooked element of instructional guidance connected to PPK is the ‘know-why’ component. Learners are often prescribed learning tasks without a rationale or justification for their utility. The underlying assumption for implementing active learning strategies is the benefits of collaboration, communication, and collective problem-solving are clear to learners (Dring, 2019; Hartikainen et al., 2019). However, these perceived benefits or rationales are often not provided explicitly to learners; instead, they are implied through use.
When implementing active learning techniques or strategies in a blended or online course one needs to consider not only the ‘know-how,’ but also the ‘know-why.’ Table 1 helps to identify the scope of instructional guidance that should be provided to students.
 
Table 1. Recommended Type of Instructional Guidance for Active Learning




 


Know How


Know Why




Activity


Steps


Purpose / Rationale




Technology


Steps


Purpose / Rationale




Outcomes / Products


Completion


Goals




 
The purpose of providing clear and explicit instructional guidance to learners is to ensure efficiency, equity, and value in incorporating active learning strategies into online and blended learning environments. Along with our argument for “know-why” (Bereiter, 2012), we draw upon Murphy (2023) who highlights the importance of “know-how’ by stating, ‘if students do not understand how a particular learning design helps them arrive at a particular outcome, they tend to be less invested in a course’ (n.p.).
Clear instructional guidance does not diminish the authenticity of various active learning strategies such as problem-based or inquiry-based techniques. In contrast, guidance serves to scaffold the activity and clearly outline learner expectations. Design standards organizations, such as Quality Matters, suggest the inclusion of statements that indicate a plan for how instructors will engage with learners, as well as the requirements for learner engagement in active learning. These statements regarding instructor engagement could be extended to include more transparency in the selection of instructional strategies. Murphy (2023) suggested that instructors should ‘pull back the curtain’ and take a few minutes to share the rationale and research that informs their decision to use strategies such as active learning. Opening a dialogue about the design process with students helps to manage expectations and anxieties that students might have in relation to the ‘What?’, ‘Why?’ and ‘How?’ for the active learning exercises.
Implications for Future Research
We contend that a blend of direct instruction and active learning strategies is optimized by instructional guidance, which provides explicit know-how and know-why for students to engage in learning tasks and activities. The present discussion does not intend to evaluate the utility of active learning as an instructional strategy. The efficacy of active learning is a recurring theme in the academic literature, and the justification for efficacy is largely anecdotal or based on self-reporting data from students (Hartikainen, Rintala, Pylväs and Nokelainen, 2019). Regardless, the process of incorporating active learning strategies with direct instruction appears to be beneficial for learning (Ahshan, 2021; Christie & De Graaff, 2017; Mintzes, 2020), and more likely, the learning experience can be harder to quantify. Our argument relates to the necessary inclusion of instructions and guidance that make the goals of active learning more efficient and effective (de Jong et al., 2023). Scardamalia and Bereiter (2006) stated earlier that knowledge about dominates traditional educational practice. It is the stuff of textbooks, curriculum guidelines, subject-matter tests, and typical school “projects” and “research” papers. Knowledge would be the product of active learning. In contrast, knowledge of, ‘suffers massive neglect’ (p. 101).  Knowledge enables learners to do something and allows them to actively participate in an activity. Knowledge comprises both procedural and declarative knowledge.  It is activated when the need for it is encountered in the action. Instructional guidance can help facilitate knowledge of, making the use of active learning techniques more efficient and effective.
Research is needed on the impact of instructional guidance on active learning strategies, especially when considering the incorporation of more sophisticated technologies and authentic problems (Rapanta, Botturi, Goodyear, Guardia and Koole 2021; Varvara, Bernardi, Bianchi, Sinjari and Piattelli, 2021). Recently, Lee (2020) examined the impact of instructor engagement on learning outcomes in an online course and determined that increased instructor engagement correlated with enhanced discussion board posts and student performance. A similar examination of the relationship between the instructional guidance provided and student learning outcomes would be a valuable next step. It could offer more explicit guidance and recommendations for the design and use of active learning strategies in online or blended courses.
Conclusion
Education was disrupted out of necessity for at least two years. This experience forced us to examine our practices in online and blended learning, as our sample size for evaluation grew dramatically. The outcome of our analysis is that effective design and inclusion of student engagement and interactions with instructors are critical for quality learning experiences (Rapanta et al., 2021; Sutarto, Sari and Fathurrochman, 2020; Varvara et al., 2021). Active learning appeals to many students (Christie & De Graaff, 2017) and instructors as it can help achieve many of the desired and required outcomes of our courses and programs. Our review and discussion highlighted the need to provide clear and explicit guidance to help minimize cognitive load and guide students through an invaluable learning experience. Further, instructors and designers who include explicit guidance participate in a metacognitive process, while they outline the purpose and sequence of steps required for the completion of active learning exercises. Creating instructions and providing a rationale for the use of active learning in a course gives instructors and designers an opportunity to reflect on the process and ensure that it aligns with the intended purpose or stated goals of the course. This reflective act makes active learning more intentional in use rather than employing it to ensure that students are present within the learning space.
 
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Click here to open the video in a new window and watch it at https://mediaspace.msu.edu


Michigan State University named Brightspace by Desire2Learn as its centrally supported Learning Management System (LMS) in July 2012. Since then, Brightspace (generally shortened to D2L here on campus) provides the platform and tools for online and web enhanced student experiences. This video gives you a quick overview of not only the D2L platform and built-in accessibility helper Spartan Ally, but also other academic services and technologies available on campus. You'll also meet a team of academic technology support folks who can help you as you learn to leverage MSU-provided digital tools in your teaching. 

Timeline of video

Introductions
1:40 - D2L Overview
31:00 - Spartan Ally
44:48 - MSU Core Academic Technologies Overview
1:10:05 - Getting Help
1:17:05 - Assessment Services Overview
1:19:11 - Q&A Fun Part 2


 

Recording Using Kaltura Capture 
Click on Add New and Kaltura Capture. Make sure you are logged into Kaltura Mediaspace

This will launch a new screen where you can download Kaltura Capture. Click on the version you want (Mac or Windows)
Download and install Kaltura Capture 
 
Once it is installed you will need to launch it from Kaltura Mediaspace by clicking on Add New > Kaltura Capture. This will launch a new window where you can Click on Open KalturaCapture 

 
This will launch the program on your computer. 

 
Using the controls you can select your camera, screen and microphone. Most will only have one option, but you may have more if you have an external camera, dual monitors or an external microphone. If you only want to record camera or screen then click on the corresponding to turn off that recording option. You will know it is off because a red line shows through the icon. 
Once you have your settings in place, click on the Big Red Button to begin recording 

 
Launch your presentation and record your lecture. 
 
When you are done, click the stop button


This will bring up a screen where you can change the name of your recording, add in a description and add tags. Then click on Save and Upload to upload it to Kaltura Mediaspace. You will find it under My Media when you go back to Kaltura Mediaspace. 

 

The NavBar in D2L is the panel at the top of your course homepage that provides links to important tools and pages. When you open a new course, the NavBar includes a default set of links and drop-down menus to various D2L features. It usually looks something like the image below.

Why customize your NavBar?

You may not use all the tools included in the default NavBar; removing unused items can simplify navigation for students.
A streamlined, relevant NavBar helps students find what they need more efficiently.
You can personalize it to fit your teaching style, whether that’s clean and text-based or visual with icons.

How to customize your NavBar

On your course homepage, locate the NavBar at the top.
Click the three-dot menu icon on the right side of the NavBar.
From the dropdown, select “Customize this NavBar.”

Note: When you customize the NavBar, you're creating a new version of the MSU NavBar for your course.

Edit NavBar Links

Under the “Name” textbox, you’ll see a “Links” section listing all current NavBar buttons.
Hover over any link to delete it or drag to reorder.
Click “Add Links” to include new tools, even ones that normally appear in dropdowns, like “Class Progress,” without adding the entire “Assessments” menu.


Enable icon-based navigation (optional):
Prefer a more visual layout?Check the box labeled “Enable Icon-Based NavBar”, located just below the “Add Links” button. This will display icons instead of (or alongside) text for each link.
Preview and Save

Click “Save and Close” to preview your updated NavBar.
You can continue editing it at any time until it feels just right.
If at any time you want to see what the NavBar looks like, click "save and close." You can edit it as much as needed.

Tips:

Students don’t see all the same tools that you do (e.g., “Course Admin” and “Intelligent Agents”). Use the View as Student feature to check how the NavBar appears from their perspective.
Avoid changing the NavBar after students have access, as it may confuse them.

Example
Here’s what my instructor NavBar looks like:It includes only the tools I use, arranged in the order students need them. I’ve removed dropdown menus since I don’t use all the tools they contain. Students see a clean, focused navigation bar that matches how the course is structured.