What role can Gameful Pedagogy play in online courses?

COVID-19 caught everyone off guard in 2020. Suddenly, all classes had to be held online and instructors and students had to react quickly with minimal help. With time to reflect on these experiences, faculty ask themselves what methods are available to keep students engaged and motivated in an online or virtual environment.

At the Center for Academic Innovation, gameful pedagogy is one approach to increasing student engagement. This method of course design takes inspiration from how good games function and applies that to the design of learning environments.

One key goal of gameful pedagogy, as one might guess, is leveraging student motivation. To achieve that, course designers draw on elements of Self-Determination Theory, or SDT for short. This theory centers the power of intrinsic motivation as a driver of behavior. It sits on three primary pillars: autonomy (the power of choice a learner can have in their learning experience), competency (a feeling of accomplishment derived from completing a challenge), and belongingness (a feeling of being included and heard by the environment one is in or the people around them) (Deci & Ryan, 2000).

Yet, gameful pedagogy isn’t just about SDT. Practitioners also believe in an additive point-based grading system instead of traditional grading. In traditional deductive percentage-paced grading, learners start at 100% and have their points deducted as they learn, which does not align with what learning is about.

In a gameful course, learners are treated as novices when they first start a learning journey, so they start from zero and then work their way up to their goals. It also provides learners the freedom to fail. From a gameful point of view, it is unfair to expect learners to be “perfect” in learning environments because mistakes are common in learning, and they are great growth opportunities. Therefore, in gameful, learning environments that leave space for learners to explore and offer chances to make up for mistakes are preferred. It is important, however, to acknowledge that this freedom does not mean creating an out-of-control environment. Educators can still apply limitations by assigning different point values, requiring the completion of certain tasks to unlock others, etc. to ensure that students are working toward the learning goals. All of these approaches and more boil down to gameful pedagogy, and this course design method has been used in a wide range of classes, from higher education down to K-12. However, most use cases occurred in person before the 2020 COVID outbreak. Does gameful also work in online environments?

That turns out to be a great question for Pete Bodary, clinical associate professor of applied exercise science and movement science in the School of Kinesiology. He has taught gameful courses for several years, including MOVESCI 241. This course teaches body mass regulation assessments, principles, and strategies. It is constructed with an additive point-based grading scheme, all-optional assignments (a student has the autonomy to complete any combination of assignments to get to their desired grade/goal), a strong supportive network, and real-world relevant topics (diabetes, disordered eating, weight control, supplements and safety, etc.).

To maintain all assignments as optional while ensuring that students are on track to the learning objectives, Bodary assigns significantly more points to certain assignments to encourage completion. Some assignments include personal dietary intake and physical activity tracking, case studies, participation and reflections on dietary and physical challenges, and more.

In Winter 2023, he decided to give students more freedom to engage with the class lectures on top of the existing setup. Students could choose from three distinct sections: the in-person section, the synchronous virtual section, or the asynchronous virtual section. In the in-person section, students were required to attend lectures in person. In the synchronous virtual section, students could participate in lectures online while being live-streamed. The asynchronous virtual section allowed students the freedom to watch lecture recordings at their convenience without the obligation to attend lectures in real-time.

Did students in different sections perform differently in this course? The short answer is no, not significantly.

“Those who are remote do not have the ease of popping out a question, [meaning the ability to raise their hand and spontaneously ask questions], so that is one difference to consider. However, we maintain a pretty active [asynchronous] Q/A space. I don’t believe that they ‘performed’ differently,” Bodary said.

Students engage with the course content differently, but they are all motivated and learning in their own way. In fact, to find out students’ motivations in this course, Bodary deployed a U-M Maizey project. U-M Maizey is a generative AI customization tool that allows faculty, staff and students to build their a U-M GPT chatbot trained on a custom dataset. Bodary set up Maizey in the Fall 2023 term for the same course with a similar structure and prompted Maizey: What is the primary motivation of students?

By evaluating students’ activity data, Maizey summarized that students are primarily motivated by finding course materials relatable and beneficial to improving their personal and loved ones’ health and well-being, connecting knowledge and issues they garnered in their daily lives to class content, and implementing course content in real-world problems.

Looking at this example, three key characteristics emerge: controlled freedom for students to choose how to engage with the course, opportunities for students to make personal connections with course content, and possibilities for students to apply course content in real-world situations.

Tying these characteristics back to gameful pedagogy, there is alignment between them and the three components of SDT – autonomy, belongingness, and competency. Furthermore, the additive grading system and all-optional assignment design support student exploration and agency to choose assignments and coursework. The course format, whether in-person or online, didn’t impact students’ motivation. Instead, the fact that students can choose their own way to participate in the class may motivate them even more.

What’s important here isn’t modality (online, in-person, or asynchronously) but rather the content and design of the course. The success of MOVESCI 241 hinges on a carefully designed course where students can successfully meet the learning goals regardless of how they engage. The design of MOVESCI 241 is gameful, but not all gameful courses are designed this way. If you want to use gameful pedagogy to increase engagement in your course, you can start with these steps. You can also check out GradeCraft, a learning management system (LMS) built at the center to support gameful courses. Some key features of GradeCraft that make it a perfect companion for gameful courses are the additive grading system, mechanisms for tracking tangible progress (points planner, levels, unlocks, and badges), and functions for flexibility (highly tailorable for both instructors and students). Finally, if you want to learn more about gameful pedagogy or GradeCraft, please email us at [email protected], and staff would be happy to set up a conversation with you.

References:

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268.

Educators can use generative AI to transform dense, technical material into clear, easily understandable content. This improves students’ comprehension and makes the learning experience more inclusive to a wider audience. While students are growing in their knowledge of complex academic topics, sometimes academic terminology can be a barrier. Particularly early in the course, students may not yet be familiar with the jargon and language of your subject matter. In addition, you may have learners in your course with a wide range of educational and cultural backgrounds. Some of your students may be from countries outside of the United States, and English may not be their first language. By demystifying complex concepts, jargon, and metaphors with generative AI, educators are empowered to create more equitable and effective learning environments for our diverse array of learners.

For example, you can use the following example prompt to get started:

“Could you please rewrite the following text to an 8-10th-grade reading level (using Flesch-Kincaid Grade Level) and clarify any complex jargon: “[Your text]” Thank you!”

In this prompt, we are asking ChatGPT to rewrite text to an 8-10th-grade reading level on the Flesch-Kincaid Grade Scale. This is the reading level recommended for a general adult lay audience. Feel free to adjust this to fit your target audience.

Example: An Online Course on Neuroscience

Drafting

Now imagine that you are a renowned neuroscientist and a highly regarded faculty member at Michigan Medicine. You are interested in developing an online course that will bring neuroscience concepts to a lay audience. You are excited to get started, but as you begin to develop content, you quickly realize that your typical content is aimed at seasoned medical students and filled with jargon that may be daunting to those without prior knowledge. You realize that generative AI may be able to assist you in breaking down concepts into simpler terms.

You fill in the example prompt with some of the text from one of your old in-person presentations with key concepts that you would like to include in this online course:

“Could you please rewrite the following text to an 8-10th-grade reading level (using Flesch-Kincaid Grade Level) and clarify any complex jargon:

In our introductory exploration of neuroscience, we delve into the intricate workings of the central nervous system (CNS), focusing on the complex interplay between neurons and glial cells. We examine the fundamental principles of neuronal communication, emphasizing the role of neurotransmitters in synaptic transmission and the significance of action potentials in neural signaling. A detailed analysis of the brain’s functional anatomy is conducted, where we dissect the various lobes of the cerebral cortex, the limbic system, and the intricate circuitry of the basal ganglia, highlighting their contributions to cognitive and motor functions. Additionally, we explore the mechanisms underlying neuroplasticity, emphasizing the synaptic and non-synaptic changes that facilitate learning and memory. The course also delves into the pathophysiology of neurological disorders, exploring the etiology and progression of conditions like Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis, and discusses the latest advancements in neuropharmacology and neuroimaging techniques.

Thank you!”

In response to your input, ChatGPT gives you the following output:

“In our first look at neuroscience, we study how the central nervous system (CNS) works. This includes understanding how neurons (brain cells) and glial cells (supporting cells) interact. We learn about how neurons talk to each other, focusing on neurotransmitters, which are chemicals that help send messages in the brain, and action potentials, which are electrical signals in neurons.

We also look closely at the brain’s structure. This includes studying the different parts of the brain, like the lobes of the cerebral cortex, the limbic system (which deals with emotions), and the basal ganglia (important for movement), and how they help us think and move.

The course also covers neuroplasticity, which is how the brain changes to help us learn and remember things. We look at both synaptic changes (changes at the connections between neurons) and non-synaptic changes (changes within the neurons themselves).

We also learn about neurological disorders like Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis. We study what causes these diseases and how they progress. Finally, we discuss new developments in treating brain disorders and ways to look at the brain, like neuropharmacology (study of how drugs affect the brain) and neuroimaging (brain scanning techniques).”

In this example, ChatGPT keeps all of the main concepts intact while using simpler language, providing definitions of terminology used (rather than removing it entirely), and breaking the large paragraph into more digestible, smaller paragraphs or chunks.

Refining

As a content expert, it is important to read through the output and ensure that all key concepts remain intact. It is also up to you to determine whether the revisions are sufficient and appropriate for your audience. You may choose to ask for stylistic revisions as well. For example, ChatGPT wrote the text as though the course is currently happening. However, you plan on delivering this information at the beginning of the course to talk about what the learner will learn. This is your preference.

You can ask ChatGPT to revise with the following:

“The course hasn’t happened yet. Please rewrite this to account for that. For example, using the phrase “we will” throughout.”

ChatGPT will then go through and make the requested revisions to the text using the appropriate tense that you indicated in your input:

“In our upcoming exploration of neuroscience, we will delve into how the central nervous system (CNS) functions. This will involve understanding the interaction between neurons (brain cells) and glial cells (support cells). We will learn about neuronal communication, focusing on neurotransmitters, which are chemicals that help send messages in the brain, and action potentials, the electrical signals in neurons.

We will also closely examine the brain’s structure, studying different parts such as the lobes of the cerebral cortex, the limbic system (which deals with emotions), and the basal ganglia (important for movement), and understanding how they influence our thinking and movement.

The course will cover neuroplasticity, which is how the brain changes to facilitate learning and memory. We will observe both synaptic changes (alterations at the connections between neurons) and non-synaptic changes (alterations within the neurons themselves).

Additionally, we will explore neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis. We will investigate the causes of these diseases and their progression. Finally, we will discuss the latest developments in treating brain disorders and methods for examining the brain, such as neuropharmacology (the study of how drugs affect the brain) and neuroimaging (brain scanning techniques).”

Continue to refine as needed. Consider feeding into the chat examples of your tone of voice so that the content is not only accessible for learners but also contains a human element. In addition, you can increase your expectation of language understanding as your students grow in their knowledge and your expectations of understanding increase.

Echoes of “Can we have a study guide?” still reverberate through the virtual classrooms, even as summer takes hold and the allure of relaxation sets in. Study guides offer a temporary solution to students’ hunger for knowledge, providing them with the fish they need to satisfy their immediate needs. This approach, however, creates a cycle of dependency, requiring another fix before the next test opens. This is not the way. Instead of spoon-feeding, students should be taught to fish.

Though study guides have their merits, their direct impact on learning is not always evident. Tests can be a significant source of stress for students, which in turn hampers their performance. Study guides can help alleviate this anxiety and improve exam scores (Dickson, Miller, & Devoley, 2005), but they don’t necessarily foster deep, long-term learning. If the goal is to guide students’ online study habits before a test, then they should receive guidance not only on what to study but also on how to study effectively. Problem Roulette is the way.

Problem Roulette is an invaluable personalized online learning tool that directs students’ attention to the study skills that work best for them. It offers a collection of previous test items for students to practice with and, starting this Fall 2023, will begin providing tailored study tips based on proven theory and algorithms designed to enhance test performance. In essence, Problem Roulette will not only feed but also teach students to fish. It will give them the confidence boost they crave through exposure to test-like items, while teaching them personally relevant study skills that can be applied to new situations.

How will Problem Roulette work in online learning environments? In short, it will harness the power of gameful learning. As students engage with practice test items, the system will collect statistics on their performance, which will then be visualized and presented on a student-facing dashboard. This feedback will include information on the number of problems completed and the number of consecutive correct answers. These metrics will be compared with predefined volume and streak goals established through previous research (Black et al., 2023), known to maximize course performance. Consequently, the game for students becomes achieving their target volume and streak goals, which intrinsically incentivizes their study. To attain these goals, however, they must study effectively by consistently answering questions correctly in a row. As students strive to meet their volume and streak targets, they will simultaneously discover the study habits that yield the best results for them individually.

In the realm of online teaching, Problem Roulette emerges as an empowering force, equipping students with the skills they need to become self-sufficient learners. It shifts the focus from mere information consumption to active engagement, encouraging students to take charge of their own learning journey. By embracing Problem Roulette, educators can foster a generation of online students who not only excel academically but also possess the essential skills to adapt, learn, and thrive in the digital age.

Generative AI can be a valuable asset to instructors looking for assistance with creating various aspects of course design. For example, generative AI, such as ChatGPT, can be a valuable tool for educators in drafting learning objectives. Using GenAI in any setting is usually a process of drafting and then refining prompts until the desired result is achieved. In this article, we will outline some ways to generate and refine learning objectives for a course.

Learning objectives are concise statements that articulate what students are expected to learn or achieve in a course. They play a crucial role in guiding both teaching strategies and assessment methods, ensuring that educational experiences are focused and effective. Clear and well-defined learning objectives are essential for aligning educational activities with desired learning outcomes. By analyzing a vast array of educational content and pedagogical methods in its training data, AI can offer a wide range of learning objective recommendations, which educators can then build off of, using their knowledge as experts in the field.

Articles

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Generative AI for Course Design: The Basics

Learn more foundational information about Generative AI

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Learning objectives and outcomes

How to craft good learning objectives for instruction

Using your preferred GenAI tool, here is an example prompt that you can use to get started:

Please create learning objectives for a course on [topic], focusing on [big idea 1], [big idea 2], and [big idea 3]. The learning objectives are concise one word sentences beginning with an action verb. Please refer to Bloom’s Taxonomy if you get stuck. Thank you!

This example prompt can be modified to fit your needs. For example, you may choose to add more ideas and give additional context about the course. The more detail and context you provide in your input, the better the AI output will be. So please feel free to add in outlines, syllabi, or any other materials that may help your GenAI assistant better understand your vision.

Example: An Online Course on the Cold War

Drafting Objectives

Now that we have our example prompt, let’s see an example of it in action. Imagine you are an instructor for an introductory online course on the Cold War. You plan to use ChatGPT to generate some ideas on potential learning objectives to get you started and guide your curriculum creation. You already have some general ideas on what you want to cover: causes, major events, and overall impact. You fill in the prompt as so:

Please create learning objectives for an online introductory course on the Cold War, focusing on its causes, major events, and impact on international relations. The learning objectives are concise one word sentences beginning with an action verb. Please refer to Bloom’s Taxonomy if you get stuck. Thank you!

You press enter and ChatGPT provides you with the following learning objectives:

“Identify the primary causes of the Cold War.

Describe major events and turning points during the Cold War.

Explain the ideologies of the United States and the Soviet Union during the Cold War.

Analyze the impact of the Cold War on international relations.

Compare and contrast different perspectives on key Cold War incidents.

Evaluate the effectiveness of diplomatic strategies used during the Cold War.

Summarize the global consequences of the Cold War in the post-war era.

Discuss the role of nuclear weapons in Cold War policies and strategies.

Interpret historical documents and sources related to the Cold War.

Assess the influence of the Cold War on contemporary international politics.”

Refining

It is now up to you as the expert to determine which learning objectives are the most relevant and how you should go about revising them. For example, you may look at the list and notice that there are no learning objectives that ask the learners to create something with the knowledge they’ve acquired throughout the course (e.g., a final project). You return to ChatGPT and ask the following:

Please craft a learning objective that has learners create something relevant to the topic at hand.

In response, ChatGPT provides you with the following:

Design a multimedia presentation that illustrates the timeline of the Cold War, integrating key events, major political figures, and their impacts on international relations.

If you disagree with this suggestion, you can reply with “More?” to get additional ideas. ChatGPT will then provide you with a longer list:

“Develop a documentary script focusing on a specific aspect of the Cold War, such as the Cuban Missile Crisis or the Space Race, incorporating primary sources and historical analysis.

Construct a series of infographics that visually represent the economic, political, and social changes in different countries during the Cold War era.

Compose a series of fictional letters or diary entries from the perspective of individuals living in different countries during key moments of the Cold War, reflecting their experiences and viewpoints.

Create a podcast series that discusses the influence of the Cold War on modern international relations, including interviews with experts or simulations of discussions between historical figures.

Produce an interactive map or timeline that allows users to explore the progression of the Cold War, highlighting significant events, treaties, and shifts in alliances.

Formulate a role-playing game where players assume the roles of world leaders during the Cold War, making decisions that could change the course of history.

Design a virtual museum exhibit showcasing artifacts, propaganda, and media from the Cold War, with descriptive and analytical captions explaining their significance.”

You can repeat this process as often as you’d like – adjusting the prompt and adding additional context (e.g., outlines, key ideas, information about your teaching style) to get better responses. When formulating responses for you, ChatGPT looks at the entire chat log so it is recommended that you continue to add to the same chat for best results.

In our next article, we’ll explore how to use Generative AI to improve accessible language in your course.

Introduction

Education is undergoing a significant transformation as generative artificial intelligence continues to develop at a rapid pace. It is now easier than ever for educators to experiment with generative AI in their practice and see for themselves how generative AI can be leveraged during the course development process to brainstorm, synthesize, and draft everything from communications to students to learning objectives.

Generative AI: The Basics

Before experimenting with Generative AI (GenAI), it is helpful to have some high level foundational knowledge of how GenAI works. Essentially, GenAI functions using advanced machine learning algorithms, specifically neural networks, which emulate human brain processing. These networks are trained with large datasets, enabling them to learn language patterns, nuances, and structures. As a result, GenAI can produce contextually relevant and coherent content, a capability exemplified in tools like ChatGPT.

To better understand how GenAI tools like ChatGPT work, let’s look at a breakdown of the acronym “GPT”:

GPT stands for “Generative Pre-trained Transformer.” It is a type of artificial intelligence model designed for natural language processing tasks. “Generative” refers to its ability to generate text based on a combination of the data it was trained on and your inputs. It can compose sentences, answer questions, and create coherent and contextually relevant paragraphs.

The term “Pre-trained” indicates that the model has undergone extensive training on a vast dataset of text before it is fine-tuned for specific tasks. This pre-training enables the model to understand and generate human-like text.

Finally, “Transformer” is the name of the underlying architecture used by GPT. Transformers are a type of neural network architecture that has proven especially effective for tasks involving understanding and generating human language due to their ability to handle sequences of data, such as sentences, and their capacity for parallel processing, which speeds up the learning process.

The GPT series, developed by OpenAI, has seen several iterations, with each new version showing significant improvements in language understanding and generation capabilities. Many of these improvements are due to the model continuously training on user inputs. OpenAI has made it transparent that your data is being used to improve model performance and you can choose to opt out by following the steps that will be outlined in the upcoming articles on how to use GenAI tools for course design, learning objectives and more.

Does it matter which GenAI Tool I use?

Not really. Individuals may find preferences for one tool or another based on response speed or comfort with the interface. You may wish to use a tool that can opt out of using personal data for training purposes. Most of the GenAI tools are generally similar.

Next Steps and Considerations

In educational contexts, the incorporation of GenAI tools, such as ChatGPT, will potentially reshape our approach to content creation and improve efficiency for educators who often find themselves pressed for time. However, it is important to note the importance of acknowledging the technology’s limitations, such as potential biases, outdated information due to insufficient training data, and incorrect information – often referred to as “hallucinations.” It is vital that you always fact-check and revise GenAI outputs to maintain the integrity and high quality of your content.

In conclusion, by leveraging GenAI tools like ChatGPT, educators can navigate course design with greater ease and efficiency. From drafting learning objectives and engaging course titles to simplifying complex academic language and brainstorming assessments, GenAI has the potential to be an invaluable asset to your design work. However, it is critical to remember that these tools come with limitations, including potential biases and inaccuracies. By combining the strengths of GenAI with the expertise and critical oversight of educators, we can efficiently create new experiences for our learners.

Introduction

If you have been anywhere where teaching is involved, you have probably heard mention of “learning styles.” “I’m a visual learner” vs. “I’m a hands-on learner” or “My instructor didn’t teach in my learning style” are all the types of commentary that are common when some individuals talk about their own learning. Although it is deeply appealing to be able to categorize individuals into easy methods of learning, unfortunately, it is deeply flawed, has little empirical evidence to support it, and might cause more problems than it solves.

What are learning styles?

To best understand why learning styles are problematic, it is important to clearly define learning styles. The idea of learning styles is that there are stable, consistent methods that individuals take in, organize, process, and remember information, and by teaching those methods, students learn better.

One popular concept in learning styles posits that the modality of information is critical – a “visual” learner learns best by seeing versus an “auditory” learner who learns best by having things spoken or described to them. Learning style theory would suggest that by using visual aids, a visual learner would organize and retain information better than say, an auditory learner. The implication is that matching modality information to the modality of learning style is critical to student success.

A note: there are additional and related concepts of “cognitive styles,” “learning strategies,” and “learner preferences” that can sometimes be used interchangeably with “learning styles.” For this article, “learning styles” refers specifically to the theory that there are ways that individuals learn best. In contrast, learning preferences suggest that there are ways people prefer to receive information, but it may not impact learning.

At face value, the concept of learning styles makes sense. Individuals learn differently. Most educational settings are trying to reach large numbers of students in personalized ways. It would be useful to have an easily applied theory that would help all students learn! As educators, we want to recognize the “uniqueness” of each student and help learners in any way we can. This desire has led educators to look for easier ways to navigate the complexities of teaching. Unfortunately, learning is not that simple.

Do learning styles really exist?

In general, most learning style theories make two presumptions:

Individuals have a measurable and consistent “style” of learning, and
Teaching to that style of learning will lead to better education outcomes, and conversely, teaching in a contradictory method would decrease achievement.

In other words, if you are a visual learner, you should learn best if you see things, regardless of the situation. If you are a kinesthetic learner, you will learn best if you can physically manipulate something, regardless of the topic. However, neither of these two assumptions shows any grounding in research. These two propositions are where we can see the concept of learning styles breaking down.

Are learning styles measurable and consistent?

Did you know that there are actually over 50 different theories of learning styles by various researchers? Researchers have been trying for years to find a correlation between individuals and how to help learning. Some theories suggest the modality of learning matters (like the common VARK theory) while others propose details like time of day and temperature of the room define a learning style. One study that suggested using a cell phone was a learning style (Pursell, 2009). Just the number of different styles makes it difficult to measure and make sense of an individual style.

In addition, most learning style inventories rely on a student’s self-report about how they perceive they learn best. These self-reports are generally not validated in any way. Generally, humans tend to be poor judges of our own learning. Therefore, these surveys are generally measuring “learner preference” rather than “learning style.” You may think you are an auditory learner but until it is validated that you objectively learn better through audio format, it is a preference, not a style.

Also, when reporting results, many studies will rely on “student satisfaction” as a measure of success, or rely on students’ reflections as a measure of success in a class. For example, many measures of learning styles will ask students how they believe they learn best. Unfortunately, satisfaction with a class or a student’s recollections of success are subjective measures, and generally not accurate (Kirschner & van Merriënboer, 2013, Kirschner, 2017). While understanding a learner’s preference is useful as is understanding student satisfaction with a lesson, it does not have the same weight as necessitating teaching to that preference.

Finally, ”styles” are unstable and unreliable. The research on learning styles has suggested that these preferences may be unstable – they be topic-specific, but they also change over time (Coffield et al., 2004). That means that although an individual may be a kinesthetic learner in history this week, that person is a visual learner in math when talking about calculus (but not about geometry), or prefers to learn how to ride a bike kinesthetically instead of reading about it in a book. This questions whether a learning style is a “trait” (or something stable and persisting for a person) or a “state” (something that is temporary and may change). Learning styles as a state of mind are not particularly useful. How can a teacher know the preference of an individual student today in a given subject?

Does teaching a learning style result in better learning?

Even more importantly, however, is the second assumption – does teaching to an individual’s learning style lead to achievement? Simply put, there is no evidence that supports teaching to a person’s specified learning style results in better learning (Alley, et. al., 2023; Cuevas, 2015; Kirschner & van Merriënboer, 2013; Krätzig & Arbuthnott, 2006; Pashler et al., 2008; Rogowsky et al., 2020). No study has shown that teaching to an identified learning style results in better retention, better learning outcomes or student success. Instead, we see that teaching to a self-identified learning style has no impact on learning in children or adults (Krätzig & Arbuthnott, 2006; Paschler et al., 2008; Rogowsky et al., 2015, Rogowsky et al., 2020). Some research suggests that some students performed better on tasks when taught in a different modality than their self-identified “learning style” (Krätzig & Arbuthnott, 2006, Rogowsky et al., 2020). Most studies of learning styles use a methodology that uses multiple styles to all learners – meaning that there is no way to isolate learning style to teaching method. This leads us to ultimately conclude that while the concept of learning styles is appealing, at this point, it is still a myth.

Alternate explanations to learning styles

Anecdotally, there are many stories about the success of leveraging “learning styles.” If learning styles are not empirically supported, how are these successes explained? There are alternative explanations for why teaching in multiple methods increases achievement that do not prescribe students into style categories. Multi-modal learning explains how learning improves with various methods of teaching.

Learning requires sustained attention. Therefore, if an educator can capture and maintain students’ attention, students’ learning outcomes likely improve. Providing engagement with content in multiple forms – be it through hands-on activities, or different modalities – makes students pay attention to content in different ways, and requires learners to integrate knowledge in new ways. If an educator is using multiple methods and modalities, it’s just more interesting, and students pay more attention, which leads to better learning. Mayer and colleagues (2001, 2003) have extensively studied how students learn with visuals and audio, and the interaction of the two. What he and his colleagues suggest is that by providing dual streams of information in multiple methods engages learners to work harder at understanding the material, which leads to better learning. It may be that the research on learning styles is actually showing that teaching with different modalities is just more interesting to students rather than catering to a particular style of learning (Krätzig & Arbuthnott, 2006).

Why learning styles are dangerous

While the intentions of learning styles are good, the implications of learning styles are more destructive than helpful. On the positive side, reflecting on how one learns is always a lesson. However, by focusing on a style suggests that learners are passive vessels at the whim of the method of teaching. Ultimately, most educators want students to actively engage in their learning. The best learning takes place when an individual can connect and incorporate information into his or her personal experiences and understanding. By focusing on a student’s learning style we reinforce a simplistic view of learning. Learning styles suggest that individuals have one way to learn best. Unfortunately, learning is complex, and not easy. This is hard and takes time! It has very little to do with the way information is handed to a learner, but rather, how the learner processes that knowledge once they have it. It is important to remember – learning is within the control of the learner.

Thinking critically about learning styles

If learning styles do not impact an individual’s ability to learn, why is there so much talk about them? Articles and books are still being published about learning styles and how to tailor teaching to reach every style. Research on teaching and learning is a complicated discipline, and being able to examine theories and concepts like learning styles critically is important to anyone working in education. The challenge is to keep a skeptical eye when you hear about research supporting learning styles and ask the right questions to make sure you are getting good information.

What should you think about the next time you encounter learning styles in the wild?

What framework of learning styles are they referring to? Some are more empirically vetted than others. The most popular learning style VARK (Visual-Auditory-Read/Write-Kinesthetic) is also the least validated. Find out more about the learning style being discussed.
How are they measuring both learning style and success? Are they self-reported? Are they looking at academic results or a self-report of satisfaction with learning?
Is the study carefully controlled? Many studies fail to tailor the learning to a particular style. Rather, the lesson uses all the styles to reach all the students. There is no way to truly measure success.
Learning styles can be controversial with some people. They aren’t necessarily harmful if they encourage people to reflect on teaching and learning in different ways. They can be harmful if students believe that their learning is outside their control.

References

Alley, S., Plotnikoff, R. C., Duncan, M. J., Short, C. E., Mummery, K., To, Q. G., Schoeppe, S., Rebar, A., & Vandelanotte, C. (2023). Does matching a personally tailored physical activity intervention to participants’ learning style improve intervention effectiveness and engagement? Journal of Health Psychology, 28(10), 889–899. https://doi.org/10.1177/13591053221137184

Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Should we be using learning styles? What research has to say to practice: Learning & Skills Research Center.

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You may hear different terminology as you begin online teaching. The following are some working definitions to help differentiate the terms used when discussing teaching leveraging technology.

University of Michigan Registrar defined instruction-mode definitions:

In-Person: Indicated by a (P) on the course listing, in-person classes are the traditional face-to-face classes. Instructors and students meet at a designated time in a designated place each week.

Online: Indicated by a (D) by the registrar, online classes do not meet in person. All learning activities take place online. Online classes can be:

Synchronous: There are at least some designated times for students and instructors to meet simultaneously in a tool like Zoom. Synchronous classes will specify the day and time for meetings in the registrar. Online synchronous classes are generally structured more like an in-person class, except in a video conference format, however, there is often more asynchronous work as well.
Asynchronous: There is no requirement for simultaneous meetings. Most of the interactions between students and instructors happen through other communication tools. Lectures may be pre-recorded. While video conferencing may be utilized, it is generally an optional component.

Hybrid: Indicted by an M by the registrar (for Mixed), these classes have both a required in-person component as well as an online component. Hybrid classes could meet once a month (rather than once a week), or have one class online and one class in person each week. Meeting days/times need to be specified.

Other Online Teaching Definitions:

Hy-flex: A course where students can choose on a day-by-day basis whether to attend class in person or via a synchronous videoconference session. Because Hyflex classes need to account for room capacity, they would be considered in-person. Currently, the University of Michigan does not have a specific indicator for hy-flex classes.
Blended learning: While some people use “hybrid” and “blended” interchangeably, generally blended learning includes any kind of online component to supplement instruction. Even if a course does not reduce face-to-face meetings, maintaining and utilizing a Canvas course to extend the classroom indicates a blended learning experience. Blended learning takes advantage of online technology to enhance in-person classrooms.
Emergency Remote Teaching: Suddenly altering teaching modalities from in-person to online due to an emergency. Emergency remote teaching is a type of online teaching, however, it generally does not involve careful planning of instruction specifically for that modality.

The rapid shift to emergency remote instruction during COVID-19 left many instructors questioning how best to assess students, even well after classes resumed. Concerns about academic integrity left some wondering if using online tests made students more likely to violate academic integrity rules. Online test proctoring made news in many higher education settings as a way to ensure academic integrity. However, others have argued it is a violation of students’ privacy.

What is Online Proctoring?

You may be familiar with proctoring in a face-to-face or residential setting where a designated authority oversees an exam in a controlled, specified environment. Similarly, online proctoring is a service that monitors a learner’s environment by either a person or an artificial intelligence algorithm during an online exam. However, the environment an online proctor oversees is a learner’s personal environment. This monitoring can take the form of videotaping, logging students’ keystrokes, browser data, location data, and even biometric data like test-taker eye movements.

Advocates of online proctoring cite concerns about academic integrity in the online environment as a reason to implement proctoring (Dendir & Maxwell, 2020). Some even suggest that students do not mind the additional security because they believe it supports the integrity of the test and/or degree.

Online proctoring in the media and research

While onsite-proctoring for academic integrity may seem reasonable, there have been questions about monitoring a learner’s home environment. monitoring a learner’s home environment has the potential for harm. Online proctoring can be perceived as invasive by students, as personal information about one’s location and physical data is recorded that is not otherwise necessary for an exam. Several institutions, like U-M Dearborn, University of California Berkeley, University of Illinois, and the University of Oregon have placed limitations on, if not discontinuing altogether the use of third-party proctoring services. Institutions cite issues of accessibility, bias, concerns about student privacy, and institutional culture as reasons to discourage third-party proctoring. Student and faculty groups have publicly advocated for institutions to discontinue security features like locked-down browsers and third-party monitoring. At the University of Michigan Ann Arbor, third-party proctoring generally involves a separate fee and may be expensive, but still available through vendor partners.

Most of the academic research involving the use of online proctoring has focused on academic integrity, rather than the impact of proctoring itself. Wuthisatian (2020) found lower student achievement in online proctored exams compared to the same exam proctored onsite. Those students who were the least familiar with technology and the requirements for setting it up performed the most poorly. In addition, students who have test anxiety may experience even more anxiety in certain proctoring situations (Woldeab & Brothen, 2019). With further research, we may find the problem may not necessarily be proctoring, but rather the burden and effort of technology on students when taking an online exam.

Problems with internet connections or the home testing environment may be beyond students’ control. The lack of ability to create a “proper” testing environment raised students concerns about being unjustly accused of cheating (Meulmeester, Dubois, Krommenhoek-van Es, de Jong, & Langers, 2021)

What are the alternatives to proctoring?

Ultimately, only the instructor can determine whether proctoring is the right choice for a class and sometimes proctoring may be the best choice for your discipline, field, or specific assessment. Particularly in a remote setting, it may feel like the integrity of your assessment (particularly a test) is beyond your control, so proctoring may feel like the only option. However, there are alternatives to proctoring exams, from using exam/quiz security measures, to re-thinking a course’s assessment strategy to deemphasize exams. If you are concerned about how and what you are assessing, the Center for Research on Learning and Teaching provides resources and consultations to discuss academic integrity and different methods of assessment. We also recommend CAI’s Faculty Proctoring document if you have questions about proctoring.

Learn more:

U-M Dearborn policy on online proctoring
Center for Academic Innovation: Faculty Proctoring Resource
Center for Academic Innovation: Student Proctoring Resource [coming soon]
Center for Academic Innovation: Student Conduct and Academic Integrity
Center for Research on Learning and Teaching: Academic Integrity in the Classroom
Center for Research on Learning and Teaching: Assessments

How this will help:

Understand key principles of ethical community engagement and how to operationalize them when designing and teaching online community-engaged courses.

Learn concrete suggestions, resources, and strategies for addressing the needs of students and community partners during online community-engaged teaching.

Discover ways that Ginsberg Center staff can support your community-engaged course, from finding remote engagement opportunities for students to helping prepare your students to partner with communities and maximize their learning.

The basics

Community-engaged learning is when “students engage in activities that address human and community needs, together with structured opportunities for reflection designed to achieve desired learning outcomes” (adapted from Jacoby, 1996). In your face-to-face classes, you may have experience working directly with community partners in various ways to integrate students into community-engaged learning.

But what does this look like in an online class? Or when the community engagement is virtual? When shifting to an online environment, many community-engaged instructors at the University of Michigan have expressed the difficulty of balancing students’ needs for accessible and empathetic virtual instruction, community partners’ rapidly-shifting needs and priorities, and general public health and safety concerns. However, online instruction does not mean isolated – instead, it is possible to leverage technology as well as use it as a lens to examine community engagement.

The Ginsberg Center is a community and civic engagement center with a mission to cultivate and steward equitable partnerships between communities and the University of Michigan in order to advance social change for the public good. Our Best Practices for Online Community Engaged Teaching and Learning provides tangible suggestions, resources, and strategies that are rooted in the 6 key principles that guide our work. The guide also synthesizes research on online service-learning and community engagement with a particular focus on the opportunities available at the University of Michigan. We offer highlights from our guide below:

1. Connecting Civic Learning Across Contexts:
We support students’ integrative learning across classroom, co-curricular, personal, and community settings. Reflection is a critical component of this integration throughout the partnership process.