Welcome to the Teaching & Learning Guide for Embracing Personalized Learning! 

This guide includes best practices and tips to help you:

• Define personalized learning and its influences

• Identify which personalized learning techniques fit your teaching

• Apply personalized learning practices to your classes

Teachers know that their students learn differently and at various paces. Yet most of our courses are set up as one-size-fits-all. “The promise of personalized learning,” according to Brod (2024), “is that the variability between and within learners can be embraced to improve education for all learners.” 

The most commonly referenced definition of personalized learning was provided in 2010 by the US Department of Education Office of Educational Technology, which defined personalized learning as “instruction that is paced to learning needs, tailored to learning preferences, and tailored to the specific interests of different learners” (US DOE, 2010). Personalized learning allows students to progress through content based on demonstrated learning instead of seat time. In turn, students feel more empowered to control their own learning and make coursework relevant to their interests. Research on personalized learning shows that it can even increase student retention (Cevikbas and Kaiser, 2022).

Scholarship on teaching and learning often focuses on two aspects of personalization: allowing students to self-regulate their learning and using adaptable learning activities for students to complete based on their current knowledge, skills, and preferences (Brod, 2024). A combination of both approaches is needed to create a successful personalized learning environment. 

The approach of handing over control to students and allowing them to self-regulate their learning starts from the assumption that students know themselves better than a teacher or even a tutor would. Research on self-regulated learning has shown that sometimes students don’t have the meta-cognitive skills to successfully self-regulate their own learning. Self-regulation, though, is an important skill for students to learn so combining it with adaptable learning can meet the needs of individual students while also teaching those students how to understand their own learning.

Adaptable learning allows students to complete the same or similar tasks in different ways and/or at different times. Adaptable learning often requires students to make sense of materials in their own way, which requires them to pull from prior knowledge and skills. Assigning adaptable learning tasks allows students to practice their self-regulating skills and empowers students to effectively manage their own learning (Bernacki et al., 2021)

The assumption many instructors have is that to successfully design a personalized learning environment, they must take into account all learners and their potential prior knowledge, motivations, goals, beliefs, interests, skills, and experience and provide an instructional experience that is responsive to these varying needs (Bernacki et al., 2021). This is a tall order for most instructors. Research has shown, though, that instructors don’t need to know everything about their students in order to successfully design for personalized learning.

Instead, instructors can design for adaptability. An adaptable learning approach is student-centered. Instructors can allow students to alter the design of a learning task by changing the timing, order, context, or more. With adaptability, the goal or outcome of a learning task can remain the same for every student, while the methods or product of the task can be individualized by the learner. Or, students can set individual outcomes or goals based on their needs and interests. Either way, instructors communicate to students that they value their individual needs and desires and trust them to adapt the course to fit those needs. 

The goal of adaptive learning is to foster students’ personal growth as they learn how to complete tasks in their own way which makes the course more relevant (Cevikbas and Kaiser, 2022). Studies in personalized learning show that when students have opportunities to personalize course content, they are more likely to achieve deep learning. In other words, “if learners are intrinsically committed to a given topic, problem, or profession, they will learn” (Zmunda et al., 2015). This deep learning can in turn increase student motivation and engagement and reduce student dropout rates.

There are many ways to add personalization to a learning environment. A course does not need to go “all in” on personalized learning to see the benefits. Instead, instructors can introduce aspects of personalized learning into current course goals. As Zmunda, et al (2015) state, “Personalized learning has students envision the investigation, idea, or challenge [...] The larger aims of a given course or program are fixed, but the content of the exploration is shaped by the individual tasks.”  

Below, you’ll find several ideas for embracing a personalized learning approach in your courses.

Goal-setting

Goal-setting is a beneficial skill for students to learn that can benefit them in any future career. Asking students to set individual goals based on the broader course goals can be a useful way to encourage students to make the content their own. Goal-setting can happen anytime throughout the course but may be most useful before significant course projects. Encourage students to set goals that are specific and achievable. For example, rather than setting a goal of “I want to get an A on this project,” encourage students to think about how this project could benefit them in their future careers or how they could make the project relevant to their communities. 

If students are struggling to set relevant goals, it may be helpful to start with passion or interest surveys. There are plenty of surveys online that can guide students to think about what they are passionate about more broadly. Or, instructors can create non-graded quizzes to gauge which aspects of the specific content each student is most interested in. 

After setting specific goals, you can choose deliverables with students based on those goals. Or, you can offer a variety of deliverables for students to choose from so they can tailor the content to their own interests and goals.

Differentiated and generative activities

Two types of adaptable learning activities are differentiated tasks and generative tasks. Both allow instructors to assign activities to students flexibly and with their individual needs in mind. 

• Differentiated tasks are tasks or questions that have multiple solutions and allow different entry points. They can be worked on in different ways according to the student’s prior knowledge and current comprehension. Examples include mathematical equations that have multiple solutions or tiered assignments that use the same core concept with various steps that students can do based on difficulty. 

• Generative tasks require learners to go beyond the provided material and to actively make sense of the material themselves. Generative tasks ask students to start with a core concept and apply their own prior knowledge, skills, and interests to make the content relevant. Examples include asking students to create a concept map to explain a theory, concept, or phenomenon. Another example is a larger project where students produce a public-facing product based on a problem.  

Flexible scheduling and pacing

Allowing students to complete activities in their own time, within reason, can be one way to add personalization. By adding flexible scheduling and student-led pacing, instructors consider the reality that people learn at different rates. 

An easy way to introduce flexible scheduling is to set an end goal and several checkpoints for students. Let students know the date range they have to complete their assignments, then require them to submit their progress incrementally. 

Another way to add flexibility is to create learning plans with individual students or offer 2-3 options for pacing and allow students to choose which option works best for them. For example, some students may prefer to turn something in once a week whereas others may like to work on a big project over a few weeks and turn it in all at once. You can have students choose which course blueprint suits their preferred pacing.

One concern many instructors have about pacing is that it requires them to grade or be ready to offer feedback constantly. It is true that flexible scheduling requires more flexibility from the instructor. You can let students work independently for a little while, though, before offering feedback. Allowing students to solve issues themselves can help them gain self-regulation skills.

Activity playlists

Once instructors consider the benefits of types of activities and student choice and flexibility, one way to add personalization is through activity playlists. Playlists combine the ideas of varying activities and flexible pacing. They can empower students to effectively manage their own learning and foster their use of learning activities to promote deeper learning (Brod, 2024). 

To create an activity playlist, include a variety of activities that require different skills and have different difficulty levels and assign each activity a point value. Then, set a minimum point value students must reach. Students will have to choose their activities based on their interests and skills in order to reach the required points. Playlists give students multiple opportunities to show their knowledge.

Activity playlists allow students to practice self-regulating their learning since they are making choices about activities based on their interests and often challenging themselves beyond their current skill level in order to reach the required minimum point value. For help setting up an activity playlist in Canvas, contact your Instructional Designer. 

Collaborative grading

Collaborative grading practices are natural compliments to personalized learning. Collaborative grading involves meeting with students individually and evaluating their work together. This practice asks students to self-evaluate their work based on what they understand to be their current skill level and the level of effort they put into their work. 

Even if you don’t have time to meet with your students individually, you can incorporate self-evaluation into grading by asking students to complete evaluations or self-reflections of their work when they submit it. Reading their reflections along with their assignment deliverables can allow you to gain a better understanding of a student’s current skill level and progress than assignments alone.  

Using technology

Educational technologies can make personalization more practical and effective. Using technology tools like Generative AI, for example, can take some of the burden off instructors by offering students unlimited, real-time access to feedback and help generate ideas. 

It may be helpful to encourage students to ask AI to evaluate their work and even track their progress. Students can ask AI for feedback or help any time they feel stuck. Instructors can also ask AI to help them create practice quizzes or activities based on differing skill levels to assign to students individually or in groups. AI can even help you create learner profiles to track student progress, skills gained, and interests. Remember to respect student privacy and never give AI students’ names or other personal information.

• Bernacki, M.L. et al. "A Systemic review of Research on Personalized Learning: Personalized by Whom, to What, How, and for What Purpose(s)?" Educational Psychology Review 52, no. 2 (2021): 123-145. https://doi.org/10.1007/s10648-021-09615-8.

• Brod, G. “There Are Multiple Paths to Personalized Education, and They Should be Combined.” Current Directions in Psychological Science 33, no. 3 (2024). https://doi.org/10.1177/09637214241242459

• Bulger, M. Personalized Learning: The Conversations We’re Not Having. Data & Society, 2016. https://www.datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf.

• Cevikbas, M. and Kaiser, G. "Promoting Personalized Learning in Flipped Classrooms: A Systemic Review Study." Sustainability 14, no. 18 (2022): 11393. https://www.mdpi.com/2071-1050/14/18/11393.

• Castaneda, K.B, et al. "Crafting Personalized Learning Paths with AI for Lifelong Learning: A Systemic Literative Review." Frontiers in Education 9, (2024): Article 1424386. https://www.frontiersin.org/journals/education/articles/10.3389/feduc.2024.1424386/full.

• Katerina, R. H. et al. "There Are Multiple Paths to Personalized Education, and They Should Be Combined." Current Directions in Psychological Science 27, no. 3 (2024): 1-19. https://doi.org/10.1177/09637214241242459.

• Zhang, L., et al. "Understanding the Implementation of Personalized Learning: A Research Synthesis." Learning and Instruction 32, no. 1 (2023): 77-90. https://www.sciencedirect.com/science/article/pii/S1747938X19306487?casa_token=gBuYUjv58G8AAAAA:TgSz3o6OYZ4vIvFrccCfqXaYLxbLpQO2XGnG7Delr12kgwqIQjANyc40YDN1TJYREl4KLHkWCyI.

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