Memory plays the central role in learning – it is “the mechanism by which our teaching literally changes students’ minds and brains” (Miller, 2014, p. 88). Thus, understanding how memory works is important for both instructional designers and instructors. According to modern theories, memory involves three major processes: encoding (transforming information into memory representations), storage (the maintenance of these representations for a long time), and retrieval (the process of accessing the stored representations when we need them for some goal or task) (Miller, 2014). Let’s briefly review these processes and see how they may inform our course design and instruction.

Encoding – What Is the Role of Attention and Working Memory?

How does encoding happen? We receive information from our senses (visual, auditory, etc.), and then we perform a preconscious analysis to check whether it is important to survival and if it is related to our current goals. If it is, this information is retained and will be further processed and turned into mental representations. Thus, attention is the major process through which information enters our consciousness (MacKay, 1987). Attention is limited, and it is to some extent under voluntary control, but it can be easily disrupted by strong stimuli. Attention is crucial for memory, and without attention we cannot remember much (Miller, 2014).

How attention is directed depends on the way the content is presented and on the nature of the content itself (Richey et al., 2011). If the content is intrinsically motivating for the student, it will catch their attention more readily. But beyond that, the manner we design our instructional materials can influence how learners focus their attention to select and process the information, and in turn on what and how much gets stored in their memory. For example, we can ensure that students are guided to the most relevant content first by making that content more visually salient. Or we can tell an engaging story to focus their attention to the concepts that come next.

Baddeley and Hitch's multicomponent model of working memory (1974). — Baddeley and Hitch’s multicomponent model of working memory (1974)

Working memory is a concept introduced in the 1970s by Alan Baddeley. This model describes immediate memory as a system of subcomponents, each of them processing specialized information such as sounds and visual-spatial information. This system also performs operations on this information and are managed by a mechanism called the central executive. The central executive combines the information from the various subcomponents, draws on information stored in long-term memory, and integrates new information with the old one (Baddeley, 1986).

Some researchers consider attention and working memory to be the same thing; while not everyone agrees, it is clear that they are highly interconnected and overlapping processes (Cowan, 2011; Engle, 2002). Attention is the process that decides what information stays in working memory and keeps it available for the current task. It is also involved in coordinating the working memory components and allocating resources based on needs and goals (Miller, 2014).

The capacity of each of the working memory components is limited. However, these components are mostly independent: visual information will interfere with other visual information, but not much with another type such as verbal information (Baddeley, 1986). Therefore, the most effective instructional materials will include a combination of media, such as images and text (or better yet, audio narration), rather than just images or just text.

Graphic by Cheese360 at English Wikipedia is licensed under CC BY-SA 3.0

Storage – How Fast Do We Forget?

Ebbinghaus's forgetting curve (1885) - the graph shows the percentage of words recalled declining sharply after one day and then more slowly — Ebbinghaus’s forgetting curve (1885)

In the late 1800s, Hermann Ebbinghaus conducted his famous series of experiments on the shape of forgetting. The result was the forgetting curve (also called the retention curve), which is a function showing that the majority of forgetting takes place soon after learning, after which less information will be lost (Ebbinghaus, 1885). A recent review of studies on the retention curve concluded that the rate of forgetting may increase up to seven days, and slows down afterwards (Fisher & Radvansky, 2018). This interval is useful to consider when planning instruction. A well-designed course will include sufficient opportunities for practice and retrieval during this time, so as to minimize the forgetting that naturally occurs.

Graphic from MIT OpenCourseWare is licensed under CC BY-NC-SA 4.0

Retrieval – How Do We Get It Out of Our Heads and Use It?

While long-term memory is considered unlimited, retrieval (or recall) can be challenging. Its success depends on a few factors. To retrieve memory representations, we use cues—information that serves as a starting point. Since a memory can include different sensory aspects, information with rich sensory associations is usually remembered more easily (Miller, 2014). Visual and spatial cues are particularly powerful: memory athletes perform some mind-blowing feats by using a special technique called “the memory palace”—imagining a familiar building or town and placing all content inside it in visual form (to learn more about this technique, check out this TED talk by science writer Joshua Foer).

Recall is also influenced by how the information was first processed: deep processing (focusing on meaning) will yield superior retrieval performance compared to shallow processing (focusing on superficial features like some key words or the layout of the information). However, equally important is a match between the type of processing that happens during encoding and the one that happens during retrieval (Miller, 2014). For instance, if the final exam contains multiple-choice questions, learners will perform better if they also practiced with multiple-choice questions when they learn the content. Finally, emotions have been shown to boost memory (Kensinger, 2009), and even negative emotions (such as fear or anger) can have a strong effect on recall (Porter & Peace, 2007).

Conclusion – Implications for Instruction

What can we do to maximize our students’ memory potential? Based on these memory characteristics, here are a few strategies that can help:

Make use of graphic design and multimedia learning principles to create attention-grabbing, well-organized instructional materials that include a combination of media.
Include plenty of retrieval practice activities, such as polling during lectures, quizzes, or flashcards. The website Retrieval Practice is a fantastic resource for quick tips, detailed guides, and research. Top things to keep in mind:
- Boost retrieval practice through spacing (spreading sessions over time) and interleaving (mixing up related topics during a practice session).
- Make sure you plan some sessions for the critical seven-day period after introducing the material.
Consider teaching students the memory palace technique for content that requires heavy memorization.
Support every type of content visually where possible.
Encourage deep processing of the material, for example through reflections, problem-solving, or creative activities.
Ensure that students have opportunities to engage with the material during learning in the same way as they will during the exam.
Try to stimulate emotions in relation to the content. While negative affect can help (for example, recounting a sad story to illustrate a concept), it is probably best to focus on positive emotions through exciting news, inspiring anecdotes, and even more “extrinsic” factors such as humor, uplifting music, or attractive visual design.

Using these strategies will help you create learning experiences where students encode, store, and retrieve information efficiently, allowing them to use it effectively in their lives, studies, and work. Do you have any related experience or tips? If so, share in a comment!

References

Baddeley, A. D. (1986). Working memory. Oxford University Press.

Cowan, N. (2011). The focus of attention as observed in visual working memory tasks: Making sense of competing claims. Neuropsychologia, 49(6), 1401–1406. https://doi.org/10.1016/j.neuropsychologia.2011.01.035

Ebbinghaus, H. (1885). Memory: A contribution to experimental psychology.

Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11(1), 19–23. https://doi.org/10.1111/1467-8721.00160

Fisher, J. S., & Radvansky, G. A. (2018). Patterns of forgetting. Journal of Memory and Language, 102, 130–141. https://doi.org/10.1016/j.jml.2018.05.008

Kensinger, E. A. (2009). How emotion affects older adults’ memories for event details. Memory, 17(2), 208–219. https://doi.org/10.1080/09658210802221425

MacKay, D. G. (1987). The organization of perception and action: A theory for language and other cognitive skills. Springer New York. http://dx.doi.org/10.1007/978-1-4612-4754-8

Miller, M. D. (2014). Minds online: Teaching effectively with technology. Harvard University Press.

Porter, S., & Peace, K. A. (2007). The scars of memory. Psychological Science, 18(5), 435–441. https://doi.org/10.1111/j.1467-9280.2007.01918.x

Richey, R., Klein, J. D., & Tracey, M. W. (2011). The instructional design knowledge base: Theory, research, and practice. Routledge.

Photo by Nathan Dumlao on Unsplash.

My interest in learning about motivation in education began many years ago when I started learning about motivation in game design. In order to better understand motivation, in a classroom, while playing a game, and in an online learning environment, I am turning to the body of research that has grown from Edward Deci and Richard Ryan’s Self-Determination Theory (SDT). This blogpost will be a continuation of my previous SDT Primer and an excellent companion to Chris Lindberg’s Games as a Model for Motivation and Engagement series of posts.

While I had intended to use this entry for discussing grades and assessment, an important piece of SDT and its application is understanding the different types of motivation explored by the SDT community of researchers. This post will define and expand on the numerous types of motivation in preparation for a discussion on grades and assessment.

Before we begin, take a brief minute to explore and reflect about what moves you to do something? As an example, what moved you to open this blog post and begin reading it?

The Autonomy-Control Continuum

The types of motivation you might be most familiar with are intrinsic and extrinsic motivation. Intrinsic motivation refers to doing something because it is inherently interesting or enjoyable, while extrinsic motivation refers to doing something because it leads to a separable outcome. I might be moved to read a chapter of a novel over lunch because it is inherently enjoyable (intrinsic), or I might be moved to run errands over lunch because of external factors, like visiting the bank or post office due to their limited open hours (extrinsic). While these opposites are often displayed and discussed as an either-or, they are really just two ends of a spectrum that contains more nuanced gradations.

(Gagné & Deci, 2005, p. 336)

The autonomy-control continuum (Ryan & Deci, 2017) is an outgrowth of the intrinsic-extrinsic spectrum, representing the spectrum between autonomous regulation, or a feeling of complete volition and controlled regulation, or a feeling of being externally or internally compelled to act. While intrinsic motivation would fall under the category of autonomous regulation, extrinsic motivation can sometimes come close to the autonomy end of the spectrum for personally important or valued tasks, or can swing all the way to the controlling side with external rewards or punishments for tasks. And on the extreme opposite end of the spectrum from intrinsic motivation is amotivation, or the complete absence of intentional regulation. Ideally, we hope that students will feel autonomous motivation, which has also been shown as optimal for learning.

Internalized Motivations: External vs. Internal

Now let’s explore some of the murky gradations between feeling autonomous and controlled. The first step is to compare two degrees of controlled regulations: external vs. internal regulations. External regulation is motivation that is controlled by external factors—a student might experience external regulation when they have to complete a group project in a course. External factors, the instructor in this case, dictates that students collaborate in groups for this project. Internal regulation (or introjection), occurs when internally controlling factors are at play, e.g. shame, guilt, or fear. Continuing with the group work as an example, a student might feel moved to complete a task for the group project by placing internal pressure on themselves, resulting in feeling guilty if they don’t perceive that they’re pulling their weight, or shame in being the last group member to respond to a discussion assignment, or fear that their lack of activity will punish everyone in the group with a lower grade. In both cases, the student feels controlled, either by an external factor or internal pressure.

Identified & Integrated Regulations

As we move closer to the autonomy end of the spectrum, we run into identified regulation, or the acceptance of extrinsic value. Our student from the example above might feel extrinsically motivated to complete the group project, but through the use of a rationale statement from the instructor, might accept the value of this group work, thus feeling more of a sense of autonomy than with external or internal regulation. Lastly, and moving even closer to autonomy, is integrated regulation, or adding the value of a task to one’s own beliefs or sense of self. Perhaps through reflection or a particularly well designed group project, a student comes around and now believes that group work is an essential part of their desired educational experience. While integrated regulation is not the same as feeling autonomous, you might be able to imagine a situation where an identified or integrated regulation would feel more motivating than an external or internal regulation.

How to Begin Thinking About Grades

In a recent Q&A with Richard Ryan, one of the authors and lead researchers of SDT, responded that “there has been no empirical justification for why we have grades in schools at all.” My next blog post will dive deeper into the role that grades and assessment play in SDT and motivation. In the meantime, I would like to pose some questions to get you started thinking about how you use grades in relation to motivation in your courses:

Do you use grades to create external regulation of behavior in your course?
- Are you grading a behavior or the demonstration of a skill?
Do you want to emphasize performance goals or mastery goals?
Are there ways to help students identify and integrate the activities and assessments in your course?
Do you need to grade this activity/assessment/task?

These are big, difficult questions! And thinking about motivation in terms of a spectrum is complicated! If you find yourself wanting to continue the discussion of motivation in course design, check out the companion blog series mentioned in the introduction above, contact your instructional designer, or keep an eye out for other opportunities to continue the discussion at various upcoming Ecampus events!

References & Resources

Center for Self-Determination Theory (CSDT). (2019).

This website is a treasure-trove of resources on SDT and its application in numerous fields, including education.

Gagné, M., & Deci, E. (2005). Self-determination theory and work motivation. Journal Of Organizational Behavior, 26(4), 331–362.

Ryan, R. M., & Deci, E. L. (2017). Self-Determination Theory: Basic psychological needs in motivation, development, and wellness. New York: Guilford Press.

This post is the second in a three-part series that summarizes conclusions and insights from research of active, blended, and adaptive learning practices. Part one covered active learning, and today’s article focuses on the value of blended learning.

First Things First

What, exactly, is “blended” learning? Dictionary.com defines it as a “style of education in which students learn via electronic and online media as well as traditional face-to-face learning.” This is a fairly simplistic view, so Clifford Maxwell (2016), on the Blended Learning Universe website, offers a more detailed definition that clarifies three distinct parts:

Any formal education program in which at least part of the learning is delivered online, wherein the student controls some element of time, place, path or pace.
Some portion of the student’s learning occurs in a supervised physical location away from home, such as in a traditional on-campus classroom.
The learning design is structured to ensure that both the online and in-person modalities are connected to provide a cohesive and integrated learning experience.

It’s important to note that a face-to-face class that simply uses an online component as a repository for course materials is not true blended learning. The first element in Maxwell’s definition, where the student independently controls some aspect of learning in the online environment, is key to distinguishing blended learning from the mere addition of technology.

You may also be familiar with other popular terms for blended learning, including hybrid or flipped classroom. Again, the common denominator is that the course design intentionally, and seamlessly, integrates both modalities to achieve the learning outcomes.

Let’s examine what the research says about the benefits of combining asynchronous, student-controlled learning with instructor-driven, face-to-face teaching.

Does Blended Learning Offer Benefits?

The short answer is yes.

The online component of blended learning can help “level the playing field.” In many face-to-face classes, students may be too shy or reluctant to speak up, ask questions, or offer an alternate idea. A blended environment combines the benefit of giving students time to compose thoughtful comments for an online discussion without the pressure and think-on-your-feet demand of live discourse, while maintaining direct peer engagement and social connections during in-classroom sessions (Hoxie, Stillman, & Chesal, 2014). Blended learning, through its asynchronous component, allows students to engage with materials at their own pace and reflect on their learning when applying new concepts and principles (Margulieux, McCracken, & Catrambone, 2015).

Since well-designed online learning produces equivalent outcomes to in-person classes, lecture and other passive information can be shifted to the online format, freeing up face-to-face class time for active learning, such as peer discussions, team projects, problem-based learning, supporting hands-on labs or walking through simulations (Bowen, Chingos, Lack, & Nygren, 2014). One research study found that combining online activities with in-person sessions also increased students’ motivation to succeed (Sithole, Chiyaka, & McCarthy, 2017).

What Makes Blended Learning So Effective?

Five young people studying with laptop and tablet computers on white desk. Beautiful girls and guys working together wearing casual clothes. Multi-ethnic group smiling.

Nearly all the research reviewed concluded that blended learning affords measurable advantages over exclusively face-to-face or fully online learning (U.S. Department of Education, Office of Planning, Evaluation, and Policy Development, 2009). The combination of technology with well-designed in-person interaction provides fertile ground for student learning. Important behaviors and interactions such as instructor feedback, assignment scaffolding, hands-on activities, reflection, repetition and practice were enhanced, and students also gained advantages in terms of flexibility, time management, and convenience (Margulieux, McCracken, & Catrambone, 2015).

Blended learning tends to benefit disadvantaged or academically underprepared students, groups that typically struggle in fully online courses (Chingosa, Griffiths, Mulhern, and Spies, 2017). Combining technology with in-person teaching helped to mitigate some challenges faced by many students in scientific disciplines, improving persistence and graduation rates. And since blended learning can be supportive for a broader range of students, it may increase retention and persistence for underrepresented groups, such as students of color (Bax, Campbell, Eabron, & Thomson, 2014–15).

Blended learning benefits instructors, too. When asked about blended learning, most university faculty and instructors believe it to be more effective (Bernard, Borokhovski, Schmid, Tamim, & Abrami, 2014). The technologies used often capture and provide important data analytics, which help instructors more quickly identify under-performing students so they can provide extra support or guidance (McDonald, 2014). Many online tools are interactive, fun and engaging, which encourages student interaction and enhances collaboration (Hoxie, Stillman, & Chesal, 2014). Blended learning is growing in acceptance and often seen as a favorable approach because it synthesizes the advantages of traditional instruction with the flexibility and convenience of online learning (Liu, et al., 2016).

A Leap of Faith

Is blended learning right for your discipline or area of expertise? If you want to give it a try, there are many excellent internet resources available to support your transition.

Though faculty can choose to develop a blended class on their own, Oregon State instructors who develop a hybrid course through Ecampus receive full support and resources, including collaboration with an instructional designer, video creation and media development assistance. The OSU Center for Teaching and Learning offers workshops and guidance for blended, flipped, and hybrid classes. The Blended Learning Universe website, referenced earlier, also provides many resources, including a design guide, to support the transformation of a face-to-face class into a cohesive blended learning experience.

If you are ready to reap the benefits of both online and face-to-face teaching, I urge you to go for it! After all, the research shows that it’s a pretty safe leap.

For those of you already on board with blended learning, let us hear from you! Share your stories of success, lessons learned, do’s and don’ts, and anything else that would contribute to instructors still thinking about giving blended learning a try.

Susan Fein, Oregon State University Ecampus Instructional Designer
susan.fein@oregonstate.edu | 541-747-3364

References

Bax, P., Campbell, M., Eabron, T., & Thomson, D. (2014–15). Factors that Impede the Progress, Success, and Persistence to Pursue STEM Education for Henderson State University Students Who Are Enrolled in Honors College and in the McNair Scholars Program. Henderson State University. Arkadelphia: Academic Forum.
Bernard, R. M., Borokhovski, E., Schmid, R. F., Tamim, R. M., & Abrami, P. C. (2014). A meta-analysis of blended learning and technology use in higher education: From the general to the applied. J Comput High Educ, 26, 87–122.
Bowen, W. G., Chingos, M. M., Lack, K. A., & Nygren, T. I. (2014). Interactive learning online at public universities: Evidence from a six-campus randomized trial. Journal of Policy Analysis and Management, 33(1), 94–111.
Chingosa, M. M., Griffiths, R. J., Mulhern, C., & Spies, R. R. (2017). Interactive online learning on campus: Comparing students’ outcomes in hybrid and traditional courses in the university system of Maryland. The Journal of Higher Education, 88(2), 210-233.
Hoxie, A.-M., Stillman, J., & Chesal, K. (2014). Blended learning in New York City. In A. G. Picciano, & C. R. Graham (Eds.), Blended Learning Research Perspectives (Vol. 2, pp. 327-347). New York: Routledge.
Liu, Q., Peng, W., Zhang, F., Hu, R., Li, Y., & Yan, W. (2016). The effectiveness of blended learning in health professions: Systematic review and meta-analysis. Journal of Medical Internet Research, 18(1). doi:10.2196/jmir.4807
Maxwell, C. (2016, March 4). What blended learning is – and isn’t. Blog post. Retrieved from Blended Learning Universe.
Margulieux, L. E., McCracken, W. M., & Catrambone, R. (2015). Mixing in-class and online learning: Content meta-analysis of outcomes for hybrid, blended, and flipped courses. In O. Lindwall, P. Hakkinen, T. Koschmann, & P. Tchoun (Ed.), Exploring the Material Conditions of Learning: Computer Supported Collaborative Learning (CSCL) Conference (pp. 220-227). Gothenburg, Sweden: The International Society of the Learning Sciences.
McDonald, P. L. (2014). Variation in adult learners’ experience of blended learning in higher education. In Blended Learning Research Perspectives (Vol. 2, pp. 238-257). Routledge.
Sithole, A., Chiyaka, E. T., & McCarthy, P. (2017). Student attraction, persistence and retention in STEM programs: Successes and continuing challenges. Higher Education Studies, 7(1).
U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. (2009). Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington, D.C.

Image Credits

Blended Learning Icon: Innovation Co-Lab Duke Innovation Co-Lab [CC0]
Leap of Faith: Photo by Denny Luan on Unsplash
School photo created by javi_indy – www.freepik.com

Ecampus Course Development and Training

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Category Archives: Research

Memory: Where All the Magic Happens