Designing learning with memory in mind

Katy Montgomery

As instructional designers, how can we help learners acquire new understanding and retain it in long-term memory? This is certainly a top priority when designing instructional materials. Before moving on to some suggestions, let’s take a look at the theoretical foundations that support those tips.

Key components in understanding memory

The path to long-term memory

To begin, we need to understand that new knowledge does not go directly into long-term memory. Unlike with the rides at Disney World, there just isn’t a “fast pass” when it comes to memory (though as we’ll see later, we can make the line move more efficiently with the right approach). 

As the information processing model shows, new knowledge has to make it through two important areas first (Lawless, 2019). The first one is sensory memory. So much sensory stimuli surrounds learners, and their brains can only notice so much. Only when some of this is given attention, does it pass into working memory. Similarly, working memory can only hold so much at once. Then, within working memory, new information must be properly encoded to make it to long-term memory.  

Cognitive load

Cognitive load is an important concept related to working memory. Let’s imagine it this way. Cars of information are driving down a highway in the brain. They arrive at the working memory zone and find that it is a limited space. It can only handle five to seven cars at a time. To make matters even more difficult, sometimes extraneous objects fall onto the road, and even fewer cars are let in at once.  

In other words, our cognitive resources can only process so much in working memory at once. The inherent complexity of what we are learning places one set of demands, and this is called the intrinsic cognitive load. Some intrinsic loads are light—just maybe a car or two; others are heavier—maybe too many cars to fit in working memory at one time. Additionally, instructional materials and the environment require their own processing, and like the fallen debris take up space on the road, this represents the extraneous cognitive load. It’s important as an instructional designer to limit this load to make more space for useful information.

Schema 

To optimize our design, we also need to think about schema. In long-term memory, information isn’t stored in discrete pieces. Instead, it is held in ever-evolving schemata of interrelated concepts. A schema is encoded from new information in working memory, and it can also be retrieved to help more efficiently process other information. 

Previously, I noted that working memory can only handle five to seven “cars,” or chunks of information, at once. But how big are those cars— how much does each chunk hold? Well, this varies by the individual depending on the schemata they have developed. A novice may require twenty chunks, for example, to hold the same information that an expert holds in just one chunk. That leaves the expert with a lot more available space in working memory. Therefore, helping learners effectively form and refine schemata is an important concern of an instructional designer. Facilitating learner retrieval of existing schemata to help in the process is another design goal.

Tips for optimizing memory

With this background in mind, let’s consider some ways to optimize memory.

Guide rather than tell learners

Whenever it is possible, try to guide learners towards discovery rather than explicitly telling them. Allowing them to arrive at the insight themselves can have beneficial effects (Flynn, 2021). This more effectively activates existing schemata and helps learners incorporate the new information.

Use images

Images can also be used to efficiently awaken an existing schema (Neumann & Kopcha, 2018). Furthermore, they also enhance formation of new schemata. When selecting images, consider the learners’ current stage of development.