Attention, Memory, and Learning: What Do We Know? So What?

Marcella L. Bullmaster-­Day, PhD
Interim Associate Dean of Academic Affairs
Graduate School of Education, Touro College
marcella.bullmaster-day@touro.edu

At the center of our shared professional vocation of teaching is the quest to connect with our students’ current interests and knowledge, to engage their attention, and to move them to higher levels of understanding and performance in relation to our course subject matter. From the vast research on attention, memory, and learning (see some sample references at the bottom), let’s walk through five rounds of “what we know,” stopping at each round to apply what we know by asking “so what?” What can I do as a course instructor to ensure that my students get and keep the concepts and skills that are my course objectives?

Round One: What we know

The human memory system is a survival mechanism. “Cues” are experiences that trigger us to “tag” information as being survival-­‐‑relevant. We pay attention to information that has the following qualities:

  • Sensory impact / vividness: Sound, visual qualities, tactile sensations, smell. Visual sense is the strongest, so judicial use of visual material is always helpful.
  • Emotional impact: We process meaning before details. Emotional arousal focuses us on the “gist” of an experience first, not the peripheral minutiae. As time passes and we retrieve the memory of some information or event, we remember the big, generalized picture first. Then we have to work further to retrieve related details. Even retrieving the general idea is quicker if there was an emotional component to encoding it.
  • Relevance to one’s own personal history: We pay attention to information that we think will be useful to us in the future. Our personal experiences, interests, and cultural associations shape what we think is important and relevant. What matters to one person does not necessarily matter to the next.
  • Structure and meaning: We pay attention to information we can interpret and put into context. When we understand the language, format, and purpose of information, we can pay attention. For example, complex mathematical formulations are of great interest to serious mathematicians, while non-­‐‑mathematicians won’t understand them, won’t know what to pay attention to, and certainly won’t remember because there’s nothing already in their memory frameworks to which the mathematical formulations can be “hooked.”
  • Personal participation (as opposed to passive exposure): We encode a richer set of cues when we are actively involved in completing a task or solving a problem, which increases the likelihood of retrieving the information later.

So what? What can I do in my course?

  • Use appropriate visuals.
  • Arouse emotion by illustrating major points with appropriately related stories; challenging students to solve problems; or assigning well-­‐‑structured group work.
  • Relate course material to students’ interests, experiences, and cultural contexts.
  • Assess students’ prior knowledge so that you can hook new information to their previous understandings and correct their existing misconceptions.
  • Keep students engaged in actively discussing, retrieving information from memory, and solving problems.

Round Two: What we know

Memory is enhanced by establishing associations between concepts. Working memory is the short-­‐‑term system of cognitive processes that maintains information, attention, and focus, with separate neural channels for processing words and pictures. Because of working memory’s limited capacity, learners are only able to concentrate on a subset of the environmental stimuli available to them at any one time.

A common mistake is to relate too much information without enough time devoted to “connecting the dots.” Most experts are so familiar with their topic that they forget what it’s like to be a novice. Expertise does not guarantee good teaching.

During instruction, students selectively attend to relevant aspects of the language and visuals presented; actively associating, organizing, and integrating new information into large-­‐‑capacity long-­‐‑ term memory via interconnected neural networks or schemas of meaning, and understanding. When learners have attained expertise in an area, their schemas are more tightly organized into unified “chunks” of interrelated information, enabling an expert’s working memory to quickly perceive patterns where a novice sees only an overwhelming array of seemingly unrelated facts.

Rehearsal is one critical step that allows material to be transferred from short-­‐‑term to long-­‐‑term memory.

So what? What can I do in my course?

  • Select the most important information for each lesson. Start with the key ideas and, in a hierarchical fashion, form the details around the larger notions.
  • Break complex material into manageable segments with examples, practice, and feedback in each segment.
  • Split lectures into modules of 10-­‐‑minutes, since that’s all the longer people can usually pay attention when listening. Each segment should deal with a single core concept. Explain relationships between ideas.
  • Leave out irrelevant content or activities that can hinder efficient processing and depress learning.
  • Integrate auditory and visual sensory information appropriately.
  • Require effortful retrieval of the new knowledge and skills at various points over
  • Repeat to remember, remember to re Have students do self-­‐‑testing that requires retrieving the material from memory, not merely identifying the right answer from a list.
  • Give students practice, using activities that are highly similar to those used in your course assessments. If you give tests, have them practice with the same test formats. If you assign papers, have them write mini-­‐‑papers. This is technically termed “transfer-­‐‑appropriate processing.”

Round Three: What we know:

Because working memory has separate information processing channels for visual/pictorial input and for auditory/verbal input, learning is optimized when instruction aligns text, relevant visuals, animations, and narration in ways that make the most efficient use of limited working memory.

So what? What can I do in my course?

A number of principles guide such effective alignment:

  • The Multimedia Principle: Use words plus judiciously selected graphics rather than words alone. Choose the type of graphic that will best support the intended learning and avoid extraneous or merely decorative graphics.
  • The Contiguity Principle: Align text in close proximity to visuals. For example, if the words of a text and the meaning of a supporting diagram must be mentally integrated, but are unintelligible apart from each other, it is better to place the text on the diagram itself rather than in a separate space to the side. Separating text and visual support creates a split-­‐‑attention effect that depletes working memory.
  • The Modality Principle: Use audio narration to explain complex visuals instead of adding written text explanations, which overload the visual channel of working memory since a learner cannot focus simultaneously on complex pictures and written words.
  • The Redundancy Principle: Use either text or audio narration to explain visuals, but not both. When the language is especially challenging, text explanations are better than audio narration.
  • The Coherence Principle: Omit extraneous visuals, words, and sounds. These may seem interesting, but will divert attention from the task of selecting, organizing, and integrating new material with prior knowledge.
  • The Personalization Principle: Engage learners through conversational first-­‐‑ or second-­‐‑person language (“you,” “your,” “we,” “our”) and audio voice quality. In online environments, make the author visible through on-­‐‑screen virtual coaches.
  • The Segmenting Principle: Present content in short sequences and allow the learner to control the rate of access.
  • The Pre-training Principle: Ensure that learners know the names and characteristics of key concepts and technical terms at the outset of the lesson, then engage them in processes or procedures to learn the concepts.

Round Four: What we know:

There are limitations on how much we can pay attention to at one time. The brain is not capable of multi-­‐‑tasking. The brain naturally focuses on concepts sequentially, one at a time. There are functional things we do at the same time (walk, breath, eat), but in terms of paying focused alert attention – only one thing at a time. When we think we are multitasking, we’re just shifting attention – disengaging and re-­‐‑engaging. Research shows that we make more errors when doing this. A person who is interrupted takes 50% longer to accomplish a task and makes up to 50% more errors.

Asking students to juggle two things simultaneously may be pushing the limits of cognitive “load.” Since it is mental activity that moves academic concepts and skills from working memory to storage in long-­‐‑term memory, any physical or behavioral activity on the part of the learner must support and maximize, rather than impede, cognitive processing within the limits of working memory. Activities such as responding to online questions, completing practice problems, participating in group collaboration, or engaging in immersive simulations can impose considerable demands on novice learners’ working memory, leaving little room for the selecting, organizing, and integrating processes essential to long-­‐‑term learning.

So what? What can I do in my course?

  • Select and plan learning activities carefully, so that they lessen cognitive load and result in retention for novice learners, rather than overtaxing their working memory capacity and becoming counterproductive.
  • Distribute practice exercises over the entire unit of instruction rather than clustering them all at the end.
  • Provide tailored explanatory feedback to learner responses.
  • Provide pre-­made graphics rather than asking students to generate or complete graphics when dealing with complex information that is new to them.
  • When new, complex information is introduced, use lecture (in 10-­‐‑minute segments) rather than group discussion initially.
  • Use still pictures rather than animation or video when introducing new, complex information.
  • Replace some practice problems with examples showing all the steps (worked examples).

Note: Of these instructional activities, the power of providing worked examples is particularly robust. Worked examples are effective when they adhere to the cognitive-­‐‑load principles presented above and are accomplished through a process of “fading.” In fading, students see all the steps of the first completed example problem, with explanations for each step. This is followed by a second example with most of the steps worked out, but in which students are asked to complete and explain one or two steps. Next they complete more steps in a partially worked out third problem, and so on until they have mastered all the steps and can complete practice problems independently, explaining each step for themselves.

References:

Blakemore, S., & Frith, U. (2008). Learning and remembering. In The Jossey‐‑Bass reader on the brain and learning. San Francisco: Jossey-­‐‑Bass, pp. 109 – 119.Bransford, J.D.,

Brown, A.L., & Cocking, R.R. (Eds.) (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academy Press.

Clark, R.C., & Mayer, R.E. (2008a). Learning by viewing versus learning by doing: Evidence-­‐‑based guidelines for principled learning environments. Performance Improvement, 47(9), 5 – 13.

Clark, R.C., & Mayer, R.E. (2008b). e-­‐‑Learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning, 2nd edition. San Francisco: Pfeiffer.

DeLeeuw, K.E., & Mayer, R.E. (2008). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100(1), 223 – 234.

Mayer, R.E. (2010). Seeking a science of instruction. Instructional Science, 38(2), 143 – 145.

Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187 – 198.

Mayer, R.E., & Moreno, R. (2002). Animation as an aid to multimedia learning. Educational Psychology Review, 14(1), 87 – 99.

Medina, J. (2008). Brain rules. Seattle, WA: Pear Press.

Miller, M.D. (2011). What College Teachers Should Know About Memory: A Perspective From Cognitive Psychology, College Teaching, 59(3), 117 – 122.

Moreno, R., & Mayer, R.E. (2000a). A coherence effect in multimedia learning: The case for minimizing irrelevant sounds in the design of multimedia instructional messages. Journal of Educational Psychology, 92(1), 117 – 125.

Moreno, R., & Mayer, R.E. (2000b). Engaging students in active learning: The case for personalized multimedia messages. Journal of Educational Psychology, 92(4), 724 – 733.

Paas, F., van Gog, T., & Sweller, J. (2010). Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives. Educational Psychology Review, 22(2), 115 – 121.

Retnowati, E., Ayres, P., & Sweller, J. (2010). Worked example effects in individual and group work settings. Educational Psychology, 30(3), 349 – 367.

Rosenshine, B.V. (2002). Converging findings on classroom instruction. In A. Molnar (Ed.), School Reform Proposals: The Research Evidence. Information Age Publishing.

Stull, A.T., & Mayer, R.E. (2007). Learning by doing versus learning by viewing: Three experimental comparisons of learner-­‐‑generated versus author-­‐‑provided graphic organizers. Journal of Educational Psychology, 99(4), 808 – 820.

Sweller, J. (2009). The many faces of cognitive load theory. T&D, 63(8), 22.

Sweller, J., & Cooper, G.A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59 – 89.

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