GUEST POST: Memorable Feedback: Lessons from Cognitive Psychology in Encoding
By Dr. Behany Brunsman and Dr. Rob McEntarffer
Rob McEntarffer taught English, psychology, and philosophy for 13 years at Lincoln Southeast high school in Lincoln, NE. While teaching, he became interested in educational measurement issues and got a Masters degree in educational measurement from the University of Nebraska, Lincoln in 2003. He started working as an Assessment/Evaluation specialist with Lincoln Public Schools in 2005, and works with the district on large scale and classroom assessment issues. Rob earned his PhD in Teaching, Learning, and Teacher education in 2013, focusing his research on how teachers make room for formative assessment processes in their classrooms. He lives with his wife, two kids, dog, and cat in Lincoln, NE and works for Lincoln Public Schools.
Bethany Brunsman has been an Assessment/Evaluation Specialist with Lincoln Public schools since 2000 and works with assessment and data reporting/use. She has a Ph.D. in psychology and Master of Arts degrees in psychology and educational measurement and statistics from the University of Iowa.
NOTE: This post is a continuation of an earlier Learning Scientists blog post “Memorable Feedback: Lessons from Cognitive Psychology in Selective Attention.” That post focused on the role of selective attention, and this post discusses encoding.
As assessment specialists in a large public school district, our jobs involve working with teachers to develop useful classroom assessments and use assessment data to help students learn. Along the way, we’ve found two bodies of literature to be useful during conversations with teachers: cognitive psychology literature related to memory models, and assessment literature related to effective feedback. The purpose of this series of blog posts is to highlight three potentially useful connections between these research areas: selective attention (discussed in an earlier blog post), encoding/deep processing (the topic of this post), and retrieval practice (new post coming soon!).
Review of the Memory Model
Several different models of human memory are used in the literature about memory and teaching/learning (1, 2, 3). For the purposes of this post, we will refer to what might be called a “standard” or traditional model of memory. Some elements of this model are borrowed from cognitive load and depth of processing theory, but this simplified “three-box” memory model has been useful during teaching and learning discussions in our district, so we use it as an overall organizing model as we discuss connections between memory theory and feedback advice from assessment literature.
Thanks to Dr. Sarah Salem for her help creating the Simplified Standard Model of Memory graphic.
Encoding/Deep Processing
An element of the memory model that may provide useful context for teachers as they make feedback decisions is the deep processing/encoding step. Teachers have to make hundreds of instructional decisions that impact what kind of cognitive “work” students do in their working memories. This deliberate attention to cognitive work in working memory is also relevant to feedback decisions. The memory model predicts that if students do particular kinds of cognitive work with feedback, it will increase the likelihood that they will encode the learning from that feedback into their long term memory system. Specifically, semantic encoding is relevant to teacher decisions about what kind of feedback to provide. “Encoding” refers to the processes operating at the time of learning that determine what information is stored in long-term memory. Different kinds of encoding lead to different kinds of storage. For example, words are typically better remembered when encoded for meaning (semantic or “deep” encoding) rather than for appearance (nonsemantic or “shallow” encoding) (4). As teachers make decisions about what kinds of feedback to provide, attending to semantic encoding—choosing feedback that encourages students to think about meaning rather than surface features of their work—may help teachers provide more useful feedback.
In addition, the closely related concept of deep processing is another useful touchstone for teachers as they provide feedback. Although technically part of a competing model of memory (depth of processing theory), the idea of the “depth” of processing as a predictor of encoding probability can help teachers design feedback. While describing their original model, Craik and Lockhart (2) explained that “...greater ‘depth’ implies a greater degree of semantic or cognitive analysis. After the stimulus has been recognized, it may undergo further processing by enrichment or elaboration” (p. 675). Teachers’ goals while providing feedback should be to increase the chances that students will think “deeply” about their work. The goal of feedback should often not be a “quick fix,” rather feedback should require students to analyze their own work in ways that reveal underlying, generalizable ideas and meanings. The purpose of feedback isn’t an immediate improvement in student work. Rather, the purpose is to help students think in more complicated and “deeper” ways about their own work, so that they encode generalizable principles into long term memory for use later.
Providing Feedback to Encourage Deep Processing: Make Sure the Student is Doing the Cognitive Work, Not the Teacher.
For encoding and deep processing to occur, students need to be engaged with the material they are learning. Before students have access to answers or solutions, they need to have attempted to remember the answer or solve the problem themselves (5). Brookhart (6) and Wiliam & Leahy (7) suggest that feedback needs to be specific enough that it is actionable by the student, but not so specific that the teacher has already corrected all of the errors. Brookhart (6) adds that feedback “…is about choosing words and phrases that show that you value the student as a person who learns…And it is about giving feedback that, when possible, helps students decide for themselves what to do next” (p. 37).
Example: Helping Students Encode and Deeply Process Through Feedback
[Note: This fictionalized example is a compilation of classroom practices based on discussions with teachers and our own experiences.]
Teachers can make feedback choices that encourage metacognition and self-regulation. For example, in Ms. Christensen’s sixth grade math class, students attempted to solve 10 long division problems, showing their work and final answers to each problem. Ms. Christensen looked through the class set of papers and made a list of common mistakes in the student work. She then provided feedback on individual students’ work by simply circling any long division problems on student papers that resulted in the incorrect answers. Ms. Christensen shared the list of common misconceptions with her students and asked them to “diagnose” mistakes in the long division problems from their paper that resulted in wrong answers. Students “matched” their incorrect answers with the common mistakes from the list (metacognition). Students then moved into small groups according to which common mistake they made in some of their work (Ms. Christensen spot checked to make sure students assigned themselves to the right group). Each group worked through another sample item to make sure they understood how to avoid the common mistake, and students used that modeling to correct their mistakes in their original long division problem assignment. This kind of feedback and requirement for students to identify which feedback applies to their thinking may not only help students learn more useful long division skills, but also enable students to identify problems in their own work and use that knowledge to correct future mistakes.
References
1. Atkinson, R. C., & Shiffrin, R. M. (1968). Chapter: Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. The psychology of learning and motivation (Volume 2). New York: Academic Press. pp. 89–195.
2. Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11(6), 671–684. https://doi.org/10.1016/s0022-5371(72)80001-x
3. Sweller, J. (1988). Cognitive Load During Problem Solving: Effects on Learning. Cognitive Science. 12 (2): 257–285.
4. Demb, J., Desmond, J., Wagner, A., Vaidya, C., Glover, G., & Gabrieli, J. (1995). Semantic encoding and retrieval in the left inferior prefrontal cortex: a functional MRI study of task difficulty and process specificity. The Journal of Neuroscience, 15(9), 5870–5878. https://doi.org/10.1523/jneurosci.15-09-05870.1995
5. Shute, V. J. (2008). Focus on Formative Feedback. Review of Educational Research, 78(1), 153–189. https://doi.org/10.3102/0034654307313795
6. Brookhart, S. M. (2017). How to give effective feedback to your students. ASCD.
7. Wiliam, D., & Leahy, S. (2015). Embedding formative assessment: practical techniques for K-12 classrooms. Learning Sciences International.
