How Students Learn
The most effective methods of teaching and learning can be counterintuitive for both instructors and students.* For instructors, teaching often means “covering” material, believing that in presenting information they are transferring knowledge. Once the material is “covered,” instructors often move on to the next topic. Students often assume that learning is the result of total study time (perhaps plus effort), usually spent in re-reading and re-writing class notes, re-reading the textbook, highlighting and color-coding material. Students favor these practices because, although they give the illusion of learning (familiarity with materials is mistaken for comprehension; recognition is confused with recall), they are not cognitively demanding and can quickly produce strong short-term memory gains.
Research shows, however, that other methods produce greater long-term retention and higher-order thinking. Bjork (1994) dubbed these methods “desirable difficulties.” These methods are “difficulties” for students because they produce slower results and highlight gaps in student knowledge, which can cause students to feel less confident and less motivated. To be successful, these methods must also be “desirable,” meaning students are motivated to complete them and have the necessary foundational knowledge and skill.
Repetition helps us remember information, but unless that repetition is intentionally spaced over time, we forget the information rapidly. For instance, while a student who is exposed to information and immediately tested on it will likely remember about ninety percent, two weeks later, without spaced study, that student will likely remember only about thirty percent. However, for students who space their study—even if exposed to it only twice—retention anywhere from eleven to twenty-four days later remains around seventy percent (Cepeda et al., 2009). In short, long-term learning is more likely to happen if a student returns to studying after some forgetting has happened; the act of difficult retrieval makes the information more likely to “stick.” (Brown et al., 2014).
Often related to spaced studying is interleaving. Students (and instructors) generally prefer to “block” study, that is, engage many examples of the same or similar types of information or problems, moving on only after there is a sense of having “mastered” the knowledge or skill. This can produce satisfactory immediate results—and give students the illusion of learning—but is ineffective for long-term retention. Interleaving, on the other hand, intentionally mixes topics or types of problems in a student’s study regimen. In one study, students who interleaved their study performed about three times better than those who blocked their study (Rohrer & Pashler, 2010). Similar results are found in all types of skills practice. One study found that baseball batters who practiced hitting fifteen identical pitches in a row benefited much less from the practice than those who practiced hitting random pitches (Brown et al., 2014). While scientists cannot fully explain these results, a leading theory is that learning entails not just mastering a body of knowledge or a skill, but the additional skills of recognizing and implementing the correct knowledge and tools to solve a problem—precisely the skill that interleaving encourages.
Students often believe that the quality of study is the same as quantity of study time. This is reinforced when instructors (and course evaluations) ask students how much time they spent studying. Furthermore, students believe that the most productive study time is in the two days before a test. In practice, this leads to “cramming” study time (even less time than students themselves say is ideal) into the days or hours before a test. But for best performance on a test, students should spend the greatest proportion of their study time nearly two weeks before the test, tapering off their study time until a simple 15- to 20-minute daily review is all that is needed in the seven days leading up to the test (Taraban et al., 1999; Rawson & Kintsch, 2015).
Generation of Information
Many students like to study by rehearsing solutions to problems (or definitions to terms and concepts), but they remember more when they must produce the information they are trying to learn. This is related to the “testing effect,” in which students remember more information, and for longer periods, when their studying involves being tested on the material (Roediger & Karpicke, 2006). Remarkably, the testing effect holds true even when students answer incorrectly in these “study tests” (Kornell, et al., 2015), when the testing takes time away from other ways of studying, and when the only test involved is a pre-test! That is, the act of attempting an answer (right or wrong) helps long-term retention.
What About Learning Styles?
Sensitivity to the way students learn has produced abundant literature on “learning styles,” the most common system of which is VARK (visual, auditory, reading/writing, kinesthetic). The premise behind this literature is generally that people receive and process information differently (visual, auditory, etc.). Proponents then conclude that students do not learn as well when the teaching methods are not matched to their learning styles. This implies that instructors should match pedagogy to students’ learning styles.
While well-intentioned, there is no empirical evidence to support the common learning styles theory (Pashler et al., 2009). Students may identify learning preferences, but these do not uniformly correlate to more successful or easier learning. In fact, one study found that both visual and auditory learners who encountered information by watching a film performed better than those in their same style group who encountered the information by listening to a story. Interestingly, the performance gap was greater for self-identified visual learners (Willingham et al., 2015).
Recommendations for Instructors
Much of the impetus for productive studying falls upon students. But instructors can encourage productive methods and discourage unproductive methods. For instance, to encourage the generation of information instructors should suggest that students make and use flashcards and NOT provide detailed study guides. Other actions an instructor can take include giving a comprehensive, non-optional final (spaced study), giving frequent, low-stakes quizzes (testing effect), mixing the types of problems or questions students engage with in-class (interleaving), and assigning study-heavy assignments early in the semester or unit (front-loaded study).
Bjork, R. A. (1994). Institutional impediments to effective training. D. Druckman and R.A. Bjork (eds.) Learning, remembering, believing: Enhancing human performance, National Academy Press, Washington, D.C., 295-306.
Brown, P. C., Roediger III, H. L., & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Cambridge, MA: Belknap Press: An Imprint of Harvard University Press.
Cepeda, N. J., Coburn, N., Rohrer, D., Wixted, J. T., Mozer, M. C., & Pashler, H. (2009). Optimizing distributed practice: Theoretical analysis and practical implications. Experimental Psychology 56(4), 236-246. https://pdfs.semanticscholar.org/2ed6/027a24493e0c6c10239e94568088297f1937.pdf
Kornell, N., Klein P. J., Rawson, K. A. (2015). Retrieval attempts enhance learning, but retrieval success (versus failure) does not matter. Journal of Experimental Psychology: Learning, Memory, and Cognition 41(1), 283-294. PMID 25329079 DOI: 10.1037/a0037850
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. http://journals.sagepub.com/doi/abs/10.1111/j.1539-6053.2009.01038.x
Rawson, K. A., & Kintsch, W. (2005). Rereading effects depend on time of test. Journal of Educational Psychology, 97, 70–80.
Roediger III, H. L., & Karpicke, J. D. (2006). Test-enhanced learning. Psychological Science, 17, 249–255.
Rohrer, D., & Pashler, H. (2010). Recent research on human learning challenges: conventional instructional strategies. Educational Researcher, 39, 406–412.
Taraban, R., Maki, W. S., & Rynearson, K. (1999). Measuring study time distributions: Implications for designing computer-based courses. Behavior Research Methods, Instruments, & Computers, 31(2), 263-269. https://doi.org/10.3758/BF03207718
Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015). The Scientific Status of Learning Styles Theories. Teaching of Psychology, 42(3), 266-271. DOI: 10.1177/0098628315589505
*Thanks to Charles Weaver, professor of psychology and neuroscience, Baylor University, whose many presentations on “the science of learning” informed this summary.