Research on Student Learning

If we were to survey most university instructors on the important components of teaching, we might get a result like this one:


The important components of teaching are clearly a good instructor, good content, and good students. And if we asked instructors to indicate where they put their focus, we would get different answers. Some focus on how to be an excellent instructor, some focus on designing ideal content, others focus on interaction with students. And all of these are clearly characteristics of good teaching.

But after you have taught a course a few times, you might begin to ask different questions. Why don’t my students remember anything from previous quarters? Why do some students work hard and others give up? Are my students skipping the reading because they don’t care about it, or because it is too confusing for them? My students always miss exam questions on topic X, even though I explain it very clearly. Why?

And at this point, we need to think less about teaching, and more about student learning. Neuroscience and psychology of learning is where we get to the part that actually matters- – students being able to think critically about the material and ideas that we are teaching them.

In the following sections, we will discuss the basics of how students learn. The framework of this content comes from the excellent book How Learning Works (Ambrose et al., 2010), available from Amazon or Wiley.

Point #1: Students are novices, instructors are experts.

Students think differently about our discipline than we think about it. If you are an expert in statistics and someone asks you, “Do you know about ANOVAs and T Tests?” then you think, “Yes! I understand how they were derived. I understand why they are used for different data sets, and that they test different hypotheses. If you give me a data set, I can determine which is appropriate, and apply it correctly–either using a calculator or using R.”

You have both declarative knowledge about these statistical tests (you understand their definitions) and procedural knowledge about these tests (you know when and how to use them). It is also easy for you to recall that information from memory, as you’ve used (and taught) these tests hundreds of times. But when a student who took Intro to Stats last year is asked, “Do you know about ANOVAS and T Tests?” and they answer, “Yes!” …. well, they might mean something else by that “yes.” They might mean that the tests were taught to them at one point, and they have used both at some point.

Novices might have learned information in a previous class, but it is less connected to other information, rarely both declarative and procedural, and very hard to retrieve from memory. Since they don’t know what it feels like to be an expert, they interpret their understanding as solid.

In a classic study by Chi and colleagues (1989), physics novices and experts were given sample physics problems and asked to sort them by type. The novices tended to sort by the look of the problem, for example, –pulley problems, or lever problems. Experts, however, sort them by physics principles: the type of force or physical law used.

As instructors, we need to design a course that novices will learn from. We should seek to make connections clear, teach both declarative and procedural knowledge, and help students improve retrieval.


  1. Explain how sections of the course connect to each other, both in the syllabus and in class.
  2. Measure student knowledge of previous content with a low-stakes test. Include both declarative and procedural questions. If you feel some students don’t recognize their lack of understanding, have students retake the test in small groups, discuss and justify their responses to one another, and then report back.
  3. Give students practice connecting previous content (earlier in the quarter) to current content. Create class activities that have them compare and contrast the current theory with a previous theory.
  4. Give students practice organizing and categorizing. Pass out a randomized selection of previous exam questions, and have students sort them by the underlying principles rather than by surface characters. Present case studies that are the boundaries of different cases (paintings with elements of different schools, for instance) that force students to more carefully examine content.
  5. Expose students to expert understanding. In introductory courses, students may think the only difference between your understanding and theirs is you are faster at vocabulary or algebra. Show interesting examples of advanced work in your discipline to show students the importance of making connections and applying basic concepts.

 Point #2: Student motivation varies.

Consider the largest course you teach. It is likely an introductory course for majors, or a survey course for non-majors. Some of the students (perhaps even most of them) have learning goals for your course. They arrive interested in your subject, are willing to spend significant time preparing for the course, and are comfortable taking intellectual risks like asking questions or volunteering comments in class. Other students have performance goals. They are motivated to do better than other students and to get grades that will further their plans for graduate or professional school. These students may study for exams, but are less willing to risk embarrassment by talking in class, and are not likely to explore optional material. If a student is motivated by performance goals but considers your class to be low value (a general ed requirement) or low expectancy (because it is reputed to be difficult), then they will also not be motivated to work hard.

You may have heard recently about “growth mindset.” In this work by Dweck and associates (see Mueller and Dweck, 1998), students who feel that intelligence is fixed are willing to work hard as long as they are confident they are intelligent enough to succeed. If they experience failure, however, they generally stop trying because they feel they have found the boundary of their intelligence. Students with a growth mindset, however, are convinced that continued practice will improve their intelligence. They are more willing to try difficult course activities and are more comfortable with failing, as they view it as providing information about how to improve.

As instructors, we can address and improve student motivation.


Generally techniques that demonstrate to students that the class is high value, that they can succeed, and that difficult tasks are designed to improve their intelligence will be more likely to improve their motivation to work hard at your learning goals.

  1. Make it clear why this course has high value for the student. Your course might teach important content for the major, or provide skills valued by employers, or enable students to determine which health care plan to purchase.
  2. Make the class environment supportive. The content and activities should clearly relate to the course learning goals, and the assessments should require effort but be achievable. Your attitude during class should encourage intellectual risk-taking and indicate that failure is a guide for future practice, not an indicator of limited intelligence.
  3. Provide real-world tasks. If possible, create projects and assessments that clearly relate to work that would be required by an employer. Discuss current issues. If you assign primary literature for students to read, emphasize why reading difficult material is required in future courses and in employment.
  4. Be enthusiastic about your discipline. You may be the first philosopher / engineer / art historian / geologist that these students have ever met, and their impression of you will be their first impression of your field.


Point #3: Student mastery requires component skills

One of the most interesting explanations of mastery I’ve seen is this scale developed in a handbook for public speaking (Sprague and Stewart, 2015):

sprague image

It points out that novices (like early undergraduates) may come from a background of success in secondary school, so when they begin study in a discipline, they assume that the level of understanding they have is an appropriate level. It requires some work to make students recognize that they do not have a complete understanding, and then an additional amount of work for them to get sufficient meta-awareness of their abilities. But instructors are generally experts who have been experts for several years, and they have now reached a point where the steps to get from Stage 1 of unconscious incompetence to Stage 3 of conscious competence have been forgotten. Those of us at Stage 4 have forgotten what we needed to move through earlier stages.

When we teach a complex skill, like how to build a philosophical argument or how to add parallel vectors, we experts often leave out steps that seem obvious to us. And if you are teaching in a large class and don’t regularly sit with students as they work on this skill, we may not realize where students are getting confused. The skills and logical steps needed to be able to take memorized basic knowledge and apply it to a more complex, new situation are called component skills.

Once you as an instructor have a complete list of component skills that must be taught, we must then consider how practice and feedback are necessary. Do students need to practice skills in isolation first? Is it better to have them practice the task in a more authentic context so they see the importance of the skill? The research in this field is mixed, and seems to depend on the skill and the experience of the student.

Lastly, consider the problem of transfer – the application of skills or knowledge to a new situation. Even students who can thoroughly explain a concept or equation may find themselves completely stumped when asked to solve a “new” problem that an expert would immediately recognize as the same type. A classic experiment (Gick and Holyoak, 1980) asked students to solve a puzzle about an army attempting to conquer a fortress. After solving this puzzle, the students were asked to solve a “different” puzzle about using x-rays to treat a tumor without damaging nearby tissue. Even though the solutions were identical (divide the attack force, surround the target), students did not apply the military solution to the medical problem.


  1. Ask for a TA’s help in enlisting component skills students may need help with.
  2. Ask colleagues for assignment prompts that have produced good results for them. Are there instructions you can add to your own assignments?
  3. Ask a smart friend or a teaching consultant to let you explain a subject to them. People who have practiced learning new skills are very good at recognizing when they are confused (Step 2) and asking for clarification.
  4. Make grading specifications clear. If you are assigning a presentation of a difficult analysis, students may get distracted by the style of the presentation slides when you really want clarity in the analysis. Consider even providing a presentation template to minimize the cognitive load on students while they struggle with the main purpose of the assignment.
  5. Describe difficult skills to the students, then provide them with both isolated and contextual practice. Gather evidence (via quizzes or short assignments) to determine which type of practice was more effective.


Point #4: Students need help with effective practice and studying.

Undergraduate students are not used to thinking about their learning. Generally, they are rewarded for doing what is assigned in the way that follows the assignment prompt. It is rare for an instructor to ask, “What part of the assignment was difficult for you? What questions do you dread seeing on an exam? What different study techniques have you tried, and which was most effective?” Because effective practice and effective studying are both painful and difficult, students rarely do them instinctively.

Deliberate practice is a term coined by psychologist K. Anders Ericsson, and refers to breaking down a skill into component parts, and then focusing practice on the parts that are most difficult for the learner. Ericsson studied violinists and pianists most famously (Ericsson, Krampe & Tesch-Römer, 1993), but similar results have been seen across disciplines. The role of a teacher in deliberate practice is to help students see the components needed to read a journal article or perform a monologue, to find the components that the student struggles most with, and then make the student accountable for practicing those specific components.

Similarly, students are generally prone to choosing ineffective study techniques. The most effective way to learn material is to be tested on it (Dunlosky et al., 2013). But students consistently choose instead to re-read lecture notes or highlight a textbook rather than quiz themselves. This is likely because re-reading notes feels like the information is clear and fully understood, while testing oneself feels like the material is NOT understood. A significant responsibility of the instructor is to provide assignments that are challenging, and include the supportive framework needed for the student to succeed at the challenge. Generally, classes with more regular exams and assessments improve student performance (Haak et al., 2011).


  1. Measure student understanding at the beginning of the term in order to design assignments at the “challenge” level of difficulty.
  2. Provide clear specifications for high-quality work.
  3. Provide opportunities for practice. Students cannot write a good term paper until they have written drafts and received feedback. Consider having fewer types of assignments and instead more drafts for one important type of assignment.
  4. Set expectations about practice, and reassure students that you expect them to struggle at some tasks. Consider sharing stories about when you used deliberate practice to learn a skill.
  5. Test students more often, even if stakes are low.


In addition to these points, there is interesting research on student development and encouraging self-directed learning which you can read in How Learning Works as linked at the start of the article. But let’s conclude by thinking generally about what we have covered. While an instructor should be clear and organized in her teaching, it is also important to recognize that students vary in background, motivation and preparation level. In order to maximize your effectiveness, consider how you can structure your course in ways that match how student brains learn.

For more references, or to schedule a conversation with a CEI teaching consultant, contact us at



Chi, M.T.H., Bassok, M., Lewis, M. W., Reimann, P. , & Glaser, R. (1989). Self – explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145 – 182.

Dunlosky, J.; Rawson, K. A.; Marsh, E. J.; Nathan, M. J.; Willingham, D. T. (2013). “Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology”. Psychological Science in the Public Interest 14 (1): 4–58.

Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological review, 100(3), 363.

Ford , M. E. ( 1992 ). Motivating humans: Goals, emotions and personal agency beliefs. Newbury Park, CA: Sage Publications, Inc.

Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive psychology, 12(3), 306-355.

Haak, D. C., HilleRisLambers, J., Pitre, E., & Freeman, S. (2011). Increased structure and active learning reduce the achievement gap in introductory biology. Science, 332(6034), 1213-1216.

Mueller, C. M., & Dweck, C. S. (1998). Praise for intelligence can undermine children’s motivation and performance. Journal of personality and social psychology, 75(1), 33.

Sprague, J., Stuart, D., & Bodary, D. (2015). The speaker’s handbook. Cengage Learning.