See also Teaching statistics for statistics-specific topics. Also, see Student assessment for ways to see what students are actually learning.
[To read] Ambrose et al., How Learning Works: Seven Research-Based Principles for Smart Teaching (2010). Recommended to me as a good review of general pedagogical research.
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological Science in the Public Interest, 9(3), 105–119. doi:10.1111/j.1539-6053.2009.01038.x
It’s tempting to worry about students’ “learning styles”: whether they prefer visual, auditory, textual, or other explanation strategies. Turns out there’s no evidence that they learn better if they’re taught in the style they prefer.
Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135–142. doi:10.1016/j.tate.2017.06.001
No, “digital native” students do not have magical technology skills – they mostly use technology for passive consumption – and do not have magical abilities to multitask. In fact, those who are “good” at multitasking do worse at the tasks; the brain is single-core with a high context-switching overhead. A review of various studies in education.
Singer, L. M., & Alexander, P. A. (2017). Reading on paper and digitally: What the past decades of empirical research reveal. Review of Educational Research. doi:10.3102/0034654317722961
A slightly odd systematic review of the literature on reading in print vs. electronically. Spends most of its time discussing whether the reviewed studies adequately defined terms or reported all details of their experiments, but does conclude “when longer texts are involved or when individuals are reading for depth of understanding and not solely for gist, print appears to be the more effective processing medium”.
Singer, L. M., & Alexander, P. A. (2016). Reading across mediums: Effects of reading digital and print texts on comprehension and calibration. The Journal of Experimental Education. doi:10.1080/00220973.2016.1143794
I don’t like any paper that uses “concomitantly” in a sentence. Regardless, a within-subjects design presenting college students either print or digital texts, followed by short-answer comprehension questions. 69% of participants thought they did best on the comprehension questions when they read the digital text, average scores were higher for the print text. Students are apparently not good judges of which medium works well for them. (There is a chi-squared test in Table 3 justifying this conclusion that I don’t understand; what exactly is presented in the table? The text says 69% preferred digital, but the table shows 69/90 = 77% did.)
Van Heuvelen, A. (1991). Learning to think like a physicist: A review of research‐based instructional strategies. American Journal of Physics, 59(10), 891–897. doi:10.1119/1.16667
Introductory physics students think in a completely different way from expert physicists: students try to match problems to formulas, rather than building qualitative understanding. An example:
The test included a conservation of energy problem in which a spring launched an object up into the air. For many of the students, this was a spring problem. They searched their minds for spring equations. Over 50% of the students used the most recent “spring” equation they had encountered – an equation for simple harmonic motion… For them, the final test was an effort to find an equation to solve spring problems, inclined plane problems, cable problems, and so forth.
Students don’t use the diagrams and qualitative reasoning used by the instructors (like free-body diagrams), possibly because they “do not understand the meaning of basic quantities and concepts that are represented in the diagrams” and because they rarely get in-class practice doing so.
There’s a lot of emphasis on having students work problems to improve their understanding, and it’s folk wisdom that students don’t really understand until they do homework problems. But:
Kim, E., & Pak, S.-J. (2002). Students do not overcome conceptual difficulties after solving 1000 traditional problems. American Journal of Physics, 70(7), 759–765. doi:10.1119/1.1484151
Looks at 27 first-year students in a physics class at Seoul National University. Because of the Korean examination system, each had solved an average of 1,500 physics practice problems while preparing for the entrance exam; however, their conceptual understanding was still quite poor, and they were not able to connect the math back to the concepts.
The small sample size and lack of any experimental control limits the conclusions that can be drawn here.
Byun, T., & Lee, G. (2014). Why students still can’t solve physics problems after solving over 2000 problems. American Journal of Physics, 82(9), 906–913. doi:10.1119/1.4881606
An expanded version with 49 students, still finding a lack of relationship between extensive problem-solving and conceptual understanding.
Webb, D. J. (2017). Concepts first – a course with many improved educational outcomes as well as parity for underrepresented minority groups. American Journal of Physics.
An experiment with a “concepts-first” physics class, in which the first portion of the semester is spent entirely on concepts (using active learning), followed by a few weeks learning the algebra to apply the concepts to actual problems. Students in this course did better on the final exam and better on the FCI than an ordinary active learning class and a lecture-based class, and underrepresented minorities also reached parity with the rest of the class.
PhysPort has some resources on encouraging student engagement in active learning, since active learning doesn’t work unless the students are active.
Peer Instruction is a teaching method from Eric Mazur and colleagues at Harvard, designed and tested on introductory physics courses. It’s a flipped classroom approach that tries to give students many opportunities to screw up and be corrected, rather than letting their misunderstandings persist until exams.
Unfortunately I haven’t seen studies testing peer instruction in statistics courses. (Please send me some, if they exist!) In physics, however, it has dramatic results, doubling student learning:
More generally, “active learning” strategies seem much more effective when compared to traditional lecturing:
Intro stats labs often take the form “Here’s a simulation of phenomenon X [the central limit theorem, sampling distributions, …]. Press this button and see what happens.”
In physics, lecture demonstrations have a similar tenor: “Here’s this apparatus, now watch what happens when I press the button.”
However, this doesn’t teach students anything unless they predict the behavior in advance, so they have a chance to realize they’re wrong: Crouch, C., Fagen, A. P., Callan, J. P., & Mazur, E. (2004). Classroom demonstrations: Learning tools or entertainment? American Journal of Physics, 72(6), 835–838. doi:10.1119/1.1707018
Further, physics demos are often misinterpreted by students, who misremember the outcome of the demo in ways consonant with their misconceptions. Asking them to predict the outcome in advance (assuming they have learned the conceptual framework needed to do so) reduces this problem dramatically: Miller, K., Lasry, N., Chu, K., & Mazur, E. (2013). Role of physics lecture demonstrations in conceptual learning. Physical Review Special Topics - Physics Education Research, 9(2). doi:10.1103/physrevstper.9.020113
Wieman, Carl E., Perkins, Katherine K., & Adams, Wendy K. (2008). Oersted medal lecture 2007: Interactive simulations for teaching physics: What works, what doesn’t, and why. American Journal of Physics, 76(4), 393–399. doi:10.1119/1.2815365
An excellent overview of the lessons learned in PhET, a project to build interactive physics simulations. Their demos require $10-40,000 to build, with a team of faculty, programmers, and science education specialists, with extensive testing with real students to ensure the demos meet their goals. They discuss several cases where apparently well-designed demos were leading the students to misconceptions, and how they found these problems and eliminated them. This process has also been discussed in more detail, below:
Adams, Wendy K et al. (2008a). A study of educational simulations part 1 – engagement and learning. Journal of Interactive Learning Research, 19(3), 397–419.
Adams, Wendy K et al. (2008b). A study of educational simulations part II – interface design. Journal of Interactive Learning Research, 19(4), 551–577.
Holmes, N., Olsen, J., Thomas, J. L., & Wieman, Carl E. (2017). Value added or misattributed? A multi-institution study on the educational benefit of labs for reinforcing physics content. Physical Review Physics Education Research, 13(1), 010129. doi:10.1103/PhysRevPhysEducRes.13.010129
Measures differences in final exam scores between students who took an optional physics lab section and those who didn’t, and finds no meaningful difference. Makes a very good point about labs:
First, goals to reinforce content often come hand-in-hand with increased structure, as it becomes important for students to observe a particular “correct” result. When one examines the cognitive activities in which students are engaged while completing such lab course activities, they are dominated by following instructions to collect specified data using unfamiliar equipment, and following specified procedures to analyze the data and write up reports in a specified format. Although the relevant physics concepts were central to the thinking of the instructor that designed and built the experiments, those concepts get little, if any, attention from the student carrying out the assigned activities using that apparatus.
Labs should be restructured to eliminate extraneous cognitive load and focus more strongly on the physics concepts.
Interteaching comes from operant psychology, and frames itself as providing positive reinforcement for learning behavior instead of aversive consequences (like course failure). Seems focused on courses with lots of reading and conceptual material, like psychology, rather than math or hard sciences. Most research on interteaching seems to come form one group.
The basic idea – read before class, discuss in class, get targeted feedback – seems to match with Peer Instruction’s methods, with the addition of a “prep guide” and some different incentive systems.
Saville, B. K., Lambert, T., & Robertson, S. (2011). Interteaching: Bringing behavioral education into the 21st century. The Psychological Record, 61(1), 153–166.
Comprehensive description of interteaching and early experiments on its effectiveness. The method:
First, the teacher constructions a preparation (prep) guide consisting of questions designed to guide students through a reading assignment. The questions cover a range of formats, often proceeding from simpler definitional-type questions to more complex application and synthesis questions… The teacher then distributes the prep guide to students (e.g., via a course Web page), who then have several days to complete the prep-guide items before class. In class, students first hear a brief clarifying lecture that reviews selected material from the previous class period. After the lecture, students form pairs to discuss the prep guide… If students discuss the material thoroughly, the pair discussions should last approximately two thirds of the class period… After students have discussed the prep guide thoroughly, they complete a record sheet, which provides the teacher with feedback on how the discussions went and which material was difficult to understand.
The method also involves frequent testing (“at least five times per semester”), plus participation points and a small incentive grade from your partner’s exam performance: if you discuss a prep guide with someone, then one of its questions appears on the exam, you get a few points if both of you do well on that exam question.
Bryan K. Saville (2013). Interteaching: Ten Tips for Effective Implementation, Observer, 26(2).