A Different Way to Think about Lectures

 



Many critics contend that lecturing leads to rote learning and low student motivation. Proponents of lectures counter that students need teachers to explain new ideas and lectures when they are done well can arouse student interest rather than squelch it. Arguing about whether lecturing is better or worse than some other teaching method seems pointless, like trying to decide whether a hammer is a better tool than a saw. But given the prominence of lecturing in the college classroom it is worth exploring, questions about when lecturing is likely to be an effective or ineffective way to support learning.

In a seminal study of college student learning, Daniel Schwartz and John Bransford (1999) explored the question of when it is a good time for telling. They demonstrated that a lecture—the same lecture—served as a very potent way to support student learning in one circumstance and was almost completely ineffective in another circumstance. Here is what they did . . . The experiment took place in a sophomore level psychology class during a unit on human memory. Students were assigned to one of three conditions in which they studied course material and then a week later took a test to determine their understanding of the subject matter. Students did all the work individually, and were carefully monitored to insure they actually studied the material. Here are the three learning conditions:

Group 1: Read + Summarize + Lecture

  • In class, students read a chapter about memory and then wrote a summary of the material.
  • Next class period they heard a lecture that explained the material from the chapter.

Group 2: Analyze + Lecture

  • In class, students analyzed contrasting data sets. Each data set described a memory experiment and what the subjects in the study remembered [see example Contrasting Data Sets file below]. The data sets contained no descriptive, explanatory or theoretical information about memory experiments or theories. Students were told to look for similarities and differences between the two data sets.
  • They did NOT read the chapter on memory.
  • Next class period students heard the same lecture as Group 1.

Group 3:  Analyze + Analyze

  • In class, students analyzed the same data sets as Group 2 and were given twice the amount of time to do so.  
  • They did NOT read the chapter on memory or hear the lecture.

Evaluating students’ learning. One week after the learning phase the researchers tested student learning in two ways:

  1. True/False Test. A short true/false test to determine what the students remembered about the memory concepts
  2. Prediction Test. Students read a description of a memory experiment they had not seen previously. They were asked to predict the outcome in terms of what people in the study would remember. The researchers reasoned that if students had gained a deep understanding of the memory concepts, then they should make relevant predictions given a new problem.

Basic Knowledge of Memory. The average scores on the T/F test for Groups 1 and 2 were equivalent. Students in both groups knew the same basic concepts. So, in terms of basic factual information about memory, the students in Groups 1 and 2 are comparable. Group 3, the double analysis group was excluded from the test because they had not been exposed to the concepts at all—through reading or lecture.


Understanding of Memory. What about their understanding of the concepts? The graph depicts the percentages of appropriate predictions for each group. On average the Group 2 students produced 47% of the appropriate predictions. This was about three times as many predictions as Groups 1 and 3. As you can see Groups 1 and 3 produced only about 15% - 17% of the predictions.

So, what’s going on here? What accounts for the superior performance of Group 2? And, why did Group 1 do so poorly? The critical difference can’t be the lecture. In Group 1 the lecture was not effective at all but in Group 2 it contributed to deep understanding. Of course, the lecture explained memory theories, concepts and research findings. But students don’t receive understanding from a lecture—they have to construct it. And, they construct understanding by using what they already know to interpret and make sense of the lecture material.

Groups 1 and 2 had very different prior knowledge going into the lecture. In Group 1 students read and summarized a text chapter. In creating a summary, a student makes decisions about the relative significance of ideas, condenses information and translates it into his or her own words. Presumably when students then heard the lecture they were able to notice similarities and differences between their own interpretations of the material and the instructor’s. But all of that made little difference in their ability to predict how people will perform in a memory experiment.

Students who analyzed contrasting data sets developed differentiated knowledge of the material. Students discerned distinctive features and patterns in the data even though they did not know the meaning or significance of the patterns. After analyzing the data sets students would be able to say something like, “I can tell you how people responded to this material in these situations—but I don’t know why or what it means.” According to Schwartz and Bransford, This differentiated knowledge prepared the students to understand deeply an explanation of the relevant psychological principles when it was presented to them, (p.475). In other words, students were able to use these patterns to interpret the memory concepts and research findings presented in the lecture. When students did not have this kind of differentiated knowledge, the lecture had little effect on their understanding. This study illustrates the importance of relevant prior knowledge in learning. Students with differentiated knowledge were better able to understand the lecture and achieved deeper understanding of the memory concepts. Students with global or generic knowledge did not benefit from the lecture, and achieved only superficial understanding of the concepts.

There is a lot of talk in higher education these days about making lectures more interactive and engaging. Teachers are using response systems and other active learning techniques to try to connect students with the subject matter. The Time for Telling study is a reminder that students’ prior knowledge plays a key role in whether they can engage the lecture material in ways that lead to deep understanding. The pedagogical challenge is to figure out ways to help students develop differentiated knowledge relevant to the topics you teach. The use of contrasting examples is one compelling technique and there may be others.

Perhaps we need to think about improving our lectures as involving not just revising the delivery of material, but designing assignments and activities that prepare students to make good sense out of the lectures they hear.

References

Schwartz. D.L & Bransford, J.D. (1999). A time for telling. Cognition and Instruction. 16(4), 475-522.

 

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William Cerbin,
May 6, 2012, 9:41 AM
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