The effects of lecture speed and note‐taking on memory for educational material

  • Chen, Ashley
  • Murphy, Dillon H.
  • Brabec, Jordan A.
  • Bjork, Robert A.
  • Bjork, Elizabeth L.

  • 1Department of Psychology University of California Los Angeles Los Angeles California USA

Correspondence Ashley Chen, Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095, USA. Email: [email protected]

Received in revised form December 07, 2023

Received July 04, 2023

Accepted December 11, 2023

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

Applied Cognitive Psychology 38(1):p e4166, January/February 2024. | DOI: 10.1002/acp.4166

1 INTRODUCTION

Since the COVID‐19 pandemic, the online learning environment has been expanding as universities worldwide began to offer students a wider variety of options for how they want to receive their education (Adedoyin & Soykan, ). As a powerful communication and instructional tool, pre‐recorded lecture videos have gained popularity not only as a supplement to in‐person learning, but also as a core component of online learning (Scagnoli et al., ). The use of lecture videos as a primary mode of instruction during online learning has allowed students to gain full control over how they self‐regulate or pace their studying, which is a considerably different feature from the more guided in‐person instruction (Yu, ). Such an option seems to be especially appealing to college students who may have various circumstances that prevent them from dedicating all their time to education (e.g., they work a full‐time or part‐time job, commute to campus, have family responsibilities; Andrade et al., ).

When students make decisions on how to engage with course content, such as the speed at which they will watch lecture videos and/or whether to take notes, they need to consider the potential trade‐offs associated with increasing lecture speed and taking notes. By increasing lecture speed, students save significant amounts of time, but perhaps at the expense of their comprehension. At 1x video speed, speech rates are typically around 150 words per minute, and speech comprehension tends to decline at around 200 to 270 words per minute (Barron, ). Considering that the auditory channel can only process a finite amount of information at any given time, it is thus reasonable to predict that increasing lecture speed beyond 270 words per minute would result in impaired comprehension. Furthermore, students may experience difficulty in taking notes when dividing their attention between note‐taking and an accelerated lecture video, resulting in their missing of important information in the lecture. Given that increasing lecture speed and note‐taking seem to be common features of students' study behaviors, we sought to determine whether, and if so, how, note‐taking behavior changes while watching accelerated lecture videos and how the interaction of these two factors affects individuals' memory for educational material.

1.1 Does lecture speed benefit or impair memory?

Speeding up lecture videos beyond 1x speed not only increases the number of words spoken per minute, but it also changes the audio pitch of narration. Moreover, graphics and animations remain static but are presented for a shorter period of time. While these conditions may make learning more challenging or unpleasant (see Ritzhaupt et al., ; Wright et al., ), students seem to hold mixed opinions about what lecture speeds are optimal for learning. Survey data collected from a sample of 204 undergraduate students revealed that accelerated lecture videos were viewed as useful and superior to 1x speed lectures on measures of speed of knowledge acquisition, their ability to stay focused, and their ability to retain information (Cardall et al., ). There seems to be a limit at which accelerated lecture videos are seen as beneficial, however, with individuals rating 1.5x speed as better for concentration and more likeable than 2x speed, which was described as difficult to listen to and watch (Nagahama & Morita, ). A recent survey administered by Murphy et al. () asked 123 undergraduate students to report the speed at which they watched lecture videos and the speed they believe is best for learning. In this survey, 85% of all students reported watching videos at accelerated speeds (i.e., faster than normal speed), and 60% of all students believed that accelerated speeds were optimal for learning.

Prior research has reported that increasing lectures up to 2x speed has little cost on retention. For example, Lang et al. () found that 21,835 students assigned to complete learning courses at 1.25x speed were more likely to obtain higher grades and complete more course content than those assigned to 1x speed (courses were made available online on the Stanford Lagunita Platform for learners worldwide). Researchers have also observed that speeding videos up to 2x speed did not adversely affect performance on an immediate or delayed memory test, but performance declined beyond 2x speed (Murphy et al., ; see also Barabasz, ; Nagahama & Morita, ; Ritzhaupt et al., ; Song et al., ; Wilson et al., ). Thus, while lecture speed can affect memory, the effect is minimal if speeds do not exceed 2x, suggesting that increasing lecture speed is a beneficial strategy when considering its efficiency (i.e., time saved). This gain in efficiency may be seen by students as especially advantageous as the time saved with accelerated lecture videos can be used for other activities or courses.

Advocates of online education have suggested that lecture videos should be kept brief, even as short as 10 minutes, since students have difficulty maintaining attention during learning (Khan, ; Koller, ; see also Wilson & Korn, ). Indeed, performance on sustained attention tasks tend to decrease as time on task increases (Young et al., ). Accelerating lecture videos may be a beneficial strategy to preserve students' attention and memory for presented content. For instance, Murphy et al. () found that faster playback speeds reduced mind‐wandering among younger adults, likely compensating for cognitive costs introduced by increased speeds. Increased attentiveness due to decreased mind‐wandering and time on task may thus explain why there are little to no costs of faster speeds on test performance (Risko et al., ; Szpunar et al., ).

1.2 The importance of note‐taking during learning

When attending lectures either live or asynchronously, students often choose to take notes to help encode presented information, maintain their attentiveness, and collect material to use for later review or assignments (Morehead, Dunlosky, Rawson, Blasiman, & Hollis, ; Nakayama et al., ; Van Meter et al., ; Witherby & Tauber, ). Research shows a robust effect of note‐taking for promoting better test performance and that information contained in notes is significantly more likely to be recalled on both immediate and delayed tests as compared to information not included in such notes (Aiken et al., ; Howe, ; Kiewra, ). According to the depth‐of‐processing model, attentively analyzing and relating incoming information to prior knowledge during note‐taking facilitates deep encoding and leads to durable memory (Craik & Lockhart, ). Not all types of note‐taking are equally attentionally demanding, however, and different types of note‐taking can be characterized as generative and non‐generative. Generative note‐taking refers to the process of summarizing, paraphrasing, and concept mapping, while non‐generative note‐taking can be described as verbatim copying (Mueller & Oppenheimer, ). Generative note‐taking is more cognitively demanding but thought to be beneficial for learning as one actively engages with the material by making connections between concepts and filtering out irrelevant information, reflective of the encoding hypothesis: active processing that occurs during the act of note‐taking improves learning and retention (Di Vesta & Gray, ; Mayer, ; Wittrock, ). During this process, valuable information is selected, organized into a coherent structure, and interpreted in the context of relevant prior knowledge, which strengthens memory (Fiorella & Mayer, ). For instance, students who take generative notes during a science lecture may form inferences about causal mechanisms based on existing knowledge or rephrase main ideas in a more accessible manner (i.e., in their own words). In contrast, with non‐generative note‐taking, information tends to be processed in a more superficial manner, resulting in weaker, less durable memory (Kiewra, ). Hence, the quality of one's notes can serve as an indication of depth of processing.

In testing how different levels of processing may impact recall performance, Bretzing and Kulhavy () had participants engage in one of four strategies (letter search, verbatim, summarizing, paraphrasing) while reading a text passage. Immediate and delayed performance did not vary between those who summarized or paraphrased presented information, but both groups significantly outperformed those who took verbatim notes. Verbatim transcription, considered a shallow strategy, provided no added benefit to comprehension above not taking any notes. The researchers concluded that note‐taking is a valuable study strategy when students engage in generative behaviors, mainly the summarizing or paraphrasing of main ideas; otherwise, verbatim transcription is discouraged as it prevents students from meaningfully processing incoming information (see also Miyatsu et al., ).

In the current study, we focus on parallel note‐taking, the act of taking notes while simultaneously listening to a lecture, which can be contrasted with spaced note‐taking, the act of taking notes during a pause in the lecture (e.g., note‐taking in between lecture segments). Parallel note‐taking is a particularly engaging activity that requires a substantial amount of attention to carry out, yet there are strong mnemonic benefits (Cook & Mayer, ). In addition to generative processes, reductions in mind‐wandering may act as a mechanism by which learning is bolstered during note‐taking. According to Smallwood and Schooler (), mind‐wandering is a state of decoupled attention during which individuals are unable to integrate information from their internal and external environments. Since successful learning depends on students' ability to maintain executive control, shifting focus from a primary to an irrelevant task (i.e., focusing on personal thoughts and feelings) during mind‐wandering often results in learning impairments. However, when students take notes, they are typically more attentive and report greater levels of concentration on the task‐at‐hand, resulting in better performance outcomes (Lindquist & McLean, ; Schacter & Szpunar, ). A study by Wong and Lim () found that mind‐wandering mediated the relationship between longhand note‐taking and recall, and relative to individuals who took pictures of lecture slides, note‐takers mind‐wandered less and demonstrated superior recall performance. As such, note‐taking may act as a protective buffer against attentional lapses that occur when watching lecture videos.

1.3 The effect of lecture speed during note‐taking

Note‐taking, particularly high quality or generative note‐taking, may be more difficult at faster speeds. Some prior work reported not observing an interaction between speed and note‐taking with audio recordings (Aiken et al., ; Peters, ), and although these studies provide some insight into the relationship between lecture speed and note‐taking, it is difficult to generalize the findings of these studies to current educational contexts. Notably, rather than audio recordings, instructors now mainly share lecture video recordings of course content, which typically includes instructor images and presentation slides, creating both an auditory and visual component to the material to be learned, with that combination most likely increasing students' engagement with content and satisfaction with their learning experiences (Scagnoli et al., ). In addition, presenting information in these two modalities has been shown to benefit retention when instructors direct students' attention and keep them engaged in the cognitive processing of important information (Colliot & Jamet, ; van Gog et al., ; Wang & Antonenko, ). Hence, we sought to examine the relationship between lecture speed and note‐taking using multimodal pre‐recorded lecture videos.

2 THE CURRENT STUDY

In the current study, we investigated whether, and if so, how watching pre‐recorded lecture videos at increased speeds while taking notes affects retention of educational material. In Experiment 1, participants were shown lecture videos at either 1x or 2x speed while taking laptop notes or not taking any notes before being tested on the presented content. In Experiment 2, participants were instructed to take longhand, or hand‐written, notes while watching the lecture videos. We proposed two competing, exploratory hypotheses regarding the interaction between lecture speed and note‐taking: (1) At normal speeds, attentional demands associated with note‐taking are manageable. However, note‐taking requires cognitive resources that are depleted when presented with information at 2x speed, interfering with the full processing of lecture information. As a result, individuals who take notes at 2x speed would be expected to perform worse at later test relative to those who do not take notes at 1x or 2x speed; (2) Note‐taking may be especially beneficial at 2x speed, as with less time to take notes, students are encouraged to summarize and paraphrase, reflective of generative note‐taking. Therefore, individuals who take notes at 2x speed would be expected to more actively encode lecture information, resulting in optimal test performance in comparison to those who take notes at 1x speed and those who do not take any notes while watching the lecture videos.

3 CONTROL STUDY

We collected a sample of 191 undergraduate participants from the University of California, Los Angeles (UCLA) Human Subjects Pool who provided a comparison control group. They did not watch the videos before taking the tests to ascertain how much learning occurred from watching the lecture videos. These control‐group participants were sampled when data collection for Experiment 1 was ongoing. They completed all 40 test questions, and their average performance (M = .34, SD = .15) is represented by the dashed line in Figures 2 and 3. As expected, participants in the control group performed significantly worse on the memory tests compared to participants who had actually watched the videos in Experiment 1, t(727) = −10.94, p < .001, d = −.79, BF10 >100, and Experiment 2, t(473) = −16.53, p < .001, d = −1.36, BF10 >100. Control participants were also presented with a number of speeds ranging from 0.25x to 2.0x, and asked both (a) to select at which speed they typically watch lecture videos and (b) to select which speed they thought would be best for learning. The proportion of participants selecting each of these various speeds with respect to these two questions is illustrated in Figure 1. As can be seen there, control participants tended to select to watch lecture videos at speeds faster than the speed they believed would be best for learning.

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FIGURE 1

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Control group participants' reported speed at which they usually watch lecture videos and the speed that they think is the best for learning.

4 EXPERIMENT 1

In Experiment 1, we investigated how memory for information presented in accelerated lecture videos is affected by laptop note‐taking. As there has been a notable shift in college classrooms from students taking longhand notes to laptop notes, we sought to explore how laptop note‐taking may affect performance outcomes (Mueller & Oppenheimer, ). Participants watched lecture videos about the Paleozoic Era and principles of microeconomics at either 1x speed or 2x speed while some simultaneously took notes. In line with prior work on effects of playback speed and note‐taking (see Kiewra, ; Song et al., ; Wilson et al., ), we expected to see a small, negative effect of watching lecture videos at 2x speed and a significant benefit of note‐taking on memory for presented content. We also predicted an interaction between lecture speed and note‐taking but were unsure whether note‐taking at 2x speed would support or interfere with individuals' learning.

4.1 Method

4.1.1 Participants

After exclusions, we recruited 201 participants (173 females, Mage = 20.89, SDage = 4.06) from the UCLA Human Subjects Pool. All participants were fluent in English and 62% were native English speakers. 35% of participants identified as Caucasian, 42% as Asian/Pacific Islander, 4% as Black, and 19% as others/unknown. Participants were excluded from analysis if they self‐reported being off task during the online experiment (e.g., using the phone, talking to someone). Participants were told that they would still receive credit for their participation regardless of their answer to the question. This process resulted in 63 exclusions. Furthermore, if they self‐reported having prior expertise on either video topic (zero participants reported having expertise on the Paleozoic Era and 10 participants reported having expertise on principles of microeconomics), their scores on that topic were excluded from analysis.

Participants received course credit for completion of the online experiment. Informed consent was acquired from all participants, and the study was conducted with approval from the UCLA Institutional Review Board. There was no a priori sampling plan, but a post‐hoc sensitivity analysis showed that with our total sample size of 201, alpha = .05, and power = .80, we could detect a small effect size (  = .04; Cohen, ).

4.1.2 Materials

Participants watched two educational lecture videos, one about geologic time periods during the Paleozoic Era and another about microeconomic principles. These videos were chosen by the experimenters as the presented content was likely to be unfamiliar to participants, who were recruited from a psychology subject pool. Thus, performance on the tests would likely reflect learning and not prior knowledge. Lecture videos included instructor images and accompanying presentation slides. No subtitles or captions were provided. The videos were sourced from YouTube and modified to play at 2x speed (further information on video duration and speech rates can be found in Table 1). Video order was counterbalanced such that half of the participants saw the video about the Paleozoic Era first. The videos and transcripts have been made available on the Open Science Framework here .

To measure learning, participants were asked to complete a test on the content immediately after watching each lecture video. Tests included 20 multiple choice questions on the information provided in each video, with four answer choices provided for each question (see Appendix A for the full list of questions). Choices such as “all of the above,” “none of the above,” “A and B” and so forth were not presented as options. Out of the total 40 questions presented, 13 tested information only presented verbally (i.e., instructor had said the answer); one tested information only presented visually (i.e., answer appeared on presentation slides); and 23 tested information presented in both modalities. Three questions from the microeconomics test were transfer questions as they asked participants to apply learned concepts to new scenarios. Test performance was calculated as the proportion of questions correctly answered for both videos.

4.1.3 Procedure

Participants were told that they were doing a study related to memory, during which they would watch a series of interesting videos and then be tested on the presented material. Participants were randomly assigned to one of four conditions: watching the videos at 1x speed while not taking any notes (n = 47), watching the videos at 1x speed while taking notes (n = 52), watching the videos at 2x speed while not taking any notes (n = 54), and watching the videos at 2x speed while taking notes (n = 48). While the lecture videos were playing, participants were instructed not to pause or skip parts of the videos. For those in non‐note‐taking conditions, participants were told not to take notes of any kind when watching the videos and to put the videos in full‐screen mode. Participants who were tasked with taking notes were asked to open a new document for note‐taking (e.g., Google document, Word document) in a separate window. They were told to adjust their browser windows so that the note‐taking document took up half the screen and the experiment window the other half.

After watching the first lecture video, participants were instructed to make judgments of learning (i.e., how likely they were to remember the contents of the video on a later test) on a scale of 0 to 100 (0 = not at all, 100 = very likely) and to share how effective they thought the instructor was at delivering the lecture on the scale of 0 to 100 (0 = not effective, 100 = very effective). Next, they were asked to report how engaged they were while watching the video on a scale of 0 to 100 (0 = not engaged at all, 100 = very engaged), how often their mind wandered on a scale of 0 to 100 (0 = not at all, 100 = all the time), and finally, their interest in the lecture video on a scale of 0 to 100 (0 = not interesting at all, 100 = very interesting). They subsequently completed a memory test, for which they were not allowed to use any notes. This entire process was then repeated for the second video. At the end of the experiment, participants indicated how familiar they were with the presented content in the two lecture videos on a five‐point scale (1 = not familiar, 5 = very familiar).

4.2 Results

In our analyses, we computed Bayes factors, which tell us the degree to which the null and alternative hypotheses are more plausible compared with each other (i.e., a ratio of the marginal likelihood of the null model and a model suggesting group differences). We provided BF01 when the data favored the null hypothesis and BF10 when the data favored the alternative hypothesis (Kass & Raftery, ).

Performance on the microeconomics test (M = .58, SD = .19) was significantly higher than performance on the Paleozoic test (M = .38, SD = .16), t(190) = 14.98, p < .001, d = 1.08, BF10 >100. Moreover, there was no effect of lecture order such that performance on the first (M = .48, SD = .20) and second test (M = .49, SD = .20) was not significantly different, t(190) = −0.24, p = .811, d = −.02, BF01 = 12.03. We collapsed performance across topics to control for possible variance in learning different topics and ensure that effects were domain general (this was the same approach used in Murphy et al., ). Analyses regarding judgments of learning, instructor effectiveness, engagement, and interest have been made available on the Open Science Framework here .

4.2.1 Performance outcomes

To analyze the possible effects of lecture speed and note‐taking requirements on test performance, a 2 (1x speed, 2x speed) × 2 (no notes, notes) ANOVA was performed (Figure 2). Analyses indicated that participants who watched the lecture videos at 2x speed scored lower on the test (M = .45, SD = .19) compared to those who watched the same videos at 1x speed (M = .52, SD = .20), F(1, 388) = 11.41, p < .001,  = .03, BF10 = 46.05. In addition, participants who took notes performed better (M = .53, SD = .19) than those who did not take any notes (M = .44, SD = .20), F(1, 388) = 18.82, p < .001,  = .05, BF10 >100. Test performance for participants watching the videos at different speeds, however, did not depend on whether they took notes or not, F(1, 338) = 0.14, p = .711,  < .001, BF01 = 7.00.

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FIGURE 2

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Test performance as a function of speed and note‐taking conditions in Experiment 1. The dashed line represents the mean performance of participants who did not watch the videos. Error bars reflect the standard error of the mean.

4.2.2 Content analysis of participants' notes

Qualitative differences in notes for those who watched the lecture videos at 1x and 2x speed were assessed. We were curious as to whether participants who took notes at 1x speed would type more words than those who took notes at 2x speed and whether taking more notes would be correlated with better performance. Thus, we measured the word count for each set of notes. Participants took significantly more notes when watching the videos at 1x speed (word count: M = 1763.60, SD = 1030.72) compared to 2x speed (word count: M = 949.71, SD = 529.02), t(129) = 6.54, p < .001, d = .99, BF10 >100. Multiple linear regression was used to investigate if lecture speed and word count significantly predicted performance. The fitted regression model was: predicted accuracy = 0.41 + 8.82e‐5*(word count) −0.02*(lecture speed). The overall regression was statistically significant, R2 = 0.18, F(3, 169) = 12.75, p < .001. While word count predicted performance (β = 8.23e‐5, p < .001), speed did not (β = −0.01, p = .776). There was no significant interaction between word count and lecture speed (β = 2.86e‐5, p = .453).

An n‐gram program measured the amount of textual overlap between participants' notes, lecture transcripts, and presentation slides (Mueller & Oppenheimer, ). For the current study, we programmed a 3‐gram algorithm to compare every possible consecutive three‐word string (trigram) of the base texts (i.e., lecture transcripts, presentation slides) to that of the target texts (i.e., participants' notes). Verbatim overlap was quantified as the number of matches between base and target text trigrams over total number of base text trigrams. Two percentages were calculated for each set of notes, one for verbal overlap between lecture transcripts and participants' notes and another for visual overlap between presentation slides and participants' notes.

Notes taken at 1x speed contained an average of 2.63% verbal overlap with the lecture transcript (SD = 2.87%), which was significantly more than that found in notes taken at 2x speed, with the latter averaging 1.10% (SD = 1.21%), t(116) = 4.57, p < .001, d = .69, BF10 >100. Results indicated that notes taken at 1x speed contained an average of 23.78% visual overlap with the presentation slides (SD = 16.44%), while notes taken at 2x speed averaged 12.09% (SD = 8.84%). Participants who took notes at 1x speed had a greater percentage of visual overlap compared to those who took notes at 2x speed, t(132) = 5.83, p < .001, d = .89, BF10 >100. In addition, proportion of visual overlap was significantly greater than proportion of verbal overlap, t(172) = 17.15, p < .001, d = 1.30, BF10 >100. Controlling for lecture speed, we observed that proportion of verbal overlap (β = .03, p < .001) and visual overlap (β = .005, p < .001) predicted performance. In addition, controlling for lecture speed, the more verbal (β = 331.39, p < .001) and visual overlap (β = 58.70, p < .001) participants' notes contained, the greater the word count.

4.2.3 Mind‐wandering and performance

Participants who watched lecture videos at 1x speed (M = 49.48, SD = 29.85) believed that they mind‐wandered more than those who watched the videos at 2x speed (M = 44.69, SD = 27.87), F(1, 388) = 4.87, p = .028,  = .01, BF10 = 0.41. Furthermore, there was a significant effect of note‐taking showing that those who took notes (M = 36.70, SD = 27.83) thought that they mind‐wandered less than those who did not take notes (M = 57.11, SD = 26.37), F(1, 388) = 57.74, p < .001,  = .13, BF10 >100. There was no interaction effect, F(1, 388) = 2.46, p = .118,  = .01, BF01 = 2.03.

After controlling for speed, self‐reported frequency of mind‐wandering (β = −.003, p < .001) was negatively correlated with test performance. Hence, the more often participants believed that they mind‐wandered while watching the lecture videos, the poorer their performance tended to be. To explore if mind‐wandering mediated the relationship between speed and test performance, we ran a mediation analysis with speed as the independent variable, mind‐wandering as the mediator, and performance as the outcome. Based on 5000 bootstrapped samples, we obtained a 95% CI that revealed that the indirect effect of speed on recall in the sample was not significantly different from zero (95% Bootstrap CI: [−0.002, 0.03]). Using the same procedure, we tested whether mind‐wandering mediated the relationship between note‐taking and retention and observed that the indirect effect of note‐taking on performance in the sample was significantly different from zero (95% Bootstrap CI: [0.03, 0.07]). Thus, the mnemonic benefits of note‐taking can be explained by reductions in mind‐wandering.

4.3 Discussion

In Experiment 1, we saw that increasing lecture speed impaired performance such that those who watched the lecture videos at 2x speed scored lower on the subsequent test than did those who watched the videos at 1x speed (about a 7% difference in test scores). Additionally, participants who took notes while watching the videos outperformed those who did not take notes (about a 9% difference in test scores), indicating that note‐taking supported the encoding of target information in memory. There was no interaction between lecture speed and note‐taking, suggesting that accelerated speeds did not significantly interfere with or enhance the effects of note‐taking. However, after looking at the data, we suspected that there may have been a compensatory effect of note‐taking at 2x speed. Thus, we conducted Tukey's post‐hoc analyses, which showed that there were no significant differences in performance between individuals who took notes at 2x speed and individuals who did not take any notes at 1x speed, t(388) = −0.66,  = .912, d = −.10. Thus, note‐taking may help compensate for any disadvantages to learning occurring as a result of greater lecture speed.

Individuals who took notes performed better on subsequent tests compared to those who did not take any notes. In general, participants took quite a lot of notes—as a comparison, participants typed an average of 1093.80 words (SD = 578.60 words) for the Paleozoic video when the lecture transcript contained 2446 words and an average of 1638.93 words (SD = 1080.05 words) for the microeconomics video when the transcript contained 2834 words. While there has been a notable shift in college classrooms from students taking longhand notes to laptop notes, it is unclear if longhand notes lead to better performance outcomes (Mueller & Oppenheimer, ) or not (Bui et al., ; Luo et al., ; Morehead, Dunlosky, & Rawson, ). A survey of 577 undergraduate students revealed that college students were flexible in their note‐taking, choosing which method to take notes depending on how fast the instructor spoke and whether lecture slides were provided (Morehead, Dunlosky, Rawson, Blasiman, & Hollis, ). As such, we were curious as to whether hand‐writing or taking longhand notes would be able to (a) assist participants who were watching lecture videos at increased speeds, similar to what we found with those taking laptop notes, and (b) encourage them to take more generative rather than transcriptive notes.

5 EXPERIMENT 2

In Experiment 1, we saw that note‐taking improved individuals' retention of information from lecture videos presented at 1x and 2x speed. In Experiment 2, we wanted to investigate whether the benefits of note‐taking would be more pronounced if individuals were asked to take longhand notes. As laptop use can encourage verbatim transcription of lecture content, perhaps encouraging individuals to handwrite their notes would motivate them to take more generative notes, especially since writing is often a slower process than that of typing (Brown, ). Furthermore, we sought to examine if any disadvantages to learning occurring as a result of greater lecture speed could be compensated for by longhand note‐taking.

5.1 Method

5.1.1 Participants

We recruited 133 participants (97 females, Mage = 20.68, SDage = 4.50) from the UCLA Human Subjects Pool. All participants were fluent in English and 69% were native English speakers. 38% of participants identified as Caucasian, 36% as Asian/Pacific Islander, 6% as Black, and 20% as others/unknown. If participants self‐reported having prior expertise on either video topic, (one participant reported having expertise on the Paleozoic Era and five participants reported having expertise on microeconomics principles), their scores on that topic were excluded from analysis. Participants received course credit for completion of the in‐person experiment. Informed consent was acquired from all participants, and the study was conducted with approval from the UCLA Institutional Review Board. We conducted an a priori power analysis, which indicates that for a factorial analysis of variance (ANOVA), assuming alpha = .05, power = .80, 128 participants would be needed to reliably detect a medium effect (  = .06; Cohen, ).

5.1.2 Materials

The same materials from Experiment 1 were used for Experiment 2.

5.1.3 Procedure

Similar to Experiment 1, participants were instructed that they would be watching a series of videos and later be tested on the presented material. Participants were randomly assigned to one of four conditions: watching the videos at 1x speed while not taking any notes (n = 35), watching the videos at 1x speed while taking notes (n = 31), watching the videos at 2x speed while not taking any notes (n = 34), and watching the videos at 2x speed while taking notes (n = 33). Instead of taking notes on their computers, participants were asked to take longhand notes using pen and paper while watching the videos in full‐screen mode. At the end of the study, participants were asked if they typically take notes while watching lecture videos, and if they did, whether they took longhand notes or laptop notes. They also were asked to indicate how familiar they were with the content on the Paleozoic Era and principles of microeconomics on a five‐point scale (1 = not familiar, 5 = very familiar).

5.2 Results

Performance on the microeconomics test (M = .70, SD = .14) was significantly higher than performance on the Paleozoic test (M = .46, SD = .16), t(126) = 16.30, p < .001, d = 1.45, BF10 >100. In addition, there were no differences in performance between the first (M = .58, SD = .19) and second test (M = .58, SD = .19), t(126) = 0.19, p = .846, d = .02, BF01 = 99.53. As in Experiment 1, we collapsed performance across topics to control for possible variance in learning different topics and thus ensure that effects were domain general. Analyses regarding judgments of learning, instructor effectiveness, engagement, and interest have been made available on the Open Science Framework here .

5.2.1 Performance outcomes

In illustrating the effects of speed and note‐taking requirements on test performance, we conducted a 2 (1x speed, 2x speed) x 2 (no notes, notes) ANOVA (Figure 3). The main effect of lecture speed was not significant, F(1, 256) = 2.24, p = .135,  = .01, BF01 = 2.80, indicating that overall, increasing speed did not significantly impair test performance. In other words, test performance of those who watched 1x speed videos (M = .59, SD = .18) did not differ from that of those who watched 2x speed videos (M = .56, SD = .20). Participants who took longhand notes (M = .62, SD = .16) while simultaneously watching the videos were observed to have outperformed those who did not take any notes (M = .54, SD = .20), F(1, 256) = 12.99, p < .001,  = .05, BF10 = 54.75. Additionally, there was no significant interaction between lecture speed and note‐taking, F(1, 256) = 0.16, p = .691,  < .001, BF01= 4.72.

Open multimedia modal

FIGURE 3

Open multimedia modal

Test performance as a function of speed and note‐taking conditions in Experiment 2. The dashed line represents the mean performance of participants who did not watch the videos. Error bars reflect the standard error of the mean.

5.2.2 Note‐taking preferences

Participants were asked if they typically take notes while watching lecture videos, and if they did, whether they took longhand notes or laptop notes. Out of the 133 participants surveyed, 54% reported that they hand‐wrote or took longhand notes, while 37% of participants reported taking laptop notes and 9% reported not taking any notes while watching lecture videos. A Chi‐Square Goodness of Fit Test indicated that the proportion of individuals who took laptop notes, longhand notes, or no notes was not equal, (3, N = 133) = 197.58, p < .001.

5.2.3 Content analysis of participants' notes

As in Experiment 1, we assessed the qualitative differences in notes taken at 1x and 2x speed. Participants took significantly more notes when watching the videos at 1x speed (word count: M = 168.25, SD = 64.76) compared to 2x speed (word count: M = 90.02, SD = 39.92), t(77) = 7.22, p < .001, d = 1.45, BF10 >100. In fitting these results to a regression model, we found that predicted accuracy = 0.47 + 1.10e‐3*(word count) + 0.01*(lecture speed). The overall regression was statistically significant, R2 = 0.22, F(3, 95) = 8.83, p < .001. While word count was significantly associated with test performance (β = 7.63e‐4, p = .020), speed was not (β = −0.12, p = .115). The interaction between word count and lecture speed was not significant (β = .001, p = .051).

Using the n‐gram program described in Experiment 1, we found that notes taken at 1x speed contained an average of 1.15% verbal overlap with the lecture transcript (SD = 0.84%), which was significantly higher than the percentage of verbal overlap in notes taken at 2x speed, which averaged 0.42% (SD = 0.38%), t(64) = 5.47, p < .001, d = 1.11, BF10 >100. In addition, notes taken at 1x speed contained an average of 13.56% visual overlap with the presentation slides (SD = 6.60%), while notes taken at 2x speed averaged 5.75% (SD = 4.30%), t(79) = 6.97, p < .001, d = 1.40, BF10 >100. Proportion of visual overlap was significantly greater than proportion of verbal overlap, t(100) = 14.28, p < .001, d = 1.42, BF10 >100. Controlling for lecture speed, we observed that proportion of verbal overlap (β = .10, p < .001) and visual overlap (β = .01, p < .001) predicted performance. Moreover, controlling for lecture speed, notes that contained more verbal (β = 53.65, p < .001) and visual overlap (β = 7.75, p < .001) tended to have higher word counts.

5.2.4 Mind‐wandering and performance

Participants believed that they mind‐wandered more when they watched lecture videos at 1x (M = 44.47, SD = 27.19) versus 2x speed (M = 36.95, SD = 29.38), F(1, 256) = 4.95, p = .027,  = .02, BF10 = 1.18. In addition, those who took notes (M = 31.74, SD = 25.26) reported mind‐wandering less than those who did not take notes (M = 49.10, SD = 28.91), F(1, 256) = 26.55, p < .001,  = .09, BF10 >100. The interaction was not significant, F(1, 256) = 0.64, p = .426,  = .002, BF01 = 4.00.

We saw that mind‐wandering (β = −.002, p < .001) was negatively correlated with test performance after controlling for speed. Thus, when individuals perceived that they were off‐task during mind‐wandering, their actual learning was lower. Similar to Experiment 1, we investigated if mind‐wandering mediated the relationship between speed and performance by testing a mediation analysis with speed as the independent variable, mind‐wandering as the mediator, and test performance as the outcome. Based on 5000 bootstrapped samples, we obtained a 95% CI that revealed that the indirect effect of speed on recall in the sample was not significantly different from zero (95% Bootstrap CI: [−0.00004, 0.04]). In addition, we found that reduced mind‐wandering mediated the relationship between note‐taking and test performance (95% Bootstrap CI: [0.02, 0.06]).

5.3 Discussion

Unlike Experiment 1, increasing lecture speed did not significantly impact test performance (about a 3% difference in test scores). There was, however, a significant benefit of longhand note‐taking, such that those who took notes outperformed those who did not take any notes (about a 8% difference in test scores). While the effects of note‐taking did not depend on the speed at which information was presented, we conducted Tukey's post‐hoc analyses to investigate if note‐taking compensated for decreased performance at 2x speed. Results indicated that there were no significant differences in performance between individuals who took notes at 2x speed and individuals who did not take any notes at 1x speed, t(256) = −1.50,  = .442, d = −.26. Because we were unable on the basis of Experiment 2's results alone to draw any conclusions as to whether laptop versus longhand notes are superior for learning, we conducted an informal cross‐experiment comparison to explore how effects of lecture speed and note‐taking may vary by note‐taking method.

6 CROSS‐EXPERIMENT COMPARISON

Using data collected in both Experiments 1 and 2, we performed a 2 (1x speed, 2x speed) × 2 (no notes, notes) × 2 (Experiment 1, Experiment 2) ANOVA. We observed a significant main effect of speed such that those who watched the videos at 2x speed performed worse on the subsequent test (M = .49, SD = .20) compared to those who watched the same videos at 1x speed (M = .55, SD = .20), F(1, 644) = 10.90, p = .001,  = .02, BF10 = 45.79. A strong benefit of note‐taking was observed as well, F(1, 644) = 30.33, p < .001,  = .04, BF10 >100, showing that those who took notes displayed better test performance (M = .56, SD = .19) than those who took no notes (M = .48, SD = .21). Additionally, participants in Experiment 2 scored higher on the test (M = .58, SD = .19) than did participants in Experiment 1 (M = .48, SD = .20), F(1, 644) = 37.48, p < .001,  = .05, BF10 >100. The interaction between speed, note‐taking, and experiment was not significant, nor were any of the two‐way interactions, all ps > .05. Thus, the apparent beneficial effects of note‐taking on memory did not depend on modality (i.e., laptop versus longhand note‐taking). As we were interested in comparing performance outcomes between those who took laptop and longhand notes, we conducted Tukey's post‐hoc analyses, which showed that participants who took longhand notes demonstrated significantly higher test performance (M = .62, SD = .16) than participants who took laptop notes (M = .53, SD = .19), t(644) = −4.23,  < .001, d = −.48, BF10 >100. Hence, taking notes by hand appears to better support learning of lecture content relative to taking notes using a laptop.

We also explored how the total amount and quality of participants' notes varied depending on speed and note‐taking modality. A 2 (1x speed, 2x speed) × 2 (Experiment 1, Experiment 2) ANOVA indicated that participants took significantly more notes at 1x speed (word count: M = 1196.36, SD = 1127.31) than at 2x speed (word count: M = 621.91, SD = 590.42), F(1, 270) = 29.63, p < .001, = .10, BF10 >100. Participants who took laptop notes also had higher word counts (word count: M = 1359.01, SD = 914.36) than those who took longhand notes (word count: M = 127.20, SD = 65.89), F(1, 270) = 224.40, p < .001,  = .45, BF10 >100. There was a significant interaction such that for those who took laptop notes, word count was significantly lower for those who watched videos at 2x speed than at 1x speed, but for those who took longhand notes, the effect of speed was not significant, F(1, 270) = 20.15, p < .001,  = .07, BF10 >100.

Participants' notes had significantly more verbal overlap with the lecture transcript when they took notes at 1x speed (M = 2.10%, SD = 2.46%) compared to 2x speed (M = .84%, SD = 1.03%), F(1, 270) = 224.40, p < .001, = .45, BF10 >100. In addition, laptop notes contained more verbal overlap (M = 1.87%, SD = 2.33%) than longhand notes (M = .77%, SD = .73%), F(1, 270) = 23.07, p < .001,  = .08, BF10 >100. There was no interaction between speed and note‐taking modality, F(1, 270) = 3.18, p = .076,  = .07, BF01 = .87. With regards to visual overlap, notes taken at 1x speed had more visual overlap with presentation slides (M = 20.15%, SD = 14.59%) than notes taken at 2x speed (M = 9.68%, SD = 8.04%), F(1, 270) = 49.66, p < .001, = .16, BF10 >100. Participants' laptop notes also contained more visual overlap (M = 17.97%, SD = 2.33%) than participants' longhand notes (M = 9.46%, SD = 6.74%), F(1, 270) = 35.85, p < .001,  = .12, BF10 >100. There was no significant interaction, F(1, 270) = 1.96, p = .162,  = .007, BF01 = .49.

7 GENERAL DISCUSSION

The implementation of online lecture videos as primary learning tools in higher education has been observed to improve student engagement and satisfaction while maintaining high learning outcomes (Choe et al., ). As such lecture videos give students flexibility with regards to how and when they want to learn, we sought to explore how the decisions made regarding lecture speed and note‐taking, both common features of students' study behaviors, may impact their learning. In investigating whether note‐taking when watching lecture videos at increased speeds affects one's memory for educational material, we had participants watch accelerated lecture videos while simultaneously taking notes or not taking notes. Across both Experiments 1 and 2, test performance numerically declined as speed increased, although the effect was not significant in Experiment 2. An informal cross‐experiment comparison revealed that individuals who watched lecture videos at 2x speed scored an average of 6% lower on the subsequent test compared to those who watched the same videos at 1x speed. Thus, lecture speed does appear to negatively affect memory, although this effect can be considered small and worthwhile when considering the time saved.

We also found that note‐taking enhanced memory regardless of the speed at which information was presented. Indeed, we saw that laptop and longhand note‐taking helped individuals effectively retain and process information presented during lectures, as evidenced by higher test scores among those who were assigned to take notes. The mechanism by which note‐taking supported learning cannot solely be attributed to motoric processes, as doodling (structured and unstructured) has not been shown to improve attention and reduce rates of mind‐wandering relative to note‐taking, which led to superior retention of information above doodling and passive listening (Meade et al., ; Spencer‐Mueller & Fenske, ). Therefore, when individuals actively engaged with to‐be‐learned information via note‐taking, whether that be using the laptop or by longhand, they remained attentive and engaged in levels of processing that enhanced memory consolidation and test performance (Bohay et al., ; Peper & Mayer, ).

Prior work has shown that the more complete students' notes are, the more successful they are in later recalling target information (Nye et al., ; Peverly et al., ). An assessment of participants' notes indicated that the more notes participants took, the better they performed on the subsequent test, even though their notes were not available during the test, which is the typical situation in most actual classroom settings. While participants took more laptop than longhand notes, longhand note‐taking was shown to be better for learning than laptop note‐taking, as taking longhand notes led to a 9% increase in performance relative to taking laptop notes. Perhaps observed differences in test performance could be explained by the observation that laptops tend to facilitate verbatim note‐taking. Indeed, an n‐gram program revealed that laptop notes had significantly more verbal overlap with the lecture transcript and visual overlap with the presentation slides than longhand notes—however, there may have been more overlap in laptop notes simply by nature of participants taking more notes (i.e., having higher word counts). As the denominator for the calculation of overlap is base trigrams (e.g., lecture transcript, slides), volume of notes affects the percent of total overlap (i.e., the more notes one takes, the more likely they match the transcript or slides). Indeed, we found that the more verbal and visual overlap participants' notes contained, the higher the word count. Although we are limited in the conclusions we can draw from this finding, as group sizes were unequal, if participants transcribed the lecture transcript or presented slides word‐for‐word, it seems unlikely that the target information would have been processed at a level necessary for later retrieval (Igo et al., ; Van Meter et al., ), resulting in the observed differences. We also saw that there was more visual than verbal overlap in participants' notes, indicating that when individuals took notes, they preferred to transcribe information that was presented visually rather than verbally. A survey of 364 former and current students conducted by Witherby and Tauber () revealed that when professors do not post lecture slides, students take notes on what is on the slides rather than what their professors say, possibly due to visual information being easier to copy onto one's notes and visual‐verbal redundancy (i.e., instructors read from lecture slides while teaching; Adesope & Nesbit, ).

We were unsure if note‐taking while watching accelerated lecture videos would be beneficial for learning. In Experiments 1 and 2, we did not observe a significant interaction between lecture speed and note‐taking, but nonetheless conducted post‐hoc analyses given our interest in how participants in each condition performed relative to each other. One might think that note‐taking would be particularly distracting with 2x speed videos, especially since it consumes time and attentional resources needed to process information at increased speeds (Cook & Mayer, ). Nonetheless, our results appear to indicate that these costs are small and worthwhile when considering the benefits of note‐taking. Interestingly, the performance of participants who took notes while watching lecture videos at 2x speed was not significantly different from that of participants who did not take notes at 1x speed. It therefore seems that note‐taking could be a method of compensating for potential deficits associated with faster playback speeds.

Reductions in mind‐wandering may explain why there were minimal costs of watching lecture videos when participants took notes. Generally, individuals who were distracted or frequently mind‐wandered while the lecture videos played were less likely to perform well on the memory test. Those who watched the videos at 1x speed and did not take any notes also reported mind‐wandering more often, supporting the idea that we mind‐wander more when engaged in an easy versus difficult task (Forster & Lavie, ). Moreover, mediation analyses revealed that decreased mind‐wandering acted as a mechanism by which note‐taking enhanced learning (Experiments 1 and 2). In the current studies, we assessed mind‐wandering by asking individuals to retroactively reflect on how often they mind‐wandered during the lecture video, which is not how mind‐wandering is typically measured. Mind‐wandering is usually evaluated by stopping participants during a task and asking them where their attention is directed at that moment (Weinstein, ). While our study did not use this approach, a recent study by Murphy et al. () presented mind‐wandering probes every 60 seconds to participants watching lecture videos at 0.75x, 1x, and 2x speed. They found that the faster the lecture speed, the less mind‐wandering participants reported. Thus, reductions in mind‐wandering associated with note‐taking suggest that increased attention may act as a mechanism by which memory is supported when individuals take notes.

7.1 Limitations and future directions

Across all experiments, participants were administered a test immediately after watching presented lecture videos, which is not how learning is typically measured. Administering a delay test to individuals learning from speeded clips would be more reflective of how traditional classroom assessments are conducted and could provide further insight into the relationship between playback speed, note‐taking, and memory. However, there is little reason to expect that the nature of the observed relationships would change with time, as learning outcomes for individuals who were shown lecture videos at speeds ranging from 1x to 2.5x speed did not vary as a function of time (Murphy et al., ), and the benefits of note‐taking tend to remain robust over long delays (Kiewra, ). According to Di Vesta and Gray (), note‐taking serves two primary functions: encoding and storage. Throughout this paper, we have demonstrated that the act of note‐taking in the absence of review can facilitate learning (encoding function), but reviewing notes stored in written format can strengthen retention as well (storage function; Hartley, ). In educational settings, students often take notes with the intention of revisiting them at a later time, notably when preparing to take an exam. As such, it is unlikely that a student would be asked to take notes and then an immediate test without an opportunity to review their notes, as participants were asked to do in the current study. Therefore, while the results of the present study provide evidence of the encoding function of note‐taking, it is difficult to generalize findings to classroom settings when there is an absence of a review opportunity. Further research is needed to clarify how the relationship between lecture speed and note‐taking changes when students are allowed to restudy their notes.

Greater verbatim overlap typically indicates that individuals are engaging in less generative processing (e.g., less paraphrasing, summarizing), which should impede learning of presented content. However, Experiments 1 and 2 revealed that the amount of verbatim overlap present in participants' notes positively predicted test performance. As mentioned previously, verbal and visual overlap positively correlated with word count, so performance may have increased as a byproduct of participants taking more notes overall. Since we assessed factual recall rather than conceptual understanding (with the exception of the three transfer questions on the microeconomics test), it is also possible that verbatim overlap benefits memory when test questions target memory of concept terminology instead of individuals' abilities to apply those concepts in new contexts (e.g., transfer of knowledge). Perhaps only when participants analyze or evaluate important concepts, which require more selective and thoughtful processing approaches, does verbatim overlap interfere with critical thinking (Jensen et al., ). Moreover, as only one out of the 40 test questions tested information that was presented visually, we did not have enough data to analyze memory as a function of modality (visual, verbal, visual and verbal), which could have provided useful comparisons with the results regarding amount of verbal versus visual overlap in participants' notes. Constructing tests that assess both factual recall and comprehension and information provided in different modalities may provide insight into how the effects of verbatim overlap depend on the type of test question. In addition, since we had participants learn science and economics concepts, it may be worth testing the generalizability of findings across other domains such as mathematics, which require both procedural and conceptual knowledge.

While note‐taking can be beneficial, it is possible that taking notes in between lecture segments would be better for learning and more reflective of actual student behavior. Notably, when watching pre‐recorded lecture videos, students are allowed to pause them, which can be advantageous for their learning. Indeed, inserting pauses in lectures gives students an opportunity to digest just‐learned information and take notes (Boyle, ; Fanguy et al., ). In addition, being able to control the rate at which information is presented may reduce the cognitive demands of note‐taking, as students can direct their full attention to watching lecture videos and pause to take notes when necessary (Marchand et al., ). In the present study, participants were not allowed to change lecture speed and pause or rewind the videos—they had to take notes simultaneously while watching the videos. Allowing students to pause and take notes can help facilitate comprehension, as shown in Aiken et al. (). In their study, individuals who took notes separately from listening to audio recording segments (spaced note‐taking) outperformed those who did not take any notes and those who took notes simultaneously while listening to the recordings (parallel note‐taking). Therefore, asking students to take notes in between lecture segments may allow students to reap the benefits of note‐taking while devoting sufficient attention to processing information at faster lecture speeds. Future studies could explore the effects of spaced note‐taking and lecture speed on memory performance.

8 CONCLUSION

In summary, the present findings inform how students should better self‐regulate or direct their own learning with pre‐recorded lecture videos. Across both studies, we observed a small, negative effect of increasing lecture videos to 2x speed and a strong advantage of note‐taking, regardless of modality. Even though individuals had to simultaneously take notes while watching the videos, which may have been cognitively taxing, there was still a prominent benefit of note‐taking. We also found that longhand note‐taking led to better test performance compared to laptop note‐taking, which may be attributed to differences in verbatim overlap and level of processing. Thus, students should strive to take generative notes during study as it can help them remain attentive and engaged, which subsequently supports their encoding and learning of lecture information. Importantly, while the effects of note‐taking did not vary by speed, further analyses showed that there were no significant differences in performance between individuals who took notes at 2x speed and did not take notes at 1x speed, indicating that note‐taking may help compensate for any disadvantages to learning occurring as a result of greater lecture speed. Thus, if choosing to watch accelerated lecture videos, it is recommended to do so while taking notes, as it supports memory for lecture content.

CONFLICT OF INTEREST STATEMENT

The authors have no conflicts of interest to disclose and certify that they have no affiliations with or involvement in any organization or entity with any financial or non‐financial interest in the subject matter or materials discussed in this manuscript. The experiments reported in this article were not formally preregistered. The stimuli and data have been made available on the Open Science Framework here .

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Complete list of test questions and their answers. Correct answers are highlighted in bold. Videos and transcripts have been made available on the Open Science Framework here .

Palezoic Era Test Questions:

  1. During the Paleozoic Era, present‐day North America was part of the _____ craton.

    1. Siberia

    2. Laurentia

    3. Gondwana

    4. Baltica

  2. Which of the following is the correct order of the four geologic periods (earliest to latest)?

    1. Cambrian, Ordovician, Silurian, Devonian

    2. Cambrian, Silurian, Ordovician, Devonian

    3. Silurian, Cambrian, Ordovician, Devonian

    4. Silurian, Ordovician, Cambrian, Devonian

  3. Mass extinctions marked the end of which geological time periods?

    1. Ordovician and Silurian

    2. Devonian and Silurian

    3. Cambrian and Devonian

    4. Devonian and Ordovician

  4. Why was having harder body parts beneficial for Cambrian organisms?

    1. Protection against predators

    2. Easier to dig for food

    3. Easier to regulate body temperature

    4. Enabled them to easily remove dissolved oxygen from water

  5. What did NOT occur during the Cambrian period?

    1. Sea level rose

    2. Appearance of gymnosperms

    3. Creation of new niche space

    4. First appearance of fish

  6. Where was Laurentia located?

    1. South Pole

    2. Arctic Circle

    3. Equator

    4. North Pole

  7. What changes did NOT occur during the Ordovician Period?

    1. Less craton exposed due to sea level rise

    2. First appearance of non‐vascular plants

    3. Creation of new niches

    4. Diversification of marine vertebrates

  8. What is one possible reason for why the Ordovician mass extinction occurred?

    1. Extensive glaciation at the South Pole

    2. Changes in sea level triggered by oceanic volcanism near Laurentia

    3. Temperature rise resulting in hotter surface waters and stress on primary producers

    4. Ocean anoxia, or severe deficiency of oxygen

  9. What occurred during the Ordovician mass extinction?

    1. Global collapse of reef communities

    2. Extinction of North American brachiopods and bryozoan families

    3. At least 100 marine invertebrate families went extinct

    4. Approximately 50% of marine vertebrates went extinct

  10. During the Ordovician period, which of the following organisms were primary producers?

    1. Brachiopods

    2. Corals

    3. Acritarchs

    4. Bryozoans

  11. During the Silurian period, we see the appearance of which organism(s)?

    1. Jawless fish

    2. Non‐vascular plants

    3. Tetrapods

    4. Placoderms

  12. Which geological time periods were characterized by major reef building?

    1. Cambrian and Ordovician

    2. Silurian and Devonian

    3. Ordovician and Devonian

    4. Ordovician and Silurian

  13. Which of the following did NOT occur during the Devonian Period?

    1. Gondwana moved away from Laurentia

    2. Mountains started to form

    3. Major reef building continued

    4. Ammonoids diversify

  14. What characteristic(s) do guide fossils have?

    1. Represent long geologic time periods

    2. Widespread geographically

    3. Hard to find since they are located in specific areas

    4. Susceptible to decay

  15. The Devonian Period is also known as the _____.

    1. Age of Fish

    2. Ammonoid Age

    3. Gymnospermic Diversification Age

    4. Age of Marine Invertebrate Diversification

  16. What is the defining characteristic of ammonoids, which evolved from nautiloids?

    1. Unique suture patterns

    2. Primary producers during the Silurian Period

    3. Distinct shell made of calcium bicarbonate

    4. Found throughout the Devonian to Permian Period

  17. What are tetrapods?

    1. A four‐limbed organism living on land that appeared during the Devonian period

    2. A four‐limbed organism living in the ocean that appeared during the Devonian period

    3. A four‐limbed organism living in the ocean that appeared during the Silurian period

    4. A four‐limbed organism living on land that appeared during the Silurian period

  18. Which of these occurred during the Devonian mass extinction?

    1. Many invertebrate families went extinct

    2. Survival of seedless non‐vascular plants

    3. Global collapse of reef communities

    4. Approximately 70% of marine vertebrates went extinct

  19. Why were tropical marine groups most severely affected by the Devonian mass extinction?

    1. Due to global cooling, marine organisms that needed to live in warm temperatures were unable to survive

    2. Despite normally thriving under warmer temperatures, they were unable to adapt quickly enough to rising sea temperatures

    3. Tropical marine groups lost their habitats because of the global collapse of reef communities

    4. Primary producers, such as acritarchs, went extinct, eliminating tropical marine group's main source of food

  20. The earliest tetrapods evolved from lobe‐finned fishes. What occurred during this evolutionary process?

    1. Lobe‐finned fish started to go on land to find alternative food sources

    2. Lobe‐finned fish started to live in shallower waters, briefly going on land as they migrated to other waters

    3. Lobe‐finned fish started to live on land since their habitats collapsed due to global warming

    4. Lobe‐finned fish lived in shallower waters due to ocean anoxia and shortly after, lived on land

Microeconomics Test Questions:

  1. _____ is the study of how people make choices over how to use scarce, limited resources.

    1. Microeconomics

    2. Macroeconomics

    3. Economics

    4. Econometrics

  2. Which field of economics studies the economy as a whole, including but not limited to topics such as inflation and monetary policy?

    1. Macroeconomics

    2. Financial economics

    3. Microeconomics

    4. Labor economics

  3. Which field of economics studies individuals and specific markets, including but not limited to topics such as monopoly and competition?

    1. Macroeconomics

    2. Financial economics

    3. Microeconomics

    4. Labor economics

  4. Which of the following is NOT a foundational principle of microeconomics?

    1. Choices are necessary because resources are scarce

    2. Markets do not often lead to efficiency

    3. People can benefit more through trade than if self‐sufficient

    4. When markets do not achieve efficiency, government intervention can improve society's welfare

  5. Which of the following is NOT an example of an economic resource?

    1. Time

    2. Attention span

    3. Motivation

    4. Oxygen

  6. The true cost of something is its _____.

    1. Opportunity cost

    2. Margin cost

    3. Market capital

    4. Market price

  7. True or False: All choices involve trade‐offs.

    1. True, we always give up something when making a choice

    2. True, except when alternatives present no opportunity for benefit

    3. False, we do not always need to give up something when making a choice

    4. False, except under rare circumstances when there is no alternative choice

  8. Suppose you receive a movie ticket for free. What is the opportunity cost of going to see the movie?

    1. $0, because you did not spend any money to buy the ticket

    2. $0, because you do not lose anything by watching the free movie

    3. Not $0, because the time spent on watching the movie could have been used for other purposes

    4. Not $0, because the movie theater spent money for the showing

  9. What does it mean to make a trade‐off at the margin?

    1. To gain more than you lose economically

    2. Sticking with an original plan of doing something

    3. Comparing costs and benefits of doing more or less of something

    4. To cut your losses when costs outweigh profits

  10. A(n) _____ is anything that offers rewards to people who change their behavior.

    1. Opportunity cost

    2. Incentive

    3. Lucrative

    4. Benefit

  11. True or False: We do not get an increase in total output when people specialize in tasks they are good at.

    1. False, we get more total things which we can then trade with others

    2. True, everyone should work independently to protect their own economies

    3. False, every country has its own natural resources to contribute

    4. True, the risk increases when more people are involved

  12. When has a market reached equilibrium?

    1. When market supply matches demand

    2. When the costs and benefits of purchasing goods and services are equivalent

    3. When the cost of goods and services equal the buying power

    4. When the supply accounts for demand and potential loss

  13. When can an economy be described as efficient?

    1. When it uses its resources to help people at the expense of others

    2. When it maximizes its resources to help achieve equity

    3. When it maximizes its resources to help some people without hurting others

    4. When it uses its resources to help achieve equality

  14. What are the consequences of maximizing the size of the proverbial ‘economic pie’?

    1. Introduces equality into the system

    2. Maximizes economic productivity

    3. Opportunity cost decreases

    4. Opportunity cost increases

  15. When the individual pursuit of self‐interest makes society worse off, this is called _____.

    1. Aggregation

    2. Market exploitation

    3. Market failure

    4. Stagflation

  16. There is a tradeoff between _____ and _____.

    1. Supply, demand

    2. Market powers, inflation

    3. Institutions, regulations

    4. Equality, efficiency

  17. Which of the following is an example of a positive statement?

    1. The government should take measures to reduce inflation

    2. Unemployment is more harmful than inflation

    3. The government should raise minimum wage

    4. An increase in minimum wage increases unemployment among teenagers

  18. Which of the following is NOT an example of a normative statement?

    1. Taxes should be lowered to promote business activity

    2. Higher interest rates will dampen house prices

    3. Unemployment is more harmful than inflation

    4. The government should raise minimum wage

  19. When markets achieve efficiency, government intervention _____.

    1. Can lead to market failure

    2. Is needed to regulate prices and monitor efficiency

    3. Will not improve societal welfare

    4. Will improve societal welfare

  20. What is an example of a market failure?

    1. When a monopoly is established

    2. When demand is low but supply is high

    3. When goods and services are made by high cost producers rather than low cost producers

    4. When the cost of goods and services do not equal buying power

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