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Laptop versus longhand note taking: effects on lecture notes and achievement

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There has been a shift in college classrooms from students recording lecture notes using a longhand pencil-paper medium to using laptops. The present study investigated whether note-taking medium (laptop, longhand) influenced note taking and achievement when notes were recorded but not reviewed (note taking’s process function) and when notes were recorded and reviewed (note taking’s product function). One unique aspect of the study was determining how laptop and longhand note taking influence the recording of lecture images in notes and image-related achievement. Note-taking results showed that laptop note takers recorded more notes (idea units and words) and more verbatim lecture strings than did longhand note takers who, in turn, recorded more visual notes (signals and images) than did laptop note takers. Achievement results showed that when taking laptop notes, the process function of note taking was more beneficial than the product function of note taking (i.e., better image-related learning and similar text-related learning). When taking longhand notes, the product function of note taking was more beneficial than the process function of note taking (i.e., better text-related learning and similar image-related learning). Achievement findings suggest that the optimal note-taking medium depends on the nature of the lecture and whether notes are reviewed.
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Vol.:(0123456789)
Instructional Science (2018) 46:947–971
https://doi.org/10.1007/s11251-018-9458-0
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ORIGINAL RESEARCH
Laptop versuslonghand note taking: eects onlecture notes
andachievement
LinlinLuo1· KennethA.Kiewra2· AbrahamE.Flanigan3· MarkeyaS.Peteranetz2
Received: 7 April 2017 / Accepted: 7 June 2018 / Published online: 15 June 2018
© Springer Nature B.V. 2018
Abstract
There has been a shift in college classrooms from students recording lecture notes using a
longhand pencil-paper medium to using laptops. The present study investigated whether
note-taking medium (laptop, longhand) influenced note taking and achievement when
notes were recorded but not reviewed (note taking’s process function) and when notes
were recorded and reviewed (note taking’s product function). One unique aspect of the
study was determining how laptop and longhand note taking influence the recording of
lecture images in notes and image-related achievement. Note-taking results showed that
laptop note takers recorded more notes (idea units and words) and more verbatim lecture
strings than did longhand note takers who, in turn, recorded more visual notes (signals and
images) than did laptop note takers. Achievement results showed that when taking laptop
notes, the process function of note taking was more beneficial than the product function
of note taking (i.e., better image-related learning and similar text-related learning). When
taking longhand notes, the product function of note taking was more beneficial than the
process function of note taking (i.e., better text-related learning and similar image-related
learning). Achievement findings suggest that the optimal note-taking medium depends on
the nature of the lecture and whether notes are reviewed.
Keywords Note taking· Lecture learning· Laptop
Nearly all college students record lecture notes (Bonner and Holliday 2006; Castelló and
Monereo 2005; Kiewra 2002), and they are wise to do so because note taking is positively
related to achievement (e.g., Kiewra 1985; Nye etal. 1984; Peverly etal. 2014). Note tak-
ing boosts achievement because it potentially serves both a process (the taking of notes is
helpful) and product (the review of notes is helpful) function. Traditionally, students have
* Kenneth A. Kiewra
kkiewra1@unl.edu
1 Department ofSchool Research, School Development, andEvaluation, University ofRegensburg,
Regensburg, Germany
2 Department ofEducational Psychology, University ofNebraska-Lincoln, Lincoln, NE, USA
3 School ofCommunication Studies, Ohio University, Athens, OH, USA
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taken notes using a pencil-paper longhand medium. Advancements in technology, how-
ever, have increased the number of students who take lecture notes using a laptop com-
puter (Fried 2008; Lauricella and Kay 2010) to the point where nearly one-third of college
students record class notes on laptops (Aguilar-Roca etal. 2012). Given this increase, the
purpose of the present study was to determine how the process and product functions of
laptop and longhand note taking differentially impact students’ notes and achievement. To
make these determinations, various note-taking indices and achievement outcomes were
examined. Because lectures often contain visual images that might be especially difficult
for laptop note takers to note (Mosleh etal. 2016; Reimer etal. 2009), the present study
also measured the presence of images in notes and image-related achievement.
Note‑Taking Function
Theoretically, note taking serves two functions: process and product (Di Vesta and Gray
1972; Kiewra 1985). The process function suggests that the activity of recording lecture
notes is more effective than listening-only. This note-taking advantage is explained by two
hypotheses. One is the translation hypothesis (Conway and Gathercole 1990; De Haan
et al. 2000), which posits that hearing and writing lesson material is better than simply
hearing it because additional writing leads to more distinctive encoding and better memory
for the lesson material. The other is the generative hypothesis (Wittrock 1974), which pos-
its that note takers better assimilate lecture ideas with prior knowledge than do listeners
(Peper and Mayer 1978, 1986; Shrager and Mayer 1989) because the note-taking process
encourages students to paraphrase, organize, and integrate new lesson material in line with
related prior knowledge.
Research on the process function is, however, mixed (Kiewra 1985; Kobayashi 2005),
with some studies favoring note taking over listening (Bligh 2000; Einstein etal. 1985;
Kiewra etal. 1991; Suritsky and Hughes 1991) and others showing no benefit of note tak-
ing over listening (Fisher and Harris 1973; Glover etal. 1980; Riley and Dyer 1979). Kie-
wra and Fletcher (1984), in fact, found no evidence for note takers linking lecture ideas to
prior knowledge even when asked to do so. Kiewra etal. (1991) argued against the genera-
tive benefits of the note taking, saying that the note-taking process is cognitively demand-
ing, potentially overloads memory (Sweller 1994), and interferes with immediate learning:
During lecture learning, students must continuously and simultaneously listen, select
important ideas, hold and manipulate lecture ideas, interpret the information, decide
what to transcribe, and record notes. Some resources are additionally spent on the
mechanical aspects of note taking such as spelling, grammar, and notational style. It
is unlikely that many resources are available for more generative processing of lec-
ture information. (Kiewra etal. 1991, p. 241)
Other researchers have also found the activity of note taking cognitively demanding and
sometimes ineffective when recorded notes are not reviewed (Bui and Myerson 2014;
Katayama and Robinson 2000; Piolat etal. 2005).
The product function suggests that reviewing an externally stored set of notes aids learn-
ing because review permits students to commit noted ideas to memory through rehearsal,
organization, or elaboration when more time permits following the lecture (Kiewra 1985;
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Laptop versuslonghand note taking: effects onlecture notes…
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Kiewra etal. 1991). Research confirms that reviewing lecture notes boosts achievement
substantially compared to no review (e.g., Armbruster 2000; Fisher and Harris 1973; Kie-
wra 1985; Knight and McKelvie 1986). When the process and product functions of note
taking are compared, the product function is usually more effective (Kiewra etal. 1991;
Kobayashi 2005; Rickards and Friedman 1978). This comparison suggests that the true
value of note taking is more in the review of notes than in their recording.1
Note‑Taking Medium
Three recent studies containing multiple experiments investigated the achievement and
note-taking advantages of laptop versus longhand note taking. In the first study (Bui etal.
2013, Experiment 1), college students listened to a brief lecture and either recorded notes
using laptop or longhand methods. With regard to note taking, laptop notes contained more
lecture ideas and more verbatim strings than did longhand notes. Regarding achievement,
laptop note takers recalled more facts (main ideas and important details) than longhand
note takers on an immediate test without review (process function).
In the second study (Mueller and Oppenheimer 2014), students watched a series of brief
lectures and either recorded notes using laptop or longhand methods. Regarding immediate
testing (Experiment 1), longhand note takers achieved more on concept, but not fact, items
than laptop note takers when notes were recorded but not reviewed (process function). The
product function was not investigated in Experiment 1. Regarding delayed testing (Experi-
ment 3), there was a note-taking function by medium interaction. Laptop and longhand
note takers performed comparably on fact and concept items when notes were recorded but
not reviewed (process function), whereas longhand note takers achieved more than laptop
note takers on those same items when notes were both recorded and reviewed (product
function). Regarding note taking, laptop notes contained more words and more verbatim
lecture strings than did longhand notes across all three experiments, even when laptop note
takers were warned against recording verbatim notes in Experiment 2.
In the third study (Fiorella and Mayer 2017, Study 2), students studied verbal informa-
tion about the human respiratory system presented on ten 3-inch by 5-inch flashcards and
recorded notes using longhand or laptop methods. Students were instructed to study one
card at a time and to not return to a previously studied card. After studying, all participants
had 3min to review their notes before taking an achievement test that contained retention,
transfer, and drawing items. Laptop note takers outperformed longhand note takers on all
three item types. This laptop achievement advantage occurred even though longhand note
takers used more spatial strategies, such as mapping and drawing, than laptop note takers.
The laptop group, however, recorded more words than the longhand group.
Looking across the three studies investigating note-taking medium, common findings
pertain to note taking. First, laptop note takers recorded more information in the form of
idea units (Bui etal. 2013) or words (Fiorella and Mayer 2017; Mueller and Oppenheimer
2014) than longhand note takers. These findings are not surprising because adults type at
1 Kiewra etal. (1991) argued that the product function of note taking is best represented by those who only
review notes (such as those provided by the instructor or borrowed from a fellow student) but do not also
record notes. They argued that both recording and reviewing notes is representative of the combined pro-
cess and product functions of note taking rather than the product function alone.
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L.Luo et al.
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a rate of 33 wpm (Karat etal. 1999) but write at a rate of only 22 wpm (Brown 1998), and
transcription speed is positively related to the quantity of recorded lecture notes (Peverly
et al. 2007). These findings are important because both note-completeness indices (idea
units and words) are positively related to achievement (Peverly etal. 2003; Williams and
Worth 2002).
Second, longhand note takers recorded more generative notes than laptop note tak-
ers. Two studies (Bui et al. 2013; Mueller and Oppenheimer 2014) reported that laptop
notes contained more verbatim lecture strings than did longhand notes. Laptop note tak-
ers approached note taking as a non-generative transcription process by writing lecture
ideas word for word, whereas longhand note takers approached note taking as a generative
process by writing lecture ideas in one’s own words. Although Fiorella and Mayer (2017)
did not examine verbatim notes, they found that laptop notes were written in words only,
whereas longhand notes contained self-generated illustrations such as maps and drawings.
Taken together, it appears that different note-taking mediums stimulate different note-
taking strategies. Laptop note takers used primarily verbal note-taking strategies and
approached note taking as a transcription process. Longhand note takers used both ver-
bal and spatial note-taking strategies and approached note taking as a generative process,
wherein lesson ideas were noted in one’s own words and complemented with illustrations.
The studies investigating note-taking medium were at odds, though, with respect
to achievement. Regarding the process function of note taking (when notes were not
reviewed), laptop note takers achieved more than longhand note takers in one study (Bui
etal. 2013, Experiment 1), whereas longhand note takers achieved more than laptop note
takers in another study (Mueller and Oppenheimer 2014). Regarding the product function
of note taking (when notes were reviewed), laptop note takers achieved more than long-
hand note takers in one study (Fiorella and Mayer 2017, Study 2), whereas longhand note
takers achieved more than laptop note takers in another study (Mueller and Oppenheimer
2014, Experiment 3). Bui etal. (2013) credit laptop achievement advantages to note-taking
quantity: laptop note takers recorded more complete notes than longhand note takers. They
contend that the process function of note taking is enhanced by recording more notes, and
previous studies support this contention (Bligh 2000; Einstein etal. 1985; Kiewra etal.
1991; Suritsky and Hughes 1991). Bui etal. (2013), however, also found that the benefits
of taking verbatim notes might be lost if the notes are not reviewed. Mueller and Oppenhe-
imer (2014), meanwhile, credit longhand achievement benefits to the better quality of those
notes compared to laptop notes. They contend that longhand note takers, relative to laptop
note takers, were more engaged and thoughtful during the note-taking process and that
their resulting more paraphrased (versus verbatim) notes were a more meaningful product
for review. Fiorella and Mayer (2017) found that although longhand note takers used spa-
tial note-taking strategies, they achieved less than laptop note takers on the drawing test.
They suggested that longhand note takers experienced greater extraneous cognitive pro-
cessing during learning compared to laptop note takers because spatial strategies required
more time and cognitive effort than verbal strategies.
Visual Images inNotes
Investigating the presence or absence of visual images (e.g., graphs, tables, figures, etc.) in
notes is important because adding visual images to verbal lesson materials helps illustrate
lesson material and increase student learning (ChanLin 1998). Mayer (2009) summarized
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Laptop versuslonghand note taking: effects onlecture notes…
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this benefit in his multimedia principle: students learn better from words and visual images
than from words alone (Mayer and Gallini 1990). The advantageous addition of images
also fits with the dual-coding theory (Clark and Paivio 1991) that learning is best when
material is processed both verbally and visually. However, visual images might be harder
to capture using a laptop because of technological constraints (such as difficulty drawing
an image or producing a graph). In fact, students reported that it was harder and slower to
draw pictures, symbols, charts, and graphs when they used a laptop, versus longhand meth-
ods, for note taking (Reimer etal. 2009).
Among the three studies that investigated laptop and longhand note taking (Bui
etal. 2013; Fiorella and Mayer 2017; Mueller and Oppenheimer 2014), only Fiorella
and Mayer’s study investigated visual images in notes. The researchers were particu-
larly interested in the types of note-taking strategies students might use when study-
ing a scientific text about respiration that contained verbal information but no images.
They proposed that different note-taking mediums might encourage different note-tak-
ing strategies and learning outcomes. Specifically, they predicted that laptop note tak-
ers would use a verbal note-taking strategy (i.e., record notes in words only), whereas
longhand note takers would use both verbal and spatial note-taking strategies (i.e.,
create visual images representing the respiratory system). Results confirmed that lap-
top note takers recorded information mainly in words and that longhand note takers
recorded more images in notes than did laptop note takers. However, recording more
images did not boost achievement. Surprisingly, laptop note takers outperformed long-
hand note takers on retention, transfer, and drawing tests.
Research Gaps andLimitations
The reviewed studies on laptop versus longhand note taking (Bui etal. 2013; Fiorella
and Mayer 2017; Mueller and Oppenheimer 2014) have gaps or limitations addressed
in the present study. First, those three studies contained a total of eight experiments,
and only one of those (Mueller and Oppenheimer 2014, Experiment 3) investigated
systematically the main and interactive effects of note-taking function (process, prod-
uct) and note-taking medium (laptop, longhand). The present study did as well. Sec-
ond, these studies used non-course related material or material presented in an uncon-
ventional way. In one study (Mueller and Oppenheimer 2014), college students viewed
five different TED Talks (Experiments 1 and 2) or listened to four lectures over four
general but unrelated topics (Experiment 3). In another study (Bui etal. 2013), college
students listened to a lecture taken from a book that compared a popular film to the
Crimean War event it depicted. In the third study (Fiorella and Mayer 2017), students
viewed materials about the human respiratory system on 10 flashcards, which is not
a typical instructional medium in college classroom learning. The present study used
a lecture pertaining to educational measurement, a relevant topic for the educational
psychology students who participated. Furthermore, although Fiorella and Mayer
(2017) investigated the presence or absence of images recorded in laptop versus long-
hand notes and found that longhand note takers made more drawings in notes than did
laptop note takers, their lesson material was presented via text rather than lecture and
contained no provided images. The present study involved note taking from lecture
rather than from text and used lecture material that contained images.
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The Present Study
The primary purpose of the present study, then, was to further investigate to what extent
different note-taking mediums (laptop, longhand) and note-taking functions (process, prod-
uct) affect (a) lecture note-taking behaviors and (b) achievement. In this regard, the present
study replicates Mueller and Oppenheimer’s (2014) Experiment 3 design by investigating
the interactive effects of note-taking function and note-taking medium. It does so, though,
using authentic material, an achievement test that assesses both text-related and image-
related learning outcomes, and a wider array of note-taking measures (verbatim notes, note
quantity, and visual notes). A unique purpose of the present study was to examine how
laptop and longhand methods influence note taking and achievement when the lecture con-
tains visual images. To meet these purposes, college students recorded laptop or longhand
notes (note-taking medium) while viewing a course-related lecture that contained several
images and then reviewed or did not review notes (note-taking function) before taking an
achievement test measuring text-related and image-related learning outcomes.
Methods
Participants anddesign
Participants were 126 undergraduate education majors enrolled in an educational psychol-
ogy course at a large Midwestern university who participated to receive research credit.
Eighty percent were female, and most were Caucasians (94%). Seventy-one percent were
juniors and seniors, and 88% held grade-point averages of 3.0 or higher. Prior to partici-
pation, equal numbers of volunteering students were first assigned randomly to either the
process or the product note-taking function group and received email notification of their
participation time and place. The process and product function groups participated at dif-
ferent times in the same classroom setting. Upon arrival at the classroom, all participants
were further assigned randomly to either longhand or laptop note-taking groups. Because
some students failed to show up for the experiment, group sizes were slightly unequal. This
2×2 design produced four groups: longhand process (n = 30), longhand product (n = 32),
laptop process (n = 30), and laptop product (n = 34).
Materials
A 23-min narrated PowerPoint lecture covered the topic of educational measurement. It
was a pre-programmed, self-advancing presentation with accompanying narration. The
audio-recorded narration contained 2696 words presented at a rate of 117 wpm. There
were 23 PowerPoint slides in total. Each slide was presented for about 30s to 1 min,
and each displayed content with a center heading along with one or two bullet points
containing brief verbal information (10 or fewer words) below the heading (an example
appears in Fig.3 in Appendix). The first slide was an overview of the lecture’s four main
topics: (a) levels of measurement, (b) measures of central tendency, (c) measures of dis-
persion, and (d) shapes of distributions. The next five slides covered the first topic, lev-
els of measurement. The first slide was an overview of the four levels of measurement:
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nominal, ordinal, interval, and ratio. Each of the next four slides covered one of the
measurement levels in turn. For each measurement level, information about definition,
characteristics, examples, and limitations was presented. A similar structure was used to
present the next two topics (i.e., the three measures of central tendency—mean, median,
and mode; and the three measures of dispersion—range, variance, and standard devia-
tion). The last nine slides covered the fourth topic—shapes of distributions. This sec-
tion first introduced frequency tables and then the four distributions: normal, negatively
skewed, positively skewed, and bimodal. These nine slides also contained images to
help participants visualize and understand the lecture’s verbal information. One image
was a frequency table, and the other eight images were graphs of distributions.Figure4
in the Appendix is an example of a lecture slide with verbal information and image.
Narrated PowerPoint lecture is a common instructional presentation mode used in
college courses such as psychology (Cramer etal. 2006), business (Keefe 2003), biol-
ogy (Moravec etal. 2010), nursing (Corbridge etal. 2010), and cinema (Young 2009).
Narrated PowerPoint lectures can be presented in class or viewed by students on their
own outside of class (Mayer 2008).
Longhand note takers received blank paper and pens for note taking. Laptop note
takers received laptops loaded with and left open to a blank Word document for note
taking. All Word functions (e.g., bold, highlight, text alignment, tables, and indentation)
were operational.
A distracter task was used to clear working memory between information acquisi-
tion and testing. It included 10 vocabulary multiple-choice questions taken from sample
Scholastic Aptitude Test items.
The multiple-choice test contained 37 items and contained a mixture of fact, relation-
ship, concept, and skill items. Items were constructed following guidelines for construct-
ing items measuring varying learning outcomes (Gagné 1977; Gronlund 1998; Kiewra
2009). The test’s overall internal consistency, as measured by Cronbach’s alpha, was .804.
Because one focus of the present study was assessing image-related learning, two
achievement scores were created: image-related and text-related. The image-related scores
were based on 11 image-related items. These items pertained specifically to graphically
represented images (e.g., images of distributions) in the PowerPoint lecture. For example,
“In which distribution is the mode always greater than the mean?” The text-related scores
were based on the remaining 26 items that assessed participants’ knowledge of lecture con-
tent not supported by lecture images. For example, “Which two measures of dispersion
indicate the average distance of scores from the center of a distribution?” The internal con-
sistency coefficient for image-related items was .60 and was .74 for text-related items.
An exit survey collected participants’ demographic information, lecture topic knowl-
edge, typical note-taking completeness, note-taking medium preference, and attitudes
about their experimental note-taking medium. Demographic information included par-
ticipants’ gender (a. Male, b. Female), ethnicity (a. Black/African American, b. Hispanic/
Latino, c. Asian/Pacific Islander, d. Caucasian/White, e. Other), class standing (a. Fresh-
man, b. Sophomore, c. Junior, d. Senior), and approximate cumulative GPA (a. 3.5–4.0,
b. 3.0–3.49, c. 2.5–2.99, d. 2.0–2.49, e., below 2.0). The prior knowledge question asked
whether participants had taken any courses that covered the topic of measurement (a. Yes,
b. No). The next two questions asked participants about their typical note-taking complete-
ness [How many notes do you usually take in class? (a) I don’t take notes, (b) I take a few
notes, (c) I take a lot of notes], and their note-taking medium preference [What note-taking
medium do you commonly use? (a) Longhand, (b) Laptop]. Finally, the exit survey asked
participants their attitudes about the experimental note-taking medium they used (laptop
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or longhand) in terms of effectiveness, ease of use, enjoyment, and likelihood of future use
using a 1–6 Likert-type scale (1 = To a small degree, 6 = To a large degree).
Procedure
Once participants arrived in the experiment classroom and were situated, the researcher
read instructions aloud informing them of the procedure. All participants were informed of
the three experimental phases: learning phase (“You’ll be presented with material to learn
about the topic of educational measurement. Record notes in a way that is most helpful to
you as in any college class”), testing phase (“You’ll take a test on educational measurement
that tests facts, relationships, recognition of new examples, and skills”), and exit survey
phase (“You’ll answer a few questions about yourself and about the methods you used dur-
ing the study”). Those in the product group were additionally informed that they would
have 15min to review their recorded notes before the testing phase. All participants then
carried out group-specific tasks. Finally, participants were debriefed and dismissed.
Scoring
All multiple-choice test items were scored objectively by computer. Image-related achieve-
ment scores were calculated using the average correct percentage of the 11 image-related
items. Text-related achievement scores were calculated using the average correct percent-
age of the 26 text-related (non-image) items.
All notes were scored for idea units, words, verbatim strings, signals, and images. The
first author scored all notes for idea units, signals, and images, and a trained rater indepen-
dently scored about one third of notes to check reliability for these measures. Words and
verbatim strings were scored by computer.
Idea units were established by assigning one point for each noted idea unit based on a
rubric that included all 213 lecture idea units. An idea unit was defined as a conceptual unit
composed of an argument and its relations (Kintsch 1988). A sample idea unit in the pre-
sent study was: range (argument) is the difference between highest and lowest scores (rela-
tion). If an idea unit was present, then one point was assigned. If an idea unit was absent,
then no point was assigned. Previous research confirms that there is a positive correla-
tion between number of idea units recorded and achievement (Kiewra and Fletcher 1984).
Interrater reliability, measured by Cohen’s kappa, was .869, indicating excellent agreement
between two raters (Cohen 1988).
To assess recorded words and verbatim strings, all longhand notes were first transcribed
to electronic format to match laptop format. The number of words was calculated using the
“word count” function in Microsoft Word so that laptop and longhand notes were counted
using the same rules and so that human errors in counting were prevented. Previous
research shows that the number of words recorded is positively correlated with achieve-
ment (Boyle and Forchelli 2014; Mueller and Oppenheimer 2014). Verbatim strings, meas-
ured by the percentages of one-, two-, and three-word textual overlap between each partici-
pant’s notes and the lecture transcript, were computed using an n-gram program (Mueller
and Oppenheimer 2014). Previous research shows that the presence of verbatim strings in
notes is negatively correlated with achievement because verbatim note taking is consid-
ered a transcription process reflective of shallow processing (Mueller and Oppenheimer
2014). Another study (Bui etal. 2013), however, suggests that verbatim note taking might
be effective for laptop note taking when those notes are reviewed.
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The number of visual signals in notes was also scored. Visual signals included mark-
ings that designated certain information as particularly important (e.g., bolding, underlin-
ing, all-capital letters, stars, arrows, boxes, and circles). The number of visual signals was
counted and interrater reliability was good (Cohen’s kappa was .817). Previous research
shows that visual signals direct attention to key information and lead to higher recall than
when signals are not used (Lorch 1989).
Finally, notes were also scored for the number of images contained (e.g., the image of
a negatively skewed distribution). Each of the nine images presented during lecture was
scored as full, partial, or absent. Using the example of a negatively-skewed distribution, a
full image depicted its shape and identified where the mean, median, and mode fell in the
distribution; a partial image perhaps included only the distribution shape; and an absent
image meant that no image was recorded. In general, the presence of images in learning
materials boosts achievement (Mayer 2009), but the noting of images did not prove benefi-
cial in a recent text learning study (Fiorella and Mayer 2017). Interrater reliability, meas-
ured by Cohen’s kappa, was .811 for full image scores and .870 for partial image scores.
Disagreements were resolved by discussion. Because of the low number of full and partial
images found in participants’ notes, these two indices were combined into one index (i.e.,
the number of images recorded) for analysis.
Results
Results pertained to preliminary analyses, achievement, note taking, and attitudes.
Preliminary Analyses
Chi square tests were conducted for demographic variables, prior knowledge, typical note-
taking completeness, and note-taking medium preference (all were categorical variables).
Table 1 provides group statistics for these analyses. The four groups (laptop process,
longhand process, laptop product, and longhand product) differed significantly on gender
(p = .13) and on note-taking medium preference (longhand versus laptop; p = .10), using
p = .20 significance level to avoid Type II errors. Based on these findings, gender and note-
taking medium preference were included as covariates in all further analyses. The groups
did not differ significantly with respect to the other demographic, prior knowledge, or typi-
cal note-taking completeness variables.
Achievement
Two-way MANCOVA—medium (laptop vs. longhand) by function (process vs. product)—
was conducted on image-related and on text-related achievement scores. Image-related and
text-related scores were moderately correlated, r = .511, p < .001. For interpretation pur-
poses, effect sizes (i.e., eta squares) around .02 are small, around .13 are moderate, and
around .26 are large (Cohen 1992).
The overall multivariate test revealed a statistically significant interaction effect of note-
taking medium and note-taking function, p = .022, η2 = .062, and a significant note-taking
function main effect, p = .013, η2 = .071, as well as a significant covariate: note-taking
medium preference, p = .024, η2 = .061. Table2 provides multivariate test statistics for all
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L.Luo et al.
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variables, and the next two subsections address image-related and text-related findings,
respectively.
Image‑related achievement scores
There was an interaction between note-taking medium and note-taking function for image-
related test scores after controlling for the covariates, F(1, 120) = 7.328, p = .008, η2 = .058.
With respect to the covariates, note-taking medium preference (p = .006, η2 = .061) was
Table 1 Group statistics for demographic variables
Laptop process Laptop product Longhand process Longhand product Chi square
test
n (%) n (%) n (%) n (%) χ2p
Gender
Male 10 (36) 6 (18) 5 (17) 4 (13)
Female 18 (64) 28 (82) 25 (83) 28 (87) 5.74 .13
Class standing
Freshman 1 (4) 0 (0) 0 (0) 0 (0)
Sophomore 9 (32) 9 (27) 9 (31) 6 (18)
Junior 7 (25) 14 (41) 5 (17) 13 (41)
Senior 11 (39) 11 (32) 15 (52) 13 (41) 10.20 .34
Ethnicity
White 27 (96) 33 (97) 27 (93) 30 (94)
Black 1 (4) 0 (0) 2 (7) 0 (0)
Hispanic 0 (0) 0 (0) 0 (0) 1 (3)
Asian 0 (0) 0 (0) 0 (0) 1 (3)
Other 0 (0) 1 (3) 0 (0) 0 (0) 12.46 .41
Overall GPA
3.5–4.0 20 (71) 23 (68) 17 (57) 18 (56)
3.0–3.4 4 (14) 8 (23) 7 (23) 12 (38)
2.5–2.9 3 (11) 3 (9) 5 (17) 2 (6)
2.0–2.4 0 (0) 0 (0) 1 (3) 0 (0)
Below 2.0 1 (4) 0 (0) 0 (0) 0 (0) 12.50 .41
Prior knowledge
Yes 16 (57) 25 (73) 20 (67) 22 (69)
No 12 (43) 9 (27) 10 (33) 10 (31) 5.12 .53
Typical note-taking completeness
Take a lot of
notes
18 (64) 20 (61) 19 (63) 18 (56)
Take a few notes 9 (32) 13 (39) 11 (37) 11 (35)
Do not take notes 1 (4) 0 (0) 0 (0) 3 (9) 6.19 .40
Note-taking medium preference
Prefer longhand 16 (57) 27 (79) 26 (87) 23 (72)
Prefer laptop 12 (43) 7 (21) 3 (10) 8 (25)
Prefer both 0 (0) 0 (0) 1 (3) 1 (3) 10.60 .10
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statistically significant, and gender (p = .246, η2 = .011) was not significant. The interaction
pattern is depicted in Fig.1, and the follow-up tests of simple effects were conducted. The
interaction can be interpreted two ways. First, regarding the simple effect of note-taking
function at each level of note-taking medium: when laptop notes were recorded, partici-
pants who only took notes without reviewing them (M = 70%, SD = 21%) outperformed
those who took notes and reviewed them (M = 53%, SD = 18%) on image-related achieve-
ment items, p = .002, η2 = .079. When longhand notes were recorded, however, review-
ing notes did not yield statistically different results from not reviewing them (M = 67%,
SD = 24% vs. M = 63%, SD = 19%, respectively), p = .531, η2 = .003. Second, regarding
the simple effect of note-taking medium at each level of note-taking function: when notes
were recorded but not reviewed (process function), the laptop group (M = 70%, SD = 21%)
and the longhand group (M = 63%, SD = 19%) did not differ significantly on image-related
Table 2 Multivariate test statistics for achievement analysis
Wilks’ Λ F(2, 119) p value Partial η2
Covariates
Gender .977 1.388 .254 .023
Note-taking medium preference .939 3.838 .024 .061
Main effects
Note-taking function .929 4.545 .013 .071
Note-taking medium .991 .519 .599 .009
Interaction effect
Note-taking function*medium .938 3.965 .022 .062
Fig. 1 Interaction of note-taking medium and function for image-related achievement scores
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achievement items, p = .241, η2 = .011. When notes were both recorded and reviewed
(product function), the longhand group (M = 67%, SD = 24%) outscored the laptop group
(M = 53%, SD = 18%), p = .007, η2 = .051. The main effect for note-taking function was not
significant, F(1, 120) = 3.452, p = .066, η2 = .028. The main effect for note-taking medium
was not significant, F(1, 120) = .981, p = .324, η2 = .008.
Text‑related achievement scores
There was an interaction between note-taking medium and function for text-related test
scores after controlling for the covariates, F(1, 120) = 4.361, p = .039, η2 = .035. The
covariates were not significant (gender, p = .246, η2 = .011; note-taking medium prefer-
ence, p = .170, η2 = .016), so including them did not affect results. The interaction pattern
is depicted in Fig.2, and the follow-up tests of simple effects were conducted. The interac-
tion can be interpreted two ways. First, regarding the simple effect of note-taking function
at each level of note-taking medium: when laptop notes were recorded, reviewing notes
or not reviewing notes did not differentially affect text-related achievement (M = 69%,
SD = 15% vs. M = 72%, SD = 15%, respectively), p = .479, η2 = .004. When longhand notes
were recorded, however, students who reviewed notes (M = 76%, SD = 15%) outper formed
those who only took notes but did not review them on text-related achievement (M = 66%,
SD = 17%), p = .025, η2 = .041. Second, regarding the simple effect of note-taking medium
at each level of note-taking function: when notes were recorded but not reviewed (process
function), the laptop group (M = 72%, SD = 15%) had higher mean scores than the long-
hand group (M = 66%, SD = 17%), but the simple effect test was not significant, p = .224,
η2 = .012. When notes were both recorded and reviewed (product function), the longhand
group (M = 76%, SD = 15%) had higher mean scores than the laptop group (M = 69%,
Fig. 2 Interaction of note-taking medium and function for text-related achievement scores
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SD = 15%), but the simple effect test was not significant either, p = .079, η2 = .025. The
main effects for note-taking function [F(1, 120) = 1.214, p = .273, η2 = .010] and for note-
taking medium [F(1, 120) = .091, p = .764, η2 = .001] were not significant.
In summary, across both achievement measures, the process function of note taking was
more beneficial than the product function of note taking when students took laptop notes
(i.e., better image-related learning and similar text-related learning). On the other hand, the
product function of note taking was more beneficial than the process function of note tak-
ing when students took longhand notes (i.e., better text-related learning and similar image-
related learning). The top portion of Table3 provides group means and standard deviations
for all achievement scores.
Note Taking
Because there were seven note-taking variables examined (i.e., idea units, words, verba-
tim strings (one-, two-, and three-word strings), signals, and images), a principal compo-
nents analysis (PCA) was used to reduce the number of variables and to compute proper
composite scores. The note-taking data met the assumptions for using PCA. First, these
note-taking variables were correlated as shown in the Table4 correlation matrix. Second,
sample size (n = 126) was adequate, providing a ratio of 18 observations per variable. The
Kaiser–Meyer–Olkin measure of Sampling Adequacy was 0.57, and the Bartlett’s test of
sphericity was statistically significant (χ2(21) = 702.12, p < .001). Furthermore, each note-
taking variable shared some common variance with other variables (all communalities
were above 0.6).
PCA results showed that the first three eigenvalues were above one. The first three
components explained 45, 25, and 14% of variance, respectively. Therefore, a three-
factor solution that explained 84% of the variance was adopted. Direct oblimin rotation
was used to allow correlation between note-taking components. As shown in the pattern
matrix (Table5), the first component included the three verbatim string indices, the second
Table 3 Mean (and standard deviations) of outcome variables by groups
Covariates values: Gender = 1.79, Note-taking medium preference = 1.29
Laptop process (n = 30) Longhand pro-
cess (n = 30)
Laptop product (n = 34) Longhand
product
(n = 32)
Achievement
Image-related 70% (21%) 63% (19%) 53% (18%) 67% (24%)
Text-related 72% (15%) 66% (17%) 69% (15%) 76% (15%)
Note taking index
Verbatim notes .52 (1.11) −.35 (.90) .26 (.92) −.43 (.76)
Note quantity .23 (1.19) −.26 (.72) .31 (1.07) −.31 (.83)
Visual notes −.72 (.39) .91 (.75) −.80 (.37) .67 (.88)
Attitude
Effective 4.23 (1.28) 4.43 (1.07) 3.76 (1.25) 4.34 (1.08)
Easy to use 5.00 (1.20) 4.20 (1.38) 4.73 (1.18) 4.45 (1.18)
Enjoyable 4.43 (1.28) 3.70 (1.37) 4.18 (1.51) 3.79 (1.08)
Future use 4.57 (1.48) 4.60 (1.50) 4.00 (1.50) 4.45 (1.50)
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component included idea units and words, and the third component included noted signals
and images. Therefore, the first component was labeled “Verbatim Notes Index,” the sec-
ond component was labeled “Note Quantity Index,” and the third component was labeled
“Visual Notes Index.
To assess the effects of note-taking medium (laptop vs. longhand) and note-taking func-
tion (process vs. product) with regard to these three note indices, a 2 × 2 MANCOVA
was conducted and included participants’ gender and note-taking medium preference for
laptop or longhand note taking as covariates. The overall multivariate test revealed a sta-
tistically significant main effect for note-taking medium, p < .001, η2 = .661. The interac-
tion effect was not significant, p = .939, η2 = .003, and neither was the main effect for note-
taking function, p = .305, η2 = .030. With respect to the covariates, gender was significant,
p < .001, η2 = .153. Note-taking medium preference was not significant, p = .499, η2 = .020.
Table6 provides multivariate test statistics for all variables, and specific findings for the
three note indices are addressed next in turn.
With respect to Verbatim Notes Index, there was a significant main effect for note-tak-
ing medium: laptop note takers (M = .38, SD = 1.02) had higher scores than longhand note
takers (M = −.39, SD = .90), p < .001, η2 = .141, meaning that laptop note takers recorded
more verbatim strings than longhand note takers as measured by one-, two-, and three-word
Table 4 Correlation coefficients between note-taking measures
**Correlation is significant at the 0.01 level
*Correlation is significant at the 0.05 level
Idea units Words Verbatim strings Signals Images
1-word 2-word 3-word
Idea units 1.00 0.67** −0.19* −0.21* −0.17 0.23* 0.16
Words 1.00 −0.08 0.24** 0.31** −0.19* −0.34**
1-word 1.00 0.70** 0.57** −0.22* −0.15
2-word 1.00 0.97** −0.45** −0.44**
3-word 1.00 −0.46** −0.47**
Signals 1.00 0.41**
Images 1.00
Table 5 Pattern matrix for principal components analysis on note-taking variables
Loadings < .4 are suppressed
Component 1 Component 2 Component 3
Verbatim notes Note quantity Visual notes
One-word verbatim strings .957
Two-word verbatim strings .873
Three-word verbatim strings .786
Idea units .923
Words .901
Images .839
Signals .773
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strings. The main effect for note-taking function (p = .428, η2 = .005) and the interaction
effect (p = .572, η2 = .003) were not significant. The covariates were not significant (gender:
p = .085, η2 = .025 and note-taking medium preference: p = .411, η2 = .006).
With respect to Note Quantity Index, there was a significant main effect for note-taking
medium: laptop note takers (M = .27, SD = 1.11) had higher scores than longhand note tak-
ers (M = −.28, SD = .78), p < .001, η2 = .120, after controlling for the covariates, meaning
that laptop note takers recorded more notes than longhand note takers as measured by idea
units and words. The gender covariate was significant (p < .001, η2 = .145), but the note-
taking medium preference covariate was not significant (p = .990, η2 = .000). The main
effect for note-taking function (p = .573, η2 = .003) and the interaction effect (p = .974,
η2 = .000) were not significant.
With respect to Visual Notes Index, there was a significant main effect for note-taking
medium: longhand note takers (M = .78, SD = .82) had higher scores than laptop note takers
(M = −.76, SD = .38), p < .001, η2 = .588 after controlling for the covariates, meaning that
longhand note takers recorded more visual notes, comprised of signals and images, than
laptop note takers. The covariates, gender (p = .346, η2 = .007) and note-taking medium
preference (p = .230, η2 = .012), were not significant. The main effect for note-taking func-
tion (p = .107, η2 = .021) and the interaction effect (p = .751, η2 = .001) were not significant.
Overall, laptop note takers recorded more notes and more verbatim notes than long-
hand note takers who, in turn, recorded more visual notes than laptop note takers. The
middle portion of Table3 provides group means and standard deviations for all note-taking
indices.
Attitudes
Participants rated their attitudes about their experimental note-taking medium (laptop or
longhand) in terms of effectiveness, ease of use, enjoyment, and likelihood of future use
using a 1–6 Likert-type scale (1 = To a small degree, 6 = To a large degree). A 2×2 MAN-
COVA was conducted and included participants’ gender and note-taking medium prefer-
ence for laptop or longhand note taking as covariates. The overall multivariate test revealed
a statistically significant main effect for note-taking medium that favored the process
group, p < .001, η2 = .204. The interaction effect was not significant, p = .689, η2 = .020, and
neither was the main effect for note-taking function, p = .262, η2 = .045. With respect to
the covariates, none were significant. Table7 provides multivariate test statistics for all
variables.
Table 6 Multivariate test statistics for notes analysis
Wilks’ Λ F (3, 118) p value Partial η2
Covariates
Gender .847 7.086 < .001 .153
Note-taking medium preference .980 .795 .499 .020
Main effects
Note-taking function .970 1.222 .305 .030
Note-taking medium .339 76.819 < .001 .661
Interaction effect
Note-taking function*medium .997 .135 .939 .003
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With respect to effectiveness, laptop (M = 3.98, SD = 1.28) and longhand (M = 4.39,
SD = 1.07) groups did not differ significantly, p = .136, η2 = .019. With respect to ease of
use, there was a significant main effect for note-taking medium: laptop note takers rated
their note-taking medium easier to use (M = 4.86, SD = 1.19) than longhand note takers
(M = 4.32, SD = 1.28), p = .022, η2 = .045. With respect to enjoyment, laptop note takers
(M = 4.30, SD = 1.40) enjoyed taking notes using their medium more than longhand note
takers did using their medium (M = 3.75, SD = 1.23), p = .028, η2 = .041. Finally, with
respect to future use, laptop (M = 4.27, SD = 1.51) and longhand (M = 4.53, SD = 1.49) note
takers did not differ significantly, p = .479 η2 = .004.
Overall, participants found the laptop note-taking method easier to use and more enjoy-
able than the longhand method. The bottom portion of Table3 provides group means and
standard deviations for all attitude measures.
Discussion
With the growing popularity of laptop use in classrooms (Fried 2008; Lauricella and Kay
2010), investigating the effectiveness of laptop note taking versus traditional longhand note
taking is important. Yet, research on this topic is still in its infancy because, as far as we
know, only three other published studies have investigated laptop versus longhand note tak-
ing for classroom learning (Bui etal. 2013; Fiorella and Mayer 2017; Mueller and Oppen-
heimer 2014). The present study makes important contributions to this research domain.
First, the present study investigated systematically the main and interactive effects of note-
taking function (process, product) and medium (laptop, longhand) on college students’ note
taking and achievement, a research direction taken in just one other experiment (Mueller
and Oppenheimer 2014, Experiment 3). Second, it examined these variables using material
that was more authentic than the non-course material used in previous studies (Bui etal.
2013; Mueller and Oppenheimer 2014) or than text material printed on flashcards (Fiorella
and Mayer 2017). Third, the present study investigated a variety of note-taking behaviors
(i.e., idea units, words, verbatim strings, and signals) and was the first to examine how stu-
dents record notes on images presented in a lecture and how recording such images affects
image-related achievement. Previous research on noted images (Fiorella and Mayer 2017)
involved text learning, and images were not actually included in the text materials.
The present study’s main purpose was investigating systematically the main and inter-
active effects of note-taking function (process, product) and medium (laptop, longhand)
Table 7 Multivariate test statistics for attitudes analysis
Wilks’ Λ F (4, 113) p value Partial η2
Covariates
Gender .976 .682 .606 .024
Note-taking medium preference .964 1.057 .381 .036
Main effects
Note-taking function .955 1.333 .262 .045
Note-taking medium .796 7.228 < .001 .204
Interaction effect
Note-taking function*medium .980 .564 .689 .020
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on college students’ achievement and note taking, with a unique focus on how students
take notes when a lecture contains visual images. Therefore, achievement was based on
image-related achievement scores and on text-related (non-image) achievement scores.
With respect to image-related achievement, laptop note takers benefited more from note
taking’s process function than product function, whereas longhand note takers benefited
equally from both functions. With respect to text-related achievement, laptop note takers
benefited equally from both functions, whereas longhand note takers benefited more from
note taking’s product function than process function.
Both achievement results can be explained by the nature of recorded notes. Laptop note
takers recorded more notes (idea units and words) and more verbatim notes than longhand
note takers who, in turn, recorded more visual notes, in the form of signals and images,
than laptop note takers. Our contention is that laptop note takers had a transcription orien-
tation and recorded mainly verbal information from the lecture (Mueller and Oppenheimer
2014), whereas longhand note takers had a generative orientation and used a combination
of verbal and spatial note-taking strategies (Armbruster 2000; Bohay etal. 2011; Fiorella
and Mayer 2017; Reimer etal. 2009). The benefit of transcription-oriented laptop note tak-
ing was that it required minimal cognitive processing—students seemed to simply copy
what they heard—compared to generative-oriented longhand note taking, where students
devoted extra resources to paraphrasing lecture ideas, drawing complex images, and signal-
ing important ideas. The spending of additional resources purportedly limited longhand
note takers’ learning during the note-taking process (see Kiewra etal. 1991) and likely
led to their lower image-related achievement scores compared to laptop note takers when
tested without review.
The more generative-oriented longhand note-taking medium was more beneficial than
laptop note taking once notes were reviewed as observed for text-related achievement.
Although recording paraphrased and visual notes hindered learning without an opportunity
for review, such notes proved effective once they were reviewed. When there was ample
processing time during review, the more generative longhand notes proved to be a bet-
ter review source than the more transcription-oriented laptop notes. Longhand note takers
might have particularly benefited from having images in their notes to review. Laptop note
takers, meanwhile, had no images to review and, consequently, their image-related achieve-
ment scores were lower following review than when there was no review.
Looking across note-taking and achievement findings from the present study and three
others (Bui etal. 2013; Fiorella and Mayer 2017; Mueller and Oppenheimer 2014) inves-
tigating laptop and longhand note taking, some commonalities and differences emerge as
shown in Table8, where present findings appear in the fourth column. Regarding com-
monalities, first, all four studies found that laptop notes contained more information (idea
units and/or words) or verbatim strings than longhand notes as shown in the top portion of
Table8. These reoccurring findings indicate that taking notes with a laptop prompts tran-
scription-oriented note taking (Fiorella and Mayer 2017; Mueller and Oppenheimer 2014).
Second, two studies (present study and Fiorella and Mayer 2017) examining noted images
found that longhand notes contained more images than laptop notes. These similar find-
ings suggest that taking longhand notes prompts generative note taking and spatial strategy
use. Recording image notes, however, did not boost image-related achievement scores. As
shown in the bottom portion of Table8, both studies found that laptop note takers outper-
formed or performed comparably to longhand note takers.
Regarding differences, there was inconsistency among the studies regarding achieve-
ment as shown in the middle portion of Table8. With respect to text-related achievement
scores, one study (Bui et al. 2013, Experiment 1) found a process advantage for laptop
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Table 8 Four studies comparing laptop versus longhand note taking
Bui etal. (2013) Mueller and Oppenheimer
(2014)
Fiorella and Mayer (2017) Present study
Note taking
Idea units and words Laptop>Longhand Laptop>Longhand Laptop>Longhand Laptop>Longhand
Verbatim strings Laptop>Longhand Laptop>Longhand Laptop>Longhand
Signals and images Longhand>Laptop Longhand>Laptop
Achievement
Text-related
When notes were not reviewed Laptop>Longhand Longhand>Laptop Longhand=Laptop
When notes were reviewed Longhand>Laptop Laptop>Longhand Longhand>Laptop
Image-related
When notes were not reviewed Laptop>Longhand
When notes were reviewed Laptop>Longhand Laptop=Longhand
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note taking over longhand note taking, whereas another study (Mueller and Oppenheimer
2014), found a process advantage for longhand note taking over laptop note taking (in two
of three experiments). In the present study, laptop and longhand note takers did not dif-
fer from one another with regard to note taking’s process function. These mixed findings
add to the already mixed findings regarding the process function of note taking (Kiewra
1985; Kobayashi 2005) and leave unclear whether recording laptop or longhand notes is
best when notes are not reviewed. This lack of clarity might not hold much practical impor-
tance, though, given that the primary reason for recording notes is producing a complete
and effective set of notes for review (Badger etal. 2001; Kiewra 1985; Van Etten etal.
1997).
When notes were reviewed (product function), two studies (present study and Muel-
ler and Oppenheimer 2014) found an advantage for reviewing longhand notes over laptop
notes, but Fiorella and Mayer’s study (2017) revealed the opposite. The difference in the
reviewing effect might be associated with review period length: the present study and Mul-
ler and Oppenheimer’s study (15 and 10min, respectively) had longer review periods than
Fiorella and Mayer’s study (3min).
Limitations, Future Research Directions, andEducational Implications
Some present study limitations and corresponding future research directions are notewor-
thy. The first pertains to randomization. Although assigning participants to note-taking
groups randomly can result in nearly equitable groups, as was the case in the present study,
doing so might run counter to participants’ note-taking medium preference and thereby
mask naturally occurring results. In the present study, for example, 75% of participants
reported that they commonly take notes longhand, whereas just 25% reported that they
commonly take notes using a laptop (see Table1). This was taken into consideration by
including note-taking medium preference as a covariate in analyses. Still, future studies
could investigate longhand and laptop note taking in more natural settings that allow stu-
dents to take notes using their preferred medium as Friedman etal. (2014) did.
A second limitation pertains to low levels of recorded images in notes. On average,
longhand note takers recorded one lecture image in notes, whereas laptop note takers
recorded none of the nine lecture images in notes. One explanation for this small number
of recorded images might be that images were presented in the last section of the lecture
when attention is likely to wane (Scerbo etal. 1992). Alternatively, lecture rates that exceed
students’ transcription rates might generally cause students to abandon image recording
throughout the lecture in favor of recording verbal information. Because image-related note
taking research is in its infancy, there are several avenues that future image-related stud-
ies might pursue including: (a) varying the number of images provided, (b) varying how
images are spread throughout the lecture, (c) varying lecture pace or lecture pauses, (d)
varying whether or not images are supplemented by verbal descriptions as occurred in the
present study, and (e) examining the potential of laptop note taking when users can use the
laptop’s camera to capture a lecture’s displayed images.
A third limitation is that testing only occurred immediately following the lecture.
Although many previous studies examined note-taking functions in this way (e.g., Kiewra
etal. 1991; Peverly etal. 2007), adding a delayed test is more comparable to what happens
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in actual classroom settings and is more likely to magnify any laptop versus longhand
achievement differences especially when notes are reviewed.
A fourth limitation pertains to the uneven instructions for the note-taking function
groups. Prior to the experiment, both the note-taking process and the note-taking prod-
uct groups were informed of a post-test, but the product group received an additional
instruction indicating they would have a 15-min period for reviewing their notes before
the test. Knowing they would have a review period might have differentially affected
the product group’s note-taking behaviors in relation to those of the process group (who
received no such instruction). Although this did not appear to be the case given that the
2×2 MANCOVA on notes showed that the process and product groups did not differ in
their note-taking behaviors (i.e., note-taking function main effect was not significant),
future studies should avoid this potential confounding factor by providing consistent
instructions to both note-taking function groups.
A final limitation is that only one lecture was used, which limited findings’ generaliz-
ability. Future studies might use multiple lectures to improve generalizability as Mueller
and Oppenheimer (2014) did.
Some educational implications follow from this and related studies. First, despite the
growing popularity of laptop note taking (Fried 2008; Lauricella and Kay 2010) and
students’ self-reports that laptop note taking is beneficial (e.g., Barak etal. 2006; Mitra
and Steffensmeier 2000; Skolnick and Puzo 2008) and is easier and more enjoyable than
longhand note-taking (present study), laptop note taking has limitations. First, laptop
note-taking discourages students from recording lecture images in their notes. There-
fore, laptops might not be the ideal medium for recording notes when lectures contain
several important images. Second, reviewing laptop notes is generally less beneficial
than reviewing longhand notes, which are more paraphrased, contain more signals and
images, and lead to higher text-related achievement when reviewed. Despite these find-
ings, the present study cannot confirm that such note taking is best for those who prefer
to record laptop notes. Previous laptop note-taking research (Bui etal. 2013) showed
that verbatim transcription resulted in higher achievement than recording organized
notes for immediate testing without review (Experiment 1) and for delayed testing fol-
lowing note review (Experiment 3), but not for delayed testing when notes were not
reviewed (Experiment 2). Taken together, it seems that laptop note taking lends itself to
verbatim note taking (Bui etal. 2013; Mueller and Oppenheimer 2014; and the present
study) and that verbatim laptop note taking is effective when testing occurs immediately
without review and when testing is delayed following review.
Second, when incorporating images in their lectures, instructors should take steps
to be sure such images are recorded in notes and later reviewed. Previous note-taking
research suggests that this can occur when instructors simply provide students with
notes containing images (Bui and McDaniel 2015; Stefanou etal. 2008), slow the lec-
ture rate to allow more complete note taking (Aiken etal. 1975), or provide pauses
likely to increase note taking (Luo etal. 2016).
Third, instructors should warn students, especially those using laptops, not to simply
transcribe notes because such shallow processing does not always boost achievement. A
mild warning, though, might not be sufficient to deter laptop note takers from “mindless
transcription” (Mueller and Oppenheimer 2014, p. 1166).
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Laptop versuslonghand note taking: effects onlecture notes…
1 3
In conclusion, different note-taking mediums promote different note-taking strate-
gies. Laptop use promotes a transcription-oriented verbatim-style note taking that con-
tains mostly verbal information. This transcription style allows laptop note takers to
record more or longer notes than longhand note takers. Longhand note takers, on the
other hand, use a generative note-taking approach and produce notes that are more para-
phrased and more visual (containing more signals and images). The full benefit of long-
hand notes, however, is only realized when those notes are reviewed.
Acknowledgment We thank Dr. Pam Mueller and Dr. Daniel Oppenheimer for supplying the program for
the verbatim strings analyses, and we thank Daniel Parr for scoring notes.
Appendix: Examples ofthenarrated PowerPoint lecture slides
See Figs.3 and 4.
Fig. 3 Sample lecture slide with verbal information only
968
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Fig. 4 Sample lecture slide with verbal information and image
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