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Published in final edited form as: Educ Psychol (Lond). 2025 Apr 23;45(5):10.1080/01443410.2025.2493257. doi: 10.1080/01443410.2025.2493257

The Effects of Notetaking Modality and Symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) on Learning

Gabrielle A Shimko a, Karin H James a,*
PMCID: PMC12393830  NIHMSID: NIHMS2074760  PMID: 40896310

Abstract

College students with attention/deficit-hyperactivity disorder (ADHD) exhibit difficulty in lecture notetaking, which may exacerbate persistent academic difficulties. Higher ADHD symptoms are related to slower handwriting speed (HWS), potentially disrupting learning during the notetaking process. This study investigated whether typing notes could compensate for slower HWS and facilitate more learning than handwritten notes in individuals with higher ADHD symptoms. College students oversampled for ADHD diagnoses watched a 15-minute TedTalk while taking handwritten, typed, or no notes and took a quiz to assess learning. Fine motor dexterity, HWS, typing speed, sustained attention, and ADHD symptoms were measured. Participants with higher ADHD symptoms learned significantly more if they took handwritten or typed notes as compared to not taking notes. Slower HWS and worse sustained attention related to higher ADHD symptoms. Thus, active notetaking facilitated learning, particularly for students with higher ADHD symptoms, and the optimal modality may depend on individual transcription abilities.

Keywords: ADHD, transcription, notetaking, lecture learning, college students

Introduction

Individuals with attention-deficit/hyperactivity disorder (ADHD) are attending college at higher rates than ever, however, despite increases in enrollment, they are less likely to graduate, earn lower grades, and are more likely to be on academic probation compared to their neurotypical peers (Advokat et al., 2011; DuPaul et al., 2018; Gormley et al., 2019; Hechtman et al., 2016). This academic achievement discrepancy may be attributed to the characteristic executive function difficulties underlying the ADHD phenotype, relating to challenges in engaging with organizational behaviors and study skills important for academic success in post-secondary settings (Allsopp et al., 2005).

One such study skill that is especially predictive of academic achievement in college settings is lecture notetaking (Morehead et al., 2019a; Suritsky & Hughes, 1991). Notetaking is the process by which a learner summarizes, paraphrases, and/or copies presented information through transcription (Voyer et al., 2022). Notetaking during lectures is the most effective way to encode and store information presented in class, and thus, assists learners in recalling information for later assessments contributing to grades (Christopoulos et al., 1987; Fazal et al., 2012; Peverly et al., 2014; Peverly et al., 2007). Accommodations for notetaking are common among students with ADHD registered with accessibility services, paralleling data suggesting difficulty in this skill (Advokat et al., 2011; Vekaria & Peverly, 2018). These accommodations include receiving copies of notes, having a designated notetaker, or allowing use of computer programs that transcribe speech into words (Harrison et al., 2020).

There is minimal evidence available evaluating whether notetaking accommodations reduce academic impairment related to ADHD. Emerging research suggests that the passive nature of these interventions is less effective compared to skill-based interventions that promote active engagement through skill development, such as taking notes (Evans et al., 1994; Harrison et al., 2020). Indeed, the in-the-moment, active process of transcribing lecture information via notetaking facilitates learning, independent of studying, coined as the encoding effect (Di Vesta & Gray, 1972). It is theorized that encoding benefits arise from the sensorimotor engagement notetaking affords through transcription, as well as the cognitively demanding nature of the notetaking process (Bui & Myerson, 2014; Jansen et al., 2017; Kiewra, 1987; Peverly, 2006). Specifically, one must verbally comprehend the material being presented, parse and select key aspects of the lecture content related to learning goals, link the selected material to prior notes and knowledge, and finally, quickly transcribe and paraphrase the material in a way that appropriately summarizes the information as related to learning goals (Piolat et al., 2005).

Although it is theorized that it is the cognitively taxing nature of notetaking that facilitates the encoding effect for learning, it may also be the reason individuals with ADHD often avoid or struggle to take notes, and learn less from them, irrespective of studying (Vekaria & Peverly, 2018). Higher ADHD symptoms are related to difficulties in sustained attention, or engaging in goal directed behavior over long periods of time, which is essential in engaging with lecture material through notetaking (Avisar & Shalev, 2011; Gmehlin et al., 2016; Weyandt et al., 2017). Indeed, the act of notetaking is suggested to facilitate sustained attention on the lecture in-the-moment through accurate transcription of information (Machida et al., 2018). Additionally, higher ADHD symptoms are related to poorer fine motor dexterity (FMD), and more specifically, handwriting speed (HWS) – processes important for efficient transcription (Duda et al., 2014; Fietsam et al., 2022; McCracken et al., 2023; Vekaria & Peverly, 2018). Previous work emphasizes the importance of HWS in relation to handwritten note quality, or more accurate and comprehensive notes, as the ability to summarize more information with minimal cognitive resources supports subsequent recall (Peverly, 2006; Peverly et al., 2014; Peverly et al., 2013; Vekaria & Peverly, 2018).

HWS may be one of the primary barriers related to notetaking and subsequent learning in students with ADHD. A recent study assessed differences in handwritten notetaking abilities between college students with and without ADHD, finding that individuals with ADHD performed significantly worse on the recall assessment and exhibited slower HWS compared to controls, but had no group differences in cognitive measures, such as attention, and note quality (Vekaria & Peverly, 2018). Importantly, it seems that college students with ADHD may exhibit difficulties with transcription during notetaking via slower HWS, rather than difficulties with content or organization of the notes. Specifically, slower HWS or fine motor difficulties may interrupt encoding during the notetaking process, even if the motor output of the notes is high quality, or has high correspondence with presented material (Jansen et al., 2017). Thus, because encoding benefits arise from the sensorimotor engagement notetaking affords through transcription, then differential modes of transcription with lecture material should be explored further to understand its potential inhibitory, or supportive, effects related to learning.

Because students with ADHD may exhibit slower HWS than neurotypical students, increasing transcription speed through typing may free up cognitive resources during the notetaking process and provide opportunity for in-the-moment encoding benefits. College students are increasingly using laptops to type notes, and if trained in touch typing, typing is often a faster form of transcription than handwriting (Morehead et al., 2019a; Mueller & Oppenheimer, 2016; Weigelt-Marom & Weintraub, 2018). Previous work suggests, however, that faster transcription via typing facilitates less encoding than handwriting during notetaking due to less selective processing of information, as typing allows more verbatim overlap with speech and less generative learning (Mueller & Oppenheimer, 2014). However, literature on neurotypical college students indicate that there may be no significant differences in learning between the two modalities, potentially due to increased laptop use (Jansen et al., 2017; Morehead et al., 2019a; Morehead et al., 2019b; Shell et al., 2021; Voyer et al., 2022). Instead, typed or handwritten notes may be more or less beneficial for encoding depending on individual differences in cognitive and fine motor skills. Indeed, college students with handwriting and/or learning difficulties trained in typing may eventually type faster than they handwrite, indicating potential learning benefits from typing notes over handwritten notes in individuals with higher ADHD symptoms (Weigelt-Marom & Weintraub, 2015, 2018).

The current study sought to examine differential encoding benefits between typed and handwritten notes while considering individual differences in attentional, fine motor, and transcription abilities related to ADHD symptoms in college students with and without an ADHD diagnosis. Additionally, we sought to understand how measures of sustained attention, FMD, and HWF, processes important for notetaking, related to ADHD symptoms in college students. Measuring ADHD symptoms continuously, rather than solely relying on diagnosed individuals, is crucial for capturing the full spectrum of cognitive and motor challenges and their impact on academic performance via learning during notetaking. For the purposes of this study, which was focused on different modes of sensorimotor engagement with lecture material, notetaking was operationally defined as the process by which participants transcribed any relevant information during a lecture while using either pen and paper (handwritten) or a laptop (typed) in their own chosen notetaking style/structure. Here, we assessed learning similar to Mueller and Oppenheimer’s (2014) study by manipulating the encoding phase of notetaking, with students either taking handwritten, typed, or no notes while watching a 15-minute TedTalk followed by a free response quiz with no opportunity to review their notes. We hypothesized that students with higher ADHD symptoms would learn more from the lecture from typed notes than handwritten notes and would learn significantly less if they did not take notes. Additionally, we hypothesized that typed and handwritten notes would facilitate more learning than no notes, irrespective of ADHD symptoms, which would validate the encoding benefits of notetaking. We predicted that slower HWS, but not typing speed, and worse sustained attention and FMD would relate to higher ADHD symptoms. Finally, we predicted lower ADHD symptoms, better sustained attention, faster HWS, faster typing speed, and better FMD would relate to more learning.

Method

Participants

A total of 152 college students were included in this study (Table 1). Participants with recall assessment scores two standard deviations from the mean were excluded from analyses (n = 8) to improve normality of the data for regression analyses. Additionally, participants with proficient (n = 4) or expert (n = 1) knowledge about telescopes and space, who reported significant hearing loss or who are deaf (n = 4), and one high school student taking college classes (n = 1), were excluded from analyses. All participants were recruited in accordance with Indiana University’s Institutional Review Board under protocol number 0905000365. Participants were recruited through an internal database and received 1.5 course credits for participation or from paper advertisements posted throughout the university’s campus and were paid $15.00 for participation.

Table 1.

Demographic Information

Sample Characteristics n Percent of sample (%) M SD
Sex Assigned at Birth
  Male 41 27.0
  Female 111 73.0
Education
  Undergraduate 145 95.3
  Graduate 7 4.7
Race
  White 115 75.7
  Hispanic or Latino 11 7.2
  Black or African American 7 4.6
  Asian or Pacific Islander 7 4.6
  Multiracial or Biraciala 6 3.9
  Native American or Alaska Native 2 1.3
Age 19.2 2.3
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Note. Self-reported demographic information from participants.

a

n = 1 indicated Black or African American and White, n = 1 indicated Hispanic or Latino and White. n = 4 did not indicate any specific race or ethnicity.

M = mean; SD = Standard deviation.

Power Analysis

An a priori power analysis was conducted using G*Power version 3.1.9.6 based on previous notetaking studies that detected medium effects (Faul et al., 2009). With power of .85 and α = .05, the projected sample size needed was N = 135, indicating that the obtained sample size of N = 152 is adequate to test the study hypotheses at medium power. Our results therefore utilize a significance level of α = .05, and sensitivity analyses were also conducted using this alpha level to assess the smallest detectable effect size given the final sample size.

Materials

Demographic Questionnaire

All participants completed a de-identified survey through Qualtrics on a Macintosh computer provided by the experimenter. The survey prompted participants to report information regarding sex assigned at birth, age, race and/or ethnicity, whether they received an ADHD diagnosis by a qualified medical provider, and whether they were an undergraduate or graduate student.

ADHD Symptoms and ADHD Diagnosis

Participants were prompted on the questionnaire to self-report whether they were previously diagnosed with ADHD by a qualified medical provider, when they were diagnosed, and their subtype, if known. Additionally, all participants were asked to report use of any stimulant medications on the day of the experiment. A total of 46 participants reported that they were diagnosed with ADHD (Table 2). A total of 22 (n = 19 with ADHD diagnosis) participants reported that they took medication meant for focus, or stimulant medication, on the day of the experiment and we included this variable as a covariate in analyses (see Supplementary Materials).

Table 2.

Self-Reported ADHD Diagnosis and Symptoms Across Notetaking Conditions

ADHD Diagnosis Notetaking Condition
n No Note Handwritten Typed
Yes 46 15 18 13
No 105 35 37 33
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Note. Participant self-reported diagnosis of ADHD and symptom endorsement in total sample and across conditions.

All participants were asked to respond to the Adult Self Report Rating Scale (ASRS), which is a symptom rating scale that corresponds with Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 symptoms of ADHD with α = 0.88, r = 0.84, sensitivity of 56.3%, and specificity of 98.3% (Adler et al., 2006; Kessler et al., 2005). The ASRS prompts participants to rate the frequency of experiencing 18 ADHD symptoms over the last 6 months using a 5-point Likert scale (0 = Never, 5 = Very Often) (Kessler et al., 2005). Responses were used to create three ADHD symptom scores for each participant based on the sum of their responses on inattention symptoms (n = 9 questions; ADHD-I), hyperactive/impulsive symptoms (n = 9 questions; ADHD-H), and total ADHD symptoms from both symptom type responses. Any blank responses was assigned as 0. A Welch two sample t-test indicated that individuals with a previous ADHD diagnosis exhibited significantly higher total ADHD symptoms (M = 41.28, SD = 11.84) than those without a previous ADHD diagnosis (M = 31.25, SD = 12.00); t(87) = 4.77, p < 0.0001. Additionally, individuals who took stimulant medication on the day of experiment endorsed significantly higher ADHD symptoms (M = 40.55, SD = 13.33) than those who did not take stimulant medication (M = 33.36, SD = 12.46); t(28) = 2.36, p = 0.02. See Supplementary Materials for additional validity analyses regarding ADHD symptoms.

Lecture

Participants watched a 15-minute Ted Talk video, similar to Mueller and Oppenheimer’s (2014) method, about the history of modern astronomy and capturing images of supernovae with telescopes (Levesque, 2020). The speaker in the video dictated in English. The lecture was presented on a 24” Macintosh computer screen and the audio was presented through internal computer speakers. Participants were unable to pause the video while watching and closed captions were disabled. This video was chosen to minimize the effects of previous knowledge on learning, and our survey included a Likert scale question to assess how much information participants knows about telescopes, space, and astronomy.

Notetaking Materials

Participants assigned to the handwritten note condition took notes with a black, ball-point pen on 8.5 x 11 inch, wide-ruled lined paper on a notepad. Participants assigned to the typed note condition took notes on a 13” MacBook Pro laptop using a butterfly QWERTY keyboard, and they transcribed their notes using Microsoft Word with settings preset to 12-point Times New Roman font and 1-point spacing between lines and half inch margins. Participants in the no note condition were not provided with any notetaking materials. All participants in notetaking conditions were required to take notes during the lecture in their own particular notetaking style. Measures of note quality, or a measure of accuracy and comprehensiveness of notes, were scored from typed and handwritten note groups and were assessed in exploratory analyses (see Supplementary Materials). However, the for the purposes of this study, analyses of verbatim overlap of notes with lecture content, beyond inclusion of relevant information, was not measured.

Recall Assessment

Recall of the lecture material was assessed using a 20-question short answer quiz. The quiz consisted of a combination of questions that asked participants to recall general facts from the lecture or extrapolate information from the lecture (Mueller & Oppenheimer, 2014). The quiz was provided as a packet consisting of 8.5 x 11-inch white pieces of paper with numbered questions and 2” of blank space for handwritten responses.

Two experimenters graded the recall quizzes and had an overall percent agreement of 93.44%. Any scoring disagreements between were reviewed and new point values were assigned to questions as appropriate. One point could be earned per question, making the maximum number of points possible 20. Answers that were fully correct based on the key were given 1 point, while answers that were partially correct were given 0.5 point, as determined by both graders. Questions that were left blank or were completely incorrect were not given any points. The total quiz score is a percentage based on the sum of all the points given out of the 20 points possible.

Sustained Attention

Measures of sustained attention were collected using the Conners’ Continuous Auditory Test of Attention (CATA). The CATA is a standardized computer assessment of sustained attention that is also reliable in detecting ADHD symptoms of inattention and hyperactivity with 82.6% sensitivity and 76% specificity (Wang et al., 2021). The CATA was administered on a PC desktop computer with a 13 x 16-inch screen and participants used a keyboard and noise cancelling, over-the-ear headphones to perform the task. The task was 15 minutes long and included 4 blocks of 50 trials (200 total trials) with a 2-minute practice prior to the main assessment. Within each block, 40 of the trials were warned trials, which presented a low pre-cue tone prior to a target high tone, and 10 of the trials are unwarned, which presented only the target high tone. Participants were instructed only respond to warned tones by pressing the space bar as quickly as possible.

We then separated measures of sustained attention into two measures. The first measure of sustained attention is detectability, which is a standardized measure (T score) of the ability to distinguish targets from non-targets by considering omission and commission errors. The second measure is HRT variability, which is a composite score that includes two standardized T scores: the standard deviation of hit reaction (HRT SD) time across all blocks, and the average change or slope of HRT across four blocks.

Fine Motor Dexterity

FMD was assessed using the Lafayette Instrument Model 32025 Grooved Pegboard with reliability ranging from .67 to .86 in adults (Dikmen et al., 1999; Levine et al., 2004; Ruff & Parker, 1993). The total time, total number of drops, and total number of pegs correctly placed in the board were added to create the total score for each hand, but we only utilized the dominant hand for analyses in consideration of the hand used for handwriting.

Transcription Speed

HWS was assessed through a simple copying paradigm. Participants were given an 8.5 x 11-inch sheet of wide-ruled lined paper on a notepad and a ball point pen. Participants were asked to copy a paragraph text prompt that appeared on a Macintosh computer screen as quickly and accurately as possible for one minute. The number of words written within one minute was defined as HWS. Spelling errors or grammar did not count toward the HWS score.

Typing speed was assessed using a similar copying paradigm as described in HWS. On a MacBook Pro laptop with a butterfly QWERTY keyboard, participants were asked to copy a text prompt that appeared on a Macintosh computer screen as quickly and accurately as possible for one minute. The number of words typed within one minute was defined as typing speed. Spelling errors or grammar did not count toward the typing speed score.

Procedure

The experiment occurred over the course of one 1.5-hour long session in a small, quiet room where participants completed the experiment individually. After the participant provided informed consent, they were asked to complete the survey and were given as much time as needed. After the survey was complete, the participant was instructed to acclimate to the laptop keyboard by typing a sentence on a blank document. Next, participants were randomly assigned to the handwritten note condition, typed note condition, or the no note condition and watched the lecture. Participants in all conditions were informed that they will take a quiz later in same the session to assess how much they remembered from the lecture. Participants in the handwritten or typed note conditions were instructed to take notes using the assigned modality in any way they pleased throughout the duration of the lecture, and to do their best to pay attention to the video. They were also informed that they were unable to review their notes after the lecture was over. Participants in the no note condition were only instructed to do their best to pay attention to the video. Once the experimenter started the lecture video, they ensured the participants took some form of notes throughout the video if in an active notetaking condition. Once the lecture was over, the experimenter immediately took the notes from the participant. Next, participants were then given the handwriting speed and typing speed tasks sequentially. Afterwards, the quiz was administered, and participants had 20 minutes to complete it. No participants went over the 20-minute limit. Participants were told to skip any questions they did not know the answer to and to do their best. After the quiz, participants did the CATA task, including a 2-minute practice and then the full assessment. Finally, the participant completed the grooved pegboard assessment. After this, the study session ended, and participants were debriefed and compensated for their participation as appropriate.

Results

Data were analyzed and summarized using R, version 4.0.0 (R Core Team, 2021), including the packages ggplot2, version 3.2.1 (Wickham, 2016), psych version 2.2.9 (Revelle, 2018), psychTools version 2.2.9 (Revelle, 2022), car version 3.1 (Fox & Weisberg, 2018), moments version 0.14.1 (Komsta & Novomestky, 2015), performance version 0.10.2 (Lüdecke et al., 2021), lsmeans version 2.30 (Lenth, 2016), apatables version 2.0.8 (Stanley, 2020), lm.beta version 1.7-1 (Behrendt, 2023), and doBy version 4.6.16 (Højsgaard & Halekoh, 2023). This study’s design and analyses were not pre-registered. All data and analysis code are available at https://osf.io/pfbcx/.

Tables 3 and 4 include summary statistics for all independent and dependent variables for this study. Additionally, see Table 5 for intercorrelations for all continuous independent and dependent variables. All continuous variables were centered prior to regression analyses, and symptoms of ADHD and quiz score were tested for normality within each notetaking condition utilizing the Shapiro Wilk method. All scores were within normal limits.

Table 3.

Descriptive Statistics for Predictor and Outcome Variables Across Sample

Variable  M SD  Min and Max Skew Kurtosis SE
ADHD Total 34.4 12.8 10.0 65.0 0.18 −0.79 1.04
ADHD-I 18.6 7.0 4.0 35.0 0.11 −0.74 0.56
ADHD-H 15.8 6.8 3.0 32.0 0.13 −0.79 0.57
HRT Variability 48.2 7.8 32.5 72.5 0.49 0.03 0.66
Detectability 48.9 9.2 36.0 90.0 1.32 2.99 0.75
HWS 26.9 4.5 17.0 41.0 −0.13 −0.11 0.36
Typing Speed 46.3 10.4 25.0 85.0 0.87 1.40 0.84
FMD 92.8 10.3 76.9 126.8 0.86 0.59 0.84
Total Recall Score 63.0 15.2 22.5 87.5 −0.45 −0.42 1.23
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Note. M = mean; SD = standard deviation, ADHD-H = hyperactive/impulsive symptoms; ADHD-I = inattentive symptoms

Table 4.

Descriptive Statistics for Predictor and Outcome Variables Across Conditions

Notetaking Condition
No Notes
n = 51		 Handwritten
n = 55		 Typed
n = 46
Variable M SD M SD M SD
ADHD Total 35.3 13.8 35.3 13.4 32.4 10.8
ADHD-I 18.9 7.5 19.4 7.3 17.2 6.0
ADHD-H 16.4 7.3 15.8 7.0 15.1 6.2
HRT Variability 47.4 8.2 48.9 8.6 48.1 6.6
Detectability 50.7 11.6 48.7 7.6 47.2 7.6
HWS 26.4 4.3 26.9 4.9 27.3 4.1
Typing Speed 45.5 10.5 46.2 11.8 47.3 8.3
FMD 91.7 9.8 92.8 11.0 94.0 10.1
Total Recall Score 57.9 17.0 66.3 14.1 64.7 13.0
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Note. Values were calculated based on raw data. M = mean; SD = standard deviation; ADHD-H = hyperactive/impulsive symptoms; ADHD-I = inattentive symptoms

Table 5.

Correlations of Predictor and Outcome Variables with Confidence Intervals

Variable 1 2 3 4 5 6 7 8
1. Recall Score
2. ADHD Total −.07
[−.23, .09]
3. ADHD-H −.00
[−.16, .15]		 .92*
[.89, .94]
4. ADHD-I −.13
[−.28, .03]		 .92*
[.90, .94]		 .70*
[.61, .77]
5. Detectability −.18*
[−.33, −.02]		 .17*
[.01, .32]		 .18*
[.01, .33]		 .14
[−.02, .29]
6. HRT Variability −.29*
[−.43, −.13]		 −.13
[−.29, .04]		 −.16
[−.32, .00]		 −.08
[−.24, .08]		 .14
[−.03, .29]
7. FMD −.09
[−.25, .07]		 −.16*
[−.31, −.00]		 −.19*
[−.34, −.03]		 −.11
[−.27, .05]		 −.01
[−.17, .15]		 .17*
[.01, .33]
8. Typing Speed .12
[−.04, .28]		 −.01
[−.17, .15]		 .04
[−.12, .19]		 −.06
[−.22, .10]		 −.22*
[−.37, −.06]		 −.09
[−.25, .08]		 −.26*
[−.40, −.10]
9. HWS .13
[−.03, .28]		 −.18*
[−.33, −.02]		 −.12
[−.27, .04]		 −.21*
[−.36, −.05]		 −.19*
[−.34, −.03]		 −.06
[−.22, .11]		 −.15
[−.30, .01]		 .49*
[.36, .60]
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Note. Values in square brackets indicate the 95% confidence interval for each correlation. ADHD-H = hyperactive/impulsive symptoms; ADHD-I = inattentive symptoms

*

Indicates p < .05.

ADHD Symptoms

A multiple linear regression was conducted to evaluate which cognitive and motor variables related to ADHD symptoms in our sample. Total ADHD symptoms was regressed on HWS, typing speed, sustained attention (HRT variability and detectability), and FMD. Sex was initially added as a covariate in the model but did not result in an improved fit and was removed from the model reported. The overall model was significant (R2 = 0.12; R2adjusted = 0.09, F(5, 138) = 3.70, p = 0.004; Table 6). Collinearity checks indicated no issues in multicollinearity (VIF 1.00 – 1.19; Tolerance 0.77 – 0.91) or heteroscedasticity. Slower HWS was significantly related to higher ADHD symptoms (Figure 1), while typing speed was not related to ADHD symptoms, in line with our hypotheses. Additionally, only detectability related to higher ADHD symptoms (Figure 1). Contrary to our predictions, FMD and HRT variability were not significantly related to ADHD symptoms.

Table 6.

Regression Results using Total ADHD Symptoms as Outcome Measure

Predictor b b
95% CI
[LL, UL]		 beta t Fit
(Intercept) 34.81* [32.75, 36.87]
HWS −0.68* [−1.20, −0.16] −0.23 −2.58
Type Speed 0.09 [−0.14, 0.33] 0.08 0.80
Detectability 0.28*  [0.01, 0.56] 0.17 2.04
HRT Variability −1.20 [−0.49, 0.05] −0.13 −1.63
FMD −0.19 [−0.40, 0.02] −0.15 −1.76
R2 = .118*
95% CI[.02,.20]
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Note. A significant b-weight indicates statistical significance for both the unstandardized regression weights (b) and standardized regression weights (beta). t-values were calculated by dividing the unstandardized regression weight by its standard error, reflecting the strength of the predictor-outcome relationship.

*

indicates p < .05.

Figure 1.

Figure 1

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(A) Total ADHD Symptoms Regressed with Handwriting Speed

(B) Total ADHD Symptoms Regressed with Detectability

Note. (A) Demonstration of main effect of HWS predicting total ADHD symptoms. (B) Demonstration of main effect of detectability in predicting total ADHD symptoms (Table 6). Grey shading surrounding the lines indicate standard error.

Sensitivity analyses (α = 0.05) indicated that the model was at the lower bound of adequate power for detecting medium effects (f2Estimated = 0.104; f2Observed = 0.136).

Lecture Recall

To understand which individual measures were most associated with lecture recall, a multiple linear regression was conducted utilizing total ADHD symptoms, sustained attention (HRT variability and detectability), FMD, HWS, typing speed, notetaking modality (handwritten notes, typed notes, or no notes), ADHD symptom x notetaking modality interaction term, and stimulant medication use on the day of experiment (covariate). Sex was removed as a covariate as it did not improve model fit. The comparison variable or contrast for notetaking condition was set to the no note condition, while the comparison variable for stimulant medication status was use of stimulant medication on the day of experiment. The overall model was significant (R2 = 0.23; R2adjusted = 0.17, F(11, 132) = 3.62 , p < 0.05; Table 7). Collinearity checks indicated no issues in multicollinearity (VIF 1.06 – 2.87; Tolerance 0.35 – 0.94) or heteroscedasticity. Individuals who took handwritten notes or typed notes recalled significantly more from the lecture than the no note condition (Figure 2). We used post-hoc, pair-wise calculations with a Tukey correction to compare the least squares means (or estimated marginal means) for each condition as extracted from the original linear regression. There was no significant difference in recall between the handwritten and typed note conditions (b = 1.80, t(132) = 0.62, p > 0.05).

Table 7.

Regression Results using Total Recall Score as Outcome Measure

Predictor B b
95% CI
[LL, UL]		 beta t Fit
(Intercept) 53.49* [46.20, 60.79]
ADHD Total −0.41* [−0.71, −0.11] −.35 −2.69
HWS 0.06 [−0.55, 0.67] .02 0.19
Type Speed 0.06 [−0.20, 0.33] .04 0.47
FMD −0.09 [−0.33, 0.16] −.06 −0.72
Detectability −0.12 [−0.44, 0.19] −.06 −0.77
HRT Variability −0.62* [−0.93, −0.31] −.32 −3.99
Medication Use 5.38 [−1.56, 12.32] .12 1.53
Notes-H:ADHD Total 0.49* [0.08, 0.90] .26 2.35
Notes-T:ADHD Total 0.59* [0.11, 1.08] .24 2.43
Notes-H 8.41* [2.75, 14.07] .27 2.94
Notes-T 6.60* [0.63, 12.56] .20 2.19 R2 = .232*
95% CI[.06,.29]
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Note. A significant b-weight indicates statistical significance for both the unstandardized regression weights (b) and standardized regression weights (beta). t-values were calculated by dividing the unstandardized regression weight by its standard error, reflecting the strength of the predictor-outcome relationship. Notes-H = handwritten notes; Notes-T = typed notes.

*

Indicates p < .05.

Figure 2. Total Quiz Score Regressed with Notetaking Conditions.

Figure 2

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Note. Demonstration of the main effect of condition on recall score (Table 7). ns = non-significant p value; * indicates p < .05

There was a significant interaction between ADHD symptoms and the typed note condition and ADHD symptoms and the handwritten note condition associated with higher lecture recall, indicating individuals with higher ADHD symptoms who actively took notes, regardless of modality, learned more from the lecture than individuals with higher symptoms of ADHD in the no note condition, partially supporting our hypothesis (Figure 3). Critically, post-hoc comparisons of interaction terms with a Tukey’s adjustment revealed that there was no significant difference between the interactions of ADHD symptoms in the typed note condition and ADHD symptoms in the handwritten note condition (b = −0.10, t(132) = −0.42, p > 0.05), suggesting there is no advantage to one notetaking modality or another while considering total ADHD symptoms. There was a significant main effect of ADHD symptoms associated with lecture recall, indicating that individuals with more ADHD symptoms learned less from the lecture, in line with our hypothesis. Additionally, less HRT variability related to higher lecture recall (Figure 4), partially supporting our hypothesis. Detectability, HWS, typing speed, and FMD did not relate to lecture recall, contrasting our predictions.

Figure 3. Total Quiz Score Regressed with ADHD Symptoms and Notetaking Condition.

Figure 3

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Note. Demonstration of interaction between condition and total ADHD symptoms on recall. Grey shading surrounding the line indicates standard error (Table 7).

Figure 4. Total Quiz Score Regressed with HRT Variability.

Figure 4

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Note. Demonstration of main effect of sustained attention in terms of the HRT variability composite score on recall score. Grey shading surrounding the line indicates standard error (Table 7).

Sensitivity analyses (α = 0.05) indicated that the model demonstrated sufficient power to detect medium effects (f2Estimated = 0.140; f2Observed = 0.299).

Discussion

The purpose of this study was to evaluate how the process of handwritten notes, typed notes, or not taking notes affected encoding of lecture material in post-secondary students in relation to their self-reported ADHD symptoms. A secondary aim was understanding how specific skills important for notetaking relate to ADHD symptoms in our sample. We found that individuals with higher ADHD symptoms learned more from the lecture if they took either handwritten or typed notes than if they did not take notes, with not taking notes being particularly detrimental to learning in those with higher ADHD symptoms. More hit reaction time (HRT) variability on the CATA and higher ADHD symptoms related to worse lecture recall, irrespective of notes. Finally, higher ADHD symptoms related to slower HWS and worse detectability scores on the CATA.

Lecture Recall

Handwriting or typing notes related to significantly more recall than not taking notes while controlling for all cognitive variables, with no differences between the two modalities. Additionally, exploratory analyses revealed no effect of note quality on learning (see Supplementary Materials). Thus, active notetaking, regardless of modality, leads to enhanced encoding of lecture material through sensorimotor engagement via transcription of information beyond merely listening to the lecture, perhaps highlighting the widespread use of laptops and skill in typing (Bui et al., 2013; Morehead et al., 2019b; Schoen, 2012; Voyer et al., 2022). Alternatively, there may be more subtle differences between the two active modalities that were not detected by the current study due to limitations in power.

The lecture learning benefits facilitated by typed or handwritten notes compared to not taking notes increased as a function of students’ higher ADHD symptoms. This indicates that active notetaking facilitates significant encoding gains in individuals with higher ADHD symptoms, critically suggesting that not taking notes is especially detrimental for those with higher symptoms. This highlights that the sensorimotor act of notetaking demands sustained and active engagement with the lecture, challenging the notion that individuals with ADHD do not benefit from active notetaking due to cognitive and fine motor difficulties. Interestingly, our exploratory analyses (see Supplementary Materials) indicated differences in predominant symptom subtype, such that higher ADHD-I symptoms related to better recall via handwritten notes and higher ADHD-H symptoms related to better recall via typed notes, suggesting that cognitive and motor diversity may determine which modality is most helpful in facilitating learning.

Participants with higher ADHD symptoms recalled significantly less from the lecture across all notetaking conditions, even while controlling for stimulant medication use. This result parallels literature indicating that college students with higher ADHD symptoms may exhibit difficulties with learning, and accounts of academic impairment even while utilizing academic services and medication (Gormley et al., 2019; Vekaria & Peverly, 2018; Weyandt et al., 1995). Alternatively, this may indicate additional cognitive or motor heterogeneity in the ADHD symptom profile contributed to difficulties not measured in this study (Kofler et al., 2019; Willcutt et al., 2005). Indeed, it seemed that students with higher ADHD-I symptoms struggled more than students with higher ADHD-H, however, which cognitive or motor skills contributing to these differences should be explored further.

Finally, more HRT variability significantly related to worse lecture recall across all notetaking conditions. This result is unsurprising, given that maintaining sustained focus is foundational for active engagement with the lecture material, and subsequently, would affect processing and encoding of information regardless of whether an individual is taking notes or not (Vekaria & Peverly, 2018). Detectability performance on the CATA did not relate to overall recall, highlighting that stimuli detection may not represent how one attends to a lecture, but rather difficulty in consistent engagement may result in reduced opportunity for encoding and detection, relating to worse recall (Huang-Pollock et al., 2012).

ADHD Symptoms

Our secondary aim was to understand which cognitive and motor variables related to ADHD symptoms in college students. The relationship between slower HWS and higher ADHD symptoms, especially ADHD-I per correlation results, parallels previous literature suggesting that handwriting difficulties may persist into adulthood, which impacts academic impairment and reveals potential targets for intervention (Duda et al., 2014, 2015; Goulardins et al., 2013; Vekaria & Peverly, 2018). Typing speed did not significantly relate to higher ADHD symptoms, validating that increasing transcription speed via typing may help facilitate increased engagement with learning material (Weigelt-Marom & Weintraub, 2018). Surprisingly, fine motor dexterity (FMD) did not relate to higher ADHD symptoms in our sample, given that HWS requires FMD (Fietsam et al., 2022). Better FMD was related to faster typing, suggesting that other sensorimotor difficulties may be contributing to slower HWS, such as visuomotor integration (Neely et al., 2016). Therefore, future work should focus on understanding more specific sensorimotor mechanisms and their relationship to either transcription mode, such as utilizing kinematic measurements taken during handwriting or typing tasks.

In terms of sustained attention, worse detectability, but not HRT variability, related to higher ADHD symptoms in our sample. These results parallel the literature emphasizing that ADHD symptoms are related to inattentiveness, or potentially inhibitory control, during continuous performance tasks (Huang-Pollock et al., 2012; Tsal et al., 2005; Wang et al., 2021). Interestingly, there was no relationship between total ADHD symptoms and HRT variability, but ADHD-H symptoms related to HRT variability per correlations (Avisar & Shalev, 2011; Gmehlin et al., 2016), highlighting that symptoms related to behavioral impulsivity/hyperactivity are related to inconsistent engagement with the task. Overall, individuals with higher ADHD symptoms exhibited more errors during the CATA, irrespective of HRT variability, while those with lower ADHD symptoms were more likely to respond correctly irrespective of HRT consistency (Huang-Pollock et al., 2012; Willcutt et al., 2005).

Limitations and Future Directions

This study critically highlights that actively taking notes, whether it be typing or handwriting, facilitates significant encoding gains even when attentional and motor skill differences related to ADHD are considered. However, this study also exhibits several limitations. First, the participants were a non-clinical sample of students enrolled in a large public university who self-reported ADHD symptoms and diagnoses. Although convenient, the homogeneity of our sample, including a lack of racial and ethnic diversity, and only including participants who exhibited a certain threshold of academic achievement to enroll in college, limits the scope of generalizability of our results. Future work should extend beyond typical university settings to include more racial, ethnic, and academic diversity. Additionally, we conducted this study in a non-naturalistic setting. Although high experimental control is necessary in understanding specific mechanisms, it is not as ecologically valid as an immersive classroom environment. Indeed, college lectures are often distracting, are typically much longer than 15 minutes, and include a variety of activities and presentation styles (Voyer et al., 2022). Moreover, students are rarely quizzed on material shortly after initial learning. Thus, generalizability of these findings in a context more consistent with a typical classroom experience, including assessing recall later time points, should be researched further.

Conclusion

This present work contributes to a growing body of research concerning lecture notetaking strategies and learning, revealing that active notetaking benefits learning generally, and especially in individuals with higher ADHD symptoms. Indeed, the magnitude of learning gains related to note modality may depend on individual differences in motor and cognitive abilities, as well as personal preferences. Importantly, this work preliminarily suggests that not taking notes may prevent individuals with higher ADHD symptoms from benefiting from initial learning gains active notetaking affords, highlighting the importance of sensorimotor engagement during the lecture. Interventionists thus should carefully determine individual needs, as well as importance of active engagement of students while developing or implementing classroom interventions.

Supplementary Material

Supp 1

Acknowledgments

This work was supported by the National Institutes of Health under award number 2T32HD007475. We have no conflicts of interest to disclose.

References

  1. Adler LA, Spencer T, Faraone SV, Kessler RC, Howes MJ, Biederman J, & Secnik K (2006). Validity of pilot Adult ADHD Self-Report Scale (ASRS) to rate adult ADHD symptoms. Annals of Clinical Psychiatry, 18(3), 145–148. 10.1080/10401230600801077 [DOI] [PubMed] [Google Scholar]
  2. Advokat C, Lane SM, & Luo C (2011). College students with and without ADHD: comparison of self-report of medication usage, study habits, and academic achievement. Journal of Attention Disorders, 15(8), 656–666. 10.1177/1087054710371168 [DOI] [PubMed] [Google Scholar]
  3. Allsopp D, Minskoff E, & Bolt L (2005). Individualized course-specific strategy instruction for college students with learning disabilities and ADHD: Lessons learned from a model demonstration project. Learning Disabilities Research & Practice, 20, 103–118. 10.1111/j.1540-5826.2005.00126.x [DOI] [Google Scholar]
  4. Avisar A, & Shalev L (2011). Sustained attention and behavioral characteristics associated with ADHD in adults. Applied Neuropsychology, 18(2), 107–116. [DOI] [PubMed] [Google Scholar]
  5. Behrendt S (2023). lm.beta: Add standardized regression coefficients to linear-model-objects (R package Version 1.7-1). https://cran.r-project.org/package=lm.beta
  6. Bui D, Myerson J, & Hale S (2013). Note-taking with computers: exploring alternative strategies for improved recall. Journal of Educational Psychology, 105, 299–309. 10.1037/a0030367 [DOI] [Google Scholar]
  7. Bui DC, & Myerson J (2014). The role of working memory abilities in lecture note-taking. Learning and Individual Differences, 33, 12–22. 10.1016/j.lindif.2014.05.002 [DOI] [Google Scholar]
  8. Christopoulos JP, Rohwer WD, & Thomas JW (1987). Grade level differences in students’ study activities as a function of course characteristics. Contemporary Educational Psychology, 12(4), 303–323. 10.1016/S0361-476X(87)80003-6 [DOI] [Google Scholar]
  9. Di Vesta FJ, & Gray GS (1972). Listening and note taking. Journal of Educational Psychology, 63(1), 8–14. 10.1037/h0032243 [DOI] [PubMed] [Google Scholar]
  10. Dikmen SS, Heaton RK, Grant I, & Temkin NR (1999). Test–retest reliability and practice effects of expanded Halstead–Reitan Neuropsychological Test Battery. Journal of the International Neuropsychological Society, 5(4), 346–356. [PubMed] [Google Scholar]
  11. Duda TA, Casey JE, & McNevin N (2014). Variability of kinematic graphomotor fluency in adults with ADHD. Human Movement Science, 38, 331–342. 10.1016/j.humov.2014.07.006 [DOI] [PubMed] [Google Scholar]
  12. Duda TA, Casey JE, & McNevin N (2015). Development of graphomotor fluency in adults with ADHD: Evidence of attenuated procedural learning. Human Movement Science, 44, 1–10. 10.1016/j.humov.2015.08.006 [DOI] [PubMed] [Google Scholar]
  13. DuPaul GJ, Franklin MK, Pollack BL, Stack KS, Jaffe AR, Gormley MJ, AnastopouIos AD, & Weyandt LL (2018). Predictors and trajectories of educational functioning in college students with and without attention-deficit/hyperactivity disorder. Journal of Postsecondary Education and Disability, 31(2), 161–178. [PMC free article] [PubMed] [Google Scholar]
  14. Evans SW, Pelham W, & Grudberg MV (1994). The efficacy of notetaking to improve behavior and comprehension of adolescents with attention deficit hyperactivity disorder. Exceptionality, 5(1), 1–17. 10.1207/s15327035ex0501_1 [DOI] [Google Scholar]
  15. Faul F, Erdfelder E, Buchner A, & Lang A (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. [DOI] [PubMed] [Google Scholar]
  16. Fazal S, Hussain S, Majoka MI, & Masood S (2012). The role of study skills in academic achievement of students: A closer focus on gender. Pakistan Journal of Psychological Research, 27(1), 37–51. [Google Scholar]
  17. Fietsam AC, Tucker JR, Kamath MS, Huang-Pollock C, Wang Z, & Neely KA (2022). Manual dexterity and strength and in young adults with and without Attention-Deficit/Hyperactivity Disorder (ADHD). Neuroscience Letters, 766, Article 136349. 10.1016/j.neulet.2021.136349 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fox J, & Weisberg S (2018). An R companion to applied regression (3rd ed.). Sage. https://socialsciences.mcmaster.ca/jfox/Books/Companion/ [Google Scholar]
  19. Gmehlin D, Fuermaier ABM, Walther S, Tucha L, Koerts J, Lange KW, Tucha O, Weisbrod M, & Aschenbrenner S (2016). Attentional lapses of adults with attention deficit hyperactivity disorder in tasks of sustained attention. Archives of Clinical Neuropsychology, 31(4), 343–357. 10.1093/arclin/acw016 [DOI] [PubMed] [Google Scholar]
  20. Gormley MJ, DuPaul GJ, Weyandt LL, & Anastopoulos AD (2019). First-year GPA and academic service use among college students with and without ADHD. Journal of Attention Disorders, 23(14), 1766–1779. 10.1177/1087054715623046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Goulardins JB, Marques JCB, Casella EB, Nascimento RO, & Oliveira JA (2013). Motor profile of children with attention deficit hyperactivity disorder, combined type. Research in Developmental Disabilities, 34(1), 40–45. 10.1016/j.ridd.2012.07.014 [DOI] [PubMed] [Google Scholar]
  22. Harrison JR, Evans SW, Baran A, Khondker F, Press K, Noel D, Wasserman S, Belmonte C, & Mohlmann M (2020). Comparison of accommodations and interventions for youth with ADHD: A randomized controlled trial. Journal of School Psychology, 80, 15–36. 10.1016/j.jsp.2020.05.001 [DOI] [PubMed] [Google Scholar]
  23. Hechtman L, Swanson JM, Sibley MH, Stehli A, Owens EB, Mitchell JT, Arnold LE, Molina BS, Hinshaw SP, & Jensen PS (2016). Functional adult outcomes 16 years after childhood diagnosis of attention-deficit/hyperactivity disorder: MTA results. Journal of the American Academy of Child and Adolescent Psychiatry, 55(11), 945–952.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Højsgaard S, & Halekoh U (2023). doBy: Groupwise statistics, LSmeans, linear estimates, utilities (R package Version 4.6.16). https://cran.r-project.org/package=doBy
  25. Huang-Pollock CL, Karalunas SL, Tam H, & Moore AN (2012). Evaluating vigilance deficits in ADHD: A meta-analysis of CPT performance. Journal of Abnormal Psychology, 121(2), 360–371. 10.1037/a0027205 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Jansen RS, Lakens D, & Ijsselsteijn WA (2017). An integrative review of the cognitive costs and benefits of note-taking. Educational Research Review, 22, 223–233. 10.1016/j.edurev.2017.10.001 [DOI] [Google Scholar]
  27. Kessler RC, Adler L, Ames M, Demler O, Faraone S, Hiripi E, Howes MJ, Jin R, Secnik K, Spencer T, Ustun TB, & Walters EE (2005). The World Health Organization Adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. Psychological Medicine, 35(2), 245–256. 10.1017/s0033291704002892 [DOI] [PubMed] [Google Scholar]
  28. Kiewra KA (1987). Notetaking and review: The research and its implications. Instructional Science, 16(3), 233–249. [Google Scholar]
  29. Kofler MJ, Irwin LN, Soto EF, Groves NB, Harmon SL, & Sarver DE (2019). Executive functioning heterogeneity in pediatric ADHD. Journal of Abnormal Child Psychology, 47(2), 273–286. 10.1007/s10802-018-0438-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Komsta L, & Novomestky F (2015). moments: Moments, cumulants, skewness, kurtosis and related tests (R package Version 0.14.1). https://www.r-project.org, http://www.komsta.net/
  31. Lenth RV (2016). Least-squares means: The R package lsmeans. Journal of Statistical Software, 69, 1–33. [Google Scholar]
  32. Levesque E (2020, February). A stellar history of modern astronomy [Address]. TEDxBerkeley, Berkeley, CA, United States. https://www.ted.com/talks/emily_levesque_a_stellar_history_of_modern_astronomy#t-18885 [Google Scholar]
  33. Levine AJ, Miller EN, Becker JT, Selnes OA, & Cohen BA (2004). Normative data for determining significance of test–retest differences on eight common neuropsychological instruments. The Clinical Neuropsychologist, 18(3), 373–384. [DOI] [PubMed] [Google Scholar]
  34. Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P, & Makowski D (2021). performance: An R package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6(60), Article 3139. [Google Scholar]
  35. Machida K, Chin M, & Johnson KA (2018). The provision of partial notes is not associated with improved student attention in lectures or subsequent understanding of the lecture material. Active Learning in Higher Education, 19(2), 101–115. [Google Scholar]
  36. McCracken HS, Murphy B, Ambalavanar U, Zabihhosseinian M, & Yielder PC (2023). Sensorimotor integration and motor learning during a novel visuomotor tracing task in young adults with attention-deficit/hyperactivity disorder. Journal of Neurophysiology, 129(1), 247–261. 10.1152/jn.00173.2022 [DOI] [PubMed] [Google Scholar]
  37. Morehead K, Dunlosky J, Rawson K, Blasiman R, & Hollis B (2019a). Note-taking habits of 21st century college students: Implications for student learning, memory, and achievement. Memory, 27(6), 807–819. [DOI] [PubMed] [Google Scholar]
  38. Morehead K, Dunlosky J, & Rawson KA (2019b). How much mightier is the pen than the keyboard for note-taking? A replication and extension of Mueller and Oppenheimer (2014). Educational Psychology Review, 31(3), 753–780. 10.1007/s10648-019-09468-2 [DOI] [Google Scholar]
  39. Mueller PA, & Oppenheimer DM (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25(6), 1159–1168. 10.1177/0956797614524581 [DOI] [PubMed] [Google Scholar]
  40. Mueller PA, & Oppenheimer DM (2016). Technology and note-taking in the classroom, boardroom, hospital room, and courtroom. Trends in Neuroscience and Education, 5(3), 139–145. [Google Scholar]
  41. Neely KA, Chennavasin AP, Yoder A, Williams GK, Loken E, & Huang-Pollock CL (2016). Memory-guided force output is associated with self-reported ADHD symptoms in young adults. Experimental Brain Research,, 234(11), 3203–3212. 10.1007/s00221-016-4718-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Peverly ST (2006). The importance of handwriting speed in adult writing. Developmental Neuropsychology, 29(1), 197–216. 10.1207/s15326942dn2901_10 [DOI] [PubMed] [Google Scholar]
  43. Peverly ST, Garner JK, & Vekaria PC (2014). Both handwriting speed and selective attention are important to lecture note-taking. Reading and Writing, 27(1), 1–30. 10.1007/s11145-013-9431-x [DOI] [Google Scholar]
  44. Peverly ST, Ramaswamy V, Brown C, Sumowski J, Alidoost M, & Garner J (2007). What predicts skill in lecture note taking? Journal of Educational Psychology, 99(1), 167–180. 10.1037/0022-0663.99.1.167 [DOI] [Google Scholar]
  45. Peverly ST, Vekaria PC, Reddington LA, Sumowski JF, Johnson KR, & Ramsay CM (2013). The relationship of handwriting speed, working memory, language comprehension and outlines to lecture note-taking and test-taking among college students. Applied Cognitive Psychology, 27(1), 115–126. [Google Scholar]
  46. Piolat A, Olive T, & Kellogg R (2005). Cognitive effort during note taking. Applied Cognitive Psychology, 19, 291–312. 10.1002/acp.1086 [DOI] [Google Scholar]
  47. R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ [Google Scholar]
  48. Revelle W (2018). psych: Procedures for psychological, psychometric, and personality research (R package Version 2.2.9). https://cran.r-project.org/web/packages/psych/index.html
  49. Revelle W (2022). psychTools: Tools to accompany the ‘psych’ package for psychological research (R package Version 2.2.9). https://CRAN.R-project.org/package=psychTools
  50. Ruff RM, & Parker SB (1993). Gender-and age-specific changes in motor speed and eye-hand coordination in adults: Normative values for the Finger Tapping and Grooved Pegboard Tests. Perceptual and Motor Skills, 76, 1219–1230. [DOI] [PubMed] [Google Scholar]
  51. Schoen I (2012). Effects of method and context of note-taking on memory: Handwriting versus typing in lecture and textbook-reading contexts [Bachelor’s thesis, Pitzer College]. Pitzer Senior Theses. https://scholarship.claremont.edu/pitzer_theses/20/
  52. Shell MD, Strouth M, & Reynolds AM (2021). Make a note of it: Comparison in longhand, keyboard, and stylus note-taking techniques. Learning Assistance Review 26(2), 1–21. [Google Scholar]
  53. Stanley D (2020). apaTables: Create American Psychological Association (APA) style tables (R package Version 2.0.8). https://github.com/dstanley4/apaTables
  54. Suritsky SK, & Hughes CA (1991). Benefits of notetaking: Implications for secondary and postsecondary students with learning disabilities. Learning Disability Quarterly, 14(1), 7–18. [Google Scholar]
  55. Tsal Y, Shalev L, & Mevorach C (2005). The diversity of attention deficits in ADHD: The prevalence of four cognitive factors in ADHD versus controls. Journal of Learning Disabilities, 38(2), 142–157. 10.1177/00222194050380020401 [DOI] [PubMed] [Google Scholar]
  56. Vekaria PC, & Peverly ST (2018). Lecture note-taking in postsecondary students with attention-deficit/hyperactivity disorder. Reading and Writing, 31(7), 1551–1573. 10.1007/s11145-018-9849-2 [DOI] [Google Scholar]
  57. Voyer D, Ronis ST, & Byers N (2022). The effect of notetaking method on academic performance: A systematic review and meta-analysis. Contemporary Educational Psychology, 68, 1–14, Article 102025. 10.1016/j.cedpsych.2021.102025 [DOI] [Google Scholar]
  58. Wang L-J, Lee S-Y, Tsai C-S, Lee M-J, Chou M-C, Kuo H-C, & Chou W-J (2021). Validity of visual and auditory attention tests for detecting ADHD. Journal of Attention Disorders, 25(8), 1160–1169. 10.1177/1087054719887433 [DOI] [PubMed] [Google Scholar]
  59. Weigelt-Marom H, & Weintraub N (2015). The effect of a touch-typing program on keyboarding skills of higher education students with and without learning disabilities. Research in Developmental Disabilities, 47, 208–217. 10.1016/j.ridd.2015.09.014 [DOI] [PubMed] [Google Scholar]
  60. Weigelt-Marom H, & Weintraub N (2018). Keyboarding versus handwriting speed of higher education students with and without learning disabilities: Does touch-typing assist in narrowing the gap? Computers and Education, 117, 132–140. 10.1016/j.compedu.2017.10.008 [DOI] [Google Scholar]
  61. Weyandt LL, Linterman I, & Rice JA (1995). Reported prevalence of attentional difficulties in a general sample of college students. Journal of Psychopathology and Behavioral Assessment, 17(3), 293–304. 10.1007/BF02229304 [DOI] [Google Scholar]
  62. Weyandt LL, Oster DR, Gudmundsdottir BG, DuPaul GJ, & Anastopoulos AD (2017). Neuropsychological functioning in college students with and without ADHD. Neuropsychology, 31(2), 160–172. 10.1037/neu0000326 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Wickham H (2016). ggplot2: Elegant graphics for data analysis. Springer. https://ggplot2.tidyverse.org/ [Google Scholar]
  64. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, & Pennington BF (2005). Validity of the executive function theory of attention-deficit/hyperactivity disorder: A meta-analytic review. Biological Psychiatry, 57(11), 1336–1346. 10.1016/j.biopsych.2005.02.006 [DOI] [PubMed] [Google Scholar]

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