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5.7: Listening and Note-Taking - Mathematics

5.7: Listening and Note-Taking - Mathematics



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5.7: Listening and Note-Taking - Mathematics

Note-taking

Note-taking (sometimes written as notetaking or note taking) is the practice of recording information from different sources and platforms. By taking notes, the writer records the essence of the information, freeing their mind from having to recall everything. [1] Notes are commonly drawn from a transient source, such as an oral discussion at a meeting, or a lecture (notes of a meeting are usually called minutes), in which case the notes may be the only record of the event.


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Taking Notes in English – PART 5 of 7: Shorthand

In the last post, I presented you with 4 systems for taking notes. Now it’s time to cover a technique you can use to write information more quickly, which is a simple form of shorthand – using abbreviations & symbols.

When you’re trying to write quickly, often there is simply not enough time to write the whole word, especially if the word is long. Look at the word “reconstruction.” This takes a long time to write out, so you should shorten it. Now, the shortest way to write this word is probably just to write “R.” But if you look at your notes later, will you know what “R” stands for? Probably not, so that’s not a good idea.

However, if you know before the instructor starts speaking that that the topic of the lecture will be all about “reconstruction” you can probably guess that “reconstruction” will be repeated many times by the teacher. If that is the case, then I suggest you write “R = reconstruction” at the top of your page, and then use “R” in your notes. Then you’ll know what it represents when you look back on your notes, and you’ll be able to take notes a lot faster. However, if “reconstruction” is not used often in the lecture, then it’s probably better to use and abbreviation like “rec.” which you may be able to understand in context.

Some words are so common in the English language, that well-recognized abbreviations have been created. It’s a good idea to learn and use some of these:

Remember that it’s not so important how you use capitalization and punctuation, as long as you can understand. Using symbols are another way to write more efficiently. These, too, are commonly used when note taking. Some of them get their meaning from mathematics:

Depending on the subject you are studying, there are also abbreviations that you might find useful because the vocabulary used in that area of study is repeated frequently. Below you can also find some common abbreviations that might be used in European history, for example:

Now, you also might want to consider using abbreviations or symbols in your own or in another language, if you know it. For example, Japanese and Chinese characters often take a lot less time to write than they would in English to express certain meanings. I’m not a Japanese native speaker, but I sometimes use Japanese characters because they are so convenient. For example, I often use 人 for people, 中 for inside, まで for until, 大 for large and 小 for small, and I also use kanji the days of the week: 月火水木金土日. Kanji or Chinese characters are clearly so much faster in some cases, if you know them.

Generally speaking, however, should you take notes in your native language? Well, if you are new to note-taking, it’s probably going to be very hard for you to write only in English, so it’s OK to write a little bit in your own language at first. However, if you care about your English skills, make an effort to write in English more and more.

If the content you are listening to is more important than the fact that it is in English, then you might think that you don’t have to worry so much and you can write in your language, right? But actually, this is not necessarily true. You need to be careful, because when you listen in English and write in another language, your brain is under a lot of stress because it has to translate. This is something to consider.

Now, there are a lot more abbreviations that you can use (see links at the end of this post). You don’t need to remember all of them. Just pick and choose what works for you, and gradually you’ll be able to use more and more of them. And of course, it’s fine if you make up your own abbreviations and symbols. Your notes are for you, after all! You can even draw pictures:

Don’t go crazy, however. If you cannot remember what your original abbreviation or symbol means, then you’ll be very confused and frustrated when you go back to study your notes! It’s probably best to stick to common abbreviations and symbols at first.

Remember not to overdo your use of abbreviations and symbols in the beginning. When you are first learning them, you might not be able to remember and write them fast enough for them to be useful. It could be faster just to write down the whole word!

OK, I’ve gone over some common abbreviations and symbols you can use to help you write faster. I’ve also advised you to gradually use more and more of them. In the next post of this series we’re going to have a look at reviewing and previewing your lecture notes. This is particularly important if you want to really solidify (or make permanent) the information and your understanding of it in your brain. Of course, it will also prepare you to do well on exams.


Studying

One of the most direct reasons note-taking skills are important is that effective notes usually serve as one of your best study aids for tests. Organized, detailed notes allow you to review the key points discussed in a class. While reading text materials is important, notes taken during class and through reading typically present a more concise view of key concepts. Marking specific lecture notes as "important" or "to appear on the test" helps you realize where to focus study energy.


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Cognitive correlates of lecture note taking handwriting speed and attention.

Note taking is a complex activity involving processing, recording, condensing and integrating information from one or many sources, and it is essential in educational and professional settings. In higher education, for example, the majority of students engage in some form of note taking during lectures. Although generally students' notes tend to be incomplete, they consistently account for higher exam performance (Armbruster, 2009 DiVesta & Gray, 1972 Kiewra & Benton, 1988 Kiewra, Benton, & Lewis, 1987 McIntyre, 1992 Peverly, Garner, & Vekaria, 2014 Peverly et al., 2007 Peverly et al., 2012 Haynes, McCarley, & Williams, 2015). Clearly, the notes taken in class provide a stable external memory readily available for later use, for example when reviewing and studying for tests (Boch & Piolat, 2005). Moreover, taking notes supports encoding, elaboration and reflection on lecture content: even if not allowed to review their notes before a test, students who take notes perform better than those who don't (Armbruster, 2009 DiVesta & Gray, 1972 Kiewra et al., 1991). Overall, in some settings, for example large-lecture courses, taking notes can provide the only active-learning component of an otherwise unidirectional and passive instructional process (Armbruster, 2009).

Note taking engages the cognitive system on multiple levels. Far from being a simple rote transcription under dictation, note taking requires parallel execution of several tasks: paying attention to what the lecturer says, extracting the gist, reformulating the content in a more concise form, integrating it with previously acquired knowledge, writing, and simultaneously monitoring incoming information (Piolat, Olive, & Kellogg, 2005). Indeed, dual- and triple-task procedures, requiring participants to take notes while concurrently attending to one or two additional tasks, showed that cognitive effort spent while taking notes is higher than the cognitive effort involved in just reading, listening to or memorizing information (Piolat et al., 2005).

Given the complexity and high cognitive resources consumption involved in note taking, it could be assumed that inefficiency in one or more of the component processes listed above would result in difficulties, such as not being able to keep up with the pace of the lecture, missing important ideas or topic transitions, or failing to accurately record what was said. Understanding to what extent individual differences in these component processes are related to the quality of the notes recorded might inform the development of targeted pedagogical aids or training programs to support successful note taking in the general student population and for students with specific learning difficulties (Maydosz & Raver, 2010 Boyle & Forchelli, 2014).

Recent research on the cognitive predictors of note-taking performance have emphasized the roles of handwriting speed (Peverly et al., 2007 Peverly et al., 2012 Peverly et al., 2014), working memory (Cohn, Cohn, & Bradley, 1995 Hadwin, Kirby, & Woodhouse, 1999 Kiewra et al., 1987 Kiewra & Benton, 1988 Peverly et al. 2007) and, to some extent, attention regulation in note taking (Peverly et al., 2014). However, the individual differences in cognition that relate to skillful note taking are still under debate. These research contributions are reviewed below, with the emphasis on the areas still needing further investigation and how these relate to the aims of the present study.

Handwriting speed is strongly correlated to the quality of written output in both children and adults. Studies on elementary and middle school students indicate that faster writers tend to produce more creative and better structured essays (Graham et al., 1997 Jones & Christensen, 1999, Study 1). Moreover, the improvement of orthographic automaticity through training parallels the improvement in writing content (Jones & Christensen, 1999, Study 2 see Peverly, 2006, for a review). Similarly, handwriting speed predicts the essay quality in college students with and without dyslexia (Connelly et al., 2006).

Unsurprisingly, handwriting speed is fundamental for successful note taking. Indeed, recent studies demonstrated a positive relationship between quality of lecture notes and handwriting speed measured as the number of consecutive alphabet letters participants were able to write in 30 seconds (r =.34 r = .23 r = .33 respectively for Peverly et al., 2007 Peverly et al., 2012 Peverly et al., 2014). Still unclear, however, is the contribution of non-linguistic graphomotor speed to the process of taking notes. In fact, handwriting speed tasks do not allow teasing apart pure graphomotor ability from processes that are more verbal in nature (for example, speed of access to the letter forms and letter-to-grapheme conversion). In an attempt to reveal the role of handwriting component processes in note taking, Peverly et al. (2007 2014) included in their protocol a non-linguistic symbol-copying task and a finger tapping task to measure fine motor speed, in addition to commonly used alphabet writing and word generation tasks. Although the more typical handwriting speed measures predicted notes quality, symbol copying and finger tapping performance tasks did not predict notes quality (Peverly et al., 2007 2014), possibly because of a ceiling effect due to the relative easiness of those tasks for the college student participants. Modifying task administration of non-linguistic handwriting speed to make it more challenging for adult writers would provide more fine-grained data on how "pure" graphomotor ability relates to note taking.

Working Memory and Attention

Intuitively, the abilities to hold information in mind, stay alert and focus on the environment seem fundamental to high quality note taking. Indeed, to gain a thorough understanding of the cognitive components of note taking, researchers have been interested in the role of working memory, and more recently, attention. Working memory is defined as a neurocognitive system devoted to holding information in mind temporarily as this information is being processed (Baddeley, 2012 Baddeley & Hitch, 1974). Working memory functioning has emerged as a predictor of academic performance in different domains, such as language comprehension (Daneman & Merikle, 1996 McVay & Kane, 2011), mathematical abilities (Raghubar, Barnes, & Hecht, 2010) and text writing (see Olive 2012, for a review). It has been hypothesized that working memory plays a role in lecture note taking, as this system is plausibly responsible for handling the flux of information a note taker is exposed to during a lecture, and for keeping that information active as it is being filtered, understood, and recorded (Bui & Myerson, 2014 Piolat et al., 2005). Yet, the attempts to investigate the relationship between verbal working memory and the quantity and quality of lecture notes have produced inconsistent results. Although some studies reported significant positive correlations between quality of lecture notes and measures of working memory (Bui, Myerson, & Hale, 2013 Kiewra et al., 1987 Kiewra & Benton, 1988), others have not replicated such a correlation (Cohn, Cohn, & Bradley, 1995 Hadwin et al., 1999 Peverly et al., 2007 Peverly et al., 2012).

Similarly, the exploration of the relationship between attention and note taking has not produced consistent results. Peverly et al. (2012) included a measure of executive control (the Stroop color-naming task Stroop, 1938) in their study on the predictors of note-taking performance, and found no correlation with notes quality. On the other hand, a subsequent study by the same group (Peverly et al., 2014) and two unpublished dissertations (Gleason, 2012 Vekaria, 2011) demonstrated that the quality of lecture notes was predicted by performance on a sustained attention task requiring participants to monitor a list of numbers and letters in order to detect a target 2-digit number (r = .33 in Peverly et al., 2014 r = .26 in Vekaria. 2011). Although current studies on the topic of the relationship between attention and notes quality yielded overall encouraging results, the inconsistencies reviewed above (Gleason, 2012 Peverly et al., 2012 Peverly et al., 2014 Vekaria, 2011), possibly reflecting the nature of the attention tasks being used, call for additional research.

The present study aims at clarifying and expanding the previous findings on the cognitive predictors of note taking ability in young adults. To fill the gaps in the handwriting speed literature outlined above, we used measures of transcription fluency similar to those previously used in note-taking research, but we adapted the administration and scoring protocols to avoid ceiling effects that might explain the lack of significant results in previous studies (for example, Peverly et al., 2007). Specifically, we administered the alphabet-writing task (adapted from Berninger et al., 1997) by requiring participants to alternate between upper case and lower case rows of letters and extending the interval to 60 seconds to allow for increased variance in participants' performance. We also included a symbol copying task (adapted from Wechsler, 1997) and we measured the execution time to capture inter-individual differences. Finally, we included a sentence-copying task (adapted from Wallen, Bonney, & Lennox, 1996) as a more complex and ecologically valid handwriting task, which engaged rote writing while minimizing the effort inherent in word generation used by Peverly and colleagues (2007).

The second goal was to clarify the role of attention functioning in note taking. According to a popular neurocognitive model (Posner et al., 2012), attention is best conceptualized as a set of interrelated neurocognitive subcomponents: alerting, orienting and executive control, which are responsible for keeping the cognitive system ready to process incoming information, disengaging and redirecting the focus as needed, and excluding distractions and conflicting information. Alerting, orienting and executive control might play different roles in note taking: by providing alertness and receptivity to what is being said by the lecturer, by supporting sustained processing of relevant information, by switching between writing and listening, and by allowing note takers to inhibit distractions, intrusive thoughts or semantic associations not pertinent to the ongoing note taking task.

Thus, to allow for a more integrated and theory-driven assessment of attention functions, we used the Attention Network Test (ANT Fan et al., 2002), an individually administered computerized reaction-time task, purposefully developed to provide an overall measure of processing speed and estimates of alerting, orienting and executive control within the same task. Moreover, in order to capture individual differences in attentional functioning style that might not be targeted by objective, laboratory tasks, we included three self-report measures of mindfulness, perceived cognitive failures and absentmindedness (see Method section, for details). Self-report measures of attentional functioning have been shown to predict outcomes of different academic and professional tasks (Davidson et al., 2012 Levy et al., 2012). However, their relationship to note taking, which intuitively appears heavily affected by distraction, has not been investigated so far.

Participants were 64 undergraduate students at two liberal arts colleges in the New York metropolitan area. The mean age was 21.7 (SD = 4.3) 73.4% were female the majority (87.5%) were right-handed. The race/ethnicity of the sample was diverse: White (46.9%), Black/African-American (23.4%), Hispanic (23.4%), Asian (1.6%) and Other (4.7%).

Approximately half of the participants (53.1%) were enrolled in psychology or behavioral science majors 15.6% in social sciences or legal studies majors 15.6% in health science majors and the remaining 12.5% in business, the humanities or undeclared majors. Overall, students in the sample had an average of 5.81 semesters of college education (SD = 3.0) with average reported GPA of 3.05 (SD = .48).

Participants reported no self-identified learning disabilities, psychiatric or neurological conditions, or sensory impairments. They received a choice of $30 or extra credit for participation.

The protocol included excerpts of audio-visual lectures, three handwriting speed tasks, one computerized attention test and three self-report questionnaires. A detailed description of the tasks and their scoring criteria are below.

Lectures. Two simulated introductory college-level lectures in zoology ("Coral reefs" 8 min 15 sec) and geology ("Irrigation patterns" 4 min 34 sec) were selected from practice materials for Test of English as a Foreign Language (TOEFL Educational Testing Service, 2009). Each recorded lecture was narrated by a fictional professor and included up to three slides to provide additional illustration of some of the content in graphic form. Specifically, the zoology lecture included four diagrams, each illustrating one of the three coral reef types discussed. The geology lecture contained four diagrams, each illustrating one of the four drainage pattern types described. The diagrams were simple line drawings and did not display text. Also, each lecture included a question by a student from a fictional audience, who requested a clarification of the content, which was followed by the professor's answer. Participants were provided with a ruled-paper notepad and a pen, and received instructions to take notes as if they were in a real classroom setting and expected to be tested on the material later on.

Scoring of the notes followed the procedure described in Peverly et al. (2007). In the design phase of the study, the main idea units for each lecture were identified (18 units in the geology lecture 20 units in the zoology lecture). Then, in the scoring phase, two research assistants rated participants' notes on each idea unit on a scale from 0 (idea is not mentioned or is incorrectly recorded) to 3 (idea is explained clearly). Scores ranged from 0 to 54 for the geology lecture and from 0 to 60 for the zoology lecture. After scoring each individual protocol, a composite note-quality score was computed by averaging the percentage scores for both lecture notes. The inter-rater agreement was calculated using Pearson's bivariate correlation on the scores of the first 25 participants provided by two independent scorers. The reliability was high (r = .95), therefore only one scorer was used for the remainder of the study.

Handwriting speed. Handwriting speed was assessed using three tasks based on existing measures (Berninger et al., 1997 Wallen et al., 1996 Weschsler, 1997), whose administration was adapted to increase the level of challenge and avoid ceiling effects. In the Alphabet task (adapted from Berninger et al., 1997), participants were asked to write the letters of the alphabet from A to Z, first in upper case then in lower case, and repeating the process as many times as they could in 60 seconds. Task score was calculated as the total number of letters produced. The Sentence Copying Task (based on the Handwriting Speed Test by Wallen et al., 1996) required copying a given 13-word sentence five times. Performance was measured by the number of seconds it took to complete the task. Finally, the Symbol Copying Task (adapted from the WAIS-III Weschsler, 1997) was used to obtain a measure of non-linguistic transcription fluency. The task required copying a string of twelve symbols repeatedly ten times. Performance was measured by the number of seconds it took to complete the task. The instructions for all three handwriting tasks emphasized legibility and participants were informed that points would be scored only for legible written output. The protocols for all handwriting speed tasks were evaluated by two independent scorers. Minor scoring discrepancies were settled by consensus.

Attention. Objective measures of attention were obtained using the Attention Network Test (ANT Fan et al., 2002 https://www. sacklerinstitute.org/cornell/assays_and_tools/). ANT is a free-access, computerized attention test, which provides an overall measure of processing speed and allows estimating the participants' scores on three specific subsystems of attention: the alerting network, the orienting network, and the executive control network. Participants were presented with a row of five arrows pointing laterally and asked to respond to the direction of the central arrow (left or right) as fast as possible, while ignoring the flanking arrows. The appearance of the stimuli on the screen was preceded by a temporally-informative cue (alerting the participants that a row of arrows was about to be displayed see Figure 1A), a spatially informative cue (orienting the participants that a row of arrows was about to be presented in a specific area of the screen see Figure 1B) or no cue (see Figure 1C). The flankers appeared on the screen pointing either in the same direction of the target or in the opposite direction (namely, congruent or incongruent conditions, see Figure 1D).

The mean reaction times (RTs) across the conditions provided a global measure of attention and processing speed. Moreover, subtracting reaction times from different cuing and stimulus conditions provided an estimate of alerting, orienting and executive control. Alerting, the ability to become alert or receptive to an upcoming stimulus, was computed by subtracting RTs on trials preceded by a temporal cue from RTs on non-cued trials. Orienting, indicating the ability to focus attention where the target is about to appear, was computed by subtracting RTs on trials preceded by spatially informative cue from RTs on trials in which a spatial cue was not given, but only a temporal cue was. Executive control, indicating how sensitive the participants are to conflicting and distracting information, and how fast they can resolve such cognitive conflict, was measured by subtracting RTs on congruent trials from RTs on incongruent trials. The ANT included a total of 24 practice trials and 288 experimental trials (72 trials per cuing condition, presented in randomized order) and took approximately 30 minutes to administer. The median reaction time and accuracy rates for each trial type were obtained for each participant then, subtractions scores were computed for each participant based on the definitions of alerting, orienting and executive control given above. Test-retest reliability for the ANT reaction times is in the moderate-high range (r = .87 Fan et al., 2002).

In addition, participants were asked to assess their level of everyday cognitive functioning, mindfulness and absentmindedness using three validated self-report instruments. The Cognitive Failures Questionnaire (CFQ Broadbent et al. 1982) included 25 items such as "Do you fail to hear people speaking to you when you are doing something else?" Participants were asked to respond to each statement using a 5-point Likert scale, ranging from Very Often (4) to Never (0). Higher scores indicated higher incidence of cognitive failures, with a maximum score of 100 points. The CFQ has moderate-high internal consistency (alpha =.89) and test-retest stability (.71-.82) (Bridger, Johnsen, & Brasher, 2013 Broadbent et al., 1982).

The Scale of Dissociative Activities (SODAS Mayer & Farmer, 2003) included 35 items aimed at capturing participants' tendency to experience dissociative states and absentmindedness, such as "There are occasions when I have the experience of watching myself and feeling like I am watching another person." Each item could be endorsed on a 5-point Likert scale ranging from Very Frequently (4) to Never (0), with a maximum scale score of 140 points. In the original validation study, the SODAS was found to have high internal consistency ([alpha] = .95) and moderate test-retest reliability (r = .77 over an average 38-day interval Mayer & Farmer, 2003).

Finally, the Five Facets of Mindfulness Questionnaire (FFMQ Baer et al., 2006) included 39 items, e.g., "I pay attention to sensations, such as the wind in my hair or sun on my face." Participants rated each item on a 5-point Likert scale, ranging from Very Often / Always True (5) to Never / Very Rarely True (1), resulting in five separate scores, each reflecting a different aspect of mindfulness: observing, describing, acting with awareness, non-judging and non-reactivity. High internal consistency was reported for this instrument ([alpha] = .85 de Bruin et al., 2012).

The data collection protocol was comprised of two blocks lasting 45 minutes each. Participants were assessed one at a time, in a quiet room. After signing the consent form and filling out the demographic questionnaire, they completed the three self-report measures. Then, participants watched the video lectures on a laptop with headphones and took notes concurrently. Following a short break, the second block of activities included the Attention Network Test (ANT) and the three handwriting tasks all described above. Participants were then debriefed and compensated. The presentation order of the two blocks and of the tasks within each block was counterbalanced between participants, resulting in 12 different rotations.

Table 1 shows descriptive statistics for all measures collected in the study. Overall, the average note quality percentage scores were low (M = 40.4%, SD = 12.4), and ranged widely from 8% to 70%. Table 2 shows intercorrelations among all measures.

Notes quality did not significantly correlate with completed semesters of college education [r = .16 p = .2] and the correlation between GPA and notes quality was marginally significant [r = .27 p = .08]. These initial analyses confirmed that note taking is not simply understood in terms of college experience and overall achievement and a more fine-grained search for its basic cognitive predictors is necessary.

Given the number of scores for each cluster of predictors relevant to the research questions (handwriting speed, objective and subjective attention), a two-step data analysis strategy was adopted. We first examined the relationship between notes quality and each cluster of measures then we assessed the contributions of the strongest predictors in the final regression analysis.

All three measures of handwriting speed were significantly correlated to notes quality as well as to one another (see Table 2), indicating that higher quality notes were associated with more letters produced on the Alphabet task and with faster sentence and symbol copying time. To evaluate the contributions of the different measures of handwriting speed, a multiple regression analysis using the enter method was conducted, with notes quality as the outcome variable and the three handwriting speed measures as predictors. The model yielded significant results [R = .51, [R.sup.2] = .26, [R.sup.2.sub.Adjusted] = .22, F (3,63) = 7.04, p = .0001]. As shown in Table 3, sentence copying emerged as a significant predictor of notes quality.

Table 2 shows the intercorrelations among notes quality and the five performance indices extracted from the Attention Network Test (ANT): mean reaction time, accuracy, and the alerting, orienting and executive subscores. The ANT mean reaction time was significantly correlated to notes quality [r = -.26, p < .05], indicating that better notes were associated with faster times in responding to the arrow orientation, regardless of cuing condition or flanker orientation.

A multiple regression analysis (enter method) using the ANT indices to predict notes quality was marginally significant [R = .39, [R.sup.2] = .15, [R.sup.2.sub.Adjusted] = .08, F (5,63) = 2.07, p = .08]. ANT mean reaction time emerged as a significant predictor of note quality. Table 4 shows a summary of the regression parameters.

The bottom portion of Table 2 shows the intercorrelations among notes quality and the subjective measures of attention, namely the Five Facets of Mindfulness Questionnaire (FFMQ), the Cognitive Failures Questionnaire (CFQ) and the Scale of Dissociative Activities (SODAS). Notes quality significantly correlated with the Observe subscore of the FFMQ [r = .27 p < .05]. Also, a marginally significant negative correlation was found between notes quality and the Cognitive Failures Questionnaire [r = -.22 p = .08]. Overall, these results suggested that better quality notes were associated with a higher sense of being present and observant, and with less frequent self-reported cognitive failures.

A multiple regression analysis (enter method) on this cluster of predictors resulted not significant [F (7,63) = 1.03, p = .42] (see summary of regression parameters in Table 5).

Lastly, a multiple regression analysis was conducted on notes quality scores using the predictors emerged from the first regression analyses: Sentence Copying and ANT mean reaction time. The regression model was significant [R = .51, [R.sup.2] = .27, [R.sup.2.sub.Adjusted] = .23, F (2,61) = 10.5, p < .0001]. Sentence Copying emerged as the only significant predictor is this analysis (see summary of regression parameters in Table 6) thus, providing the evidence for the primary role of handwriting speed in predicting the quality of notes.

This study aimed at identifying the predictors of note taking quality by examining the relationship between note taking performance and handwriting speed, objective and subjective measures of attention. Some of our results replicated previously established findings, while others extended previous findings by reporting on attentional measures never used before in the context of note taking research. Below we discuss our results and their implication for education.

Handwriting speed has been found to be strongly related to the quantity and quality of written output in both children's and adults' essays and summaries of reading material (Connelly et al., 2006 Graham et al. 1997 Jones & Christensen, 1999, Study 1). This factor has also been found to predict the quality of lecture notes in college students (Peverly et al., 2007 Peverly et al., 2012 Peverly et al., 2014) and to differentiate note-taking performance of male and female students (Reddington, Peverly, & Bock, 2015). In line with previous findings, our results confirmed handwriting speed to be the strongest predictor of the quality of notes.

Understandably, when handwriting is fast, effortless and automated, more resources are available for use by other processes necessary for note taking, such as sustained attention, gist extraction, and integration of lecture content with previously existing knowledge. Conversely, slow and effortful writing reduces the chances of fully attending to explanations and topic transitions, thus resulting in fragmented and/or inaccurate notes (for a similar argument on essay writing from a developmental perspective, see Kellogg, 2008). The design of the present study included measures of handwriting speed, all of which required fine motor skills but differently engaged the language system: the alphabet task and the sentence copying task required dexterity and access to linguistic codes (phoneme-to-grapheme conversion) whereas the symbol copying task selectively measured fine motor skills with no connection to language. Although all three measures significantly correlated with notes quality, only sentence copying time remained a significant predictor in the regression analysis. Similarly, Peverly and colleagues (2007 2014) did not report a correlation between notes quality and fine graphomotor skills measured with two relatively easy tasks. Here we further confirmed their results with a symbol copying task modified to be more challenging in order to minimize ceiling effects. It is worth pointing out that in the present as well as in the previous studies (for example, Peverly et al. 2007 2014), handwriting speed measures were highly intercorrelated and thus, multicollinearity might have interfered with teasing apart the contributions of each component of handwriting speed to notes quality.

Interestingly, the scientific evidence on the importance of handwriting in learning and note taking is coming at a time of decreased use of writing by hand in educational contexts and in everyday life. In the United States, cursive training is being phased out, and the availability of computers, laptops and handheld devices in classrooms, at home and on-the-go, makes typing a preferred modality for recording written information. Nevertheless, evidence shows that handwriting has advantageous effects on cognition. For example, cognitive neuroscience research has demonstrated that the process of writing by hand rather than typing promotes the acquisition of visual recognition of letters in children (Longcamp et al., 2005) and in adults that are acquiring a novel alphabet system (Longcamp et al., 2008). Neuroimaging evidence indicates that such advantage of writing over typing is due to distinctive neural signatures produced in the motor cortex when each character is produced compared to the more "generic" motor command associated with pressing the character's key on the keyboard (Longcamp et al., 2008). Moreover, Mueller and Oppenheimer (2014) have recently shown that notes taken by hand tend to be more complete and have a more conceptually organized structure possibly because longhand allows note takers to easily and quickly update previously written day information, going back to what was previously noted down to add details, examples or clarifications. Instead, typed notes tend to be more verbatim and follow the linearity of the lecture, making note taking in this medium less active and engaging (Mueller & Oppenheimer, 2014). Nevertheless, Bui, Myerson, and Hale (2013) found that students recorded more lecture idea units when taking their notes by typing compared to writing by hand. Future research will need to address how the advantages and disadvantages of taking notes using handwriting and typing will shift over time, as new generations of learners will become exposed to typing increasingly earlier in their literacy training and as new digital devices become available, that allow typing, stylus handwriting and touch-screen technology to be integrated seamlessly. Despite not comparing typing and handwriting note-taking specifically, our study adds to the existing body of evidence on the advantageous effects of handwriting on information processing and provides additional support for the continued use of handwriting in education.

Attention allows the cognitive system to focus on task-relevant information through alertness, orienting to the relevant aspects of the stimuli, and inhibiting distractions and interferences. Given the multitasking nature of note taking, we hypothesized that some aspects of attention would be related to the quality of notes taken during lecture. Indeed, participants who recorded notes of higher quality tended to have faster reaction times on the Attention Network Test (Fan et al., 2002). The predictive role of ANT reaction times on notes quality is in line with results reported by Peverly et al. (2014), who demonstrated that participants' scores at a sustained attention task predicted notes quality. In our study no significant correlations emerged between notes quality and the accuracy, alerting, orienting, and executive control scores of the ANT. Similarly, two previous studies (Peverly et al., 2012 Peverly et al., 2014) reported no significant correlations between notes quality and either group-administered or individually-administered measures of executive control, despite the importance of executive control in regulating attention in tasks relevant to academic performance (Diamond, 2013).

Several explanations for the lack of an effect of specific attention measures are possible. First, the size of our sample, as the size of the sample used by Peverly et al. (2014), might have limited the statistical power of the analyses employed. In addition, the nature of the attention tasks used, which required processing simple visual, non-linguistic stimuli, might have been too simple to account for the complexity involved in the process of note taking. Overall, the present results suggest that general attentional processing speed is more important for note taking than specific attentional functions measured using computerized tasks.

In addition to the reaction-time based ANT, our study employed subjective, self-report measures of mindfulness, absentmindedness and cognitive failures intended to capture more complex aspects of attention related to feeling present, observant and consciously aware of one's actions in the environment. Better notes were produced by the participants who reported feeling more observant and mindful in their everyday lives, as reflected by a significant correlation between the Observe scores on the Five Facet of Mindfulness Questionnaire (FFMQ Baer et al., 2006) and notes quality. However, in a regression analysis no subjective attention measure significantly predicted note-taking quality, possibly due to the high number of predictors and small sample size.

Limitations, implications and future directions

Simulating complex academic tasks in a controlled laboratory setting poses clear limitations thus, our results should be interpreted with caution. Indeed, the simulated lecture employed in the present research, as is the case with previous note taking studies, was not a part of a formally graded academic assignment and, therefore, did not expose participants to the motivating pressure that they might naturally experience in the class. Also, the relatively short duration of the audiovisual lectures used, the individual delivery via headphones, and the lack of an opportunity for interaction with classmates or lecturer separates the lecture simulation from the real classroom experience. Finally, the lack of a retention test due to time-limits constraints in our design, does not allow us to relate note-taking quality to performance at a later test, although this has been extensively documented in the literature (Armbruster, 2009 DiVesta & Gray, 1972 Haynes, McCarley, & Williams, 2015 Kiewra & Benton, 1988 Kiewra, Benton, & Lewis, 1987 McIntyre, 1992 Peverly, Garner, & Vekaria, 2014 Peverly et al., 2007 Peverly et al., 2012). Nevertheless, the present study strengthened and extended the knowledge base on the importance of handwriting speed for successful note taking.

We hope that our findings will provide an evidence base for the development of pedagogical interventions that might result in students' improved notes. Students should be informed of the advantageous functions of note taking in the learning process and should be encouraged to self-assess their strengths and weaknesses with regard to the core skills that support note taking. Instructors should keep in mind the importance of handwriting speed, processing speed (that is, attention reaction times) and mindfulness in note taking. Partial or "skeletal" outlines rather than complete verbatim lecture handouts should be provided to encourage students to take notes and scaffold the note taking process while leaving them responsible for the recording of their own notes (DeZure, Kaplan, & Deerman, 2001). Indeed, some evidence indicates that partial outlines and handwriting are associated with deeper processing and higher retention of lecture information (Konrad, Joseph, & Eveleigh, 2009 Marsh & Sink, 2010 Russell et al., 1983). Other evidence-based strategies for instructors to support the component processes of note taking include monitoring the speed of lecture delivery, adapting lecture speed according to the novelty and complexity of the material, and pausing to allow students to catch up (DeZure et al., 2001). Providing specific feedback on how to discern relevant from irrelevant words in a lecture (Williams et al., 2016) as well as encouraging comparison with peer students' notes and assigning notes restructuring at the end of the lecture (Cohen et al., 2013) may also be helpful in compensating for the limitations of slow handwriting and fluctuating attention. Finally, when note-taking difficulties require remedial intervention, a targeted training to improve writing fluency might be indicated together with explicit instruction on the use of symbols and abbreviations to reduce the load on the graphomotor component of the note-taking process (Boyle & Rivera, 2012). In situations where handwriting is entirely replaced by typing (for example in computer-aided instruction) or for those students who prefer typing due to the lack of penmanship training, typing speed should be assessed and, if necessary, typing instructions should be provided. Regardless of the level of proficiency with technology, students should be advised that certain note taking tasks would be more fruitful if carried out on paper.

Future applied research should address the potential effects of cognitive training of attention and mindfulness practices on the component processes of note taking and whether these techniques may result in increased performance. Computerized training protocols targeting specific cognitive functions, for example, working memory, are being piloted in remedial interventions and performance augmentation in reading and math (Chein & Morrison, 2010 Loosli et al., 2012 Witt, 2011). Moreover, pilot studies introducing contemplative practices in mainstream education have documented improved attention and academic performance (Eberth & Sedlmeier 2012). It could be hypothesized that elevating attention processing speed and fostering presence through training would result in improved engagement in the lecture experience, better notes and successful learning. However, this approach is speculative, and generalizability of cognitive training to academic tasks is a matter of debate and does not warrant an endorsement as a best practice at this time (Noack, Lovden, & Schmiedek, 2014).

We hope that our findings together with those from related studies will contribute to initiating educational reforms and mitigating the temptation to supplant traditional methods of instruction with more recent, technological-driven ones whose appeal might not translate into better skill acquisition and educational outcomes. Ideally, the fast pace of technological advancement will soon produce devices that easily allow the integration of typing and handwriting on touchscreens of high sensitivity, therefore making handwriting a feasible, and hopefully preferred, alternative to typing when using electronic devices, and reviving the declining art of note taking.

Alberto Manzi, Steven Martinez

Borough of Manhattan Community College, CUNY.

Author info: Correspondence should be sent to: Alberto Manzi, Ph.D., School of Social and Behavioral Sciences, Mercy College, 555 Broadway, Dobbs Ferry, NY 10522 or [email protected]

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Authors' notes: The authors would like to thank Mr. David Goldstein, Mr. David Ryan, & Ms. Kaitlyn Friedlander for their assistance in recruitment of participants and data collection. Funding for this project was provided by a Mercy College Faculty Development Grant and by grant P20MD002717 from the National Institute on Minority Health and Health Disparities (NIMHD) to the Mercy College Research Infrastructure in Minority Institutions--Career Opportunities in Research (RIMI-COR) Program.

Caption: FIGURE 1. Attention Network Test (ANT)

Please note: Illustration(s) are not available due to copyright restrictions.


2. IELTS Listening Actual Test

The book includes a collection of real exams from 2008 – 2013, ebooks are only available until 2013, and you can go to bookstores to buy the latest updates in recent years.

Who should practice this series?
This is a test preparation exam, for all subjects. However, ITN encourages you band 5.0 and above – that is, have certain listening skills. If you do not reach this level, you should start listening before starting to work to get the best effect.

How to make the best use of the series?
– If you have time, do it again at least 2 times.
– Don’t just watch the key. Note the transcript to know why I am wrong, learn more vocab and even the expression of native speakers.

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