ONLY for One Touch! Psych Critical Evaluation Paper

Computers in Human Behavior 34 (2014) 148–156

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Computers in Human Behavior

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c o m p h u m b e h

Research Report

An experimental study of online chatting and notetaking techniques on college students’ cognitive learning from a lecture

http://dx.doi.org/10.1016/j.chb.2014.01.019 0747-5632/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +1 9012991212. E-mail address: [email protected] (F.-Y.F. Wei).

Fang-Yi Flora Wei ⇑, Y. Ken Wang, Warren Fass University of Pittsburgh, 300 Campus Drive, Bradford, PA 16701, United States

a r t i c l e i n f o

Article history: Available online 22 February 2014

Keywords: Cognitive learning Multitasking Online chatting Notetaking Recall

a b s t r a c t

This experimental study investigated the effects of college students’ online chatting behavior and note- taking techniques (handwritten vs. computer-mediated) on their cognitive learning. The results showed that regardless of notetaking technique, students who did not participate in off-learning online chatting during class, compared to those who did, demonstrated better recall of lecture content and higher quality of note. In terms of cognitive learning, students who used laptops to take notes were least negatively affected by online chatting during class than those who took handwritten notes or took no notes during the lecture. The findings suggest that task switching and interruption result in reduced effectiveness of learning and notetaking; moreover, switching from handwriting on notepads to typing chat messages on computer keyboards demonstrated a motor delay compared to students who used the same devices to multitask.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

In March 2013, Google introduced a new notetaking service, Google Keep, which allows users to quickly record notes on An- droid devices (Covert, 2013). Similar products and services, such as Microsoft OneNote, Evernote, and Apple Notes, are being adopted by an increasing number of college students for classroom notetaking. Affordable laptops, tablets, and mobile devices, along with ubiquitous wireless networks, have created a generation of ‘‘classroom multitaskers,’’ meaning that students can take notes on electronic devices and, simultaneously, listen to a lecture, chat with friends on social networks, and engage in other online activ- ities. Thus, electronic devices have brought convenience and effi- ciency to students to the extent that ‘‘note-taking was reported as the largest benefit of using a laptop in class’’ (Kay & Lauricella, 2011, p. 6).

Students’ use of laptops to take notes during lectures, however, may have become a potential disturbance to teachers. For example, in 2011, Dr. Frank Rybicki was teaching a course on Law and the Ethics of Media at the Valdosta State University, when he observed a female student surfing the internet during the lecture and closed her laptop screen after urging the student not to engage in irrele- vant classroom activities (e.g., accessing Facebook; for details, see Johstono & Smith, 2011) because those activities may shift her

attention from his lecture. That situation leads to an important re- search question regarding the extent to which the use of electronic devices to take notes during class lectures influences students’ classroom learning (Junco, 2012; Karpinski, Kirschner, Ozer, Mel- lott, & Ochwo 2013; Young, 2006).

Notetaking during class has been an ignored communicative behavior by teachers (Titsworth, 2001). That lack of attention is unfortunate, as researchers have shown that notetaking can en- hance students’ retention of information (e.g., Carter & Van Matre, 1975; Kiewra, 1989), with the quantity (Nye, Crooks, Powley, & Tripp, 1984) and quality of notes (Fisher & Harris, 1974) positively related to students’ test performance. However, given the popular use of electronic devices during class, notetaking gradually has transformed from a handwritten to a computer-mediated experi- ence (e.g., typing on keyboards or touching screens). Whether using computers to take notes facilitates students’ cognitive learn- ing as effectively as does handwritten notetaking, however, is unclear.

Although computers routinely are used to take notes during class, students, simultaneously, may use those computers during class for other online activities, such as chatting with peers on so- cial networks or playing electronic games (Kay & Lauricella, 2011); consequently, banning students from using laptops during class has become common in classroom instruction (Fried, 2008). Many students ‘‘dislike the restrictions, arguing that people raised in the era of multitasking can balance Internet use and classroom partic- ipation’’ (Young, 2006, p. A27), and they believe that multitasking

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online activities do not negatively influence their notetaking behavior, nor retention of lecture material.

Because off-learning multitasking behavior potentially can interrupt students’ sustained attention and weaken their cognitive learning during class (Wei, Wang, & Klausner, 2012), the main con- cern is whether off-learning multitasking behavior (see Lindroth & Bergquist, 2010), such as chatting online during classroom note- taking, can interfere with the learning task and jeopardize students’ learning outcomes.

To address this important issue, this experimental study inves- tigated whether computer-mediated notetaking influences stu- dents’ cognitive learning with respect to two dimensions. First, we compared the effect of no notetaking, handwritten notetaking, and computer-mediated notetaking conditions on students’ cogni- tive learning outcomes from a lecture. We then examined whether off-learning classroom behaviors, such as online chatting per- formed simultaneously as students were taking handwritten and computer-mediated notes, decreased the quality of students’ notes and their cognitive learning.

2. Literature review

Research on student notetaking and cognitive learning can be traced back to Crawford (1925a, 1925b), who first found a positive relationship between notetaking during lectures and academic performance on pop quizzes, and then discovered that students who took notes during class, compared to those who did not, tended to demonstrate higher academic performance on both immediate recall and delayed retention quizzes. A possible reason for this relationship has to do with the process vs. product func- tions of notetaking.

2.1. Process vs. product functions of notetaking

Kiewra (1985) divided the functions of notetaking into two cat- egories: process and product functions. The process function has been related to the encoding of information, whereas the product function, historically, is associated with the external storage of note- taking (see Di Vesta & Gray, 1972; Knight & McKelvie, 1986). As Kiewra (1985) explained, the process function emphasizes stu- dents’ encoding practice as a unique way to select and reconstruct lecture content, and, therefore, it may reinforce students’ reflection of important information presented in lectures. Thus, ideally, note- taking during lectures may cultivate deeper learning, compared to simply listening to lectures, because self-driven organization of lecture notes may enhance students’ recall of information (Post- man, 1972). As Di Vesta and Gray (1973) explained, ‘‘Note taking as an activity may function to direct the student’s attention to cer- tain parts of the material, perhaps at the expense of attention to other parts, but in the process allowing the important points to ‘mature’’’ (p. 173). Consequently, notetaking may foster in students the practice of andragogical (independent) learning (Kiewra, 1989; Kobayashi, 2006).

For most students, lecture notes serve as a rehearsal tool for preparing for an examination (Fisher & Harris, 1973); indeed, many researchers (e.g., Hartley, 1983; Kiewra, 1989) have reported that students who review lecture notes prior to taking examinations, compared to those who do not, demonstrate higher test scores. As Carter and Van Matre (1975) pointed out, ‘‘It appears that it is not note taking, per se, but note having and reviewing which facil- itate performance’’ (p. 903). Hence, in contrast to the process func- tion, the product function focuses on students’ review of notes as a means to prevent memory loss or to increase familiarity with lec- ture content over time (Kiewra, 1985). Therefore, the product func- tion (reviewing notes) seems to be an extension of classroom learning that is influenced by students’ private efforts, such as

preferable study strategies (Annis & Annis, 1982) and cognitive styles (Annis & Davis, 1978), whereas the process function (taking notes) reflects processing information and executing attention dur- ing lectures.

Rickards and Friedman (1978) tested both functions simulta- neously and suggested that the product function (external storage) affects students’ recall more than does the process function. How- ever, emphasizing the product function does not mean that the encoding function should be neglected completely, because if stu- dents cannot initially encode information accurately, the value of having their notes for review, subsequently, might suffer. Indeed, Howe (1970) had students take notes as they listened to a 160- word recorded passage and found that there was a higher probabil- ity (.340) of students recalling an item that appeared in their notes, compared to recalling an item that was not in their notes (.047). Locke (1977) also observed that students in a classroom setting took more notes about new information than about the content pertaining to their existing knowledge, and that completion of lec- ture notes and course grades were positively correlated; however, a positive relationship existed only for verbally presented lecture content rather than a lecture that contained visual aids. Addition- ally, Kiewra and Fletcher (1984) found that words recorded by stu- dents in their notes were positively correlated with their immediate recall performance. Moreover, notetaking efficiency is believed to be positively associated with students’ recall of the pre- sented information; for example, Kuznekoff and Titsworth (2013) found that if the disturbance of mobile phones (i.e., text messag- ing) was absent during class, students could write down 62% more information in their lecture notes, resulting in their ability to recall more details from the lecture content on a multiple-choice test. However, as Peverly and Sumowski (2011) suggested, students’ notes are best used to predict their performance on essay and mul- tiple-choice tests (text-explicit items/recall of the stated content), but that their notes could not effectively predict students’ infer- ences (problem solving skills).

3. Cognitive learning: Recall of content from encoding information

Although information recall is considered to be a rudimentary educational objective (Bloom, 1956; Bloom, Englehart, Furst, Hill, & Krathwohl, 1956), achieving such retention of knowledge is a not a simple process. As Cappa (2001) described, ‘‘Memory pro- cessing can be subdivided into several phases: the encoding of information from the external world through perceptual analysis, the storage of memory track, and, finally, the retrieval of the stor- age information in response to adequate cues’’ (p. 61). Thus, whether students learn content and recall information effectively may begin with how well that content is encoded. Importantly, encoding information (or creating enduring codes for long-term memory; Dehn, 2008) depends, in part, on working memory (Baddeley, 1986), in which people ‘‘have to hold and manipulate information in the mind over short periods of time’’ (Gathercole & Alloway, 2008, p. 2). Due to a limited capacity of attention to pro- cess information (Cowan, 2005; Dehn, 2008), students’ working memory has to simultaneously maintain access to relevant on-task information and block irrelevant interferences (Baddeley & Hitch, 1974). Even though working memory processes do not guarantee ‘‘permanent learning’’ (p. 60), those processes may determine how well information is encoded and retrieved (Cappa, 2001).

However, attention to information is selective (Broadbent, 1952); working memory processes not only perform an encoding function during information processing but they also monitor the allocation of cognitive resources that are needed to perform tasks (Baddeley, 1996). As Dehn (2008) articulated, three of the five core

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functions of the central executive (i.e., the allocator of resources) are

(a) Selective attention, which is the ability to focus attention on relevant information while inhibiting the disruptive effects of irrelevant information; (b) switching, which is the capacity to coordinate multiple concurrent cognitive activity, such as time- sharing during dual tasks; (c) selecting and executing plans and flexible strategies (p. 23).

If working memory processes do influence selective attention and switching, notetaking as an aid to encoding content might sus- tain students’ attention on the learning task and limit their atten- tion switching to irrelevant off-task behavior.

However, the confusion is that if the quality of performing a sin- gle task is better than simultaneously performing multiple tasks (Rubinstein, Meyer, & Evans, 2001), it is not clear why notetaking is considered to be a coordinated dual task during class rather than an overloaded task that burdens students’ learning attention (Gathercole & Alloway, 2008). To answer that interesting question, we examine studies of classroom multitasking behaviors.

3.1. Classroom multitasking

Bowman, Levine, Waite, and Gendron (2010), testing whether multitasking behaviors could negatively influence college students’ reading time, found that students who used instant messaging (IM) during their reading took longer to complete those reading tasks than those students who did not use IM simultaneously. In terms of classroom observations about laptop usage, Lindroth and Berg- quist (2010) pointed out that when students use IM during lectures to entertain themselves, their attention to the lecture content might be lost due to the interference of the off-learning multitask- ing behavior with the primary learning task. As a result of students’ responses to a questionnaire, Wood et al. (2012) found that stu- dents who used Facebook and other IM tools as they were typing lecture notes demonstrated a poorer cognitive learning outcome than did students who used pencil-and-paper to take notes. Kar- pinski, Kirschner, Ozer, Mellott, and Ochwo (2013) found that when U.S. college students access social networking websites and study, simultaneously, they tend to have a lower grade point aver- age compared to students who did not engage in both tasks at the same time. Kuznekoff and Titsworth (2013) also found that stu- dents could earn higher scores on a multiple-choice test if they limited their texting activities during notetaking. Furthermore, the results from Hembrooke and Gay’s (2003) study showed that regardless of whether online content was relevant to lectures, allowing students to use laptops (e.g., to browse and search infor- mation) as a supplemental activity during lecture negatively influ- enced their immediate recall of the lecture material. Although the notion of insufficient sustained attention or the limited processing capacity supports the aforementioned research findings, research- ers (e.g., Baddeley, Chincotta, & Adlam, 2001; Rogers & Monsell, 1995; Wickens & McCarley, 2008) never assertively deny the pos- sibility of performing a dual task or multitasks simultaneously; in- stead, study results imply that multitasking might influence the quality of performance by increasing switch costs (i.e., prolonging response time or task errors), especially when people shift their attention back and forth between two types of tasks. Because attention has a limited capacity to process information (Cowan, 2005), switching between learning and off-learning activities dur- ing lecture, potentially, may increase errors of recording lecture notes.

Furthermore, Piolat, Oliver, and Kellogg (2005) stated that effec- tive notetaking from a lecture is a working memory resource that demands activity and requires working memory’s central

executive to generate rapid decisions about the appropriateness of information from a lecture that is important to include in one’s notes. That is, multiple cognitive tasks must be performed simulta- neously during notetaking; the performance of those tasks places demands on students’ limited resources that are allocated to the relevant information. Thus, if students use a laptop to take notes, and, simultaneously, perform additional off-learning attention- demanding activities (e.g., IM or Facebook), they may not have suf- ficient cognitive resources to simultaneously listen to a lecture, take notes effectively from that lecture using their laptops, and perform additional off-task activities. More important, students switching back and forth between learning and off-learning tasks during class tend to present a low level of sustained attention (i.e., ‘‘focusing attention on a stimulus or activity for an extended period of time;’’ Schmeichel & Baumeister, 2010, p. 31), resulting in lower cognitive learning outcomes (Wei et al., 2012).

4. Rationale and hypotheses

Researchers (e.g., Di Vesta & Gray, 1973; Kiewra, 1985) have de- voted much attention to the effects of notetaking on students’ cog- nitive learning, and their findings have shown that notetaking by hand is associated with positive cognitive learning outcomes (e.g., Kiewra, 1989; Kobayashi, 2006). However, little empirical study (For exceptions, see e.g. Fried, 2008; Hembrooke & Gay, 2003; Wood et al., 2012) has examined whether computer-medi- ated notetaking during a class lecture facilitates or interferes with cognitive learning. Thus, focusing on the process function of note- taking, we investigated potential effects of computer-mediated notetaking during a lecture on whether performing multitasking behavior, such as online chatting during notetaking, hampers stu- dents’ cognitive learning, as well as the quality of their notes.

Overall, researchers (e.g., Crawford, 1925a, 1925b; Di Vesta & Gray, 1972, 1973) have found that college students who take notes during class demonstrate better cognitive learning outcomes than students who do not take notes. However, it is uncertain whether handwritten and computer-mediated notetaking would produce a similar outcome on students’ cognitive learning.

Moreover, because researchers (e.g., Crawford, 1925a, 1925b; Di Vesta & Gray, 1972) have employed the recall of lecture notes as the most common method to assess cognitive learning outcomes, we also used the immediate recall of lecture notes to demonstrate cognitive learning. Thus, the first hypothesis was posed to test the effect of notetaking conditions on cognitive learning:

H1. Students in no-notetaking, handwritten notetaking, and com- puter-mediated notetaking conditions demonstrate differential levels of classroom cognitive learning.

Researchers (e.g., Bowman et al., 2010; Wood et al., 2012) focusing on online chatting have found that multitasking behaviors may negatively influence learning outcomes. When students use computers (laptops) to take lecture notes, they simultaneously may engage in online activities, such as chatting with peers about irrelevant subject matter (Kay & Lauricella, 2011). Given the poten- tial interference of irrelevant content during information process- ing, multiple switching among tasks may increase demands on limited resources (see Wickens & McCarley, 2008) that, potentially, might influence recall of lecture content. Thus, the second hypoth- esis tested the difference between off-learning online chatting and no online chatting on students’ cognitive learning:

H2. Students in online chatting conditions demonstrate a lower level of classroom cognitive learning than those in no-online chatting conditions.

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Peverly and Sumowski (2012) found that students’ note quality had a positive impact on performance on multiple-choice tests. Therefore, note quality should be assessed because that quality might predict whether students persistently attended to the lec- ture content when they take notes during class; specifically, Howe (1970), Kiewra and Fletcher (1984), and Locke (1977) found a po- sitive relationship between students’ note completion and their re- call of information. Thus, instead of using only immediate recall of lecture information, note quality (specifically, its accurate comple- tion; see Kuznekoff & Titsworth, 2013) in relation to lecture con- tent may be a crucial variable to examine the potential effects of notetaking on students’ sustained attention. Thus, the third hypothesis related to students’ note quality via handwritten and computer-mediated notetaking:

H3. Students in handwritten notetaking conditions demonstrate a differential level of note quality than those in computer-mediated notetaking conditions.

Additionally, to encode lecture material accurately during note- taking (Kiewra, 1985), students need to maintain their sustained attention on the lecture content. As Wood et al. (2012) indicated, students may have limited resources to process information during notetaking if they access Facebook and other IM tools during class; switching attention between irrelevant online chatting and listen- ing to lectures, consequently, may negatively influence the accu- racy of their notes. Because irrelevant online chatting may interrupt students’ information selection and increase the chances of making errors during note taking, there is a need to examine whether irrelevant interference during notetaking affects notetak- ing quality. Therefore, the fourth hypothesis was posed:

H4. Students in the online chatting conditions demonstrate a lower level of note quality than those in the no-online chatting condition.

5. Methods

5.1. Participants

The volunteer sample consisted of 127 undergraduate college students (male = 60, female = 67, Mage = 21.9%, 78.7% of partici- pants’ age range: 19–22 years) at a small-sized northeast U.S. uni- versity. Of those participants, 79.6% were Caucasian, 12.6% were African American, and the remaining were of other ethnicities. Par- ticipants had to identify that they had the ability to write lecture notes by hand, as well as comfortably engage in typing activities on laptops or computers prior to participating in the experiment. To avoid familiarity with the lectures being given, participants who had taken the Survey of Broadcasting course (the employed lecture content) were not eligible to participate in the study.

5.2. Experimental design

The experiment was a 3 (notetaking methods) � 2 (chatting conditions) between subjects factorial design; the two dependent variables were cognitive learning (test scores) and notetaking qual- ity. The experiment involved two independent variables (notetak- ing methods and chatting conditions). Participants were randomly assigned to one of three notetaking methods (no-notetaking, hand- written notetaking, or computer-mediated notetaking), and one- half of the participants from each of the notetaking conditions were assigned to one of two chatting conditions (no chatting or on- line chatting), and their cognitive learning and notetaking quality were assessed. Prior to the experimental manipulations, all

participants in a group-administered setting were asked to com- plete an online questionnaire containing questions related to demographic information, their use of laptops during classroom notetaking (i.e., the frequency of typing lecture notes via comput- ers), and sustained attention (i.e., focusing attention on the learn- ing task over time).

5.3. Materials

A 10-min scripted video lecture was recorded in the Survey of Broadcasting course.

The video recorded lecture was saved on a DVD disk and dis- played on a large projector screen in the experimental room. Two undergraduates, who never took the course and were blind to the purpose of this study, individually watched the recorded lec- ture and answered a list of questions, such as ‘‘In comparison to a real classroom situation, how would you rate the presenter’s pace in the given lecture?’’ and ‘‘In comparison to a real classroom situ- ation, how would you rate the verbal expression?’’ Using a percent- age (1 = extremely poor, 100 = excellent) to score the lecturer’s performance, all questions were rated above 90% by the two stu- dents, which suggests that the recorded lecture mirrored an actual classroom lecture.

A chatroom application was developed by the researchers for online chatting tasks involved in this study. The application was designed to simulate an online chatting environment similar to other instant messengers. The chatting window remained on the computer desktop alongside another text editor (for the computer- ized notetaking condition only) allowing participants to simulta- neously chat with peers and take notes without having to close each application window. The chat transcript was recorded in a log file. The same undergraduate reviewers who evaluated the re- corded lecture were asked to rate their experiences with the use of this chatroom application by answering questions, such as ‘‘How well did the chatroom function when you chatted with the other reviewer?’’ using a 5-point Likert scale (1 = poorly, 2 = below aver- age, 3 = average, 4 = very good, and 5 = excellent). The two under- graduates rated their chat experiences as being 5 (excellent).

5.3.1. Preliminary test about content reliability An online questionnaire was developed to collect participants’

demographic information, pretest their knowledge of the material covered in the lecture, and posttest their cognitive learning out- comes. The two undergraduates who evaluated the recorded lec- ture and chatroom experience also rated how well the pretest and posttest questions reflected the lecture, using a percentage (0 = absence of the tested content, 100 = excellent correspondence) after watching the lecture, and rated the lecture as a having 100% content agreement with the pretest and posttest questions.

5.3.2. Preliminary coding A content-analytic coding sheet was developed to code stu-

dents’ original handwritten notes and their computer-mediated notetaking printouts. The coding sheet was developed based on the lecture script, with the content of questions determined from the recall test. Three coders who were familiar with the recorded lecture content and who were experienced in qualitative content analyses, examined and coded participants’ handwritten notes and computerized notetaking printouts, respectively. Each correct recorded keyword or major theme on the notes was marked as one point, whereas any absent content was given zero points, with 10 being the highest available points for notetaking quality. The initial intercoder reliability was 74%, which was satisfactory; after discussion, the three coders reached 100% agreement on all items and then finalized the cumulative points to represent each partic-

Table 1 Dependent variable: cognitive learning.

Chat condition Notetaking condition n M SD

No chatting No notetaking 24 5.21 1.67 Hand notetaking 20 5.00 1.45 Computer-mediated notetaking 15 3.93 1.94

Chatting No note-taking 23 2.04 1.69 Hand note-taking 18 2.94 1.73 Computer-mediated Notetaking 27 3.74 1.85

152 F.-Y.F. Wei et al. / Computers in Human Behavior 34 (2014) 148–156

ipant’s notetaking quality. Notetaking quality scores were coded into the SPSS after the completion of the content analysis.

5.4. Measurement

The questionnaire was designed to measure one dependent var- iable (cognitive learning), two covariates (use of laptops during classroom notetaking and sustained attention), and demographic information (e.g., gender, age, and ethnicity). The independent variables (notetaking methods and chatting conditions) were embedded into the experimental procedure, and the other depen- dent variable (notetaking quality) was coded quantitatively, as pre- viously explained.

5.4.1. Cognitive learning Based on the lecture to which research participants were ex-

posed, 10 multiple-choice (text-explicit) questions listed at the end of the questionnaire, discussed previously, were employed to test students’ recall of the lecture material presented about radio, such as ‘‘Which of the following is ranked as the top radio format for FM stations?’’ ‘‘Which of the following is the largest radio group owner? ‘‘How many radio stations do we have in the United States?’’ ‘‘According to current market trend, what are the three C’s of radio?’’ Participants selected one of the correct answers from a five-item list. The questions were used in the pretest (prior to the lecture) to determine participants’ knowledge about radio history, and then were presented in a random order immediately after the completion of the lecture to test participants’ cognitive learning of the factual information presented. Each correct answer given was awarded one point, with the highest score being 10 points. To re- flect how much participants actually learned from the lecture, cog- nitive learning outcomes were calculated as the difference between posttest and pretest scores (no notetaking without online chatting: n = 24, M = 5.21, SD = 1.67; no notetaking with online chatting: n = 23, M = 2.04, SD = 1.69; handwritten notetaking with- out online chatting: n = 20, M = 5.00, SD = 1.45; handwritten note- taking with online chatting: n = 18, M = 2.94, SD = 1.73; computer- mediated notetaking without online chatting: n = 15, M = 3.93, SD = 1.94, and computer-mediated notetaking with online chat- ting: n = 27, M = 3.74, SD = 1.85).

5.4.2. Use of laptops during classroom notetaking Participants indicated their frequency of using laptops to take

notes during class by answering the question, ‘‘As a college stu- dent, how frequently do you use computers to take notes during class?’’ using a 6-point scale (0 = not at all, 1 = rarely, 2 = occasion- ally, 3 = often, 4 = frequently, 5 = almost every class). The mean of students’ laptop use during classroom notetaking was 1.80 (SD = 1.30). The scores from each participant were used as a covar- iate in one of the analyses.

5.4.3. Sustained attention Participants’ self-reported sustained attention during a lecture

was measured based on the pre-established six-item Sustained Attention (SA) scale (Wei et al., 2012). Participants used a 7-point Likert-type scale (1 = not at all true of me, 7 = very true of me) to rate six statements: ‘‘I pay full attention to that lecture during class,’’ ‘‘I pay my full attention to classroom discussions in that class,’’ ‘‘My attention to classroom lecture is more than other leisure activity,’’ ‘‘I never shift my attention to other non-task-oriented learning activities in this class,’’ ‘‘I can sustain my attention to learning throughout the class,’’ and ‘‘I have difficulty to sustain my learning attention during the lecture.’’ Reversed coding was applied to one item, and one item regarding classroom discussion was removed after an item analysis procedure to increase the scale’s reliability. The five items represented students’ sustained attention during

class (M = 6.48, SD = 2.26, a = .85), and those scores were used as another covariate in the second analysis.

5.4.4. Demographic information Participants identified their gender, ethnicity, and age.

5.5. Procedures

This study obtained Institutional Review Board approval in the 2011 Fall semester. Students then were recruited voluntarily by their psychology professors to participate in the study via Experim- etrix (a laboratory registration system). Participants were ran- domly assigned into one of the six conditions without any prior notification (see Table 1). Group administration was adopted in all data-collection conditions, and the experiment took place in a psychology laboratory. Participants first completed the online questionnaire about their demographic information, use of laptops, and sustained attention. They also completed the online version of the pretest to assess their knowledge of radio history. The experi- menter then displayed the videotaped lecture on the large projec- tor screen.

In the no-notetaking conditions, participants were asked to avoid any notetaking behavior as they viewed the recorded video lecture, whereas participants in the handwritten notetaking condi- tions were required to take notes via a pencil on a piece of paper. Participants in the computer-mediated notetaking conditions were asked to type notes using Microsoft Word and then to print out their notes. Both handwritten and computer-mediated notes were collected immediately by the experimenter at the end of viewing the lecture. With no recall aids (student lecture notes) present, all participants were given 10-min to complete the online version of posttest.

In comparison to the no-online chatting conditions, participants who engaged in online chatting conditions, regardless of the note- taking condition, followed the same procedure mentioned in the previous paragraph. However, those participants were asked to chat online about what they did during spring break with other participants in the laboratory as they, simultaneously, viewed the lecture that was displayed on the screen. All participants chatted, according to the chatting logs. On average, participants entered 15 lines (SD = 8.45) and 456.26 characters (SD = 229.32) during the 10-min lecture time across all chatting sections. The SPSS was used to analyze the collected data set.

6. Results

A two-way ANCOVA was employed to test students’ cognitive learning outcomes in the experimental conditions, with students’ frequent use of laptop scores entered as the covariate. A Levene’s test was conducted to assure the equality of error variances across the conditions. The nonsignificant result of Levene’s test, F(5, 121) = .239, p > .05, suggested that acceptable homongeneity of variances across the conditions was warranted.

Table 3 Dependent variable: note quality.

Chat condition Notetaking condition n M SD

No chatting Hand notetaking 20 8.15 1.60 Computer-mediated notetaking 12 8.58 1.51

Chatting Hand notetaking 18 5.22 1.59 Computer-mediated notetaking 26 5.19 1.83

Table 4 Interaction effect of notetaking and chatting conditions on Notetaking Quality (NQ).

Group F P Partial g2

Between chat conditions 28.205 0.000* 0.284 Between notetaking conditions 0.684 0.411 0.010 Chat � notetaking 0.565 0.455 0.008

* p < .005.

F.-Y.F. Wei et al. / Computers in Human Behavior 34 (2014) 148–156 153

The ANCOVA results showed that students in the no-notetaking, handwritten notetaking, and computer-mediated notetaking conditions did not demonstrate significant differences in cognitive learning, F(1, 120) = .309, p > .05. Thus, H1 was not supported. However, students in the online chatting conditions demonstrated a lower level of cognitive learning than those in the no-online chat- ting conditions, F(1, 120) = 35.286, p < .001, g2 = .227. Thus, H2 was supported.

There was a significant interaction effect between the notetak- ing conditions and the online chatting conditions, F(2, 120) = 5.938, p < .05, g2 = .090 (see Table 2 and Fig. 1). Specifically, students’ cog- nitive learning in different notetaking conditions was affected dif- ferently by online chatting. Post hoc analyses revealed that for students who did not take notes during the lecture, their cognitive learning was more negatively impacted by online chatting, F(1, 85) = 14.780, p < .001, compared to those who took notes using a computer. For students who took handwritten notes during the lecture, their cognitive learning also was more negatively affected by online chatting, F(1, 76) = 5.408, p < .05, compared to those who took notes using a computer. However, there was no significant difference due to online chatting on cognitive learning for students who did not take notes or who took handwritten notes, F(1, 81) = 6.453, p > .05. The results, thus, indicated that students who used computer-mediated notetaking were least affected by online chatting than students who did not take notes and those who took handwritten notes. The covariate, frequent use of laptops during classroom notetaking, was significant, F(1, 120) = 7.249, p < .05, g2 = .057, suggesting that students’ use of laptops had a strong influence on their classroom cognitive learning.

Another two-way ANCOVA was employed to determine if dif- ferent notetaking methods and chatting conditions affected stu- dents’ notetaking quality, with students’ sustained attention entered as the covariate. The Levene’s test of equality of error var- iance was not significant F(3, 72) = 1.088, p > .05.

The ANCOVA results showed that regardless of whether students used handwritten or computer-mediated notetaking

Table 2 Interaction effect between notetaking and chatting conditions on cognitive learning.

Group F P Partial g2

Between chat conditions 35.286 0.000* 0.227 Between notetaking conditions 0.309 0.735 0.005 Chat � note-taking 5.938 0.003* 0.09

* p < .005.

Fig. 1. Significant interaction between notetaking and chatting conditions on classroom cognitive learning.

methods, the quality of their notes was not significantly different, F(1, 71) = .684, p > .05, whereas the online chatting condition sig- nificantly influenced students’ notetaking quality, F(1, 71) = 28.205, p < .001, g2 = .284. Thus, H3 was not supported but H4 was supported. Specifically, students who participated in the online chatting conditions recorded significantly lower quality of lecture notes than those who did not participate in the online chat- ting condition. The interaction effect was not significant, F(1, 71) = .565, p > .05 (see Tables 3 and 4), but the covariate, students’ sustained attention, was significant F(1, 71) = 7.517, p < .05, g2 = .096, meaning that students’ sustained attention during note- taking had a strong influence on their notetaking quality.

7. Discussion

Researchers (e.g., Crawford, 1925a, 1925b; Di Vesta & Gray, 1972, 1973) have discovered that students who took notes during a lecture tend to perform better in an immediate recall test than students who did not take notes. As a follow up, this study exam- ined the effect of notetaking methods on students’ recall perfor- mance but failed to observe an overall statistically significant difference in students’ immediate recall of lecture content as a function of three notetaking conditions. Such an unexpected result can be accounted for via at least two possible explanations. First, as Cluskey, Elbeck, Hill, and Strupect (2011) suggested, ‘‘Students have an attention span of around 15–20 min’’ (p. 4). When there is no classroom interference to interrupt their attention, it is possi- ble that students easily can sustain their attention to lecture con- tent for a short period of time and maintain sufficient attention to process that information. However, whether students can suc- cessfully sustain their attention and still remember the lecture materials over time is questionable. Second, with regard to the lack of difference between handwritten and computer-mediated notes on students’ cognitive learning, as Connelly, Gee, and Walsh (2007) pointed out, ‘‘as mechanical low level handwriting skills be- come fluent they have less impact on cognitive load and are less likely to constrain the expression of ideas in written text’’ (p. 481). More important, even though handwriting requires more motor process than does typing to form each character (Connelly et al., 2007), research (e.g., Connelly et al., 2007; Rogers & Case- Smith, 2002) has shown a positive relationship between handwrit- ing and keyboarding skills. When the undergraduate participants in the present study could perform handwriting and keyboarding skills fluently, taking notes either by hand or via a computer pro- duced little cognitive demands to sustain their attention as they,

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simultaneously, listened to the lecture. Although learning new information does require students to devote sustained attention to the content, both handwriting and typing behaviors seemed to be performed by participants habitually, with minimum effort. In line with the perspective that minimum attention is paid to per- forming such habitual motor skills (Shiffrin & Schneider, 1977), the results showed that notetaking methods (handwriting vs. typ- ing), per se, did not influence note quality when off-learning online disturbance was absent during the lecture.

In examining the main effect of no-chatting versus online chat- ting conditions, the results showed that students demonstrated a lower level of immediate recall of lecture content and note quality in online- rather than no-chatting conditions. As Cowan (2005) and Dehn (2008) noted, attention has a limited information processing capacity; thus, when participants were engaged in off-learning on- line chatting and took lecture notes simultaneously, online chat- ting, potentially, weakened their ability to sustain their attention on the content of the lecture. Repeated online chatting produced several irrelevant interruptions that led students to either experi- ence ‘‘information loss’’ or resulted in errors (switch costs) during their notetaking. Not only did the overloaded operation in atten- tion potentially lead to negative cognitive learning but off-learning content also increased the level of difficulty that students had pro- cessing two diverse data sets simultaneously (see Pashler, 1994). Hence, despite different notetaking conditions, students who were not involved in online chatting during their notetaking demon- strated a better classroom learning outcome and a higher level of note quality than did those students who engaged in online chat- ting when they listened to the lecture. This negative impact of off-learning chatting on students’ recall of lecture content was con- sistent with previous results (e.g., Kuznekoff & Titsworth, 2013; Wood et al., 2012).

In addition to significant differences in recall scores between students who chatted online and those who did not participate in any off-learning activity, the most interesting finding regarding classroom cognitive learning was the interaction between the type of notetaking and online chatting. Specifically, for students who did not take any notes during the lecture and chatted online about content unrelated to the lecture, their immediate recall of the lec- ture material demonstrated the smallest cognitive learning out- come, compared to the other experimental groups. One explanation for this finding is that notetaking might help students to sustain their attention to the content of lectures when their attention during the lecture was diverted to the irrelevant online activity. For students who did not take notes during the lecture, apparently, they did not have preventive means to block the irrel- evant off-task online behaviors during class. Hence, across all experimental groups in the online chatting condition, students who took notes, regardless of the method, showed a higher reten- tion rate than did students who did not take notes. It is worth not- ing that even though notetaking did not reflect significant encoding function when participants’ immediate recall scores were compared in the no-notetaking, handwritten notetaking, and com- puter-mediated notetaking conditions, when participants were distracted by online chatting, notetaking seemed to become an important strategy to remind students to sustain their learning attention over off-learning activities.

Furthermore, given online chatting interference during notetak- ing, one of the unanticipated interaction results was that students who took notes via computers demonstrated better recall than did those who took handwritten notes, meaning that students who used computers to take notes were the least negatively affected by online chatting interruptions, compared to students who either did not take or took handwritten notes. If online chatting condi- tions already have increased participants’ switching costs (task er- rors) during notetaking and then lower their quality of notes and

retention of information, a potential explanation for this unantici- pated interaction finding also may be due to students’ engage- ments in the rapid motor switches from handwriting on notepads to typing their chatting messages on computer key- boards. Indeed, students not only had to perform both learning and off-learning tasks at the expense of increasing their switching costs (task errors), as most studies indicated (see Wickens & McCarley, 2008), but they also may have had to physically experi- ence a motor delay between handwriting and typing, compared to students who used the same devices simply to perform both learn- ing and off-learning tasks. With respect to processing two diverse data sets (learning vs. off-learning content) simultaneously, appar- ently students’ off-learning multitask switching is disruptive to sustained attention; however, using a different electronic device to perform two motor processes (rapidly changing the physical modes from handwriting to typing back and forth) with a re- stricted time also may negatively influence cognitive learning (see Kuznekoff & Titsworth, 2013).

Furthermore, even though handwritten and computer-medi- ated notetaking conditions did not significantly influence students’ note quality, the results revealed that online chatting (off-learning content interruption) was the major reason why students could not accurately select (encode) and record the material presented in the lecture. Moreover, sustained attention was an important covariate that influenced notetaking quality in the present study, suggesting that if students can sustain their attention on lectures, they might have a greater possibility of taking higher quality notes.

Even though researchers have found a positive relationship be- tween students taking notes and their better performance in immediate recall tests (Crawford, 1925a, 1925b), little has been re- ported in the literature as to how students’ note quality is associ- ated with their learning performance. The results from the present study suggest that a positive relationship may exist be- tween note quality and cognitive learning, but that relationship needs to be interpreted with caution. Kiewra’s (1985) distinction of the process function and product function of notetaking sug- gested a gap between notetaking and students’ learning outcome, with the process function helping with the encoding of informa- tion, which, in turn, may facilitate immediate recall of the pro- cessed information. However, the extent to which immediate recall may help with cognitive learning outcome over time is sub- ject to the product function, such as note reviewing (Carter & Van Matre, 1975), study strategies (Annis & Annis, 1982), and cognitive styles (Annis & Davis, 1978). Further research on possible mediat- ing roles of the product function may explain the chronological effect of note quality on students’ cognitive learning outcome.

7.1. Implications

The results of this study showed that computer-mediated note- taking did not necessarily lower immediate recall and note quality; however, chatting about off-learning content online did have a negative effect on students’ information processing during lecture. If internet access is a persistent problem that interrupts classroom teaching, teachers may request network services to be temporarily disconnected in a specific location for a certain period of time. However, if teachers seek to integrate students’ online access via electronic devices as a type of classroom activity in certain courses (e.g., social media), it is important to allow students to have suffi- cient time to switch between activities. Practically, it might be too difficult for students to concentrate on lectures and engage in on- line discussion simultaneously. Even though certain students might be interested in using an electronic device to communicate with peers during class as they listen to lectures, multitasks that consume more of the limited amount of attentional resources may not produce the best learning outcomes.

F.-Y.F. Wei et al. / Computers in Human Behavior 34 (2014) 148–156 155

7.2. Limitation and future studies

Although important findings were obtained in this study, those findings need to be interpreted in light of at least four limitations. First, the 10-min learning task employed in this experiment may not be equivalent to students’ 50-min learning in a genuine class- room. However, our finding is similar to the result from previous researchers (e.g., Wood et al., 2012) who measured students’ note- taking in a classroom setting. Second, in line with the process ap- proach of notetaking, this experimental study was not designed to allow prediction of students’ recall of content over time; hence, it is important for future research to extend the theoretical and methodological scopes by employing the product approach using a delayed recall test. Third, the chatting tasks assigned in the study tended to be specific off-learning topic. What remains unknown is whether assigned topic in relation to lecture content would influ- ence cognitive learning. Finally, notes are best used to predict col- lege students’ performance on essays and multiple-choice tests (Peverly & Sumowski, 2012); the multiple-choice questions in the present study were unable to assess higher levels of cognitive processing such as analytic and problem solving skills. Future stud- ies should examine whether notetaking strengthens college stu- dents’ ability to develop a higher level of cognitive learning as demonstrated in the writing of an essay.

8. Conclusion

This study was conducted to determine whether students’ note- taking and online chatting can influence their recalls of lecture content and note quality. Not surprisingly, students who did not participate in off-learning online chatting during lecture demon- strated better recall of lecture content and took higher quality notes than did students who engaged in off-learning chatting. Additionally, students who engaged in off-learning online chatting with an absence of notetaking behavior demonstrated the worst cognitive learning outcomes. Even though notetaking may not be the only method that enhances students’ immediate recall of infor- mation, the experimental results revealed that notetaking, poten- tially, helped college students to sustain their attention on the lecture, especially when online interferences occurred to shift stu- dents’ attention away from the lecture.

Although laptops or tablets have become popular notetaking de- vices used by millennial college students during lecture, the dilem- ma is that banning that technology might limit off-learning activities during class; however, forcing students to restrict their dexterity of operating notetaking devices during class may limit their opportunities to prepare for a ‘‘paperless’’ work environment. Thus, the findings from this study regarding the learning effects of using such devices does not deny the possibility of performing mul- titasking behaviors or lead to banning that technology for class- room applications; instead, the findings imply that engaging in off-learning online chatting and listening to lectures simulta- neously can decrease the quality of students’ notes and, subse- quently, their recall of lecture content. Therefore, to increase the possibility of encoding and recalling lecture content effectively, stu- dents should avoid engaging in off-learning online communication.

Aside from the switching costs (errors or prolonged time during multiple switches among different tasks), there might be an under- estimated cost of motor switching from handwriting mode to typ- ing via computers. The rapid change of motor modes within a short period of time also may force students to delay their responses to record the information accurately from the lecture. Hence, reduc- ing unnecessary rapid task switching, such as blocking off-learning chatting for hand notetakers during lecture, may enhance students’ cognitive learning. Future research is necessary to examine the

impact from both switching costs and motor switching on the quality of students’ handwritten notes. Researchers should also consider students’ notetaking abilities (e.g., experienced and inex- perienced) as a factor that may influence switching costs and mo- tor switching, and, in turn, to have an impact on cognitive learning.

Acknowledgements

The authors extend their appreciation to the anonymous reviewers for their suggestions and thank Dr. Lawrence R. Frey’s assistance with proofreading the manuscript.

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