summary

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Laptop multitasking hinders classroom learning for both users and nearby peers

Faria Sana a, Tina Weston b,c, Nicholas J. Cepeda b,c,*
a McMaster University, Department of Psychology, Neuroscience, & Behaviour, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada
b York University, Department of Psychology, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
c York University, LaMarsh Centre for Child and Youth Research, 4700 Keele Street, Toronto, ON M3J 1P3, Canada

a r t i c l e i n f o

Article history:
Received 11 September 2012
Received in revised form
5 October 2012
Accepted 12 October 2012

Keywords:
Laptops
Multitasking
Attentional control
Pedagogy

a b s t r a c t

Laptops are commonplace in university classrooms. In light of cognitive psychology theory on costs
associated with multitasking, we examined the effects of in-class laptop use on student learning in
a simulated classroom. We found that participants who multitasked on a laptop during a lecture scored
lower on a test compared to those who did not multitask, and participants who were in direct view of
a multitasking peer scored lower on a test compared to those who were not. The results demonstrate that
multitasking on a laptop poses a significant distraction to both users and fellow students and can be
detrimental to comprehension of lecture content.

! 2012 Elsevier Ltd.

1. Introduction

Multitasking is ingrained in our daily lives. As you read this article, you may also be attending to a text message, sipping coffee, or writing
out a list of to-dos. Such a lifestyle is intended to increase efficiency; however, there are limitations to how well multiple tasks can be carried
out concurrently (Posner, 1982). Multitasking places considerable demands on cognitive resources, which, in turn, degrades overall
performance, as well as performance on each task in isolation (Broadbent,1958). The issue of multitasking and its consequences has become
a growing concern in education, as students are more commonly found engaged with their laptops or smartphones during class time. The
current study investigated the effect of laptop multitasking on both users and nearby peers in a classroom setting.

There is a host of theoretical and experimental research on divided attention and dual-task interference, terms that we consider
homologous to multitasking and therefore relevant to the current discussion. Research suggests that we have limited resources available to
attend to, process, encode, and store information for later retrieval (Posner, 1982). When focused on a single primary task, our attentional
resources are well directed and uninterrupted, and information is adequately processed, encoded, and stored (Naveh-Benjamin, Craik,
Perretta, & Tonev, 2000). When we add a secondary task, attention must be divided, and processing of incoming information becomes
fragmented. As a result, encoding is disrupted, and this reduces the quantity and quality of information that is stored (Pashler, 1994). When
we eventually retrieve information that was processed without interruptions, as a primary task, we are likely to experience minimal errors.
When we retrieve information that was processed via multitasking or with significant interruptions from a secondary task, we are more
likely to experience some form of performance decrement (Wickens & Hollands, 2000).

Indeed, managing two or more tasks at one time requires a great deal of attention. Attentional resources are not infinite (Konig, Buhner, &
Murling, 2005; Pashler, 1994). When the level of available attentional resources is less than what is required to complete two simultaneous
tasks, performance decrements are experienced since both tasks are competing for the same limited resources. This is especially true if both
tasks are competing for resources within the same sensory modality (Navon & Gopher, 1979; Wickens, 2002; Wickens & Hollands, 2000).
Limits to attentional resources means the quality (accuracy) and efficiency (reaction time) at which multiple tasks are processed will be
compromised (Rubinstein, Meyer, & Evans, 2001). Numerous experimental studies have shown performance decrements under conditions
of multitasking or divided attention (e.g., Broadbent, 1958; Tulving & Thomson, 1973).

* Corresponding author. York University, LaMarsh Centre for Child and Youth Research, 4700 Keele Street, Toronto, ON M3J 1P3, Canada. Tel.: þ1 416 736 2100×33266;
fax: 1 416 736 5814.

E-mail addresses: sanaf@mcmaster.ca (F. Sana), westont@yorku.ca (T. Weston), ncepeda@yorku.ca (N.J. Cepeda).

Contents lists available at SciVerse ScienceDirect

Computers & Education

journal homepage: www.elsevier.com/locate/compedu

0360-1315 ! 2012 Elsevier Ltd.
http://dx.doi.org/10.1016/j.compedu.2012.10.003

Computers & Education 62 (2013) 24–31

Open access under CC BY-NC-ND license.

Open access under CC BY-NC-ND license.

Delta:1_surname

mailto:sanaf@mcmaster.ca

mailto:westont@yorku.ca

mailto:ncepeda@yorku.ca

www.sciencedirect.com/science/journal/03601315

http://www.elsevier.com/locate/compedu

http://dx.doi.org/10.1016/j.compedu.2012.10.003

http://dx.doi.org/10.1016/j.compedu.2012.10.003

http://creativecommons.org/licenses/by-nc-nd/3.0/

http://creativecommons.org/licenses/by-nc-nd/3.0/

emyil LU
笔记本电脑在大学教室里很常见。根据认知心理学关于多任务相关成本的理论，我们在模拟教室中研究了课堂内使用笔记本电脑对学生学习的影响。我们发现，在一次讲座中在笔记本电脑上进行多任务处理的参与者在一次测试中的得分低于没有进行多任务处理的参与者，而直接面对多任务处理同伴的参与者在一次测试中的得分低于没有进行多任务处理的参与者。研究结果表明，笔记本电脑上的多任务处理对用户和同学来说都是一个很大的干扰，并且可能会对课堂内容的理解造成损害。
一。介绍
多任务处理在我们的日常生活中根深蒂固。当你读到这篇文章的时候，你可能也在处理一条短信，喝咖啡，或者写一份待办事项清单。这样的生活方式旨在提高效率；然而，多个任务同时执行的程度是有限制的（波斯纳，1982）。多任务处理对认知资源提出了相当大的要求，反过来，认知资源又会降低总体性能，同时也会降低单独执行每个任务的性能（Broadbent，1958）。多任务处理问题及其后果已经成为教育界日益关注的问题，因为学生在上课时间更常使用笔记本电脑或智能手机。目前的研究调查了在教室环境中，笔记本电脑多任务处理对用户和附近同龄人的影响。
关于注意力分散和双重任务干扰，有大量的理论和实验研究，我们认为这些术语与多任务是同源的，因此与当前的讨论有关。研究表明，我们可以处理、处理、编码和存储信息以供以后检索的资源是有限的（Posner，1982）。当我们专注于一个单一的主要任务时，我们的注意力资源得到了很好的引导和不间断的处理，信息得到了充分的处理、编码和存储（Naveh Benjamin、Craik、Perretta和Tonev，2000）。当我们添加第二个任务时，必须分散注意力，对传入信息的处理变得支离破碎。结果，编码被中断，这降低了存储信息的数量和质量（Pashler，1994）。当我们最终检索在没有中断的情况下处理过的信息时，作为主要任务，我们可能会遇到最小的错误。当我们从次要任务中检索通过多任务处理或有重大中断的信息时，我们更可能会经历某种形式的性能下降（Wickens&Hollands，2000）。
实际上，一次管理两个或多个任务需要大量的关注。注意资源不是无限的（Konig，Buhner，&Murling，2005；Pashler，1994）。当可用注意力资源的水平低于完成两个同时任务所需的水平时，由于两个任务都在争夺相同的有限资源，因此会出现性能下降。如果两个任务都在同一感官模式下竞争资源，这一点尤其正确（Navon&Gopher，1979；Wickens，2002；Wickens&Hollands，2000）。注意资源的限制意味着处理多个任务的质量（准确性）和效率（反应时间）将受到损害（Rubinstein，Meyer，&Evans，2001）。大量的实验研究表明，在多任务或注意力分散的情况下，表现会下降（例如，Broadbent，1958；Tulving&Thomson，1973）。

Theoretical and empirical findings on multitasking are especially significant when considered in the context of student learning. In
classroom environments, students tend to switch back and forth between academic and non-academic tasks (Fried, 2008). This behavior
poses concerns for learning. The presumed primary tasks in many university classes are to listen to a lecture, consolidate information spoken
by the instructor and presented on information slides, take notes, and ask or respond to questions. On their own, these activities require
effort. If a secondary task is introduced, particularly one that is irrelevant to the learning context, attention must shift back and forth
between primary and secondary tasks, thereby taxing attentional resources. This multitasking can result in weaker encoding of primary
information into long-term memory (Bailey & Konstan, 2006; Ophira, Nass, & Wagner, 2009).

The personal computer provides a compelling source of classroom distraction and has become commonplace on university campuses.
Survey data estimates that 99% of incoming freshmen own a laptop (University of Virginia, 2009) and about 65% of students bring their laptop
to class (Fried, 2008). Research on educational laptop use addresses both the pros and cons of using this technology in the classroom. On the
one hand, laptops have been shown to assist learning through active approaches to teaching (Finn & Inman, 2004) and promotion of academic
success (Lindorth & Bergquist, 2010; Weaver & Nilson, 2005). When used for academic purposes such as taking notes and using software
programs (Driver, 2002), accessing supplemental resources and web-based activities (Debevec, Shih, & Kashyap, 2006), and viewing Power-
Point slides (McVay, Snyder, & Graetz, 2005), in-class laptop use can increase satisfaction, motivation, and engagement among students (Fried,
2008; Hyden, 2005; Weaver & Nilson, 2005). On the other hand, studies suggest that students who use laptops in class report low satisfaction
with their education, are more likely to multitask in class, and are more distracted (Wurst, Smarkola, & Gaffney, 2008). Student self-reports and
classroom observations suggest that laptops are being used for non-academic purposes, such as instant messaging and playing games (Barak,
Lipson, & Lerman, 2006; Driver, 2002), checking email and watching movies (Finn & Inman, 2004), and browsing the Internet (Bugeja, 2007).
Access to online entertainment makes it increasingly difficult for instructors to be “more interesting than YouTube” (Associated Press, 2010,
p. 10), especially if students aren’t intrinsically motivated by the subject materials. Moreover, time spent multitasking with these activities is
significant; data from one study estimates that students multitask for approximately 42% of class time (Kraushaar & Novak, 2010).

Importantly, distractions from in-class multitasking correlate with decrements in learning. Students who multitask on laptops during
class time have impaired comprehension of course material and poorer overall course performance (Barak et al., 2006; Hembrooke & Gay,
2003; Kraushaar & Novak, 2010). In a recent study, Wood et al. (2012) measured the detriments of technology-based multitasking in
a classroom setting. This study is one of few in the field that employed an experimental design (much of the literature is self-report). Students
were assigned either to a single multitasking condition (using Facebook, MSN, email, or cell phone texting), a control group (paper and pencil
notes-only or word processing notes-only), or a free-use-of-technology condition (where participants could choose to multitask or not
multitask on their laptop as much as they wished). Over the course of three class lectures, participants’ comprehension of the material was
assessed via quizzes. In general, paper and pencil control participants outperformed multitasking participants on the quiz assessments
(particularly MSN and Facebook users). However, as the authors admit, there were limitations to the methodology of this study. Most
noteworthy was that 43% of participants self-reported that they did not adhere to their assigned instructions across all three lectures. For
example, a participant assigned to the Facebook multitasking condition may have multitasked on Facebook and on MSN (i.e., two forms of
multitasking when they were instructed only to use one form), or chosen not to multitask on Facebook at all. Therefore, the experimental
manipulation was not successful, calling into question the validity of the quiz data. Although the authors corrected for this limitation in post-
hoc analyses, the results should be interpreted with caution as they are reliant on self-report and the sample size of each group was
significantly reduced. Wood et al.’s findings are relevant but restricted in terms of the pedagogical recommendations that can be offered. One
goal of the present study was to replicate the findings of Wood et al. using a more controlled design and more stringent fidelity measures.

Disrupting one’s own learning is an individual choice; harming the learning of other students in the class is disrespectful. Laptop
distractions due to movement of images and laptop screen lighting (Melerdiercks, 2005) and multitasking activities (Crook & Barrowcliff,
2001) may cause involuntary shifts of attention among students in close proximity to laptop users (Barak et al., 2006; Chun & Wolfe,
2001; Finn & Inman, 2004). These studies suggest that students are annoyed and distracted by laptop use. However, to our knowledge,
no studies have directly measured the effects of distraction caused by laptop users on surrounding peers’ learning. Therefore, a second goal
of the present study was to examine the indirect effects of laptop multitasking on student learning.

2. Experiment 1

In Experiment 1, we investigated whether multitasking on a laptop would hinder learning as measured by performance on a compre-
hension test. All participants were asked to attend to a university-style lecture and take notes using their laptops as a primary task. Half the
participants, by random assignment, received additional instructions to complete a series of non-lecture-related online tasks at any
convenient point during the lecture. These tasks were considered secondary and were meant to mimic typical student web browsing during
class in terms of both quality and quantity. We hypothesized that participants who multitasked while attending to the lecture would have
lower comprehension scores compared to participants who did not multitask.

2.1. Method

2.1.1. Participants
Forty-four undergraduate students from a large comprehensive university in a large Canadian city participated in the study (25 females;

M age ¼ 18.9 years, SD ¼ 2.0). All participants were enrolled in an Introductory Psychology course and received course credit for partici-
pating in the experiment. Participants represented a variety of undergraduate disciplines (i.e., not only psychology). They were recruited
using an online portal designed for psychology research, which explained that the study involved listening to a class lecture and filling out
a few questionnaires. Only students who could bring a personal laptop to the experiment were invited to participate. Forty participants were
included in the final data analysis, which included two experimental conditions: multitasking (n ¼ 20) and no multitasking (n ¼ 20). Of the
four participants removed from the analysis, two had previous knowledge of the lecture content (as measured by a screening questionnaire),
one performed below chance on the comprehension test, and one failed to follow instructions. The former two participants were removed
from the no multitasking condition, and the latter two participants were removed from the multitasking condition.

F. Sana et al. / Computers & Education 62 (2013) 24–31 25

emyil LU
从学生学习的角度来看，关于多任务的理论和经验发现尤其重要。在课堂环境中，学生倾向于在学术和非学术任务之间来回切换（Fried，2008）。这种行为引起了对学习的关注。在许多大学课堂上，假定的主要任务是听讲座，巩固讲师所讲的信息，并在信息幻灯片上展示，做笔记，提出或回答问题。这些活动本身就需要努力。如果引入次要任务，特别是与学习上下文无关的任务，则注意力必须在主任务和次要任务之间来回移动，从而征税注意力资源。这种多任务处理会导致初级信息在长期记忆中的编码减弱（Bailey&Konstan，2006；Ophira，Nass，&Wagner，2009）。
个人电脑提供了一个令人信服的来源，教室分心，并已成为常见的大学校园。调查数据估计99%的新生拥有笔记本电脑（弗吉尼亚大学，2009年），约65%的学生带着笔记本电脑来上课（Fried，2008年）。关于教育用笔记本电脑的研究既解决了在课堂上使用这项技术的利弊。一方面，笔记本电脑通过积极的教学方法（Finn&Inman，2004）和促进学术成功来帮助学习（Lindorth&Bergquist，2010；Weaver&Nilson，2005）。当用于学术目的时，例如记笔记和使用软件程序（Driver，2002），访问补充资源和基于网络的活动（Debevec，Shih，&Kashyap，2006），以及观看Power-Point幻灯片（McVay，Snyder，&Graetz，2005），课堂上使用笔记本电脑可以提高满意度和动力，以及学生之间的参与（Fried，2008；Hyden，2005；Weaver&Nilson，2005）。另一方面，研究表明，在课堂上使用笔记本电脑的学生对自己的教育满意度较低，更有可能在课堂上进行多任务处理，而且更容易分心（Wurst、Smarkola和Gaffney，2008）。学生自我报告和课堂观察表明，笔记本电脑被用于非学术目的，例如即时信息和玩游戏（Barak，Lipson，&Lerman，2006；Driver，2002）、查看电子邮件和观看电影（Finn&Inman，2004）以及浏览互联网（Bugeja，2007）。网络娱乐使得教师变得越来越难“比YouTube更有趣”（美联社，2010年，第10页），尤其是如果学生不是天生受主题材料的激励。此外，使用这些活动的多任务处理时间是显著的；来自一项研究的数据估计学生的任务约占课堂时间的42%（K劳什哈尔和诺瓦克，2010）。
重要的是，课堂上多任务的分心与学习能力的下降有关。上课时间在笔记本电脑上进行多任务处理的学生对课程材料的理解能力受损，整体课程表现较差（Barak等人，2006年；Hembrooke&Gay，2003年；Kraushaar&Novak，2010年）。在最近的一项研究中，伍德等人。（2012）衡量了课堂环境中基于技术的多任务处理的危害。这项研究是该领域少数采用实验设计的研究之一（大部分文献是自报告的）。学生被分配到一个单独的多任务状态（使用Facebook、MSN、电子邮件或手机短信）、一个控制组（仅纸条和铅笔笔记或文字处理笔记）或一个免费的技术使用状态（参与者可以选择在笔记本电脑上执行多任务或不执行多任务）。在三堂课的过程中，参与者对材料的理解通过测验来评估。一般来说，纸笔控制的参与者在测验评估中的表现优于多任务参与者（尤其是MSN和Facebook用户）。然而，正如作者所承认的，这项研究的方法有局限性。最值得注意的是，43%的受试者自我报告说，他们在所有三次讲座中都没有遵守指定的指导。例如，分配到Facebook多任务条件的参与者可能在Facebook和MSN上有多任务（即，当他们被指示只使用一个表单时，有两种多任务形式），或者选择根本不在Facebook上执行多任务。因此，实验操作并不成功，这就对测验数据的有效性提出了质疑。尽管作者在事后分析中纠正了这一局限性，但结果应谨慎解释，因为它们依赖于自我报告，并且每组的样本量显著减少。伍德等人的发现是相关的，但就可以提供的教学建议而言，这一发现受到了限制。本研究的一个目标是复制伍德等人的发现。采用更可控的设计和更严格的保真度测量。
扰乱自己的学习是个人的选择；损害班上其他学生的学习是不尊重的。笔记本电脑分散注意力，由于移动的图像和笔记本电脑屏幕照明（MeLedieCKS，2005）和多任务活动（克鲁克和BARROKLIFY，2001）可能导致无意间转移注意力的学生之间的接近笔记本电脑用户（Barak等人，2006；春和沃尔夫，2001；芬恩和英曼，2004）。这些研究表明，学生对使用笔记本电脑感到恼火和分心。然而，据我们所知，还没有研究直接测量笔记本电脑用户分心对周围同伴学习的影响。因此，本研究的第二个目标是研究笔记本电脑多任务处理对学生学习的间接影响。
2。实验1
在实验1中，我们研究了笔记本电脑上的多任务是否会阻碍学习，这是通过综合测试的表现来衡量的。所有参与者都被要求参加一个大学式的讲座，并以笔记本电脑为主要任务做笔记。一半的参与者，通过随机分配，在讲座期间的任何方便的时间点，收到了完成一系列与讲座无关的在线任务的额外指示。这些任务被认为是次要的，旨在从质量和数量上模拟课堂上典型的学生网络浏览。我们假设参加讲座的多任务参与者与没有多任务的参与者相比，理解分数更低。
2.1条。方法
2.1.1条。参与者
来自加拿大大城市一所大型综合性大学的44名本科生（25名女性；M年龄1/4 18.9岁，SD 1/4 2.0）参加了该研究。所有参与者都参加了心理学入门课程，并获得了参与实验的学分。参与者代表各种本科学科（即，不仅是心理学）。他们是使用一个为心理学研究设计的在线门户网站招募的，该网站解释说，这项研究包括听一堂课堂讲座和填写一些问卷。只有能带个人笔记本电脑参加实验的学生才被邀请参加。40名参与者被纳入最终数据分析，其中包括两个实验条件：多任务（n 1/420）和无多任务（n1/420）。从分析中剔除的四名受试者中，有两名之前对课程内容有所了解（通过筛选问卷进行测量），一名在理解测试中的表现低于预期，还有一名未能按照说明进行。前两名参与者被从无多任务状态中移除，后两名参与者被从多任务状态中移除。

2.1.2. Materials
A 45-min PowerPoint lecture on meteorology was created by one of the experimenters (TW) in conjunction with her peer colleagues and

a faculty member with many years of teaching experience. The lecture was based on topics taken from an introductory meteorology
textbook (Ahrens, 1999; e.g., discriminating cloud types, pressure systems, thunderstorm development). A second faculty member with
expertise in Geology and Earth Sciences reviewed the lecture and approved the accuracy of its content, level of difficulty, and consistency
with materials presented in related undergraduate classes (S. Carey, personal communication, August 16, 2012). The same experimenter
(TW), an upper-level graduate student with lecturing experience, acted as the “professor” and presented the lecture live to the class. At the
time of data collection, TW had practiced and given this lecture over a dozen times for an independent study. She followed a memorized
script.

For the multitasking condition, a set of 12 online tasks was created. An example of a task was “What is on Channel 3 tonight at 10 pm?”
These online tasks were meant to mimic typical student browsing during class in terms of both quality (i.e., visiting websites of interest to
a young adult sample, such as Google, YouTube, and Facebook) and quantity [w40% of class time, as suggested by Kraushaar and Novak
(2010)]. A pilot study (n ¼ 5), confirmed that completion of the tasks was not overwhelming; tasks could be completed in w15 min (or
33% of the lecture time). Although we do not know if the participants of our study multitask more or less than 33% during lectures in a real-
world setting, the online tasks were within the critical range of previously reported time spent on multitasking in real-world classrooms
(w40%), and therefore unlikely to artificially increase the costs of multitasking.

The primary measure of learning was a four-option multiple-choice comprehension test with 20 questions evaluating simple knowledge
(i.e., basic retention of facts from the lecture) and 20 questions evaluating application of knowledge (i.e., applying a concept from the lecture
to solve a novel problem). Question type was included as variable to examine whether multitasking outcomes might differ depending on the
difficulty level of the material being tested. There is some evidence to suggest that multitasking may be particularly detrimental to complex
knowledge (e.g., Foerde, Knowlton, & Poldrack, 2006). The ordering of the questions on the test (simple vs. complex) was intermixed.

Participants also completed a brief questionnaire that collected demographic data (e.g., age, gender, and fluency in English) and screened
participants for prior familiarity with the lecture content and general interest in the lecture presentation. Additionally, there were two
questions, both listed on a 7-point Likert scale, directed toward participants in the multitasking condition: (1) To what extent do you think
the act of multitasking hindered your learning of the lecture material? (1 ¼ did not hinder my learning; 4 ¼ somewhat hindered my
learning; 7 ¼ definitely hindered my learning) and (2) To what extent do you think your multitasking hindered the learning experience of
other students? (1 ¼ did not hinder others’ learning; 4 ¼ somewhat hindered others’ learning; 7 ¼ definitely hindered others’ learning).
Responses to these questions allowed us to measure subjective student views on multitasking outcomes.

2.1.3. Design and procedure
All participants were asked to bring their personal laptops to the experiment. They received an instruction sheet and a consent form. The

instruction sheet asked participants to attend to the lecture and use their personal laptop to take notes on the information being presented,
just as they might normally do in class. In addition to taking lecture notes, half of the participants (randomly selected) were instructed to
complete the 12 online tasks at some point during the lecture.

The experiment was conducted in a classroom with four rows of tables, each with five chairs. Therefore, since there was a maximum of 20
seats, we repeated the experiment three times to obtain a total sample of 44 participants [each repeat included roughly the same number of
total participants (range: 14–15), and an equal divide of participants within the two experimental conditions]. Participants faced a projector
screen at the front of the classroom. Instruction sheets were randomly placed at each seat. Thus, seat location of participants in each
condition was fully random. Participants were randomly presented with a seat number as they entered the classroom and were asked to
settle in and read the instruction sheet and consent form at their assigned seat. While all participants were instructed to take notes on their
laptops during the lecture, some were also required to complete the series of online tasks. An experimenter (FS) remained at the back of the
classroom during the lecture presentation and used a seating map to track participants’ seat location, monitor participants’ screen activities,
and ensure that all instructions were being followed. At the end of the lecture, participants were asked to email their notes and (if
applicable) their responses to the online tasks to the experimenters and, finally, to put away their laptops. The comprehension test
immediately followed and a 30 min time limit was enforced (as time limits are realistic of typical university examinations). All participants
completed the test within the time limit. Once the experimenters collected all the tests, participants responded to the questionnaire, were
debriefed and dismissed.

2.1.4. Fidelity measures
FS closely monitored participants’ activities throughout the lecture presentation, observing each participant’s activities at least once

every 3–4 min interval. This was done to ensure that all participants adhered to their assigned instructions. If a participant was not on task
(e.g., a non-multitasker engaging in multitasking activities, a multitasker browsing on a website irrelevant to the online tasks, or a multi-
tasker completely ignoring the online tasks), they were probed once and reminded of their specific instructions. If a participant was probed
more than 2 times, their data were discarded from the final analysis (n ¼ 1).

Participants’ notes and online task answers were analyzed for completion and quality. In terms of the online tasks, all multitaskers attempted
to answer at least some of the tasks. On average, multitaskers successfully completed 9 out of the 12 tasks (M ¼ 0.75; SD ¼ 0.25). In terms of
participants’ notes, all participants took some form of notes on the lecture content. Notes were scored for quality (1–5) by the experimenter
most familiar with the material (TW). She was blind to participants’ condition while scoring. A score of 1 meant the participant attempted to
copy the lecture slides verbatim, but the notes were disorganized and/or missing information. A score of 3 meant the participant copied the
lecture slides verbatim, but did not include additional information presented verbally by the lecturer. A score of 5 meant the participant copied
all slide information and included all information presented verbally by the lecturer. Analysis of the quality scores revealed that multitaskers’
notes (M ¼ 2.7, SD ¼ 1.2) were of a poorer quality than non-multitaskers’ notes (M ¼ 4.1, SD ¼ 1.0), t(34) ¼ 3.6, p ¼ .001, u2 ¼ .23.

Therefore, our fidelity measures (i.e., participant monitoring, discarded data, analysis of notes and online tasks files) clearly show that
participants stayed on task throughout the experiment. It is evident that multitasking played a role in impairing participants’ note-taking
ability.

F. Sana et al. / Computers & Education 62 (2013) 24–3126

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2.1.2条。材料
其中一位实验者（TW）和她的同事以及一位有多年教学经验的教职员工共同制作了一个45分钟的气象学PowerPoint讲座。讲座的主题取自气象学入门教材（Ahrens，1999；例如，区分云类型、压力系统、雷暴发展）。第二位具有地质学和地球科学专业知识的教师对讲座进行了回顾，并对其内容的准确性、难度水平以及与相关本科课程中所提供材料的一致性进行了认可（S.Carey，《个人沟通》，2012年8月16日）。同一名实验者（TW）是一名有授课经验的高级研究生，他担任“教授”，并将讲座现场呈现给全班同学。在收集资料的时候，TW已经为一项独立的研究做了十多次的练习和演讲。她遵循一个熟记的剧本。
对于多任务情况，创建了一组12个联机任务。一个任务的例子是“今晚10点3频道有什么节目？“这些在线任务旨在模拟典型的学生在课堂上浏览的质量（即访问年轻人感兴趣的网站，如谷歌、YouTube和Facebook）和数量（Kraushaar和Novak（2010年）建议的课堂时间的40%）。一项试点研究（n 1/4 5）证实，任务的完成不是压倒性的；任务可以在15分钟内完成（或33%的授课时间）。尽管我们不知道在真实世界的课堂上，我们研究的参与者多任务处理的比例是否在33%以上，但在线任务在之前报道的真实课堂多任务处理时间（w40%）的临界范围内，因此不太可能人为地增加多任务处理的成本。
学习的主要衡量标准是一个四选项的多项选择理解测试，其中20个问题评估简单知识（即基本保留讲座中的事实）和20个问题评估知识的应用（即应用讲座中的概念解决新问题）。问题类型作为变量被包括进来，以检验多任务处理的结果是否会因被测材料的难度水平而不同。有证据表明，多任务可能对复杂知识特别有害（例如，Foerde、Knowlton和Poldrack，2006）。测试中问题的顺序（简单与复杂）是混合的。
参与者还完成了一份简短的问卷调查，收集了人口统计数据（例如，年龄、性别和英语流利程度），并筛选了参与者之前对演讲内容的熟悉程度和对演讲的总体兴趣。此外，还有两个问题，都列在7分利克特量表上，针对多任务情况下的参与者：（1）你认为多任务行为在多大程度上阻碍了你对讲稿的学习？（1 1/4没有妨碍我的学习；4 1/4在一定程度上妨碍了我的学习；7 1/4肯定妨碍了我的学习）和（2）你认为你的多任务处理在多大程度上妨碍了其他学生的学习体验？（1 1/4不妨碍他人学习；4 1/4在一定程度上妨碍他人学习；7 1/4肯定妨碍他人学习）。对这些问题的回答使我们能够衡量学生对多任务结果的主观看法。
2.1.3条。设计和程序
所有参与者都被要求带上他们的个人笔记本电脑参加实验。他们收到了一份说明书和一份同意书。教学表要求参与者参加讲座，并使用他们的个人笔记本电脑记录所呈现的信息，就像他们通常在课堂上做的那样。除了做课堂笔记，一半的参与者（随机选择）被要求在课堂上的某个时间点完成12项在线任务。
实验是在一间教室里进行的，教室里有四排桌子，每排有五把椅子。因此，由于有最多20个座位，我们重复实验三次，以获得44名参与者的总样本[每个重复包括大致相同数量的总参与者（范围：14至15），以及在两个实验条件下均等的参与者的分界线]。参与者面对着教室前面的投影仪屏幕。在每个座位上随机放置说明书。因此，在每种情况下，参与者的座位位置都是完全随机的。参与者在进入教室时随机获得一个座位号，要求他们在指定的座位上安顿下来并阅读教学表和同意书。虽然所有的参与者都被要求在讲座中用笔记本电脑做笔记，但也有一些人被要求完成一系列的在线任务。一名实验者（FS）在演讲期间留在教室后面，并使用座位图跟踪参与者的座位位置，监控参与者的屏幕活动，并确保所有指示都得到遵守。在讲座结束时，参与者被要求将他们的笔记和（如果适用的话）他们对在线任务的回应发送给实验者，最后，把他们的笔记本电脑收起来。紧接着进行了理解测试，并强制执行了30分钟的时间限制（因为时间限制对于典型的大学考试来说是现实的）。所有参与者都在规定的时间内完成了测试。一旦实验者收集了所有的测试，参与者回答了问卷，听取了汇报，并被解雇。
2.1.4条。忠实度
四季酒店在演讲过程中密切监控参与者的活动，每3-4分钟至少观察一次参与者的活动。这样做是为了确保所有参与者都遵守他们指定的指示。如果参与者没有执行任务（例如，一个非多任务者从事多任务活动，一个多任务者浏览与在线任务无关的网站，或者一个多任务者完全忽略在线任务），他们会被探测一次并被提醒他们的特定指令。如果参与者被调查超过2次，则他们的数据将从最终分析中丢弃（n 1/41）。
分析参与者的笔记和在线任务答案的完成情况和质量。就在线任务而言，所有多任务处理者都试图回答至少一些任务。平均而言，多任务者成功完成了12项任务中的9项（M 1/40.75；sd1/40.25）。就参与者的笔记而言，所有参与者都对讲座内容做了某种形式的笔记。笔记的质量（1-5）由最熟悉材料的实验者（TW）评分。她在得分时对参与者的情况视而不见。分数为1表示参与者试图逐字复制演讲幻灯片，但笔记杂乱无章和/或缺少信息。分数为3意味着参与者逐字复制了演讲幻灯片，但不包括讲师口头提供的附加信息。5分意味着参与者复制了所有幻灯片信息，并包含了讲师口头陈述的所有信息。对质量分数的分析显示，多任务者的笔记（M 1/42.7，SD 1/41.2）质量比非多任务者的笔记（M 1/44.1，SD 1/41.0），t（34）1/43.6，p 1/4.001，U21/4.23）差。
因此，我们的忠诚度测量（即参与者监控、丢弃的数据、笔记分析和在线任务文件）清楚地表明，参与者在整个实验过程中都在执行任务。很明显，多任务处理在削弱参与者记笔记的能力方面起到了一定的作用。

2.1.5. Results and discussion
There were no demographic differences between participants of the two conditions in terms of age, gender, fluency in English, or high

school GPA. To examine potential differences between conditions on the comprehension test, a 2 (condition: multitasking, no multi-
tasking) # 2 (question type: simple, complex) mixed factorial ANOVA was conducted with condition as a between-subjects factor and
question type as a within-subjects factor. The main effect of condition was significant, F(1,38) ¼ 10.2, p ¼ .003, u2 ¼ .20. Participants who
multitasked during the lecture (M ¼ 0.55, SD ¼ 0.11, n ¼ 20) scored significantly lower than participants who did not multitask (M ¼ 0.66,
SD ¼ 0.12, n ¼ 20). The main effect of question type was also significant, F(1,38) ¼ 17.7, p < .001, u2 ¼ .30. Participants scored higher on simple factual questions (M ¼ 0.60, SD ¼ 0.13, n ¼ 20) than on complex apply-your-knowledge questions (M ¼ 0.56, SD ¼ 0.13, n ¼ 20). This main effect simply reflects the difficulty of the questions created. The interaction was not significant, F(1,38) ¼ 0.79, p ¼ .380. These findings demonstrate a strong, detrimental effect of multitasking on comprehension scores. Overall, participants who multitasked scored 11% lower on a post-lecture comprehension test (Fig. 1).

3. Experiment 2

In Experiment 2, we investigated whether being in direct view of a multitasking peer would negatively influence learning as measured
by performance on a comprehension test. A new group of participants was asked to take notes using paper and pencil while attending to the
lecture. Some participants were strategically seated throughout the classroom so that they were in view of multitasking confederates on
laptops, while others had a distraction-free view of the lecture. Confederates mimicked multitaskers from Experiment 1 by typing notes on
the lecture and performing other concurrent, irrelevant online tasks. We hypothesized that participants who were seated in view of
multitasking peers would have lower comprehension scores compared to participants who had minimal or no visual distraction from
multitasking peers.

3.1. Method

3.1.1. Participants
Thirty-nine undergraduate students from the same university participated in the study (26 females; M age ¼ 20.3 years, SD ¼ 4.2). None

had participated in Experiment 1. Recruitment procedures and participant incentives were the same as in Experiment 1. Thirty-eight
participants were included in the final data analysis, which included two experimental conditions: in view of multitasking peers (n ¼ 19)
and not in view of multitasking peers (n ¼ 19). The one participant excluded from the analysis was removed because of prior familiar with the
lecture content (as measured by a screening questionnaire). Thirty-six undergraduate students were recruited to be confederates.

3.1.2. Materials
The same materials were used as in Experiment 1 with the exception of two questions on the questionnaire. Instead of asking about

whether or not multitasking hindered self and peer learning, the two questions in this experiment were directed toward participants in
view of technology: (1) To what extent were you distracted by other students’ laptop use around you? (1 ¼ not distracted at all;
4 ¼ somewhat distracted; 7 ¼ very distracted) and (2) To what extent do you think being in view of other students’ laptop use hindered your
learning of the lecture material? (1 ¼ did not hinder my learning; 4 ¼ somewhat hindered my learning; 7 ¼ definitely hindered my
learning). Responses to these questions provided us with subjective student measures on whether or not their multitasking peers were
a distraction, and whether they perceived this distraction to be a barrier to learning.

3.1.3. Design and procedure
As in Experiment 1, participants were asked to bring their personal laptops to the experiment; however, only those assigned as

confederates actually used their laptops (n ¼ 36). The experimental participants (n ¼ 38) were instructed to keep their laptops in their
knapsacks.

Fig. 1. Proportion correct on the comprehension test as a function of condition (multitasking vs. no multitasking). Multitasking lowered test performance by 11%, p < .01. Error bars represent standard error of the mean.

F. Sana et al. / Computers & Education 62 (2013) 24–31 27

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2.1.5条。结果和讨论
两种情况的参与者在年龄、性别、英语流利程度或高中平均成绩方面没有人口统计学差异。为了检验理解测试条件之间的潜在差异，采用2（条件：多任务，无多任务）2（问题类型：简单，复杂）混合因素方差分析，条件为被试间因素，问题类型为被试内因素。条件的主要影响是显著的，F（1,38）1/4 10.2，p 1/4.003，u2 1/4.20。在讲座中进行多任务处理的参与者（M 1/4 0.55，SD 1/4 0.11，n 1/4 20）的得分显著低于未进行多任务处理的参与者（M 1/4 0.66，SD 1/4 0.12，n 1/4 20）。问题类型的主要影响也很显著，F（1,38）1/417.7，p<0.001，u2 1/4.30。参与者在简单事实问题（M 1/4 0.60，SD 1/4 0.13，n 1/4 20）上的得分高于复杂应用知识问题（M 1/4 0.56，SD 1/4 0.13，n 1/4 20）。这一主要效果只是反映了问题产生的难度。相互作用不显著，F（1,38）1／40.79，p 1／4.380。这些发现表明，多任务处理对理解成绩有很强的不利影响。总的来说，多任务的参与者在课后理解测试中的得分要低11%（图1）。 三。实验二 在实验2中，我们研究了在理解测试中，直接面对多任务同伴是否会对学习产生负面影响。一组新的参与者被要求在听课时用纸和铅笔做笔记。一些参与者策略性地坐在教室里，这样他们就可以在笔记本电脑上看到多任务联盟，而另一些人则可以自由地观看讲座。同盟者模仿实验1中的多任务者，在讲座上输入笔记，并执行其他并发的、不相关的在线任务。我们假设坐在多任务同伴面前的受试者的理解得分低于那些对多任务同伴的视觉干扰最小或没有的受试者。 3.1条。方法 3.1.1条。参与者 来自同一所大学的39名本科生（26名女性；M年龄1/4 20.3岁，SD 1/4 4.2）参加了研究。无 参加了实验1。招聘程序和参与者激励措施与实验1相同。38名参与者被纳入最终数据分析，其中包括两个实验条件：针对多任务同伴（n 1/419）和不针对多任务同伴（n1/419）。被排除在分析之外的一名受试者因事先熟悉讲座内容（通过筛选问卷测量）而被剔除。36名大学生被招募为同盟军。 3.1.2条。材料 除问卷上的两个问题外，实验1使用的材料与实验1相同。这个实验中的两个问题不是问多任务是否妨碍了自我学习和同伴学习，而是从技术的角度对参与者提出的：（1）你在多大程度上被周围其他学生使用笔记本电脑分散了注意力？（1 1/4完全没有分心；4 1/4有点分心；7 1/4非常分心）和（2）您认为考虑到其他学生使用笔记本电脑的情况，在多大程度上妨碍了您学习讲义？（1 1/4没有妨碍我的学习；4 1/4在一定程度上妨碍了我的学习；7 1/4肯定妨碍了我的学习）。对这些问题的回答为我们提供了一个主观的学生衡量标准，即他们的多任务同伴是否是一种干扰，以及他们是否认为这种干扰是学习的障碍。 3.1.3条。设计和程序 与实验1一样，参与者被要求携带他们的个人笔记本电脑参加实验；然而，只有那些被指定为同盟者的人才实际使用他们的笔记本电脑（n 1/436）。实验参与者（n 1/438）被要求将笔记本电脑放在背包中。 所有参与者都收到了一份指导表和一份同意书。同盟会的说明书上解释说，他们是同盟会成员，他们被要求在浏览互联网（如电子邮件、脸谱网）和在演讲时假装对演讲内容做笔记之间进行切换。事实上，他们被告知不需要注意讲座。参与者的教学用纸要求他们保存笔记本电脑，并使用实验者提供的纸和铅笔对授课内容进行书面记录，就像他们在课堂上通常做的那样。 房间布置与实验1相同。再次，由于最多有20个座位，我们重复实验四次，以获得39名实验参与者和36名联盟成员的总样本[每个重复包括大致相同数量的总参与者（范围：18～20），在课堂中与参与者相比（在2:1比）大致更多。在每个座位上都战略性地放置了说明书和同意书。参与者在进入教室时随机获得座位号。一些参与者坐在两个多任务联合会的后面（即，他们在左视野中看到一个笔记本电脑用户，在右视野中看到另一个笔记本电脑用户；图2）。这些参与者是从多任务同伴的角度考虑的。其他的参与者坐在和他们一样，被要求在讲座上做书面记录的参与者后面。这些参与者被认为没有考虑到多任务对等（图2）。 讲座如实验1所示。演讲时，一名实验者留在教室后面，用座位图跟踪参与者和同盟者的座位位置，监控笔记本电脑屏幕活动和记录，并确保所有指示都得到遵守。讲座结束后，从参与者那里收集书面笔记，要求同盟者完成他们的工作并存储他们的笔记本电脑。这时，盟军被要求离开这个房间。他们被其中一个实验者听取了汇报，然后被解雇了。其余的参与者接受了理解测试，并执行了30分钟的时间限制，所有参与者都在该时间限制内成功完成了测试。为了保持动机水平，参与者被告知，那些离开教室的学生将在一天后返回，届时他们将完成延迟的理解测试。一旦实验者收集了所有的测试，参与者回答了问卷，听取了汇报，并被解雇。

All participants received an instruction sheet and a consent form. The confederates’ instruction sheet explained that they were
confederates, and they were required to use their laptops to flip between browsing the Internet (e.g., email, Facebook) and pretending to
take notes on the lecture content as the lecture was presented. In fact, they were told they were not required to pay attention to the lecture.
The participants’ instruction sheet asked them to keep their laptops stored, and to use the paper and pencil provided by the experimenters
to take written notes on the lecture content, just as they might normally do in class.

The room set-up was the same as in Experiment 1. Again, since there was a maximum of 20 seats, we repeated the experiment four times
to obtain a total sample of 39 experimental participants and 36 confederates [each repeat included roughly the same number of total
participants (range: 18–20), with more confederates in the classroom than participants (roughly a 2:1 ratio)]. Instruction sheets and consent
forms were strategically placed at each seat. Participants were randomly presented with a seat number as they entered the classroom. Some
participants were seated so that they were behind two multitasking confederates (i.e., they were in view of one laptop user in their left
visual field and another laptop user in their right visual field; Fig. 2). These participants were considered in view of multitasking peers. Other
participants were seated behind participants who, like themselves, were asked to take written notes on the lecture. These participants were
considered not in view of multitasking peers (Fig. 2).

The lecture was presented as in Experiment 1. While the lecture was being presented, an experimenter (FS) remained at the back of the
classroom and used a seating map to track participants’ and confederates’ seat locations, monitor laptop screen activities and note-taking,
and ensure that all instructions were being followed. When the lecture ended, written notes were collected from the participants, and
confederates were asked to finish up their work and store their laptops. At this point, confederates were asked to leave the room. They were
debriefed by one of the experimenters and dismissed. The remaining participants were given the comprehension test and a 30 min time
limit was enforced, with all participants successfully completing the test within the time limit. To maintain motivation levels, participants
were told a cover story that those students who had left the classroom were going to return one day later, at which time they would
complete a delayed comprehension test. Once the experimenters collected all the tests, participants responded to the questionnaire, were
debriefed and dismissed.

3.1.4. Fidelity measures
FS closely monitored participants’ activities throughout the lecture presentation, as in Experiment 1. This was done to ensure that all

participants adhered to their assigned instructions. If a participant or confederate was not on task (e.g., a confederate not using their laptop,

Fig. 2. Visual representation of participants who were and were not in view of a multitasking peer. In view participants were strategically seated behind two confederates, with one
confederate’s laptop screen w45$ to the participant’s right and the other’s w45$ to the participant’s left. Not in view participants were seated similarly behind two experimental
subjects who took handwritten notes.

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3.1.4条。忠实度
在整个演讲过程中，如实验1，FS密切监控参与者的活动。这样做是为了确保所有参与者都遵守他们指定的指示。如果参与者或联盟成员没有执行任务（例如，联盟成员没有使用他们的笔记本电脑，或者参与者没有做任何笔记），则会对他们进行调查并提醒他们具体的指示。所有同盟国和参与者都遵守了他们的指示。
使用实验1中报告的相同量表对参与者的笔记质量进行评分。所有参与者都对讲座内容做了某种形式的笔记。不考虑多任务同伴的参与者笔记（M 1/4 3.6，SD 1/4 1.3）的质量与考虑多任务同伴的参与者笔记（M 1/4 3.7，SD 1/4 1.2）的质量相似，t<1。 因此，我们的忠实度测量（即参与者监控、笔记分析）清楚地表明，参与者在整个实验过程中坚持任务，并做了全面的笔记。考虑到多任务的同龄人并没有降低笔记质量。 3.1.5条。结果和讨论 两种情况的参与者在年龄、性别、英语流利程度或高中平均成绩方面没有人口统计学差异。为了检验理解测试中不同条件之间的潜在差异，采用2（条件：考虑多任务，而不是考虑多任务）2（问题类型：简单，复杂）混合因子方差分析，条件为被试间因素，问题类型为被试内因素。病情的主要影响显著，F（1,36）1／4 21.5，p<0.001，u2 1／4.36。多任务同伴的参与者在测试中的得分（M 1/4 0.56，SD 1/4 0.12，n 1/4 19）明显低于没有多任务同伴的参与者（M 1/4 0.73，SD 1/4 0.12，n 1/4 19）。问题类型的主要影响也很显著，F（1,36）1/411.3，p 1/4.002，u21/4.21。参与者在简单问题（M 1/4 0.69，SD 1/4 0.14，n 1/4 20）上的得分高于复杂问题（M 1/4 0.60，SD 1/4 0.15，n 1/4 20）。相互作用不显著，F（1,36）1／40.91，p 1／4.347。这些发现表明，同龄人的多任务处理分散了那些试图只关注演讲的参与者的注意力。那些考虑到多任务的同龄人在课后理解测试中的得分要低17%（图3）。 四。一般性讨论 我们的实验复制了一个重要的发现并引入了一个新的发现。首先，当参与者在学习过程中执行多个任务时，他们的理解能力会受到损害，一个是听讲稿和做笔记的主要任务，另一个是完成不相关的在线任务的次要任务。这一结果并不令人惊讶，并且与其他研究（例如，Barak等人，2006；Hembrooke&Gay，2003；Kraushaar&Novak，2010；Wood等人，2012）报告的结果一致，但我们使用了一种更为可控的程序来确认。第二，坐在多任务环境中的参与者的理解能力受损。这一发现表明，尽管这些参与者积极地尝试学习材料（如综合笔记所证明的那样，其质量与那些对演讲有清晰看法的人相似），但他们的同伴的选择使他们处于不利地位。 我们的实验应用于自然界，结果，并没有对多任务或注意力理论做出重大贡献。然而，研究结果与理论一致，即分配给任务的注意程度直接关系到所处理信息的质量和数量。虽然我们没有直接测量注意力，但所有参与者都在积极地倾听和记录所提供的信息。我们有很强的保真度指标，这使得我们能够更确定（与之前的研究相比）与我们的操作无关的因素不会给数据增加其他方差来源。因此，我们可以推测，注意力是由于我们的操作而受损的，无论是以自我多任务的形式还是以多任务同伴的视角。 在实验1中，参与者听讲座，做笔记，完成在线任务。这种同时执行多个任务的做法似乎阻碍了测试时的信息检索，可能是由于学习过程中编码不好（多任务者笔记质量较差）和分配有限注意力资源的效率低下所致。在实验2中，受试者在他们的周边视觉中有分散注意力的活动时，倾听所呈现的信息并做笔记。邦联的笔记本电脑屏幕可能分散了参与者的注意力，使他们无法将全部注意力集中在演讲上。参与者仍然能够在讲座上做笔记；但是，缺乏完全的注意力集中可能会影响对所写信息的详细阐述和处理，从而降低理解测试期间成功的检索尝试。未来的实验应旨在弥合记忆和注意的认知原则与应用教学研究之间的鸿沟，以明确和直接地测试这些围绕多任务和注意的理论主张。更严格的方法可以提供进一步的实验证据来检验这些假设。例如，可以使用眼睛跟踪方法来确定学生注意力转移的时间、地点和持续时间，以及从讲课时间向外看是否与特定于学生错过的信息的考试成绩相关。

or a participant not taking any notes), they were probed and reminded of their specific instructions. All confederates and participants
complied with their instructions.

Participants’ notes were scored for quality using the same scale reported in Experiment 1. All participants took some form of notes on the
lecture content. The notes of participants not in view of multitasking peers (M ¼ 3.6, SD ¼ 1.3) were similar in quality to the notes of
participants in view of multitasking peers (M ¼ 3.7, SD ¼ 1.2), t < 1.

Therefore, our fidelity measures (i.e., participant monitoring, analysis of notes) clearly show that participants stayed on-task throughout
the experiment and took comprehensive notes. Being in view of multitasking peers did not reduce note quality.

3.1.5. Results and discussion
There were no demographic differences between participants of the two conditions in terms of age, gender, fluency in English, or high

school GPA. To examine potential differences between conditions on the comprehension test, a 2 (condition: in view of multitasking, not in
view of multitasking) # 2 (question type: simple, complex) mixed factorial ANOVA was conducted with condition as a between-subjects
factor and question type as a within-subjects factor. The main effect of condition was significant, F(1,36) ¼ 21.5, p < .001, u2 ¼ .36. Participants in view of multitasking peers scored significantly lower on the test (M ¼ 0.56, SD ¼ 0.12, n ¼ 19) than participants not in view of multitasking peers (M ¼ 0.73, SD ¼ 0.12, n ¼ 19). The main effect of question type was also significant, F(1,36) ¼ 11.3, p ¼ .002, u2 ¼ .21. Participants scored higher on simple questions (M ¼ 0.69, SD ¼ 0.14, n ¼ 20) than on complex questions (M ¼ 0.60, SD ¼ 0.15, n ¼ 20). The interaction was not significant, F(1,36) ¼ 0.91, p ¼ .347. These findings suggest that peer multitasking distracted participants who were attempting to pay sole attention to the lecture. Those in view of a multitasking peer scored 17% lower on a post-lecture comprehension test (Fig. 3).

4. General discussion

Our experiments replicate one important finding and introduce a new finding. First, participants’ comprehension was impaired when
they performed multiple tasks during learning, one being the primary task of attending to the lecture material and taking notes, and the
other being the secondary task of completing unrelated online tasks. This result is not surprising and is consistent with those reported by
other studies (e.g., Barak et al., 2006; Hembrooke & Gay, 2003; Kraushaar & Novak, 2010; Wood et al., 2012), but we confirm it using a more
controlled procedure. Second, comprehension was impaired for participants who were seated in view of peers engaged in multitasking. This
finding suggests that despite actively trying to learn the material (as evidenced by comprehensive notes, similar in quality to those with
a clear view of the lecture), these participants were placed at a disadvantage by the choices of their peers.

Our experiments were applied in nature and, as a result, do not make major contributions to multitasking or attention theory. However,
the results are consistent with theory, namely that the degree of attention that is allotted to a task is directly related to the quality and
quantity of information processed. Although we did not directly measure attention, all participants were actively listening to and taking
notes on the information presented. We had strong fidelity measures that allowed us to be more certain (compared to previous studies) that
factors unrelated to our manipulation were not adding other sources of variance to the data. Thus, we can speculate that attention was
impaired due to our manipulation, either in the form of self-multitasking or being in view of a multitasking peer.

In Experiment 1, participants were listening to the lecture, taking notes, and completing the online tasks. This exercise of carrying out
multiple tasks at the same time seemed to have impeded retrieval of information at the test, likely as a result of poor encoding during
learning (as evidenced by multitaskers’ poorer quality of notes) and inefficiency at allocating limited attentional resources. In Experiment 2,
participants were listening to the information being presented and taking notes while in the presence of distracting activity in their
peripheral vision. Confederates’ laptop screens may have distracted participants from directing their full attention to the lecture. Partici-
pants were still able to take notes on the lecture; however, a lack of complete attentional focus may have compromised the elaboration and
processing of the information being written, thereby lowering successful retrieval attempts during the comprehension test. Future
experiments should aim to bridge the gap between cognitive principles of memory and attention and applied pedagogical research to
definitively and directly test these theoretical claims surrounding multitasking and attention. More stringent methods could provide further
experimental evidence to test these hypotheses. For example, one could use eye-tracking methodologies to determine when, where, and for

Fig. 3. Proportion correct on the comprehension test as a function of condition (view to multitasking vs. no view to multitasking). Being in view of multitasking peers lowered test
performance by 17%, p < .001. Error bars represent standard error of the mean.

F. Sana et al. / Computers & Education 62 (2013) 24–31 29

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问卷调查的结果增加了对技术对学习影响的讨论。实验1的回答表明，处于多任务状态的参与者意识到，在讲座期间进行多任务处理会“在一定程度上阻碍”他们的学习（M 1/4 5.5，SD 1/4 2.0）。然而，他们估计同龄人的学习“几乎不会受到阻碍”（M 1/4 3.3，SD 1/4 1.9）。相比之下，同伴分心（实验2）观察到的效果大小几乎是自我分心（实验1）观察到的效果大小的两倍。多任务者似乎能够以某种程度上减少分心的方式来安排多任务活动的时间。那些考虑到多任务的人似乎被引诱去观看其他学生的笔记本电脑屏幕，即使是在课堂上不合适的时刻，因此对那些考虑到多任务的人来说，与实际的多任务人相比，造成了更糟糕的学习。这些结论应谨慎解释，并应在今后的研究中得到贯彻。我们的结论仅仅基于效应大小；由于方法上的差异，我们不能直接比较不同实验的测试性能。
实验2的问卷调查结果表明，受试者被附近的同盟者“有点分心”（M 1/4 3.3，SD 1/4 2.1），并且考虑到多任务同伴，“几乎”阻碍了他们自己的学习（M 1/4 2.7；SD 1/4 1.6）。因此，总的来说，调查问卷评分表明学生没有接触到同龄人行为的间接后果。
尽管有文献表明多任务可能对复杂知识的学习特别有害（例如，Foerde等人，2006年），但我们的结果表明，多任务对简单事实学习和复杂应用学习的损害程度相同。因此，即使学习了一个新的事实（例如，“哪种云类型在大气中发现得最高？”？）可能会被自我多任务干扰，或者被同龄人的多任务干扰。
相关的，多任务可能有不同的整体效果取决于任务的难度被耍。一些研究表明，如果一项主要任务更困难或更新颖，那么它必然需要更大程度的注意力资源才能在令人满意的水平上完成任务（Kahneman，1973；Posner&Boies，1971；Styles，2006）。因此，只有在没有其他任务必须同时完成，或者任何次要任务相对简单或自动（即，如果次要任务不需要很多注意力资源；Kahneman&Treisman，1984年）的情况下，才能很好地完成主要任务。后一种情况是我们实验1的场景。参与者被要求在一项主要任务（需要很多注意力资源）中学习一些新奇的东西，同时参加一项简单的次要任务（尽管程度不同，但仍然需要注意力资源）。我们设计了初级和中级任务的难度级别，以模拟已报告为典型课堂行为的内容（即，学生在参加课堂讲座和查看电子邮件、Facebook和与朋友聊天之间来回切换）。我们的研究结果表明，尽管第二项任务对大学生来说是相当不理智的（即随意的上网浏览），但它仍然会对第一项任务的表现产生影响，正如多任务者的测试分数降低所证明的那样。未来的研究可以通过操纵主要和/或次要任务的难度水平来进一步研究课堂上多任务的影响，而不是当前设计的操纵。根据双重任务理论（如Pashler，1994），随着初级或中级任务难度的增加（如参加物理讲座的学生，但选择把大部分时间花在为下一次历史考试而学习上。
根据这项研究中报告的证据，我们可以向教育者推荐什么方法来管理课堂上的笔记本电脑使用？禁止使用笔记本电脑是极端的，也是没有根据的。不可忽视的是，笔记本电脑在适当使用时会促进积极的学习成果（例如，基于网络的研究、流行测验、在线案例研究和讨论线索；例如，Finn&Inman，2004年）。当笔记本电脑被严格用于记笔记时，打字笔记对学习的积极影响与书面笔记相似（Quade，1996）。我们的结果通过初步的交叉实验比较证实了这一发现，也就是说，我们发现，在实验1的无多任务状态（输入笔记的参与者）和实验2的无技术状态（分别为M 1/4 4.1和M 1/4 3.6）以及随后的理解测试方面，实验1的参与者（输入笔记的参与者）和实验2的参与者（写笔记的参与者）之间没有显著差异分数（分别为M 1/4 0.66和M 1/4 0.73，记住实验1中的非多任务者有时会考虑多任务者，这可以解释他们的理解测试分数较低的原因）。因此，出于各种原因，笔记本电脑应该仍然是现代教室的一种工具，也许有一些合理的限制。
一个建议是教师在课程开始时与学生讨论使用笔记本电脑的后果（Gasser&Palfrey，2009）。教师可以告知学生滥用笔记本电脑的负面教育结果，并将他们的观点与学生的观点进行比较和对比。在本次讨论中，全班同学可以集体提出一些在整个学期课堂上实施的技术礼仪规则（例如，如果你打算同时处理多项任务，坐在教室后面，这样至少其他学生不会感到困扰；McCreary，2009）。这样一来，技术和分心的问题就凸显出来了，学生可以做出明智的选择，而不是假设他们（和他们的同龄人）对多任务缺陷免疫。
另一个建议是明确禁止在学习不需要技术的课程中使用笔记本电脑。有人可能会争辩说，在课本和讲稿幻灯片上通常提供信息的课程，不需要笔记本电脑，其程度与动手学习是课程的一个综合组成部分的课程相同，可能是以专门的计算机软件的形式。这个建议是谨慎的，因为有些学生可能不会从没有笔记本电脑的课程中受益。例如，残疾学生通常依靠计算机技术来帮助学习（Fichten等人，2001年）。因此，也许可以允许笔记本电脑在所有课程中使用，但仅限于基于课程的网站（如果可能的话）。
最终，学生必须为自己的学习负起责任；然而，热情的教师可以影响学生在课堂上选择如何引导他们的注意力。第三个建议是为教育工作者提供资源，帮助他们创建丰富的、信息丰富的、交互式的课程，与非课程网站的吸引力相竞争，这样学生就不会在第一时间滥用笔记本电脑。这可能包括将笔记本电脑融入实时课堂练习。例如，教师可以要求学生在互联网上搜索缺失的讲座信息，或者找到一个有趣的在线视频与全班分享。此外，教师可以使用一个共享的网站，让学生能够对演讲概念的难度等级进行排名，从而允许教师在课堂上评估学生的理解水平。然后，教师可以复习这些概念，并在课程结束前向学生提供反馈。事实上，有创造力的教师可以塑造学生在课堂上选择使用笔记本电脑的方式，从而使笔记本电脑的使用具有建设性。

how long a student’s attention is diverted, and whether looking-away-from-lecture time correlates with test performance specific to the
information missed by the student.

Results from the questionnaires add to the discussion of technology’s impact on learning. Responses from Experiment 1 show that
participants in the multitasking condition were aware that multitasking during the lecture would “somewhat hinder” their learning
(M ¼ 5.5, SD ¼ 2.0). However, they estimated peers’ learning would be “barely hindered” (M ¼ 3.3, SD ¼ 1.9). By contrast, the observed effect
size from peer distraction (Experiment 2) was nearly twice as large as the observed self-distraction effect size (Experiment 1). Multitaskers
appear to have been able to time their multitasking activities in a manner that reduced distraction to some degree. Those in view of
multitasking appear to have been lured into watching other students’ laptop screens even during inopportune moments of the lecture, thus
creating worse learning for those in view of a multitasker compared to the actual person who was multitasking. These conclusions should be
interpreted with caution and deserve follow up in future studies. Our conclusions are based solely on effect size; we cannot directly compare
test performance across experiments due to methodological variation.

Questionnaire responses from Experiment 2 suggest that participants reported being “somewhat distracted” by nearby confederates
(M ¼ 3.3, SD ¼ 2.1), and that being in view of a multitasking peer “barely” hindered their own learning (M ¼ 2.7; SD ¼ 1.6). Thus, overall, the
questionnaire ratings suggest students are not in touch with the indirect consequences of their peers’ actions.

Despite literature suggesting that multitasking may be particularly detrimental to the learning of complex knowledge (e.g., Foerde et al.,
2006), our results show that multitasking impaired both simple factual learning and complex application learning to the same degree.
Therefore, even the learning of a new fact (e.g., “Which cloud type is found highest in the atmosphere?”) can be interrupted by self-
multitasking or distraction from peers who are multitasking.

Relatedly, multitasking may have different overall effects depending on the difficulty of the tasks being juggled. Some studies suggest
that if a primary task is more difficult or novel, it will inherently require a greater degree of attentional resources to perform the task at
a satisfactory level (Kahneman,1973; Posner & Boies,1971; Styles, 2006). Therefore, the primary task may only be performed well if no other
tasks must be completed at the same time, or if any secondary task is relatively simple or automatic (i.e., if the secondary task does not
require many attentional resources; Kahneman & Treisman, 1984). This latter case was the scenario of our Experiment 1. Participants were
asked to learn something novel in a primary task (where many attentional resources were required), while simultaneously attending to
a simple secondary task (where attentional resources were still required, albeit not to the same extent). We designed the difficulty level of
the primary and secondary tasks to mimic what has been reported as typical classroom behaviors (i.e., students who switch back and forth
between attending to a classroom lecture and checking e-mail, Facebook, and IMing with friends). Our results suggest that even though the
secondary task was rather mindless for an undergraduate student (i.e., casual Internet browsing) it still had an impact on the performance of
the primary task, as evidenced by multitaskers’ lowered test scores. Future studies could further examine the impact of multitasking in the
classroom by manipulating the level of difficulty of the primary and/or secondary tasks beyond the manipulations of the current design.
According to dual task theories (e.g., Pashler,1994), one would expect to see greater deficits in learning performance as the difficulty level of
either primary or secondary tasks increases (e.g., a student who attends their physics lecture, but chooses to spend most of the class time
studying for a history exam taking place during the next period).

In light of the evidence reported in this study, what might we recommend to educators as a means of managing laptop use in the
classroom? A ban on laptops is extreme and unwarranted. It cannot be overlooked that laptops foster positive learning outcomes when used
appropriately (e.g., web-based research, pop quizzes, online case studies, and discussion threads; e.g., Finn & Inman, 2004). When laptops
are used strictly for note-taking purposes, typed notes have been shown to have similar positive influences on learning compared to written
notes (Quade, 1996). Our results confirm this finding through a rudimentary cross-experiment comparison; that is, we saw no striking
differences between participants in the no multitasking condition of Experiment 1 (participants who typed notes) and participants in the no
view of technology condition of Experiment 2 (participants who wrote notes) in terms of quality of notes (M ¼ 4.1 and M ¼ 3.6, respectively)
as well as subsequent comprehension test scores (M ¼ 0.66 and M ¼ 0.73, respectively, keeping in mind that non-multitaskers in Exper-
iment 1 sometimes were in view of multitaskers, which could explain their qualitatively lower comprehension test score). Thus, for a variety
of reasons, laptops should remain a tool of the modern classroom, perhaps with some sensible constraints.

One suggestion is for teachers to discuss the consequences of laptop use with their students at the outset of a course (Gasser &
Palfrey, 2009). Teachers are in a position to inform students about negative educational outcomes of laptop misuse, as well as to
compare and contrast their views with the views of their students. In this discussion, the class could collectively come up with a few
rules of technology etiquette that are enforced in the classroom throughout the semester (e.g., sit at the back of the classroom if you
plan to multitask, so at least other students are not bothered; McCreary, 2009). In this way, the issue of technology and distraction is
highlighted and students can make informed choices, rather than assuming they (and their peers) are immune to multitasking
deficits.

Another suggestion is to explicitly discourage laptop use in courses where technology is not necessary for learning. One could argue that
courses where information is generally presented in textbooks and on lecture slides do not require a laptop to the same extent as courses
where hands-on learning is an integrated component of the course, likely in the form of specialized computer software. This recommen-
dation is made with caution as some students might not benefit from a course without laptops. For example, students with disabilities often
rely on computer technology to assist in learning (Fichten et al., 2001). Therefore, perhaps one could allow laptops in all courses but restrict
the use of the Internet to course-based websites only (if possible).

Ultimately students must take accountability for their own learning; however, enthusiastic instructors can influence how students
choose to direct their attention during class time. A third suggestion is to provide educators with resources to help them create enriching,
informative, and interactive classes that can compete with the allure of non-course websites, so that students are deterred from misusing
their laptop in the first place. This could include incorporating the laptop into real-time classroom exercises. For example, instructors could
ask their students to search the Internet for missing lecture information, or to find an interesting online video to share with the class.
Furthermore, instructors could use a shared website where students are able to rank the difficulty level of lecture concepts, thereby allowing
the instructor to gauge student comprehension levels in class. The instructor could then review these concepts and provide feedback to
students prior to the end of the class. Indeed, inventive instructors can shape how students choose to use their laptops during class time, so
that laptop use is constructive.

F. Sana et al. / Computers & Education 62 (2013) 24–3130

emyil LU
为了有效地将技术融入课堂，我们必须继续研究技术使用对学习的积极和消极影响。虽然本研究只从基础讲座（即即时学习）基础学习，未来的研究可以检查多任务对长期保留的影响，并可以调查主题材料的差异。分散注意力和双重任务执行的认知理论可以帮助我们理解学习的本质以及什么会分散我们的注意力。应用研究，使用随机化实验设计，将允许我们研究在学习过程中任务活动可以最大化和分心最小化的方式。我们必须扪心自问：在什么情况下使用笔记本电脑的好处大于坏处？归根结底，有吸引力的教师和专注的学习者需要努力工作并保持专注，以使课堂学习保持在最佳水平。
致谢
这项研究部分得到了约克大学健康学院的资助。我们感谢伊琳娜·卡普勒帮助制作讲座和理解测试材料。

In order to effectively integrate technology into classrooms, we must continue to examine the consequencesdboth positive and neg-
ativedof technology use on learning. While the present research examined only foundational learning from a lecture (i.e., immediate
learning), future research could examine the effects of multitasking on longer-term retention, and could investigate subject material
differences. Cognitive theories of divided attention and dual-task performance can help us understand the nature of how we learn and what
distracts us. Applied research, using randomized experimental designs, will allow us to examine ways in which on-task activities during
learning can be maximized and distraction minimized. We must ask ourselves: Under what conditions do the benefits of laptop use
outweigh the detriments? Ultimately, engaging instructors and dedicated learners will need to work hard and stay focused to keep
classroom learning at an optimal level.

Acknowledgments

This research was supported in part by a grant from the York University Faculty of Health. We thank Irina Kapler for helping to create the
lecture and comprehension test materials.

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Laptop multitasking hinders classroom learning for both users and nearby peers
1. Introduction
2. Experiment 1
2.1. Method
2.1.1. Participants
2.1.2. Materials
2.1.3. Design and procedure
2.1.4. Fidelity measures
2.1.5. Results and discussion

3. Experiment 2
3.1. Method
3.1.1. Participants
3.1.2. Materials
3.1.3. Design and procedure
3.1.4. Fidelity measures
3.1.5. Results and discussion

4. General discussion
Acknowledgments
References