The Relationship between Media Multitasking and Academic Performance among College Students: A Comparison between China and the Netherlands
Shijing Xiao 10390545
Graduate School of Communication University of Amsterdam
Master thesis written for 2012-2014 Youth and Media (Research) program Supervisor: Susanne E. Baumgartner
Abstract
With the popularity of information and communication technologies (ICTs), media users, especially young generations, are presented with more media content than ever before, which brings about media multitasking as an information management strategy. This study investigated the relationship between two types of media multitasking (i.e. media -media multitasking and media-academic multitasking) and academic performance among college students in China (N = 99) and in the Netherlands (N = 102). Academic distractibility and academic procrastination were included as mediators in the relationship. Results suggest media-academic multitasking is a common phenomenon in both countries, and students in China have higher scores over students in the Netherlands. Neither media-academic multitasking nor media-media multitasking is predictable of academic performance in either country. The indirect effect of academic distractibility was found in the relationship between media-academic multitasking and academic performance in the total model including both countries, but the indirect effect of academic procrastination was not found. General media-media multitasking was not found to be related to academic performance, academic distractibility or academic procrastination, suggesting that previous assumption on the negative influence of media multitasking lifestyle on learning process and outcome may be exaggerated.
The Relationship between Media Multitasking and Academic Performance among College Students: A Comparison between China and the Netherlands
The rapid development of media technologies has led to great changes in consuming media content. The convenient access to the Internet and high ownership rate of mobile phones present users with more data and information than ever before, and thus have increased media saturation (Rideout, Foehr, & Roberts, 2010). On the other hand, facing such large number of information within limited time, humans are unable to process it separately, thus turning to multitasking as an information management strategy (Chun, Golomb, & Turk-Browne, 2011). Media consumers especially young generations are reported to increasingly engage in media multitasking (Carrier, Cheever, Rosen, Benitez, & Chang, 2009; Foehr, 2006; Wallis, 2006). Rideout, Foehr, & Roberts (2010) found out that media multitasking is quite common among teenagers from 8 to 18 years old in the U.S., with 73% of respondents multitask while listening to music, 68% while watching TV and 66% while using a computer. The average amount of multitasking proportion has increased from 16% in 1999 to 29% in 2009 among teenagers in the U.S. (Rideout et al., 2010), showing that media-multitasking has become more trendy among youngsters.
Media multitasking often refers to concurrent use of more than one medium or one stream of content (Ophir, Nass, & Wagner, 2009; Shih, 2013), including multiple processes on a single media platform and/or on multiple media (Vega, 2009). It has at least two distinguished types: media-media multitasking, for example, watching TV while texting, and media-nonmedia multitasking, for example, listening to radio while doing housework (Wallis,
2010).
This study aims to investigate the relationship between media multitasking and academic performance among college students, especially the underlying mechanism in this relationship, in which academic distractibility (AD) and academic procrastination (AP) are examined as mediators. Extant research has only investigated the relationship between media multitasking and academic performance, but has not paid attention to the underlying mechanisms yet. Distinction is made between media-media multitasking (MMM) and media-academic multitasking (MAM) (i.e. using media content while doing academic-related works), and it is predicted that both types of media multitasking will have influences on academic performance. Also, comparison between cases in China and in the Netherlands is provided to explore whether the relationship and the underlying mechanism can be established in both countries with different media consumption style and educational systems. By including a Chinese sample, this study also fills the research gap on media multitasking and academic performance in China.
Theoretical Background Negative Influences of Media Multitasking
Under most conditions, the brain is unable to conduct two or more complex tasks (e.g. doing homework and being active on Facebook) at the same time as complex tasks require the same function area in the brain (i.e. the prefrontal cortex) (Heitin, 2013). Also, as human information processing system has limited capacity, cognitive overload occurs when processing demands exceed the processing capacity (Mayer & Moreno, 2003). Thus, negative
effects of media multitasking were mostly found in current research. Though cognitive needs drive media multitasking in the first place, they are not satisfied by media multitasking (Wang & Tchernev, 2012). On the contrary, negative effects are associated with media multitasking on cognition and information processing. Ophir et al.(2009) found out that heavy media multitaskers performed worse on a test of task-switching ability than light media multitaskers, probably due to their failure on filtering irrelevant information. Three key executive functions (i.e., working memory, shifting, and inhibition) were demonstrated to be negatively associated with the level of media multitasking among adolescents, showing that those who media multitask tend to have more problems in daily executive functions (Baumgartner, Weeda, van der Heijden, & Huizinga, 2014).
Cognition and information processing is very important to academic study in college, which requires students to have a high level of competence in reading, comprehension and writing. Having problems in processing information may lead to failure in academic performance. Negative associations with media multitasking were found in studying. Concurrent IM use was found negatively affecting comprehension efficiency while reading (Fox, Rosen, & Crawford, 2009). Specifically, results from metacognitive evaluation revealed that compared with media-multitasking condition, deeper understanding and retrieval of memory was only gained in the non-multitasking condition, which indicated that media-multitasking seems to affect both the quality of learning and whether we can process the content we learned in the brain later (Foerde, Knowlton, & Poldrack, 2006).
Media Multitasking and Academic Performance
among college students. In a study with 500 college students, 73% claimed that they were not able to study without e-devices and 38% were not able to study for over 10 minutes without checking their e-devices such as laptop and smartphones (Keller, 2011). Negative moderating effect of multitasking was found between Social Networking Sites (SNS) use and Grade Point Average (GPA), indicating that engagement with SNSs while doing schoolwork may decrease students’ capacity for cognitive processing and preclude in-depth learning which requires longer time of concentration (Junco & Cotten, 2012; Karpinski et al., 2013; Kirschner & Karpinski, 2010). Students who media multitask while doing academic works were found to have lower GPAs than those who don’t (Jacobsen & Forste, 2011; Junco, 2012b; Rosen et al., 2013). Thus in the current study, it is hypothesized that media multitasking with academic work (MAM) has negative effects on academic performance.
H1a: media-academic multitasking has negative effects on academic performance. However, previous studies only investigated media multitasking with academic work while neglecting the effects from general media multitasking in daily life. It has been argued that human’s brain would be changed in the case of media multitasking life style, leading to thirst for instant gratification, settle for quick choices, and lack of patience (Anderson & Rainie, 2012), which is harmful to cognitive processes and will bring on influences on one’s academic performance. Therefore, the current study hypothesized that both media-media multitasking and media-academic multitasking have negative effects on academic performance.
H1b: media-media multitasking has negative effects on academic performance.
Performance
While there are many studies on the relationship between media multitasking and academic performance, academic distractibility is proved to be related to media multitasking and academic performance. Firstly, there are growing number of studies showing that media multitasking with academic work interferes with attention on academic work. It is reported that students with laptop in class were likely to multitasking and had more distraction than those who don’t (Fried, 2008). Students who are sending instant messages while doing academic tasks were reported to have higher ratings of academic distractibility (Levine, Waite, & Bowman, 2007).
Secondly, distraction on academic work is proved to have influence on academic performance. Academic performance was impaired when there were audio interruptions during class as students distracted themselves from what they should focus on in class (Pashler, Kang, & Ip, 2013). Attention deficit, of which symptoms include having difficulty staying focused and paying attention, is suggested to have negative influence on academic performance. Students with Attention Deficit Hyperactivity Disorder (ADHD) have problems in academic environment as they are inattentive and have problems with working memory, thus leading to academic impairment (Daley & Birchwood, 2010). Though academic distractibility is not a developmental disorder as ADHD, it also has presence of inattention, which could possibly have negative influence on academic performance. Since media multitasking may lead to academic distractibility and distraction influence academic performance, it is hypothesized that media multitasking with academic work distracts attention on academic work, and leads to poorer academic performance:
H2a: Academic distractibility mediates the relationship between media-academic multitasking and academic performance.
However, research so far focuses only on the relationship between media multitasking with academic work (MAM) and academic distractibility, while neglecting the situation of general media-media multitasking. As Wallis (2006) pointed out that, “habitual multitasking may condition their (teens’) brains to an overexcited state, making it difficult to focus even when they want to”. Regarding the situation of academic study, it could be inferred from Wallis (2006) that habitual multitasking may develop hyper-attention, making it difficult for college students to focus on academic work when they want to. This will bring on high academic distractibility, and exert negative influence on one’s academic performance. Thus, current study has broadened the research scope to general media-media multitasking, and hypothesized that:
H2b: Academic distractibility mediates the relationship between media-media multitasking and academic performance.
Academic Procrastination as a Mediator between Media Multitasking and Academic Performance
Procrastination has been defined as an interactive dysfunctional and behavior avoidance process in which individual has the tendency to needlessly delay necessary tasks to reach certain goals (Ellis & Knaus, 1979; Lay, 1986; B. Tuckman, 2002). Academic procrastination is seen to have serious consequences for students “whose lives are characterized by frequent deadlines” (Tuckman, 2002, p.1). Many studies have shown that academic procrastination is negatively associated with academic performance among both
high school students and college students (e.g. Beck, Koons, & Milgrim, 2000; Lakshminarayan, Potdar, & Reddy, 2013; Wesley, 1994). Students with high level of procrastination performed below average in their academics compared with those with low level of procrastination. Compared with high school students, college students, who have more flexible timetable and more access to e-devices (e.g. personal laptop and smart phone), have more tendencies to multitask during doing academic works, especially considering that nowadays, many of academic works such as writing term paper, are done with computer. While doing academic work on laptops, students are often concurrently staying with SNSs, browser tabs and other forms of media; their mobile phones are always on the table, waiting them to check; their headphones are on with background music. When students are multitasking with academic works, they not only distract themselves from academic works, but also procrastinate on academic works as they switch to irrelevant tasks. Given enough time, students who media-academic multitask will not only develop procrastination behavior, but also gradually incline to procrastination consciously especially when they find schoolwork difficult, and finally influence their academic performance. Thus, it is hypothesized:
H3a: Academic procrastination mediates the relationship between media-academic multitasking and academic performance.
Similar to previous hypotheses, here it is also hypothesized that general media -media multitasking have influences on academic procrastination. It is predicted that the habitual media multitasking will drive people to be impatient. As people get used to quick switch between tasks, it is likely that they will have problems when dealing with tasks which
requires concentration, such as academic work, and lead to escaping from it by procrastination. Thus, it is hypothesized:
H3b: Academic procrastination mediates the relationship between media -media multitasking and academic performance.
The model
Differences in Media Use and Education System in Two Countries Differences in media use.
One difference in media use is online shopping. Online shopping in China has been developing rapidly in recent years: by the end of 2013, the amount of online shoppers in China has increased to 302 million, contributing to a total amount of 1850 billion RMB (approx. 230 billion euros). Amongst all the online shoppers, 56.4% are from 20 to 29 years old, and 9.8% are under 20 years old (CNNIC, 2014a). These results indicate that online shopping is quite popular in China while young people are the major consumers. On the other hand, only 14% Dutch netizens (i.e. people who have access to Internet) have experience of online shopping by the end of 2013 (Starcom, 2014). Thus it is expected that while Chinese young people do much more online shopping, they have more opportunities to multitask while they are shopping online.
Media-academic multitasking (MAM)/ Media-media
multitasking (MMM)
Academic distractibility (AD)
Academic procrastination (AP)
Academic performance (GPA)
Secondly, media use in TV watching is different between Chinese college students and students in the Netherlands. Most Chinese full-time university students are required to live on campus in shared dormitories, while Dutch students have more freedom to choose their residence. As they are living in shared dormitories where there is no TV set under most conditions, Chinese students’ access to traditional TV is limited. Normally, they can only watch TV in canteens or in the common rooms of dormitories. On the other hand, with more freedom to choose residence, students in the Netherlands have the opportunity to get their own TV set, thus having more time watching TV. Therefore, it is expected that multitasking with TV will be more prevalent in China than in the Netherlands.
Differences in higher education system.
One of the major differences in higher education between China and the Netherlands is the allocation of study load. In most universities in China, a four-year undergraduate student needs 150 to 170 credits to graduate (the minimum credits differ from different disciplines), which leads to an average of 40 credits per year. According to some major universities (e.g. Peking University and Zhejiang University), one credit equals to 15 or 16 hours in class time1, which means that the total amount of in class time is approximately 600 hours per year for an undergraduate student. According to Dutch law, one credit represents 28 hours of work and 60 credits represents one year of full-time study (Nuffic, 2014), which leads to 1680 hours per year. Typically, a 6-credit course requires students to attend class for 32 hours in total, thus the total amount of in class hour is approximately 320 hours per year, which is much fewer than that in China. Thus, it can be inferred that while Dutch universities
1
http://dean.pku.edu.cn/2011xssc/bkkcrdxfzh.htm and
require fewer hours spent in class than Chinese universities, the study load after class is much more than that of China (since there is no specific rule on the total study load in Chinese universities, here it assumes that universities in two countries do not differ much on total study load). As students spend much more time in class in China, Chinese students have more opportunities to multitask during class. On the other hand, multitasking after class while doing academic work may be more prominent in the Netherlands as students spend more time on academic work after class.
Another important difference is the evaluation and expectation of higher education in two countries. According to Dutch Qualifications Framework (NLQF), both undergraduate and graduate education emphasize the ability of applying knowledge, including reproduce, analyze and integrates knowledge, and analyze complex professional and scientific tasks and execute it (European Qualification Frameworks, 2014). Thus, the evaluation of students’ performance also emphasizes the creativity and application ability. Though China does not have such qualification framework for higher education, it has been argued that the assessment on college students in China is more focused on basic knowledge, skills and theories, with close-book exams as the most common way of assessment (C. Yang, 2008). Because the way of evaluating academic performance differs between two countries, it is expected that the influence of academic distractibility on academic performance will be different in two countries. As the evaluation of close-book exams emphasize on short-term memorization instead of long-term effort, it requires Chinese students to cram for the exam by reading and memorizing academic materials. Therefore, academic distractibility is expected to be vital to gain a high GPA. While for students in the Netherlands, distraction on
doing academic work may not be a significant predictor of GPA. The Current Study
The current study was to investigate the relationship between media multitasking and academic performance. It predicts that the relationship between media multitasking and academic performance is mediated by academic distractibility and academic procrastination. All variables were measured by self-report questionnaires.
The current study filled in the research gap in two domains: 1) So far, no research has investigated the relationship between media-academic multitasking, academic procrastination and academic performance. However, from observation and news report (Goodwill, 2013), academic procrastination is found to be associated with media multitasking, which would finally influence academic performance. The study also included academic distractibility to gain a deeper insight into the relationship between media multitasking and academic performance. 2) The current study investigated college students studying in both the Netherlands and China. It is expected that differences in media use and higher education system would lead to differences in media multitasking and its influence on academic performance. While it is predictable that multitasking with online shopping would be more prominent in China, multitasking with watching TV would be more prominent in the Netherlands.
Method Sample and Procedures
Data was collected among 201 college students from both undergraduate and graduate, aged from 17 to 40 years old (M = 22.9, SD = 3.66, 72.6 % female) with 102 studying in the Netherlands (Age: M = 24.12, SD = 4.52, 62.7% female) and 99 studying in China (Age: M = 21.60, SD = 1.77, 83.1% female). Among all participants, 55.2 % are Chinese (including Chinese studying in both the Netherlands and in China), 30.8 % are Dutch, and the rest are from other nationalities.
The English version of online survey link was sent to students in University of Amsterdam randomly using UvA student email directory. In total, 1305 emails were sent t o students’ UvA email addresses. Participants were also approached by Facebook personal groups and contacts, in which the link of the online survey was posted. The Chinese version of online survey link was posted on QQ (instant message software similar to MSN that is widely used in China) groups in university students as group members, and via online forums (e.g. www.douban.com) which has sub-groups of university students as members. All participants were requested to fill in the survey online, which took a pproximately 15 minutes to complete. The data collection lasted for 17 days, from 28 April to 13 May, 2014. While the Netherlands is a technology-savvy country which ranks third in the Internet penetration rate in the world (EurostatNewsrelease, 2013), China has much lower rate of 45.8%. However, statistics show that the majority of Chinese people who have access to Internet are from 20 to 29 years old (31.2%)(CNNIC, 2014b), and research revealed that Internet access rates among college students in China’s major cities (i.e. Beijing, Shanghai, Guangzhou, Wuhan and Xi’an) has reached 84.1% to 96.9%(Cai & Li, 2010; Liu et al., 2005; You & Zhong, 2004). Therefore, the coverage of online survey mode is not biased.
Measures
Media use and media multitasking.
Media multitasking was measured by self-report questionnaire which was adapted from Baumgartner (2014), Pea et al. (2012) and Ophir et al. (2009). In previous studies, Ophir et al. (2009) used long media list which consisted of twelve media activities. While the list included most media activities, it is hard to be executed due to the large amount of media multitasking combination, especially when it combines measures on other variables. Baumgartner's (2014) short media multitasking measure for adolescents consisted of four major media activities (i.e. watching TV, listening to music, sending messages via phone or computer and using social networking sites), and was demonstrated to be internally and externally valid and capture the main media multitasking activities (Baumgartner, 2014). Based on Baumgartner's (2014) measure, six media activities were assessed: 1) watching TV on TV set, 2) watching computer-based videos, 3) texting, 4) using social networking sites, 5) listening to music and 6) shopping online. Shopping online was included as it is prosperous in China and especially popular among college students (CNNIC, 2014a). Only electronic media activities were chosen as it is suggested when using electronic media, college students tend to multitask (Jacobsen & Forste, 2011). Except for listening to music, the other five media activities were assessed as both primary and secondary activities (listening to music is only assessed as secondary activity). Two academic-related activities were also assessed: 1) having classes/lectures at school and 2) doing academic work outside of school hours.
Students were asked how many minutes they usually spend on these eight activities on an average day. Moreover, students were asked when they were engaging in each medium
or in each academic-related activity, how often they use other media simultaneously. For example, “while doing academic works, how often are you listening to music at the same time?” Answer categories range from “Never” (1) to “Most of the time” (4).
Individual level of media multitasking is presented by media-media multitasking index (MMMI) (China: M=10.21, SD=4.08; Netherlands: M = 5.62, SD = 3.67) and media-academic multitasking index (MAMI)(China: M = 3.93, SD = 1.67; Netherlands: M = 2.84, SD = 1.47) adapted from Ophir et al.’s and Pea et al.’s (2012) measures. To create these two indexes, the categories on frequency of multitasking were assigned to numeric values as follows: “Most of the time” (1), “Some of the time (0.67)”, “A little of the time” (0.33), and “Never” (0). For each primary medium activity or academic-related activity, the multitasking time with other media was the sum of the calculated results of each simultaneous activity. The MMMI or MAMI was created by summing all the multitasking time across primary activities and divided by the total number of minutes per day spent with all primary media or all academic-related activities. The formula is as follows:
MMMI = ∑m𝑖× h𝑖 h𝑡𝑜𝑡𝑎𝑙 6 i=1 MAMI = ∑m𝑖× h𝑖 h𝑡𝑜𝑡𝑎𝑙 2 i=1
where m𝑖 is the sum of multitasking level of primary activity i (e.g. m𝑖= 0.33+0.67+0.33+0+0=1.33), h𝑖 is the amount of time spent on primary activity i, and h𝑡𝑜𝑡𝑎𝑙 is the total amount of minutes spent on all media or academia activities per day. High MAMI or MMMI on a primary activity i indicates that the people are often engaged in other media
content while using media i. The correlation between MMMI and MAMI is r = .47, p < .001 in China and r = .41, p < .001 in the Netherlands.
Academic procrastination.
Academic procrastination was measured by Tuckman's (1991) shortened procrastination scale which contained 16 items (China: M = 2.39, SD = .47; Netherlands: M = 2.46, SD = .54). The shortened scale “was recommended for use as a means of detecting students who may tend to procrastinate in the completion of college requirements” (Tuckman, 1991, p.473) and was proved to be reliable among students in western countries (Tuckman, 2002; Steel, 2010) and in China (Zhang, 2007). An example of the items is “I needlessly delay finishing my works, even when they are important”. The response categories used a four-point Likert scale which was 1 (“that’s me for sure”), 2 (“that’s my tendency”), 3 (“that’s not my tendency”) and 4 (“that’s not me for sure”). Lower score means higher level of academic procrastination. The Chinese version of Tuckman’s academic procrastination was translated by the author. The Cronbach’s alpha of English version and Chinese version are .90 and .85 respectively.
Academic distractibility.
Academic distractibility was measured by 4 items developed by Levine et al. (2007) which were specifically to measure distractibility for academic tasks among college students (China: M = 2.79, SD = .73; Netherlands: M = 2.92, SD = .75). A five-point Likert scales ranging from “strongly disagree” (1) to “strongly agree” (5) were used as response categories. Higher score means higher level of academic distractibility. An example of the item is “I find it easy to focus on assigned readings”. The Chinese version of academic distractibility scale
was translated by the author. The Cronbach’s alpha of English version and Chinese version are .63 and .70 respectively. The reliability is a bit problematic as the English version does not meet the standard of .70 for early stages of research (Nunnally & Bernstein, 1994). The results of Principal Component Analysis (using varimax rotation) showed that one component was extracted in both the English version and the Chinese version. From the results of Principal Component Analysis, the first item “I find it easy to focus on assigned readings” have a much lower factor loading (.40) than the other items (all above .80) in the Chinese version, but the factor loadings of all items in the English version do not differ significantly (.67, .77, .68 and .63 respectively). Therefore, all four items are kept in analysis.
Academic performance.
The academic performance was measured by GPA. In the Netherlands, as GPA is measured by a 10-point scale, participants in English version were asked to fill in their GPA on a scale of 0 to 10. In China, both 4.0 scale and 5.0 scale are used by different universities. Participants were asked to indicate their scale first before they fill in their GPA. All scales of GPA were standardized to z-score in comparison among two countries. The original average GPA in the Netherlands is 7.10 (N = 102, SD = 1.16), 3.50 (5.0 scale, N = 39, SD = .57) and 3.05 (4.0 scale, N = 60, SD = .56) in China.
Control variables.
Participants’ gender, age, year of school, time spent on schoolwork and field of study were also recorded as control variables in measuring the effect of media multitasking on academic performance.
All analyses were conducted by SPSS 20.0 and consisted of four stages. MMMI and MAMI which were generated based on six media activities and two academia activities were predictor variables. Three types GPA scores were standardized. Firstly, descriptive analysis on media use and academia time spent was conducted. Secondly, independent t-test was conducted between Chinese group and Dutch group on their differences on media multitasking. Thirdly, correlation analysis was used to explore the relationship between all mediators, independent and dependent variables. Finally, regression analysis was used for H1a (media-academic multitasking as predictor variable, and academic performance as dependent variable) and H1b (media-media multitasking as predictor, and academic performance as dependent variable). The PROCESS SPSS macro developed by Hayes (2012) was used for H2a, H2b, H3a and H3b in which academic distractibility and academic procrastination were used as mediators respectively. PROCESS SPSS estimates direct paths as well as indirect paths of mediators and produces bootstrap confidence intervals for testing the indirect effect.
Results Descriptive Statistics
Table 1 presents the means and standard deviations of overall time spent on media activities and academia activities as well as two types of media multitasking in two countries. Chinese students spent more time on both media activities and academia activities. Chinese students spent much less time on watching TV on TV set and listening to audio, and students in the Netherlands spent much less time on online shopping, texting and SNSs. Regarding
multitasking level, the average MMMI is 1.70 in China and .94 in the Netherlands, which are both lower than previous research among American students (Ophir et al., 2009) and Chinese students (Yang & Zhu, 2014). Both students in Chinese group and in Dutch group have higher score in MAMI than in MMMI. Chinese students have significantly higher scores than students in the Netherlands in both MMMI and MAMI.
Independent t-test results show that the differences between two countries are significant in MAMI, t(201) = 4.86, p <.001, and in MMMI, t(201) = 8.34, p <.001. Media multitasking was most prominent during class among Chinese students, followed by multitasking with academic-related work after class. While in Dutch group, the ranking of these two multitasking activities is reversed. The least prominent multitasking activity is media multitasking while watching TV in Chinese group, and shopping online in Dutch group. Gender differences in MAMI and MMMI are only found in Chinese group, in which girls are found more likely to media multitasking while doing academic-related work (i.e. in class and while doing academic work after class), t(101) = -3.17, p = .002, and while engaged in other media activities, t(101) = -3.33, p = .001. Differences in MAMI and MMMI are not found in different fields of study in both countries. The correlations between all independent variables and dependent variables are presented in Table 2.
Table1. Means and standard deviations of overall time spent on all media and academia activities, media multitasking of each primary activity, media-academic multitasking index (MAMI) and media-media multitasking index (MMMI) in two countries.
Country Total time (min) Multitasking index
China TV 7.39 14.28 .03 .06 Computer-based video 103.01 80.00 .47 .36 Audioa 53.99 64.87 / / Texting 136.43 150.21 .52 .40 SNSs 112.65 130.96 .45 .35 Online shopping 56.10 48.85 .24 .21 In class 221.90 104.43 1.32 .86 Doing academic assignments 76.16 66.66 .65 .52
All media activities 470.77 336.85 1.71 .68
All academic activities 298.96 120.30 1.97 .84
Netherlands TV 36.16 51.79 .12 .19 Computer-based video 105.16 87.54 .31 .32 Audioa 139.36 131.67 / / Texting 52.08 56.70 .23 .27 SNSs 55.20 63.70 .25 .24 Online shopping 9.18 23.44 .04 .09 In class 108.94 95.59 .33 .42 Doing academic assignments 153.90 149.44 1.11 .70
Note. MAMI is the multitasking index of “All academia activities” and MMMI is the multitasking index of “All media activities”.
a
Listening to audio is not taken as a primary activity, thus the multitasking index is not applicable.
Table 2. Correlations between all mediators, independent and dependent variables MMMI GPA Distractibility Procrastination
China MAMI .46** .05 .16 -.15 MMMI -.04 -.03 -.03 GPA -.29** .09 Distractibility -.24* Procrastination Netherlands MAMI .39** -.03 .36** -.28** MMMI -.12 .12 -.13 GPA -.15 .06 Distractibility -.43** Procrastination *p <.05, **p <.01
Relationships between two types of media multitasking and academic performance Multiple linear regressions were conducted to investigate the relationships between two types of media multitasking (i.e. media-media multitasking and media-academic multitasking) and academic performance. Time spent on schoolwork, gender, age, year of
school and field of study were controlled for in the analysis. According to H1a and H1b, the two types of media multitasking were entered separately as predictors. The regression analysis with the overall sample were insignificant, with B = .05, SE = .09, β = .04, p = .56 for the relationship between MAMI and GPA, and B = -.03, SE = .10, β= -.03, p = .80 for MMMI and GPA. Results in Dutch group indicated that neither MAMI nor MMMI predicted the GPA, with B = -.04, SE = .13, β = -.03, p = .80 and B =-.17, SE =.17, β = -.10, p =.34 respectively. In Chinese group, the effects of MAMI and MMMI were also insignificant, with B = .13, SE = .13, β = .11, p = .32 and B = -.11, SE = .16, β = -.08, p = .47 respectively. Thus, H1a and H1b were rejected. The level of media-media multitasking and media-academic multitasking are not significantly related to GPA in this study.
In the Chinese sample, bivariate correlation results indicated that after class media multitasking was significantly correlated with time spent on academic work afterschool, with r = .48, p <.05, and time spent on after school academic work was a significant predictor of GPA, with B =.004, SE = .002, β =.24, p < .05. In the Netherlands, in class media multitasking was significantly correlated with in class time, with r = .44, p < .05, but time spent on either in class or after class academic work was not predictable of GPA.
The indirect effect of academic distractibility
Haye’s PROCESS (model 4 with 1000 bootstrap samples) was conducted to investigate the mediator effect of academic distractibility in the relationship between two types of media multitasking and academic performance. Firstly, the model was tested across two countries in which MAMI and MMMI were used as independent variables separately, standardized GPA as outcome variable, and country as covariate. The model results revealed
a significant indirect effect of academic distractibility in the relationship between MAMI and GPA, with a p = .002 and a bias-corrected and accelerated 95% confidence interval of -.15 to -.03. The indirect effect of inclusion of academic distractibility as mediator is B = -.07, SE = .03, p = .002. The indirect effect of academic distractibility in the relationship between MMMI and GPA is not significant, with a p = .56 and a bias-corrected and accelerated 95% confidence interval of -.07 to .02.
Secondly, the model was tested in two countries separately. The bootstrap samples were increased to 10,000 to be trustworthy. The indirect effect of academic distractibility in the relationship between MAMI and GPA in Chinese group is not significant, with a p = .16 and a bias-corrected and accelerated 95% confidence interval of -.17 to .00. The indirect effect is not significant in the relationship between MMMI and GPA in Chinese group, with a p = .56 and a bias-corrected and accelerated 95% confidence interval of -.06 to .10. In the Dutch group, the indirect effects in the relationship between MMMI and GPA as well as MAMI and GPA were both insignificant, with p value of .18 and .40 respectively. Thus, H2b is rejected as indirect effect of academic distractibility was not found in the total model with samples from two countries, or either of the separate models with samples from China or the Netherlands. H2a is only confirmed in the total model with samples from two countries, but rejected in separate models.
The indirect effect of academic procrastination
Haye’s PROCESS (model 4 with 1000 bootstrap samples) was conducted with academic procrastination as mediator. Firstly, the model was tested across two countries in which MAMI and MMMI were used as independent variables separately, standardized GPA
as outcome and country as covariate. In the relationship between MAMI and GPA, the indirect effect of academic procrastination is not significant, with a p = .31 and a bias-corrected and accelerated 95% confidence interval of -.08 to .01. The indirect effect is not found in the relationship between MMMI and GPA, with a p = .51 and a bias-corrected and accelerated 95% confidence interval of -.06 to .00.
Secondly, the model was tested in the Chinese and Dutch sample separately. Again, the indirect effects were not found in either group with MMMI and MAMI as predictors separately (see Table 3).
The complete model
The final step of PROCESS tested the whole model (model 4 with 1000 bootstraps), in which MAMI and MMMI were used as predictors separately, academic procrastination and academic distractibility as mediators, and GPA as outcome. The model was tested in the Chinese sample, the Dutch sample and in the overall sample including both countries.
In the model with sample across two countries, the country was entered as covariate. In the relationship between MAMI and GPA, the total effect of the model is not significant with p = .99, and the total effect of MAMI on GPA is not significant, with 95% confidence interval of -.17 to .18. The total indirect effect of academic distractibility and academic procrastination on GPA is significant, with 95% confidence interval of -.15 to -.02, SE = .35, B = -.08. However, the individual indirect effect of academic procrastination is not significant, with 95% confidence interval of -.05 to .03. The individual indirect effect of academic distractibility is significant, with a 95% confidence interval of -.15 to -.02, SE = .32, B = -.07. The total effect of MMMI on GPA and the indirect effect in the relationship between MMMI
and GPA are not significant. In the individual model for each country with either type of media multitasking, no indirect effect of academic performance and academic distractibility is found in either country.
Table 3. Indirect effect of academic distractibility (AD) and academic procrastination (AP)
Model Predictor Mediator
AD only AP only AD and AP
95% CI interval Total MAMI [-.15, -.03]* [-.08, .01] [-.16, -.02]* MMMI [-.07, .02] [-.06, .00] [-.07, .03] China MAMI [-.17, .00] [-.09, .00] [-.17, -.00] MMMI [-.16, .10] [-.10, .01] [-.16, .11] Netherlands MAMI [-.21, .02] [-.12, .04] [-.23, .04] MMMI [-.14, .01] [-.10, .02] [-.15, .03] *p <.05
Figure 1. Unstandardized coefficients in each model with both mediators Sample=China and the Netherlands (N = 201)
.02 -.14** .09 -.31** .24*** Media-academic multitasking (MAM)
Academic distractibility (AD)
Academic procrastination (AP)
Academic performance (GPA)
Sample=China (N = 99) Sample=the Netherlands (N =102) -.01 -.06 -.10 -.30** .05 Media-media multitasking (MMM)
Academic distractibility (AD)
Academic procrastination (AP)
Academic performance (GPA) .08 -.09 .12 -.41** .14 Media-academic multitasking (MAM)
Academic distractibility (AD)
Academic procrastination (AP)
Academic performance (GPA) .05 -.02 -.07 -.39* .04 Media-media multitasking (MMM)
Academic distractibility (AD)
Academic procrastination (AP)
Academic performance (GPA) .02 -.21** .02 -.21 .37*** Media-academic multitasking (MAM)
Academic distractibility (AD)
Academic procrastination (AP)
Academic performance (GPA) -.02 -.12 -.16 -.20 .15 Media-media multitasking (MMM)
Academic distractibility (AD)
Academic procrastination (AP)
Academic performance (GPA)
*p <.05, **p <.01, ***p <.001
Figure 1 shows the results of unstandardized coefficients in each model with different samples. The results reveal that the effects from academic distractibility on GPA are significant except in the Dutch sample. The effects from media-academic multitasking on academic distractibility are significant except in the Chinese sample. Academic procrastination is significantly correlated with media-academic multitasking except in the Chinese sample.
Discussion
In the recent decade, new media technologies have revolutionized media users’ media consumption, bringing about more opportunities to multitask with media while consuming other media content or doing non-media activities. Media multitasking has been reported to be an increasing phenomenon among young generations (Carrier et al., 2009; Foehr, 2006; Rideout et al., 2010), and research has revealed that it has negative effects on academic performance among students (Jacobsen & Forste, 2011; Junco & Cotten, 2012; Junco, 2012a; Karpinski et al., 2013; Kirschner & Karpinski, 2010; Rosen et al., 2013). Therefore, the current study explored the relationship between two types of media multitasking (i.e. media-media multitasking and media-academic multitasking) and academic performance among college students in China and the Netherlands, and developed models to investigate the underlying mechanism in this relationship with inclusion of academic distractibility and academic procrastination as mediators.
is a common phenomenon among college students. Students in both countries have higher media multitasking level in media-academic multitasking than in media-media multitasking. This is in line with Goodwill's (2013) observation on college students’ media multitasking. Chinese students have higher levels of media-academic multitasking and media-media multitasking than students in the Netherlands. While Chinese students multitask more during class, mostly with texting and SNSs, students in the Netherlands multitask more during doing their schoolwork, mostly with listening to audio and texting. One possible explanation could be that Chinese students spend much more time in class than students in the Netherlands, thus providing them with more opportunities to multitask in class. Texting and using SNSs on mobile phone are not detected easily by lecturers compared with other activities such as listening to audio. While for students in the Netherlands with more time spent on schoolwork, they have more chances to multitask while doing academic work after class.
Inconsistent with prior studies, the effects of media-media multitasking and media-academic multitasking on GPA were not significant. Firstly, there might exist other variables that are more predictable of academic performance than media multitasking level. Previous research has proved that GPA is also influenced by demographic factors, such as highest education of parents and ethnicity (Junco & Cotten, 2012). High school GPA and time spent on preparing schoolwork have influences on GPA in college (Junco & Cotten, 2012; Karpinski et al., 2013). This is in accordance with the result in the Chinese sample, in which time spent on afterschool academic work is a significant predictor of GPA.
multitasking on GPA varied depending on the specific type of media activities, with some activities, for instance, multitasking while doing schoolwork with SNSs associated with low GPA while other concurrent media activities not. This could also be an explanation for the insignificant relationship between media-academic multitasking and GPA in the current study, as it only measured media-academic multitasking as whole, but did not distinguish the effects among each media-academic multitasking activity. It could be that while some specific types
of media multitasking activities contributed to low GPA, others did not, thus making the relationship insignificant.
Finally, the results of media-academic multitasking with each secondary media activity yielded that multitask with texting is the most prominent multitasking activity in both countries (China: texting in class: .60, texting while doing schoolwork: .58; the Netherlands: texting in class: .47, texting while doing schoolwork: .36). According to Fox, Rosen, & Crawford (2009), texting is “a type of negotiated interruption” (Fox, Rosen, & Crawford , 2009, p.53) in which a participant has freedom to decide whether and when to switch to rather than being forced to do so upon an intrusive interruption. As participants are aware of the interruption before the switch, they are also likely to be prepared of the interruption, such as remembering where they have stopped their primary activities, and can be transitioned back to the primary activity once they have finished the interruption activity. Research has also proved that when participants are warned before the occurrence of an interruption during their primary activity, the disrupted level is less (Adamczyk & Bailey, 2004). In this way, media multitasking with texting while doing schoolwork will not have detrimental influence on one’s study, as students are able to decide when to switch between tasks.
However, although the direct effect of media multitasking on academic performance was not significant, the direct effects of media-academic multitasking on academic distractibility and of academic distractibility on GPA were both found significant in the sample of two countries. The indirect effect of academic distractibility between media-academic multitasking and academic performance was also found in the model with two countries’ sample, but the effect size is weak. This indicated that across two countries, though the mediator effect of academic distractibility was not found, the level of media-academic multitasking is positively correlated with academic distractibility, and the level of academic distractibility is negatively correlated with GPA.
Several possible explanations may account for this. Firstly, there might be potential suppressors that suppressed the relationship between media multitasking and academi c performance. These variables may have had opposing effects from media multitasking on academic performance. For example, while previous research (Jacobsen & Forste, 2011; Junco & Cotten, 2012; Rosen et al., 2013) argued that high level of media multitasking leads to lower GPA, students with high level of media multitasking while doing schoolwork may also spend more time on schoolwork, thus contributing to higher GPA. The positive correlation between media-academic multitasking while doing schoolwork and the time spent on school work, and the positive correlation between time spent on school work and GPA in China supported this presumption.
Secondly, the significant negative effect of academic distractibility on GPA in the model with two countries’ sample may imply that, instead of a mediator between media-academic multitasking and GPA, academic distractibility itself is a predictor of GPA.
Since no direct effect was found between media-academic multitasking and GPA, it is assumed that zero-correlation was established between these two variables. As the effect from academic distractibility on GPA is significant, and academic distractibility correlated with media-academic multitasking well, it is possible that while high level of academic distractibility leads to low GPA, media-academic multitasking and academic distractibility are mutually correlated.
In separate model, the direct effect of academic distractibility on GPA is only significant in China. Since the items measuring academic distractibility are all about reading tasks after school (e.g. “I get distracted easily when reading class assignments”), it could be inferred that the distraction of reading assignments is an important factor to GPA in China. This is in line with the evaluation of academic performance in Chinese universities, which has been argued to have more close-book exams, thus requiring students to cram for the exam by reading and memorizing academic materials in short time before the exam (C. Yang, 2008). While in the Netherlands, multiple types of evaluations including term paper, presentation and teamwork assignment are applied to measure students’ academic performance, and emphasizes on application ability. Thus, it can be inferred that academic distractibility, especially on reading academic materials, is not a key predictor of GPA in the Netherlands.
Secondly, the direct effect from media-academic multitasking on academic distractibility is only significant in the Netherlands. Compared with Chinese students, students in the Netherlands spend more time on after school academic work and also multitask more while doing academic work. This matches with the measurement of academic distractibility, in which the items measured the level of distraction while reading academic
materials after school. While in China, with less time spent on after school aca demic work and lower level of media multitasking while doing academic assignments, the relationship between media-academic multitasking and academic distractibility is not established. Also, though Chinese students multitask much more during class, the effect on academic distractibility is not found. Thus, it could be inferred that, in class media multitasking is not significantly related to academic distractibility of reading academic materials after school.
The indirect effect of academic procrastination in the relationship between two types of media multitasking and academic performance was not found in any situation. Inconsistent with previous studies, the direct effect from academic procrastination on academic performance was also not found. While academic procrastination was demonstrated to be influential in many studies (e.g. Akinsola, Tella, & Tella, 2007; Lakshminarayan, Potdar, & Reddy, 2013; Wesley, 1994), as discussed above, there are other factors that also contribute to academic performance, such as demographic factors and high school GPA. Specifically, the direct effect from media-academic multitasking on academic procrastination was only found in the Netherlands’ sample. The explanation may be similar to the effect from media-academic multitasking on academic distractibility, that students in the Netherland spend more time on after school academic work than in class, and most items in academic procrastination measured the procrastination situation after class, such as “when I have a deadline, I wait until the last minute”. With more time spent on after school academic work and more media multitasking while doing academic work after school, it is not surprising to find a significant negative effect on academic procrastination after class. However, the direction could be reversed, as it is likely that students with more tendencies to procrastinate
incline to multitask with media activities to escape from doing academic assignments.
Finally, media-media multitasking does not seem to have any effects on academic performance, nor it has any influences on academic distractibility and academic procrastination. Though it has been argued that, media multitasking life style might change our brain functions, impair the ability to focus and understand complex concepts (Goodwill, 2013; Wallis, 2010), the results from current studies does not support this point of view. The level of general media-media multitasking does not influence the learning process or the outcome. However, this may also be because of the relatively lower media multitasking index compared with previous studies (Ophir, Nass, & Wagner, 2009; Yang & Zhu, 2014), which may exclude high level multitaskers in the sample.
Limitations
The major limitation of this study lies in the method. Firstly, the gender distribution in the sample is biased with more female than male. Previous study has shown that female are more likely to engage in media multitask (Baumgartner et al., 2014). However, confined by the relatively small sample size, it is not meaningful to compare across genders.
Secondly, media use questionnaire is not the best method to measure media multitasking. Participants found it difficult to estimate the total amount of media use. Previous research has applied survey of previous day to make it easier to recall the amount of media use (Shih, 2013), however, this method is often combined with other methods such as media diary as it is inaccurate to represent daily media use with the situation of yesterday. Also, the perception on the frequency of multitasking differs among participants, for example, texting for 15 minutes during a one-hour study may be seen as “some of time” for one
participant, but “a little of time” for another, so they are transferred into different numeric numbers, resulting in different amount of multitasking. To estimate media multitasking more accurately, it is suggested using media diary as the main method in the future.
Thirdly, the questionnaire needs improvement. Wwhile the calculation of media multitasking index seems to be easy by asking each combination of multitasking twice to distinguish from primary and secondary activities, participants may find it confusing to answer the same question twice, and may wonder the differences between doing A while doing B and doing B while doing A. Total time spent on texting was reported to be hard to
estimate as it is an inconsecutive activity. In future research, the design of survey needs
elaboration to fit in participants’ needs.
Fourthly, GPA should not be taken as the only measurement of academic performance especially in a cross country study as evaluation standards differ among countries. Also, as discussed above, academic performance is influenced by many factors, and some of them cannot be measured through survey, such as IQ. On the other hand, experiment can be used to evaluate the effect of multitasking on learning competence. In previous research, Wood et al. (2012) found that students who multitasked with SNS during class scored significantly lower on tests of comprehension on lecture material than those who did not.
The final limitation in method is the measurement on academic distractibility. All items only measured the situation after school, while neglecting possible distractibility in class. Since in class media multitasking in China is relatively high compared with in the Netherlands, future study may want to include items on in class academic distractibility, such as listening to class and taking notes in the measurement, and explore the relationship
between in class media multitasking and academic distractibility specifically.
In spite of limitations in method, the cross-sectional design of this study determines that it is not possible to establish causal relationships between any two variables. Though the media-academic multitasking was found related to academic distractibility in the Netherlands and also in the model with two countries’ sample, the direction of this effect cannot be determined. It could also be that students with high level of academic distractibility tend to media multitask during class or while doing schoolwork as they find it difficult to concentrate on class or schoolwork. This also applies in the situation with academic procrastination, with possibility of heavy procrastinators use media multitasking as a strategy to avoid doing academic work. Further longitudinal studies are needed to establish the causal relationships, and also controlled for variables such as study motivation, personality characteristics and time management skills (Quan-Haase, 2010).
Conclusion
Results from the present study shows that media-academic multitasking is a common phenomenon among college students in China and in the Netherlands, with texting as the most prominent multitasking activity. Neither media-academic multitasking nor media-media multitasking is correlated with academic performance. The indirect effect of academic distractibility is only found in the relationship between media-academic multitasking and academic performance in the total model including both countries, and the effect size is weak. The indirect effect of academic procrastination is not found in any relationships between either media multitasking type and academic performance, but is related to media-academic
multitasking in the Netherlands. Thus, the underlying mechanism in the relationship between media multitasking and academic performance needs further discussion of inclusion with other mediators. The measurement of media multitasking needs elaboration to improve accuracy. General media-media multitasking was not found to be related to academic performance, academic distractibility or academic procrastination. Therefore, it might be exaggerated to assume that media multitasking lifestyle has negative influence on learning process and outcome.
Reference
Adamczyk, P., & Bailey, B. (2004). If not now, when? The effects of interruption at different moments within task execution. In Proceedings of the ACM Conference on Human Factors in Computing Systems,, 271–278. Retrieved from
http://citeseer.uark.edu:8080/citeseerx/showciting;jsessionid=F0F786E3F3F07553F838 324DD140D42D?cid=130865&sort=recent
Akinsola, M., & Tella, A. (2007). Correlates of academic procrastination and mathematics achievement of university undergraduate students. Eurasia Journal of Mathematics, Science & Technology, 3(4), 363–370. Retrieved from
http://www.sciencedirect.com/science/article/pii/002244059390033F Anderson, J., & Rainie, L. (2012). Millennials will benefit and suffer due to their
hyperconnected lives. Pew Research Center. Retrieved from
http://www.pewinternet.org/files/old-media/Files/Reports/2012/PIP_Future_of_Internet_ 2012_Young_brains_PDF.pdf
Baumgartner, S. E. (2014). Development of a short measure of media multitasking for adolescents(in submission), 15, 1–7.
Baumgartner, S. E., Weeda, W. D., van der Heijden, L. L., & Huizinga, M. (2014). The relationship between media multitasking and executive function in early adolescents. The Journal of Early Adolescence. doi:10.1177/0272431614523133
Beck, B. L., Koons, S. R., & Milgrim, D. L. (2000). Correlates and consequences of
behavioral procrastination: The effects of academic procrastination, self-consciousness, self-esteem and self-handicapping. Journal of Social Behavior & Personality, 15(5), 3– 13.
Cai, X., & Li, L. (2010). 在校大学生上网行为的调查研究——基于某高校问卷调查的分 析. 电子商务, 12, 37–39. Retrieved from
http://qkzz.net/article/8b31b65b-2d09-44cc-a28f-e24a57e8e91b.htm
Carrier, L. M., Cheever, N. a., Rosen, L. D., Benitez, S., & Chang, J. (2009). Multitasking across generations: Multitasking choices and difficulty ratings in three generations of Americans. Computers in Human Behavior, 25(2), 483–489.
doi:10.1016/j.chb.2008.10.012
Chun, M. M., Golomb, J. D., & Turk-Browne, N. B. (2011). A taxonomy of external and internal attention. Annual Review of Psychology, 62, 73–101.
doi:10.1146/annurev.psych.093008.100427
CNNIC. (2014a). 2013 statistic report on online shopping market in China. Beijing. CNNIC. (2014b). 33rd statistical report on Internet development in China. Beijing.
Daley, D., & Birchwood, J. (2010). ADHD and academic performance: Why does ADHD impact on academic performance and what can be done to support ADHD children in the classroom? Child: Care, Health and Development, 36(4), 455–64.
doi:10.1111/j.1365-2214.2009.01046.x
Ellis, A., & Knaus, W. (1979). Overcoming procrastination: Or how to think and act rationally in spite of life’s inevitable hassles. Signet. Retrieved from
http://www.amazon.com/Overcoming-Procrastination-Rationally-Inevitable-Hassles/dp/ 0451141865
European Qualification Frameworks. (2014). National Coordination NLQF - NLQF levels. Retrieved June 15, 2014, from http://www.nlqf.nl/nlqf-niveaus
EurostatNewsrelease. (2013). Internet access and use in 2013.
Foehr, U. (2006). Media multitasking among American youth: Prevalence, predictors and pairings. Henry J. Kaiser Family Foundation, (December). Retrieved from
http://files.eric.ed.gov/fulltext/ED527858.pdf
Foerde, K., Knowlton, B. J., & Poldrack, R. a. (2006). Modulation of competing memory systems by distraction. Proceedings of the National Academy of Sciences of the United States of America, 103(31), 11778–11783. doi:10.1073/pnas.0602659103
Fox, A. B., Rosen, J., & Crawford, M. (2009). Distractions, distractions: does instant
messaging affect college students’ performance on a concurrent reading comprehension task? Cyberpsychology & Behavior, 12(1), 51–53. doi:10.1089/cpb.2008.0107
Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50(3), 906–914. doi:10.1016/j.compedu.2006.09.006
Goodwill, L. (2013). College students Media-savvy multitaskers or scatterbrained procrastinators. Yahoo News. Retrieved from
http://news.yahoo.com/college-students--media-savvy-multitaskers-or-scatterbrained-pr ocrastinators--184404200.html
Heitin, L. (2013). Media multitasking: The new marshmallow test. Retrieved April 21, 2014, from
http://blogs.edweek.org/teachers/teaching_now/2013/05/media_multitasking_the_new_ marshmallow_test.html
Jacobsen, W. C., & Forste, R. (2011). The wired generation: academic and social outcomes of electronic media use among university students. Cyberpsychology, Behavior and Social Networking, 14(5), 275–280. doi:10.1089/cyber.2010.0135
Junco, R. (2012a). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58(1), 162–171. doi:10.1016/j.compedu.2011.08.004
Junco, R. (2012b). Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance. Computers in Human Behavior, 28(1), 187–198. doi:10.1016/j.chb.2011.08.026
Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59(2), 505–514.
doi:10.1016/j.compedu.2011.12.023
Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An exploration of social networking site use, multitasking, and academic performance
among United States and European university students. Computers in Human Behavior, 29(3), 1182–1192. doi:10.1016/j.chb.2012.10.011
Keller, S. (2011). 38% of college students can’t go 10 minutes without tech [STATS]. Retrieved April 05, 2014, from
http://mashable.com/2011/05/31/college-tech-device-stats/
Kirschner, P. a., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237–1245. doi:10.1016/j.chb.2010.03.024 Lakshminarayan, N., Potdar, S., & Reddy, S. G. (2013). Relationship between procrastination
and academic performance among a group of undergraduate dental students in India. Journal of Dental Education, 77(4), 524–528. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/23576599
Lay, C. H. (1986). At last, my research article on procrastination. Journal of Research in Personality, 20(4), 474–495. doi:10.1016/0092-6566(86)90127-3
Levine, L. E., Waite, B. M., & Bowman, L. L. (2007). Electronic media use, reading, and academic distractibility in college youth. Cyberpsychology & Behavior, 10(4), 560–566. doi:10.1089/cpb.2007.9990
Liu, L., Lixin, J., Guang, S., Dongying, Z., Shuang, W., & Haiyan, H. (2005). Survey on Internet addicted symptons among college students. Chinese Journal of Behavioral Medical Science, 14, 1123. Retrieved from
http://medcontent.metapress.com/index/A65RM03P4874243N.pdf
Mayer, R. E., & Moreno, R. (2003). Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 43–52. doi:10.1207/S15326985EP3801_6
Nuffic. (2014). Higher education system in the Netherlands, 1–4.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd editio., pp. 264–265). New York: NY: . McGraw- Hill Series in Psychology, McGraw-Hill, Inc. Retrieved from http://www.amazon.com/Psychometric-Theory-Jum-C-Nunnally/dp/007047849X Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers.
Proceedings of the National Academy of Sciences of the United States of America, 106(37), 15583–7. doi:10.1073/pnas.0903620106
Pashler, H., Kang, S. H. K., & Ip, R. Y. (2013). Does Multitasking Impair Studying? Depends on Timing. Applied Cognitive Psychology. doi:10.1002/acp.2919
Quan-Haase, A. (2010). Self-regulation in instant messaging IM: failures, strategies, and negative consequences. International Journal of E-Collaboration, 6(3), 22–42. doi:10.4018/jec.2010070102
Rideout, V., Foehr, U., & Roberts, D. (2010). Generation M2.media in the lives of 8- to 18-year-olds. Henry J. Kaiser Family Foundation. Menlo Park, CA: The Henry J. Kaiser Family Foundation. Retrieved from
http://medcontent.metapress.com/index/A65RM03P4874243N.pdf
Rosen, L. D., Mark Carrier, L., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948–958. doi:10.1016/j.chb.2012.12.001
Shih, S. I. (2013). A null relationship between media multitasking and well-being. PloS One, 8(5), e64508. doi:10.1371/journal.pone.0064508
