Effects of multitasking on retention and topic interest
Introduction
In today's technology-rich world, where Internet connections and mobile devices are increasingly available, individuals' interaction with digital media begins at very early ages (Tandon, Zhou, Lozano, & Christakis, 2011). This interaction reaches particularly high levels among adolescents and young adults (Davies & Eynon, 2013). Several terms such as digital natives (Prensky, 2001) are used to describe individuals who are surrounded by these digital technologies. The ability to multitask across various multimedia environments is regarded as a significant characteristic of digital natives (Prensky, 2001, Veen and Vrakking, 2006). Other common features include effective communication, self-directed learning, and digital thinking (Prensky, 2001, Veen and Vrakking, 2006).
Observing children doing their homework, surfing the web, and instant messaging simultaneously may lead one to assume that they are skillful multitaskers. However, this can also be an urban legend in education (Kirschner & van Merrienböer, 2013). The current study aims to challenge this assumption through an experiment, which investigates the effects of different multitasking conditions on content retention and topic interest. Additional variables, such as digital device experience, daily media exposure, current multitasking habits, and two different working memory constructs were also investigated.
Multitasking can be defined as being exposed to different information sources and switching between different media (Ophir, Nass, & Wagner, 2009). Watching TV while texting or listening to music while surfing the web can be examples of this behavior, also called as media multitasking (Foehr, 2006). In another approach, multitasking is classified as either CPU- or human-based multitasking (Adler, 2012). The CPU-based multitasking refers to computer processors switching between tasks, during which the execution of tasks is perceived as simultaneous. The human-based multitasking occurs when people use their cognitive or psychomotor resources simultaneously to handle multiple habitual activities (e.g., eating and watching TV) or to switch between different PC applications (Adler, 2012). Online information seeking behavior as an iterative process of handling multiple searches can also be labeled under information-driven multitasking (Spink, 2004).
On the other hand, Kraushaar and Novak (2010) focus on task efficiency and define multitasking as either productive or distractive. Productive multitasking involves multiple acts of listening, watching, formulating, and taking notes while studying, whereas distractive multitasking includes activities that are unnecessary for learning and deplete the limited cognitive resources. In this regard, switching between different information resources using search engines can be a productive activity whereas switching between searching and informal chatting can be distractive.
There is a comprehensive literature on multitasking, which extends back to contexts where digital media were not available (Meyer & Kieras, 1997). Recently, Salvucci, Taatgen, and Borst (2009) proposed the domain-free Unified Theory of Multitasking. Their theory is strongly based on empirical findings in psychology. Salvucci et al. (2009) categorize multitasking as either sequential or concurrent based on the time spent on each task before switching to another. If the switching between the tasks is very short in duration (e.g., driving and talking on the phone), it is considered as concurrent multitasking. However, if the switches occur in longer durations (e.g., cooking and reading), it is regarded as sequential multitasking (Salvucci et al., 2009). The theory explains concurrent multitasking through Anderson’s (2007) Adaptive Control of Thought-Rational (ACT-R) model, which posits that human cognitive architecture is comprised of interacting but independent modules. Each module can keep an active set of task, which is called a thread. Although threads can operate in parallel, only one task can be executed at a time (Salvucci et al., 2009). Active threads use available cognitive resources in a “greedy but polite manner”, that is, tasks on a longer hold are given priority (Salvucci & Taatgen, 2008). Previously, the sequential multitasking has been explained with the Memory for Goals Theory (Altman & Trafton, 2002) and Threaded Cognition Theory (Salvucci & Taatgen, 2008). By reformulating these theories, Salvucci et al. (2009) stated that a goal in one's mind should be strengthened until it surpasses all other possible goals, and becomes the primary source of attention. Otherwise, the goal decays and requires more time to resume if interrupted. Hence, when a task is interrupted and another task is initiated, the interrupted task should be rehearsed in an active thread. This should continue until the next resumption in order to minimize the decrease of performance in the interrupted task (Salvucci et al., 2009).
As there is such an amount of theoretical perspectives on multitasking, there is a need for further empirical evidence to fine-tune these existing frameworks (Wallis, 2010). The strong empirical background of the Unified Theory of Multitasking and its domain-free explanations are considered useful in the current study.
Recent large scale studies reveal that multitasking is particularly common among youngsters (Carrier et al., 2009, Voorveld and van der Groot, 2013). Empirical evidence which involves longitudinal observations and interviews (Ragan, Jennings, Massey, & Doolittle, 2014), tracking tools to record learners' digital activities (Moreno, Jelenchick, Koff, Diermyer, & Christakis, 2012), and modern technologies such as eye-tracking (Calderwood, Ackerman, & Conklin, 2014) reveal that learners tend to multitask very often during learning activities. Common multitasking activities during learning are listed as social networking, chatting, texting, listening to music, studying another lesson, e-mailing, video gaming, note-taking, eating, and drinking (Burak, 2012, Fried, 2008).
The pervasiveness of multitasking in learning contexts has triggered numerous studies investigating its instructional consequences. Some scholars studied the implications of concurrent multitasking with mobile phones during lectures (Ellis et al., 2010, Lawson, 2013, Rosen et al., 2008), which revealed controversial findings. For instance, Rosen et al. (2008) observed 185 undergraduate students in three experimental conditions where learners were distracted with varying numbers of text messages. Findings showed that learning success decreased as the amount of texting increased. Another experimental study found that using mobile phones during lectures interfered with the learning gains of undergraduate students regardless of the degree of texting (Ellis et al., 2010). On the other hand, Lawson (2013) designed a similar experiment with 120 university students where receiving instant messages or texting during video lectures did not have any effect on performance.
Several researchers investigated whether multitasking with other mobile devices affected learning. In this regard, Coens, Reynvoet, and Clarebout (2011) randomly assigned undergraduate students to four different multitasking situations. While the control group watched a multimedia learning content on their iPods, the intervention groups were asked to tighten bolts and nuts as a secondary task. The first intervention group was told to pay attention to both learning content and the tightening simultaneously. The second group was asked to prioritize the learning content whereas the last group was told to do the opposite while multitasking. The only significant difference was observed between the control group and the group who prioritized tightening bolts and nuts. The difference was in favor of the control group. A second experiment was conducted in the same study where participants practiced tightening bolts and nuts prior to the experiment. Yet, the results remained similar. In another study, Doolittle and Mariano (2008) investigated the role of working memory and mobility in multitasking with audio players (i.e., iPod). Students were randomly assigned to one of two groups in which they either watched a multimedia content in a seated manner or walked around the school. Findings revealed that students in the seated condition learned better than the walkers. In addition, students with lower working memory capacity performed most poorly in the mobile condition.
In a similar study, Coens, Degryse, Senecaut, Cottyn, and Clarebout (2011) investigated how listening to audio podcasts affected learning in different situations where learners were either seated, walking or jogging. In the first experiment, no difference was observed among the three situations. However, in the second experiment, students who were seated outperformed the ones who were jogging. It should be noted that students in the second experiment listened to a learning content of 11 min for once whereas students in the first experiment listened to a learning content of 4 min twice. In addition, students solved a Sudoku puzzle before answering post-test questions in the second experiment. An interesting finding was that prior possession of audio player had partially affected learning outcomes in multitasking conditions.
Another group of scholars investigated whether the use of laptops with Internet connections affected learning outcomes in the classroom. Even though a fully controlled multitasking scenario was not available, interventions in these studies may be considered as examples of concurrent multitasking. For instance, an experiment with university students showed that retention of the course content was significantly worse among laptop users (Hembrooke & Gay, 2003). Other studies have further revealed that students who do not use laptops, but sit close to laptop users show lower performance on content retention (Fried, 2008, Sana et al., 2013).
Scholars have further investigated the effects of online messaging on retention (Bowman et al., 2010, Fox et al., 2009, Pashler et al., 2013, Tran, 2012) and problem solving (David et al., 2013, Lingbei, 2008, Wang et al., 2012). Such online messaging tasks can be considered as instances of concurrent multitasking if the learners are not allowed to pause the learning content. In two of these studies, a multitasking group was given a reading task during which they were allowed to do instant messaging, while non-multitaskers were not (Bowman et al., 2010, Fox et al., 2009). Although it lasted longer for the multitaskers to complete the reading tasks, no retention difference was found between them and non-multitaskers. Similarly, Tran (2012) compared the retention scores of different multitasking groups with non-multitaskers in a web-based experimental setting. In the study, participants had to read a text from a slide show and respond to online messages. As indicated above, one of the experimental groups was allowed to pause between the slides to respond to the messages which was an instance of sequential multitasking. The other group was not allowed to do so, which involved concurrent multitasking. No retention difference was observed between the groups. However, it should be noted that participants were given extra time to respond (i.e., 15 s) in the concurrent multitasking scenario. Similar to the findings of Tran (2012) and Bowman et al., 2010, Pashler et al., 2013 observed no retention loss when participants were allowed to pause the learning content while learning from text or audio materials. However, unlike Tran’s (2012) findings, retention scores of the multitasking group decreased when they were not allowed to pause the audio content (Pashler et al., 2013).
In their studies on the effects of multitasking on problem solving, David et al. (2013) and Lingbei (2008) asked participants to offer solutions to worst-case scenario problems and to multitask through online messaging. No difference was observed between multitaskers and non-multitaskers in terms of the accuracy of their solutions. On the other hand, multitaskers completed fewer problems in Lingbei’s (2008) study, but no difference was found by David et al. (2013). Wang et al. (2012) further reported a significant decrease in problem solving accuracy in multitasking situations where participants were asked to complete visual pattern-matching activities as the primary task where the secondary task was either voice chatting or instant messaging.
Further investigations revealed a negative association between the frequency of multitasking in learning settings and grade point average (GPA) (Burak, 2012, Jacobsen and Forste, 2011, Junco and Cotten, 2012, Kraushaar and Novak, 2010, Ravizza et al., 2014). In contrast, Clayson and Hailey (2012) observed no relationship. Some scholars further underlined the influence of cognitive capacity on multitasking (Colom et al., 2010, Donohue et al., 2012, Hambrick et al., 2010, Ophir et al., 2009). More specifically, the working memory capacity was found to be the strongest predictor of successful multitasking (Colom et al., 2010, Hambrick et al., 2010). This may be due to the non-multitaskers focusing their attention on relevant parts of the task better than the multitaskers (Ophir et al., 2009). It should be also noted that, working memory is not a single construct but comprised of three components: phonological loop, visuo-spatial sketchpad, and the central executive (Baddeley & Hitch, 1974). However, the effect of each component on individuals' multitasking performance has not been studied previously.
As summarized above, several investigations have emphasized a negative influence of multitasking on learning outcomes. However, a significant shortcoming remain regarding multitasking scenarios in the education literature. More specifically, multitasking scenarios have mostly been investigated through media-based activities rather than theory-driven approaches. In this regard, diverse affordances of different multitasking tools in terms of medium, tasks, and multitasking scenarios make it hard to reach a consensus on the empirical findings. Such a diversity may also interfere with the literature inclusion criteria of further meta-analytic studies. Therefore, it is necessary to investigate the phenomena through the lens of a grounded theory such as the Unified Theory of Multitasking. This domain-free theory classifies the multitasking events as either sequential or concurrent in learning contexts, rather than defining them according to the type of multitasking devices. Such an approach is plausible to form a universal and media-free understanding of the construct in educational settings.
Another important issue about the current multitasking literature is that almost all previous studies have focused on cognitive outcomes to address learning. However, the affective domain of knowledge is also a crucial construct (Krathwohl, 2002), which is regarded as “equally, if not more important than the cognitive domain” in educational settings (Martin & Reigeluth, 2013). The affective domain can be defined as one's positive attitudes towards a subject area (Martin & Briggs, 1986), developing and sustaining positive attitudes, value and interest in educational endeavors (Bloom, 1956, Martin and Reigeluth, 2013). Along with several other components (e.g. self-concept, motivation, values, curiosity), interest has been one of the core attributes of the affective domain (Bloom, 1956, Martin and Briggs, 1986). Within this framework, the topic interest is regarded as the outcome of the interaction between the learner and the content (Krapp, 2000, Renninger and Wozniak, 1985), which triggers the learner's actions to expand their knowledge (Ainley, 2006). The topic interest is strongly associated with both cognitive and affective functioning of individuals (Hidi and Berndorff, 1998, Krapp, 2000, Renninger, 2000, Schiefele, 1996). In general, the affective component of this interest involves positive emotions while engaging with the learning content, whereas the cognitive component is about perceptual and representational features of the learning activity (Hidi & Renninger, 2006). Unfortunately, the effects of multitasking on the affective domain, specifically on interest development have been neglected so far.
Finally, many multitasking experiments in educational settings can be criticized since the common approach was merely assigning participants to different multitasking conditions and comparing the learning outcomes without paying attention to critical personal attributes of learners. Some of these critical attributes were listed as digital device experience, daily media exposure, multitasking habits (Prensky, 2001), and working memory capacity (Colom et al., 2010). However, except for a few empirical works (e.g., Coens, Degryse, et al., 2011), previous studies did not take these variables into consideration. Hence, it is necessary to continue to investigate the instructional implications of multitasking conditions with rigorous experimental methodologies through the inclusion of such variables. A particular focus on prominent real-life multitasking activities would also be useful to question both the digital nativity arguments and the effects of multitasking on learning.
Considering the variety in approaches on digital nativity and multitasking success, the current experimental study aimed to investigate whether contemporary students or so-called digital natives learn effectively through multitasking. More specifically, the first research objective was to compare the learning gains in different multitasking conditions (i.e., concurrent vs. sequential) through measuring content retention, which is a common approach (Dietz and Henrich, 2014, Fox et al., 2009, Lawson, 2013, Pashler et al., 2013, Rosen et al., 2008, Tran, 2012).
In order to address the interaction between the affective domain and multitasking, the effect of multitasking on topic interest was included in the current study as the second objective. That is, comparing the change in topic interest scores in different multitasking conditions may inform us about the affective outcomes.
Prensky (2001) claimed that digital natives are better at multitasking because they multitask in their daily lives very often. Hence, the third objective of the study was to see whether multitasking in daily study environments improved performance in actual multitasking scenarios. This investigation can help us to see whether multitasking skills can be improved through practice.
The fourth objective was to explore if there was a relationship between digital device usage, daily media exposure, and multitasking performance, as proposed by Prensky (2001). Similar to the previous objective, this investigation helps us to question the perceived link between immersion to the digital world and learning in multitasking settings.
The last objective was to test previous assumptions that underline the role of working memory capacity on multitasking performance (Colom et al., 2010, Donohue et al., 2012, Hambrick et al., 2010) rather than immersion in the digital media (Prensky, 2001). Since the potential relationship between different working memory components and multitasking types has not been investigated previously, revealing the nature of such associations could be a unique contribution of the current experiment. Therefore, we delved into the relationship between sequential and concurrent multitasking performance and two working memory constructs (i.e., phonological loop and visuo-spatial sketchpad).
Section snippets
Participants
Participants were 572 students in the faculty of education at a state college in Turkey (age: 20.24; SD: 2.24). They were randomly assigned to seven experimental conditions. Participation was voluntary. 99 participants were chosen with a lottery and rewarded with books after the experiment. Demographics are provided in Table 1.
Background information questionnaire
The background information questionnaire had three sections. In the first section, participants indicated their demographic information such as age, gender, field of
Results
In the data analysis, mean scores were used for the topic interest and media use questionnaires as suggested by the developers. On the other hand, total achievement scores were utilized when exploring the content retention and working memory capacity. Evaluation of skewness and kurtosis values signaled acceptable and normal distributions to conduct further parametric tests (Tabachnick & Fidell, 2007). Descriptive statistics pertaining to the dependent variables and further variables of interest
Discussion
The current study examined the effects of multitasking on learning of so-called digital natives. A web-based environment was used to simulate both sequential and concurrent multitasking experiences. While the former condition required switching between two instructional videos, the latter involved responding to chat questions while watching the videos. Along with the impact of the experimental conditions on content retention and topic interest, the study also investigated the relationship
Acknowledgment
This study is the summary of the first author's PhD dissertation, which is supervised by the second author and financially supported by the Scientific and Technological Research Council of Turkey (TUBITAK grant ID: 114K633).
Muhterem Dindar is a graduate research assistant at the Department of Computer Education and Instructional Technology at Anadolu University, Turkey. He has a Ph.D. in Computer Education & Instructional Technology. His research interests are massively multiplayer online games (MMOGs), multimedia learning and cognitive psychology.
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Muhterem Dindar is a graduate research assistant at the Department of Computer Education and Instructional Technology at Anadolu University, Turkey. He has a Ph.D. in Computer Education & Instructional Technology. His research interests are massively multiplayer online games (MMOGs), multimedia learning and cognitive psychology.
Yavuz Akbulut is an associate professor at the Department of Computer Education and Instructional Technology at Anadolu University, Turkey. He has an M.A. in computer assisted language teaching and a Ph.D. in Computer Education & Instructional Technology. He conducts research on cyberpsychology, behavior and learning, cyberbullying, online gaming behaviors and computer ethics.