Multitasking effects on learner achievement, the motivations driving multitasking and strategies for mitigating multitasking’s negative effects
Precis: The best way to multitask is to not to.
Multitasking is frequently used as a strategy for accomplishing more with time, however, few people realize gains in productivity when multitasking. This review of current peer-reviewed journals, gathered from the Education Source and ERIC databases, sought to define, and understand the effects and motivations for multitasking, then identify strategies for mitigating multitasking’s negative effects. Motivations to multitask come from the gratification of staying connected, perceived efficiency, and entertainment. However, no studies were found that identified any performance benefits from multitasking beyond practicing habitualized, routine tasks. Some cognitive, behavioral, and pedagogical strategies are identified that mitigate, but do not eliminate, multitasking’s negative impact on learning and performance.
“There is time enough for everything in the course of the day, if you do but one thing at once, but there is not time enough in the year, if you will do two things at a time.”
Philip Dormer Stanhope, 4th Earl of Chesterfield
Letter IX, London, April 14, 1747
Lord Stanhope’s letter illustrates the longstanding concerns about multitasking. Even the earliest education journals studied the issue of distractibility and spreading attention too thinly (Bailey, 1889; Denio, 1897; Henderson, Crews, & Barlow, 1945; Poyntz, 1933). With digital technology, not only has the issue persisted, there are concerns that the impact on learning is even greater than before (Bowman, Levine, Waite, & Gendron, 2010; Fox, Rosen, & Crawford, 2009; Levine, Waite, & Bowman, 2007). Ubiquitous, always-on technology is changing the way humans engage with information and interact with each other. While offering tremendous personal and social enhancements, it also demands an increasingly greater share of our attention.
The term “multitasking” became well-known in the late 1980s describing a process by which computers could process several tasks simultaneously (Scott, 1985). Over the next decade, multitasking was used to describe an individual’s management of more than one task at a time and was seen as a desirable skill at home and at work (Chiavenato, 2001; Frand, 2000; Gray, 2000; “Multi-tasking with your baby,” 2001).
Multitasking refers to the choices people make about when and where they focus attention while attending to more than one task (Kenyon, 2008). The urge to multitask arises when more than one goal must be accomplished at more or less the same time and the individual has to balance pursuit of all goals independently and without cues to change tasks (Burgess, 2000). “Media multitasking” is the term used to describe multitasking that involves at least one form of digital technology (Judd, 2013) or, more commonly two or more forms of technology (Brasel & Gips, 2011; Lin, Lee, & Robertson, 2011; Rideout, 2013).
Cognitively, human multitasking is better understood as task switching, or continuous, often rapid redirections of attention. (Firat, 2013a, 2013b). Multitasking, it seems, has three dimensions: direction, duration, and depth.
Attention switching between tasks having different, unrelated goals are those that serve either to replace or interrupt study; attention switching among tasks related to achievement of the same goal serve as companions to study and advance the learner toward his goal (Benbunan-Fich, Adler, & Mavlanova, 2011; Levine et al., 2007). Benbunan-Fich (2012) further categorized patterns in changing attention direction as sequential (completing one whole task after another), interleaved (alternating between portion of multiple tasks), and embedded (completing one entire task within the timeframe of another task).
Time on task is one dimension of multitasking measuring the time between task switching. Salvucci, Taatgen, and Borst (2009) present a continuum of multitasking based on such measures that can span fractions of a second to several minutes. Short time spans between tasks are seen to be more simultaneous or concurrent involving cognitive control of attention while longer time spans are more sequential and operate on a more rational level. Duration of a switch affects the individual’s ability to return to the original task and is also dependent on the complexity of the task.
Rapid task switching has given rise to the notion of “continuous partial attention” (Stone, 2009) where an individual is aware of several stimuli but deeply engaged with none. Activities with low demand on working memory allow individuals to more rapidly switch and scan for points of interest. Where task complexity is high, though, the time required to make cognitive shift is greater and the new task is more likely to displace the old task from working memory requiring the individual to reorient herself to the original content (Salvucci & Taatgen, 2011, p. 116; Salvucci, 2010).
Effects of Multitasking
Despite the allure of multitasking’s supposed benefits, there is considerable empirical evidence demonstrating its impact on achievement and performance is largely negative.
Instant messaging while reading increases the time required to complete the reading task accounting for the time off task (Bowman et al., 2010). This is attributed to ineffective attention, the time required for interruption recovery, and time needed for task re-orientation. Bowers (2000) also described how task performance was slowed when concurrently processing more than one message. Introduction of novel stimuli can cause draw attention from the primary task at hand as cognitive capital is invested in making sense of the new information (Cheshire, 2015) such as occurs with push notifications from a digital device.
It was found that during independent study periods, a students’ average attention to tasks was less than 2.5 minutes (Judd, 2013). These findings were mirrored in IT specialists who averaged only 3 minutes between tasks (Salvucci et al., 2009). Shorter time on task and more frequent interruptions introduce a state of cognitive crisis (Firat, 2013a, 2013b; Stone, 2009) and reduce the depth of engagement (Rosen, 2008).
Exposure to new information elicits the slow onset of a cognitive phase of reflection and evaluation. Gilbert (as cited in McLarney-Vesotski, Bernieri, & Rempala, 2011) describes how a learner examines new information against existing schema. Individuals with low multitasking ability, in highly stimulating environments, can have this process interrupted resulting in incorrect or inaccurate judgements about the situation. Studies of an individual’s ability to recover from interruptions reveal that the more challenging the task, the harder it is to transition back to the original task after attention is diverted (Salvucci, 2010; Trafton, Altmann, Brock, & Mintz, 2003).
W. Zhang (2015) measured a negative correlation between laptop multitasking and the learner’s midterm grade. Self-regulation as manifest in the learner’s control over laptop multitasking was also found to affect grades. Several other studies support the conclusion that multitasking during class and self-study are correlated with lower achievement (Burak, 2012; Gaudreau, Miranda, & Gareau, 2014; Jacobsen & Forste, 2011; Ragan, Jennings, Massey, & Doolittle, 2014; Sana, Weston, & Cepeda, 2013; W. Zhang, 2015).
Negative impact on grades from multitasking affect not only the multitasker, but other learners in the same space. Close proximity to media distractions can negatively impact learning through distraction and some content is more distracting than others (Lin et al., 2011; Sana et al., 2013). Serious, somber, or banal content is less likely to create distractions than attractive, funny, or engaging content. The negative individual and proximal effects on others are made clearer in light of the user’s activity with on-task usage accounting for less than 40% of undergraduate’s time using a laptop (Ragan et al., 2014). Similarly, Benbunan-Fich and Truman (2009) found only 13% of an employee’s laptop use during meetings reflects “compliant use”. The introduction of what is likely more engaging stimuli than a class lecture or office meeting pulls attention away from the primary goal.
Analysing interviews with undergraduate students, Bardhi, Rohm, and Sultan (2010) categorized both costs and benefits to multitasking as identified by the participants. Those interviewed recognized multitasking’s inefficiencies and potential for distraction. They also describe the chaotic nature of rapid task switching and an obsessive connection with the device which pulls them away from being cognitively and emotionally present in their physical environments.
In addition to impact on learning, multitasking may also negatively influence executive function in adolescents (Baumgartner, Weeda, van der Heijden, & Huizinga, 2014), university students’ high-risk decisions regarding sex and substance abuse (Burak, 2012) and judgement in new social situations (Lieberman & Rosenthal, 2001; McLarney-Vesotski et al., 2011).
Motivators for Multitasking
If the negative effects of multitasking are so clear, it begs the question why people persist. Bardhi, Rohm, and Sultan (2010) go on to describe the perceived rewards of multitasking as expressed by their undergraduate subjects. Study participants valued the content control their and the convenience of access to so many tools from one device. Participants also valued access to pleasurable content while pursuing more banal goals.
E-mail, instant messaging, unrelated web browsing, and gaming are common in-class targets of multitasking amongst undergraduate students (Gaudreau et al., 2014). Social engagement speaks to the individual’s desire to stay connected with others (Firat, 2013a, 2013b) as the former is increasingly omnipresent in the latter. The rewards of human interaction and the desire to be up to date with news leads can lead to a multitasking state of “Continuous Partial Attention” (CPA) motivated by the desire to do more with one’s time as well as the desire not to miss anything (Stone, 2009). In this state, the individual is continually but superficially monitoring several information streams.
Gaming is another common part of multitasking. Frequent feedback, rewards, and a sense of accomplishment are strong motivators that can be achieved quickly and easily. Used as short breaks within more long-term and challenging pursuits a multitasker may mistakenly attribute gratification from gaming to the effectiveness of multitasking in completing the primary task (Wang & Tchernev, 2012).
Ubiquitous computing continually pushes entertainment and social media into our stream of consciousness through alerts of new content. In contrast to sometimes banal learning activities or work obligations, individuals may be enticed away from the primary goal and suffer experience drops in productivity and achievement.
Motivation to multitask is also connected to task appeal. Individuals engaged in mundane but important tasks are easily redirected when an exciting but irrelevant task is introduced either externally in the physical world, or internally through mind-wandering (Srivastava, 2010). If the task is sufficiently engaging, the individual may be unable to resist distraction.
While Strayer and Watson (2012) suggest that those who multitask most are also the least effective at it and Wang and Tchernev (Wang & Tchernev, 2012) call out effective multitasking as a myth, it is, nevertheless, a significant factor in performance and achievement. Technology’s ubiquity, the prevalence of multitasking, and the changing information and communication landscape, make it prudent to seek interventions that regulate and minimise its’ negative effect on learning (Kenyon, 2010; Wallis, 2010).
Regardless of an individual’s multitasking abilities, people may have a natural propensity for either single-tasking or multi-tasking (Y. Zhang, Goonetilleke, Plocher, & Liang, 2005) though less than 3% of the population demonstrate high capacity to multitask without significant loss of performance (Strayer & Watson, 2012). Frequent multitaskers and those who are good at multitasking are not necessarily one and the same. Additionally, people may be oriented to a particular kind of multitasking such as continuous partial attention rather than task switching (Ophir, Nass, & Wagner, 2009). Beyond existing inclinations and abilities, research points to behavioural, environmental, and pedagogical strategies for managing multitasking’s negative effects on learning.
Behavioural and Cognitive Strategies. Self-regulation is the effort put into directing one’s own faculties toward achievement of a goal (Grawitch & Barber, 2013). Effective multitaskers exercise self-regulation over their technology use in order to achieve a goal and it is possible for others to learn self-regulation strategies (Gaudreau et al., 2014; Perels, Otto, Landmann, Hertel, & Schmitz, 2007; W. Zhang, 2015). Such self-regulation strategies include: goal setting, planning, self-motivation, attention control, flexible use of learning strategies, self-monitoring, appropriate help-seeking, and self-evaluation (Perels et al., 2007; Zumbrunn, Tadlock, & Roberts, 2009). The extent to which learned self-regulation can specifically mitigate the negative effects of multitasking, though, is still unclear.
The importance of self-regulation is highlighted in Tabachnick, Miller, and Relyea’s findings (2008) that inherently self-motivated individuals with clear goals made decisions about their actions and environment that contributed to their ability to focus. This exercise of metacognition contributes to a greater sense of self-awareness and ability to correct ineffective work strategies (Bonds, Bonds, & Peach, 1992). Extrinsically motivated learners may be enticed to employ self-regulation strategies through the use of external motivators (W. Zhang, 2015). Learning experiences that include self-reflection and metacognitive development expose the learner to strategies for improving self-regulation.
Cognitive processing time is another element of multitasking. Insufficient time between tasks interferes with transference of information to memory; too much time between tasks may tempt learners to seek other stimuli (Y. Zhang et al., 2005). The benefits of multitasking decrease as the rapidity of task switching increases. Where task switching is a matter of choice, an individual aware of the need for transitional processing time may better appreciate the need to reduce task-switching to maintain focus (Salvucci, 2010; Trafton et al., 2003) and the faster one can process information, the better able one is to multitask (Dux et al., 2009).
Lee and Taatgen (2002) explore time factors relating to how people approach tasks. Traditional planning prepares an individual for managing several tasks while reactive planning deals with issues as they arise. While it is not always possible to anticipate issues, it may be possible to improve multitasking by reconceptualising unique processes for several different problems into a unified process that addresses all problems. For educators, deconstructing processes can help students identify actions that may serve more than one purpose and to ignore extraneous information (Lee & Anderson, 2001). In this way, learners will focus better and capitalise on efficiencies when managing more than one task.
Dux (2009) found that repetition and training can also lead to efficiencies in information processing thus decreases the negative effects of multitasking. This effect, it should be noted, was observed with the repetition of simple but novel tasks that, with familiarity, required less and less cognitive processing. Low cognitive load activities therefore are less impacted by multitasking though there are initial performance concerns and the time required to complete the task is likely to increase (Bowman et al., 2010).
Off-task multitasking using technology may also be evidence of a disorganized approach to learning, unclear or ill-defined motivations for pursuing the primary goal, or possibly a low sense of efficacy or engagement with the primary task (Gaudreau et al., 2014). This highlights the value of study skills and learner awareness of organizational strategies to manage their workload.
Environmental Strategies. Simple and seemingly obvious environmental controls that reduce distractions and temptations to multitask are easily achieved but not always practiced such as reducing the amount of technology in a study space (Foehr, 2006) or enabling “do not disturb” features on mobile devices.
Bowers (2000) found that multitasking was enhanced when sensory input channels were designed not only to reduce conflict, but to supplement the task at hand. Reflecting that understanding, Clark and Mayer (2008) offer multimedia design principles that align with our capacity to take in and process information, or working memory. It may be possible to translate these principles to the design and use of physical spaces and learning activities. Doing so will reduce demands on working memory which increases one’s opportunity to take in and process new information. Additionally, working memory can be enhanced by increasing one’s sense of efficacy in a given task (Autin & Croizet, 2012) through computer gaming (Hawes et al., as cited in Morris, Croker, Zimmerman, Gill, & Romig, 2013) and controlled exposure and experiences with simulated multitasking situations (Morrison & Chein, 2011).
Pedagogical Strategies. Anecdotal reports from teachers express concerns about declining student attention spans and greater reliance on digital devices (Fullan & Langworthy, 2014; Purcell et al., 2012). Based on their experiences, many educators identify a poor fit between traditional behaviourist pedagogy and modern children’s learning needs.
21st century pedagogies like social constructivism and connectivism, see knowledge creation as a social activity (Fagan, 2010; Siemens, 2004) and technology as a tool for creating learning communities beyond the school’s walls (Couros, 2009; del Moral, Cernea, & Villalustre, 2013; Kop & Hill, 2008). These learning approaches are well-suited to embrace what Gee (2013) calls humans’ collective “synchronized intelligence” expressed in “affinity groups” that exist easily in online spaces. Engaging with online learning networks gives students the opportunity to use their devices in positive, task-oriented ways that address the need for social connections. This kind of intentional and planned use of technology, W. Zhang (2015) suggests, focuses student engagement and reduces multitasking.
Junco (2012), however, cautions that media rich lessons with frequent on-task changes of focus still introduce the potential for distraction, though narrowly-focused multimedia learning objects in constructivist learning settings do contribute to learner achievement (Mayer, Moreno, Boire, & Vagge, 1999). Similarly, Davis (2014) describes the benefit of “small-chunk learning modules”, a strategy for aligning engagement with attention spans leading to improved learning experiences.
Effective scaffolding keeps the learner in a position of regular challenge and achievement offering the opportunity for frequent and motivating reinforcement. When there is sufficient warning and time between task switching learners are better able to make task transition (Trafton et al., 2003). Extending time on task contributes to greater ability to resist distraction (Randall, Oswald, & Beier, 2014). Such learning experiences will help the learner stay focused and motivated and serve to reducing the appeal of digital distractions, or focus multitasking to completion of the primary goal.
Though multitasking may have some utility with routine tasks and cursory monitoring of many information streams, it is clear that multitasking has a broad range of negative effects with more complex and cognitively challenging tasks. It is likely that both learner-initiated multitasking and imposed multitasking in the form of distraction will always be a concern. Multitaskers believe they are effective and erroneously attributed gratification reinforces their sense of efficacy. While the negative effects of multitasking cannot be completely eliminated, they can be reduced by learning and practicing self-regulation and metacognition. Self-regulation equips learners with behavioral and attitudinal strategies for goal-oriented motivation, organization, and effort. Learner metacognition brings about greater capacity for honest self-reflection and evaluation and promotes continuous refinement of cognitive strategies. For educators, recognizing why students multitask opens the door to show students how to more effectively meet their goals. Understanding the motivations driving multitasking and intervention strategies for managing multitasking will help educators guide learners to more effective and efficient learning.
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