Elsevier

Computers & Education

Volume 58, Issue 1, January 2012, Pages 365-374
Computers & Education

Examining the impact of off-task multi-tasking with technology on real-time classroom learning

https://doi.org/10.1016/j.compedu.2011.08.029Get rights and content

Abstract

The purpose of the present study was to examine the impact of multi-tasking with digital technologies while attempting to learn from real-time classroom lectures in a university setting. Four digitally-based multi-tasking activities (texting using a cell-phone, emailing, MSN messaging and Facebook™) were compared to 3 control groups (paper-and-pencil note-taking, word-processing note-taking and a natural use of technology condition) over three consecutive lectures. Comparisons indicated that participants in the Facebook™ and MSN conditions performed more poorly than those in the paper-and-pencil use control. Follow-up analyses were required to accommodate the substantial number of students who failed to comply with the limited use of technology specified by their assigned conditions. These analyses indicated that participants who did not use any technologies in the lectures outperformed students who used some form of technology. Consistent with the cognitive bottleneck theory of attention (Welford, 1967) and contrary to popular beliefs, attempting to attend to lectures and engage digital technologies for off-task activities can have a detrimental impact on learning.

Highlights

► Off-task multi-tasking with technology while learning in real-time detracts from performance. ► Familiarity with off-task technology use did not improve performance across 3 lectures. ► Off-task use of technologies was compelling when technologies were available and use permitted.

Introduction

Computers, especially laptops, and other digital technologies that allow wireless access to the Internet, have become standard technologies in education (Weaver & Nilson, 2005). In general, there is a consensus that existing and emerging digital technologies have the potential to expand the reach and effectiveness of current educational tools. Ongoing advances in digital technology have provided educators with increasingly smaller, affordable and portable digital devices for use as teaching and learning tools in the classroom (Crippen and Brooks, 2000, Liu, 2007, Motiwalla, 2007). Because the increased availability of new portable digital technologies has made it possible to use these technologies anywhere and anytime, many individuals regularly access and interact with technologies in every context in their lives—including the classroom. Although these technologies can be harnessed for positive educational outcomes, recent research suggests that these same digital technologies can impair performance and distract learners if used inappropriately (e.g., Fried, 2008, Kraushaar and Novak, 2010, Wainer et al., 2008). In addition, students who engage in multi-tasking (i.e., engaging in more than one activity simultaneously; Pashler, 1994), might also be expected to show decrements in performance (Junco & Cotton, 2011). Indeed, questions regarding our ability to engage in multi-tasking behaviors have become increasingly prevalent in both the popular press and in research (e.g., Eby, Vivoda, & St. Louis, 2006).

Although multi-tasking is not a new phenomenon, what is new, are the number and types of digitally based activities in which people can now engage in simultaneously. In addition, multi-tasking with technologies is perceived to be “easy”, especially among younger adults who are likely to be engaged in educational studies (Carrier, Cheever, Rosen, Benitez, & Chang, 2009). The combination of availability, perceived ease of use, and the wide range of activities that are available through portable digital technologies, increases the possibility that learners, especially young, adult learners, will engage in off-task behaviors in instructional contexts. The present study addresses learning performance when university students engage in multi-tasking with digital technologies while attending real-time classroom lectures.

In order to understand the implications of multi-tasking it is first important to understand attention. A precise definition of attention is challenging and depends to some extent on the nature of the task at hand. For example, Posner (1990) classified existing definitions of attention within three main categories. The first category acknowledged the importance of alertness or arousal to the task at hand. The second category noted the issue of selectivity, whereby, some stimuli would be acknowledged more so than others. The third category acknowledged limited processing based on competing demands within a limited system. Johnston and Heinz (1978) further characterized attention as flexible, such that individuals have voluntary control over what stimuli they choose to attend to at any given time. Each of these attributes seems necessary for understanding how learning occurs within classrooms and how learning occurs when multi-tasking is an option.

As mentioned above, multi-tasking can be defined as doing more than one activity simultaneously (Pashler, 1994). Within the extant literature, multi-tasking is typically indirectly defined via the interference it produces. For example, the inability to simultaneously perform two or more overlapping tasks when each requires selecting a response (i.e., a decision task) due to a general slowing in the performance of the second task (Levy and Paschler, 2001, McCann and Johnston, 1992, Pashler et al., 2008, Schumacher et al., 2001, Welford, 1952). This interference arises from a constraint in decision-making also referred to as Cognitive Bottleneck (Welford, 1967). Although there are several theories that propose the constraint of a cognitive bottleneck and they differ with respect to where in the process the bottleneck occurs (Deutsch and Deutsch, 1963, Norman, 1968, Solso et al., 2007), generally, the effects of a cognitive bottleneck and the related slowing in the performance of the secondary task have been very well established.

However, some researchers have demonstrated conditions under which these effects can be overcome. For example, Meyer et al. proposed an alternate model of dual-task interference, called Executive-Process/Interactive-Control (EPIC), where practice plays an important role (Meyer et al., 1995). Specifically, skilled performance is accomplished by converting declarative knowledge into procedural knowledge through practice. Once this conversion has been accomplished, the processes required to complete two tasks at once can be performed simultaneously (Meyer et al., 1995, Schumacher et al., 2001). While acknowledging this finding, some researchers have argued that the removal of the slowing of performance associated with a cognitive bottleneck can only be circumvented in very simple and highly practiced tasks and not in more complex real-world situations (Pashler et al., 2008).

Some researchers also propose that different tasks produce different kinds of interference: general vs. specific (Brooks, 1968, Hirst and Kalmar, 1987). General interference occurs in dual-tasking situations in which a person performs two unrelated tasks, such as reading a sentence (a verbal task) and pushing a button in response to a certain word (motor task). On the other hand, specific interference occurs when a person performs two closely related tasks, such as listening to a message (verbal task) and producing a verbal response to that message (also a verbal task). When two tasks draw on the same overall resources, as well as the same processes, performance is expected to be especially low (Brooks, 1968, Hirst and Kalmar, 1987). In other words, the allocation of resources to a verbal and a motor task may be easier than the allocation of resources to two verbal tasks (e.g., writing and listening to a lecture). Although in both cases attempting to complete two tasks draws upon same limited available resources, the first draws on different processes and the second draws on the same processes (competing verbal), thus leading to a “double” interference. In terms of multi-tasking using a digital technology during a lecture, it should be easier to listen to a lecture (and process the meaning) while looking at pictures on Facebook™ (verbal/visual task) than it would be to listen to a lecture and type messages on MSN (verbal/verbal task).

Would multi-tasking with digital technologies elicit the slowing of tasks or interference typically associated with a cognitive bottleneck? If so, would the effects persist when individuals were experienced users of the digital technologies? In order to address these questions, it is first important to understand how individuals might use digital technologies in a multi-tasking situation. Posner (1990) identified two different kinds of attentional tasks that learners can employ: divided attention or rapid switching between tasks (Posner, 1990). Divided attention is synonymous with dual-tasking and refers to attending to more than one stimulus at a time. When this form of attention is used, the selection of information is imperfect, and therefore, subject to dual-task slowing (Smith & Kosslyn, 2007). Rapid Attention Switching refers to shifting attention from one stimulus to another stimulus in a rapid succession, but only one stimulus is attended to at any given time (Posner, 1990) and information from one task may be undetected while attending to the other task.

Although distinctions between these two types of attention are important for understanding basic cognitive functions, the present study did not directly manipulate or control attention. However, the present study did ensure selection of competing activities that would require multi-tasking. Specifically, in three conditions learners were required to use digital technologies that employed verbal information (e.g., texting, emailing, MSN) as a secondary task while the primary task required attention to a verbally based lecture with pictorial supports. An additional condition (Facbook™) combined verbal and pictorial information as the secondary task during the lecture. Attempting to attend to these competing forms of information would be understood as forms of multi-tasking, where one task is a primary task (the learning task) and the other task is secondary (using digital media), and would be expected to impair performance.

Consistent with theories of attention, Rubinstein, Meyer, and Evans (2001) found that people who were required to multi-task took longer to finish their two tasks, than it would take them to finish both tasks if they concentrated on one task at a time. The increase in time for multi-tasking was attributed to lost time from switching back and forth between the tasks, especially when the tasks became more complex (Rubinstein et al., 2001). A neuro-imaging study on learning while multi-tasking supported this finding (Foerde, Knowlton, & Poldrack, 2006). Specifically, participants who learned without distractions were able to correctly learn information presented to them, and apply it flexibly to new situations, On the other hand, participants who multi-tasked were not able to apply this information flexibly to new contexts, though they were still able to correctly learn factual information. The authors concluded that while multi-tasking did not seem to affect rote memorization, it might hamper higher-order tasks that involve understanding material and application of the material to novel situations. Together, the results of these studies are consistent with both the cognitive bottleneck theory of multi-tasking and provide evidence that attention, especially for complex tasks, can be impaired when multi-tasking is involved.

Interestingly, a recent study examining the impact of instant messaging while reading also found that students took longer to read when messaging than those who read without messaging, however, comprehension performance, which would be considered a complex task, did not differ between the two groups (Bowman, Levine, Waite, & gendron, 2010). The authors suggested that learners who multi-tasked may have required additional time in order to review previously read material and re-engage on-task behaviors. In this case, additional time could compensate for the disruptions from instant messaging and hence, no performance differences were found. When additional time is not available, however, it may be more likely to find performance decrements. In summary, the results of the above studies suggest that off-task use of digital technologies while learning might be especially harmful to performance in a real-time classroom context.

Within the University setting, initiatives, often referred to as Anywhere Anytime Learning (AAL) (Milrad & Spikol, 2007), promote the use of digital technologies, especially personal use technologies such as laptops, as a complement to more traditional teaching and learning tools. The newest addition to personalized digital technologies is mobile technologies (e.g., Blackberrys, iPhones, Smartphones, iPads and cell-phones). These devices, when connected to wireless access to the Internet, offer the promise of shifting learning into even more environments than had been envisioned with laptops.

Although many educational systems have quickly embraced digital technologies, the effective inclusion of these technologies into teaching practice has encountered, and continues to encounter, practical and pedagogical barriers (e.g., Wood, Specht, Willoughby, & Mueller, 2008). In addition, the limited extant research provides contradicting evidence regarding the outcomes associated technology use (Wainer et al., 2008). With respect to multi-tasking, several studies show that when students have access to laptops in the classroom, they often engage in distractive multi-tasking behaviors, which is associated with a decrement in performance (Fried, 2008, Grace-Martin and Gay, 2001, Hembrooke and Gay, 2003, Junco and Cotton, 2011, Kraushaar and Novak, 2010, Wainer et al., 2008, Wurst et al., 2008). For example, research examining use of instant messaging as an instructional tool found that students engaged in off-task messaging in addition to the expected instructional use and the off-task messaging impacted negatively on the teaching environment (Murphy & Manzanares, 2008). A recent study with mobile technologies also found that students self-reported engaging in off-task activities with these devices when the technology was supposed to be used for instructional purposes (Mueller, Wood, & De Pasquale, in press). In addition, distraction from multi-tasking need not be firsthand as research indicates that using laptops in classrooms can distract not only its users, but also other students in close proximity to the laptops (Fried, 2008).

Off-task multi-tasking, in particular, poses concerns for learning. Consistent with cognitive load theory (Sweller, 2003) when learners engage in activities that are not directly related to the goals of the instructional task at hand, then learning becomes less effective (Chandler & Sweller, 1991). Cognitive load theory identifies three types of load; intrinsic, germane, and extraneous. The first two types of load are related to the learning task and activities which facilitate these benefit learning. Extraneous load is associated with activities not directly contributing to learning. Multi-tasking with off-task activities increases extraneous load which would be expected to interfere with learning as was noted in the studies above.

Together the correlational and self-report studies above suggest that off-task multi-tasking in the classroom is most likely detrimental to learning. One purpose of the present study therefore, was to directly test the impact of multi-tasking in a real-time classroom context for learning.

The present study extended current multi-tasking research by directly assessing the learning outcomes following off-task multi-tasking in when learning from real-time classroom lectures. In addition, the study contrasted the relative impact of differing digital technologies when multi-tasking. Technologies included the use of laptops for conducting Facebook™ searches, or for communicating (email/MSN messenger) and the use of cell-phones for responding to social messages (i.e., texting). Performance in these groups was compared to a variety of controls. Specifically, a paper-and-pencil control, a word-processing note-taking only control and a natural use control (in which participants were allowed to use technology in an unlimited manner, if they chose to do so, as they normally would during lectures). The natural use control group was included in order to determine the proportion of students who use digital media in a classroom and in what way they use the technology. To test the impact of familiarity with technologies, the study required students to use the same technologies over three consecutive lectures.

In total, two main hypotheses and one methodological issue were addressed.

  • 1)

    Given the potential for multi-tasking to tax the resources and distract the learner, it was expected that learning performance would be lower for the multi-tasking conditions when compared with the note-taking conditions.

    • i)

      It was expected that participants in the natural condition who chose not to multi-task, would score higher on learning performance than those who chose to multi-task.

  • 2)

    If practice facilitated the ability to multi-task, it was expected that performance in all multi-tasking conditions would increase over the three sessions.

  • 3)

    As a result of access to technologies, it was anticipated that students might engage in multi-tasking beyond what was instructed. To determine whether this occurred, a fidelity measure was included where participants indicated what multi-tasking activities they engaged in during the lecture. This measure was an exploratory measure to allow an estimate of how many multi-tasking activities students engaged in when given the opportunity. In addition, the results of the fidelity measure were utilized for reassigning participants to conditions based on the modal behavior indicated by each participant.

Section snippets

Participants

All 145 participants (116 females and 29 males) were randomly assigned to one of seven conditions (with n = 21 in Facebook™, Texting, Natural Technology Use, Word Processing only and paper-and-pencil conditions and n = 20 in the MSN and email conditions). Approximately equal proportions of males (Mage = 20.67, SD = 2.33) and females (Mage = 19.56, SD = 1.19) were represented within each condition. Participants were recruited from 2nd year research methods and statistics courses. The

Results

Two sets of analyses were performed. The first set of analyses examined learning performance in class as a function of assigned condition. The second set of analyses examined fidelity to instructions and a reanalysis of data as a function of fidelity outcomes.

Discussion

The primary purpose of the present study was to examine the relative impact of multi-tasking with various digital technologies while attempting to learn from a real-time classroom lecture. A summary of outcomes is presented below.

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