Technological advancements and widespread distribution of products such as smartphones and laptops have allowed us to become more productive and efficient in almost every aspect of our daily lives. For instance, smartphones are constantly evolving and improving its computational capabilities as well as providing desirable features to attract users.
Consequently, such technological advancements and widespread consumption of media have affected students the most during learning times (Carrier, Rose, Cheever, & Lim, 2015). Furthermore, Carrier and colleagues (2015) reported that students’ use of technology in universities, despite limitations imposed by the lecturer, are being driven by individualistic desires and perception of the surrounding environment.
We don’t multitask. We task switch. The word “multitasking” implies that you can do two or more things at once, but in reality our brains only allow us to do one thing at a time and we have to switch back and forth.
Interestingly, Ophir, Nass, and Wagner (2009) have created an index system in attempt to categorize multitasking behaviour: heavy media multitaskers (HMM) and light media multitaskers (LMM). In addition, study conducted by Ophir and colleagues (2009) tries to answer whether: individuals consistently engaging in multitasking behaviour are paying more attention to irrelevant information resulting in cognitive control deficits; multitasking behaviour is potentially advantageous for cognitive control being able to selectively pay attention to information in the surrounding environment.
Heavy Media Multitaskers
HMM
Individuals who consistently consume multimedia (e.g., music, video, computer games, instant messaging, etc.) during activities that require information processing.
Three experiments were conducted to study: environmental distractions measured by participants’ ability to filter out distracting information (recognition test involving working memory); participants’ ability to filter out irrelevant information in working memory; and participants’ tendency to switch between tasks that may be relevant or irrelevant to the provided cues (Ophir et al., 2009).
Heavy media multitaskers were found to show difficulty in processing relevant information that are presented in the environment (e.g., inability to filter out irrelevant information); increased in behaviours that resemble impulsivity (e.g., switching tasks frequently); and ultimately suggesting that HMM are especially prone to be distracted by any forms of media (Ophir et al., 2009).
Light Media Multitaskers
It is apparent that program developers and multimedia device manufacturers are incentivized to engage in research and development to maximize profit, meeting the needs and trends sought after by the consumers. For example, screens are becoming bigger and slimmer for easy accommodation and increased content consumption; programs are developed to endorse multitasking (e.g., Google Chrome tabs and webcast); and central processing units and computer components are becoming cheaper due to increased corporate competition. By doing so, Ophir and colleagues (2009) suggests that constant improvements in areas mentioned above could potentially place additional demands to an individual’s cognitive ability, thus, creating new challenges to overcome the negative side effects associated with content consumption (e.g., media multitasking).
Data collected by Ophir and colleagues (2009) suggests that LMM participants are potentially better equipped to deal with distractions – having increased ability to selectively take in relevant information and successfully filter out irrelevant ones.
LMM
Individuals that consume multimedia at a much less frequent rate (e.g., music, video, computer games, instant messaging, etc.) during activities that require information processing.
Cognitive load and electroencephalogram
Interestingly, a recent study conducted by Orun and Akbulut (2019) utilized electroencephalogram (EEG) to measure research participants’ cognitive load under multitasking conditions. While previous studies indicated that measuring cognitive load with EEG could be possible by analysing alpha and beta frequencies: beta frequencies correlated with mental effort and alpha frequencies correlated with surrounding environments, EEG measures applied in the field of psychology are still in infancy (Orun & Akbulut, 2019). Orun and Akbulut (2019) emphasized that their findings of a few significant correlative data (cognitive load) could have occurred by chance. However, their research highlighted how incorporation of EEG in learning environments were non-invasive – indicating that data between groups that measured EEG and non-EEG groups did not differ significantly (Orun & Akbulut, 2019).
Laptop use in classrooms
Multitasking in an academic setting is consistently receiving increased attention due to the nature of computing devices endorsing productivity and efficiency, which is advertised to students encouraging the use (Carrier et al., 2015). As Sana and colleagues (2013) were able to replicate findings of other studies indicating impairments in information processing due to multitasking behaviour, their research also analysed the impact of screen exposure to surrounding participants.
Exposure to laptop screens
Research participants were strategically positioned to be exposed to confederates exposing their laptop screens showing content irrelevant to the lecture material (e.g., checking mail, web-browsing; Sana et al., 2013). On the other hand, control group were not exposed to laptop screens and data analysis revealed that participants exposed to distracting conditions performed worse (17%) in comprehension test that followed (Sana et al., 2013).
Not all multitasking is negative
Despite many studies indicating negative factors associated with multitasking behaviour, research conducted by Shin, An, and Kim (2016) added another interesting factor: individual’s intent and purpose of using electronic devices influencing cognitive performance. Shin and colleagues (2016) highlighted that multitasking behaviour aiding in information processing (e.g., looking up definitions, peer collaboration) demonstrated decreased level of anxiety and higher performance in comprehensive tests. In comparison, simultaneous multitasking behaviour and non-collaborative environment demonstrated significant decrease in performance (Shin et al., 2016).
References
Bates, S. (2018, October 25). A decade of data reveals that heavy multitaskers have reduced memory, Stanford psychologist says. Stanford News. Retrieved from https://news.stanford.edu/2018/10/25/decade-data-reveals-heavy-multitaskers-reduced-memory-psychologist-says/
Carrier, L., Rosen, L., Cheever, N., & Lim, A. (2015). Causes, effects, and practicalities of everyday multitasking. Developmental Review, 35(C), 64-78.
Eyal Ophir, Clifford Nass, & Anthony D. Wagner. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences,106(37), 15583-15587.
Örün, &., & Akbulut, Y. (2019). Effect of multitasking, physical environment and electroencephalography use on cognitive load and retention. Computers in Human Behavior, 92, 216-229.
Sana, F., Weston, T., & Cepeda, N. (2013). Laptop Multitasking Hinders Classroom Learning for Both Users and Nearby Peers. Computers & Education,62, 24.
Shin, D., An, H., & Kim, J. (2016). How the second screens change the way people interact and learn: The effects of second screen use on information processing. Interactive Learning Environments,24(8), 2058-2079.