The Correlation Between Academic Media Multitasking and Achievement-a Meta-Analysis

Current research predominantly investigates the adverse impacts of multitasking across three pri mary domains: cognition and acade mic performance, health outcomes, and interpersonal relationships ( Za-

• ^*Corresponding author: kristina.randjelovic@pr.ac.rs

  
  
  

• © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

Adolescence and early adulthood is a period when digital media are frequently used during academic activities ( Carrier et al., 2015 ; Junco and Cotten, 2012 ; Wallis, 2010 ), therefore, our study includes research focused on populations of high school students and college students. Young people find it difficult to distance themselves from their phones, which is supported by the fact that the smartphone is perceived as an “extension of the body” with a strong sense of emotional attachment ( Gertz et al., 2021 ). The phone is always “at hand” during young people’s daily activities, including academic ones. Students often justify device use for course-related tasks, while significant time is spent on non-course-related activities, impacting focus and potentially leading to lower academic performance ( Kraushaar and Novak 2010 ; Rosen et al., 2013 ). The use of digital media is how adolescents and young adults mostly spend their time, on average more than 7.5 hours a day – which is almost equivalent to the length of a typical workday ( Rideout et al., 2010 ). Students and young adults increase their digital media absorption by using two or more media simultaneously through multitasking, experiencing 10h and 45 minutes of media content within their 7.5 hours per day. This behavior also manifests in educational institutions, where the use of digital media is largely uncontrolled and unregulated. If students do not invest enough time in completing academic tasks, they will not fully utilize their potential ( Fox et al., 2009 ). Researchers who have examined the relationship between media use during academic activities and academic achievement assume that media use during academic activities can lead to negative consequences for young people’s academic performance ( Bellur et al., 2015 ; Fox et al., 2009 ; Karpinski et al., 2013 ; Kokoç, 2021 ; Kostić and Ranđelović, 2022 ). Several cognitive learning theories assume that using multiple streams of information reduces information processing as a result of the limited cognitive capacity of humans ( Salvucci and Taatgen, 2010 ; Junco and Cotten, 2011 ). Cognitive theories related to information processing ( Mayer, 1998 ) and multimedia learning ( Mayer and Moreno, 2003 ) highlight that “meaningful learning” happens when individuals are actively engaged with information, concentrate on new inputs, and systematically integrate this new information into their pre-existing knowledge structures. These theories indicate that multitasking, or frequently switching between tasks, causes individuals to be only partially engaged with each task, leading to decreased attention and poorer learning and performance outcomes ( Kraushaar and Novak, 2010 ). The cognitive control deficit resulting from frequent digital media multitasking can also cause difficulties in maintaining focus on academic tasks, leading to lower achievement ( Ophir et al., 2009 ; Wallis, 2010 ). Frequent multitasking is associated with a lower GPA ( Bellur et al., 2015 ; Junco, 2012 ; Rosen et al., 2013 ; Walsh et al., 2013 ) since the time spent using digital media takes away from time devoted to academic activities. Junco (2012) found that overall GPA dropped 0.12 points for every 93 minutes above the average of 106 minutes per day spent on Facebook . Ophir, Nass, and Wagner (2009) suggest that high media multitaskers have poorer cognitive control abilities, meaning they have more difficulty managing attention and executive functions. This reduced self-regulation capacity makes it harder for them to focus on tasks and learn effectively, which can result in lower GPAs. Similar research by Rosen et al., (2013) , indicates that frequent task-switching caused by media distractions can diminish the quality of studying and negatively affect GPA. Lepp et al., (2014) confirm that the use of mobile phones and other media can pose a significant distraction that interferes with the learning process and lowers GPA. Additionally, Langberg et al. (2013) explore how selfregulation impacts academic success and emphasize that students with better self-regulation skills can more effectively manage distractions, including media multitasking, and achieve better academic results. To avoid distractions and remain focused on learning, selective attention is crucial ( Dayan and Solomon, 2010 ). Learners need to understand their attention state and employ effective strategies to regulate their attention. If a student has a clear and specific goal and sufficient motivation, such as studying for an upcoming exam, they are less likely to multitask and vice versa ( Judd and Kennedy, 2011 ).

Although numerous studies confirm a negative correlation between media multitasking and academic achievement, some also report small to moderate effects ( Burak, 2012 ; Junco and Cotten, 2012 ;

Ravizza et al., 2014 ), as well as no correlation ( Karpinski et al., 2012 ; Wei et al., 2012 ; Clayson and Haley, 2013 ). This indicates a lack of consistency among researchers regarding the correlation between media multitasking and academic achievement in high school and college students.

This topic is crucial because it provides empirical evidence on how digital distractions impact learning outcomes, helping educators and policymakers develop effective strategies to mitigate these effects and enhance student performance in educational settings. Our study is a meta-analysis and attempts to systematize the results of previous studies on the topic of the correlation between academic media multitasking for non-academic purposes and student achievement. This approach aggregates data from various studies to provide more robust conclusions, offering insights into the consistency and strength of the association between media multitasking behaviors and academic outcomes that individual studies alone may not achieve. A meta-analysis is important in this study because it allows for the comprehensive integration of findings from multiple studies, increasing statistical power and generalizability. Even though this field began developing more than a decade ago, the present times and unique environmental factors during the pandemic greatly increased its significance.

Materials and Methods

Variable operationalization

Academic media multitasking – operationalized through a questionnaire measuring the frequency of digital media use (social networks, email, games, websites, search engines, watching/listening to videos, talking on the phone) during academic activities, either in class or at home, for non-academic purposes. The scales research authors identified as adequate measures of media multitasking were also taken into consideration. These measure the use of various digital media during academic activities or assess attitudes about being able to efficiently follow lessons/complete tasks while using some of the media (e.g. social networks). The measures are intercomparable, and higher scores indicate greater frequency/ inclination for multitasking.

Academic achievement – operationalized as the current GPA, the semester average grade, the average grade from the previous level of education (high school), or the average grade in compulsory subjects. All measures are equivalent.

Inclusion and Exclusion Criteria

In order to be included in the meta-analysis, the research had to meet the following criteria:

• 1.    It was published in a scientific journal with an impact factor (Clarivate JCR).

• 2.    The publication language of the journal is English.

• 3.    The study is not older than 2010 (statistical data indicate a continuous increase in the use of the internet itself, as well as social networks at the end of the first decade of the 2000s, while Instagram, the currently most popular social network, was founded in 2010).

• 4.    The independent variable relates to the use of media multitasking.

• 5.    Media multitasking refers to the use of digital tools in an educational context for non-academic purposes.

• 6.    The study must report correlation coefficients between the variables or provide sufficient data to calculate these coefficients. The correlation coefficient was chosen as the measure of effect size because it was consistently reported across studies, allowing for a standardized comparison of the relationship between media multitasking and academic achievement. This approach maintains the internal and external validity of the meta-analysis by ensuring that the effect sizes are comparable across different studies.

• 7.    The dependent variable must be operationalized as an average grade (GPA or equivalent measure). GPA is used due to its standardization and comparability across different educational contexts and time periods, providing a reliable measure of academic performance. This decision helps maintain both internal and external validity by ensuring that the measure of academic achievement is consistent and comparable across all included studies.

After all the criteria were applied, 11 correlational studies were included in the final sample. The table with the studies contained in the sample can be seen in Appendix A.

Data Extraction Process

The data extraction process involved systematically reviewing each included study to obtain the necessary information for the meta-analysis. This comprehensive approach ensured that we had consistent and comparable data across all studies, which is crucial for maintaining the validity and reliability of the meta-analysis. For each study, we extracted detailed information including the study title, authors, year of publication, and the journal in which the study was published. We also recorded the impact factor (IF) of the journal to ensure the quality and credibility of the included studies. From the sample details, we documented characteristics such as the type of participants (e.g., students, adolescents), along with the sample size. Regarding the measures used, we extracted specific information on media multitasking, including the types of digital media use (e.g., social networks, email, games) and the context of use (e.g., during class or at home for non-academic purposes). Similarly, we noted the measures used to assess academic achievement, such as current GPA, semester average grade, or average grade from the previous level of education. Importantly, each study reported the effect size measure required for our analysis in the form of correlation coefficients. This consistency eliminated the need to use other statistics or perform additional calculations to derive these values. Each study provided a single relevant correlation measure between media multitasking and academic achievement, so there was no need to merge multiple effect sizes from individual studies.

Quality Assessment of Studies

The quality of the included studies was assessed using established criteria to evaluate risk of bias and methodological rigor. This process involved a thorough review of each study’s research design, data collection methods, and analysis techniques. To ensure a comprehensive and objective assessment, multiple researchers independently evaluated each study. Each study was first evaluated for potential sources of bias, including the selection of participants, measurement of variables, and control of confounding factors. Additionally, the methodological rigor of each study was assessed based on the clarity of research questions, appropriateness of study design, and robustness of data collection and analysis methods. The validity of the measures used for both media multitasking and academic achievement were also considered. To enhance the reliability of the quality assessment, three researchers independently coded each study. This involved assigning ratings for each criterion based on predefined scales. The initial ratings were then compared to identify any discrepancies. In cases where discrepancies were identified, researchers discussed the differences and reached a consensus on the final ratings. This quality assessment process ensured that the included studies were evaluated consistently and objectively, enhancing the validity and reliability of the meta-analysis.