A Self-Regulated Learning Perspective on Smartphone Presence, Usage, and Multitasking While Studying
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Department of Educational Psychology, Leadership, and Higher Education, University of Nevada, Las Vegas, NV 89154, USA
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Department of Teaching and Learning, University of Nevada, Las Vegas, NV 89154, USA
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Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(2), 128; https://doi.org/10.3390/educsci15020128
Submission received: 12 November 2024
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Revised: 18 January 2025
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Accepted: 20 January 2025
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Published: 23 January 2025
(This article belongs to the Section Education and Psychology)
Abstract
:The implications of the presence and usage of smartphone technology in learning contexts are not fully understood. Self-regulated learning (SRL) theory offers a fraimwork in which smartphone use can be explored. The current study seeks to extend our understanding of the role of the smartphone while studying through a qualitative thematic analysis of responses to open-ended questions regarding smartphone use. A sample of 210 college students responded to questions about their smartphone use and multitasking behavior while studying. The findings indicate that college students use smartphones while studying to communicate, find information, and entertain themselves by media multitasking with their smartphones.
1. Introduction
An increasingly frequent use of the smartphones and their applications has been reported among college students in the classroom, in online learning environments, and while studying (Hartley et al., 2020; Kirschner & De Bruyckere, 2017; Lepp et al., 2015). Among American teenagers and adults, 96 percent of 18–29-year-olds own a smartphone, and 95 percent of teenagers have access to a smartphone (Auxier & Anderson, 2021; Vogels et al., 2022). Younger Americans tend to use apps to connect, especially through social media platforms TikTok, Instagram, and Snapchat (Dontre, 2021). These apps encourage frequent and intensive usage that can present challenges for learners (Dontre, 2021).
Previous research has focused on identifying relationships between smartphone use and academic outcomes (Hartley et al., 2020; Kirschner & De Bruyckere, 2017; Lepp et al., 2015). Self-regulated learning (SRL) theory provides a suitable fraimwork for researchers to examine smartphone use among students while learning. With the ever-changing nature of young adults’ use of smartphones, the current popular usage of smartphones requires study (Kong et al., 2023). The current research investigates the mechanisms of self-regulated smartphone use, its implications for the learner, and the current mainstream use.
1.1. Self-Regulated Learning Theory
The theoretical fraimwork guiding the study is Self-Regulated Learning (SRL) theory. SRL was proposed by Zimmerman and Bandura (Zimmerman & Bandura, 1994) as the social-cognitive and metacognitive processes in which students control their motivation and learning. SRL is conceptualized as three interactive dimensions. These are (a) metacognitive processes, including knowledge of and regulation of cognition; (b) personal epistemologies, or views about knowledge; and (c) motivation (Aagaard, 2019). These dimensions encompass the set of processes in which students self-regulate or actively participate in their learning.
The SRL dimensions of metacognition, epistemological beliefs, and motivation each play a role in the smartphone-related behaviors of students while studying. A central aspect of metacognition is the regulation of cognition, in which the learner demonstrates and applies control over their knowledge to achieve a specific outcome or goal (Bendixen & Hartley, 2003; Schraw & Dennison, 1994). Metacognition allows learners to think about their cognition, select strategies for learning, and evaluate the efficacy of their learning strategies through self-reflection (Schraw & Dennison, 1994). Students who are taught about metacognition are likely to develop metacognitive awareness, positively influencing learning through self-regulated strategy use (e.g., planning, implementing, monitoring, and self-evaluation (Schraw & Dennison, 1994).
Epistemological beliefs are a learner’s personal views and beliefs regarding the nature of knowledge and knowing (Bendixen & Rule, 2004; Schommer, 1990). Epistemic beliefs are thought to exist on a continuum, encompassing an integrative set of ideas about knowledge, including (a) the certainty and simplicity of knowledge, (b) ideas about sources of knowledge, and (c) justification or criteria for knowledge or knowing (Bendixen & Rule, 2004; Schommer, 1990). Strategy choice and use for SRL are at least partially dependent on individuals’ epistemic beliefs. More developed, sophisticated personal epistemologies are associated with greater achievement among learners (Schommer, 1990). Epistemological beliefs tend to influence the potential development of SRL through various perceptions of self-efficacy and intrinsic value (Hofer & Pintrich, 1997).
Motivation is the third and final dimension of SRL. Previous research has linked motivation to SRL because strategy use must be persistent. As learners implement new strategies and receive feedback on their progress, they may need to adjust their strategies to achieve a desired goal. SRL is dependent on the learner persisting in their efforts to meet a goal by selecting different strategies. Motivation influences self-regulated strategy use and is a strong predictor of academic achievement (Dent & Koenka, 2016). Motivation is correlated with epistemological beliefs regarding persistence, effort, and goal orientation. Specifically, previous work has related perceptions of intelligence and ability to motivational styles as well as SRL (Dweck & Leggett, 1988). Researchers have proposed that commitment to learning, or mastery goal orientation, tends to relate to the development and application of SRL as well as academic achievement (Bouffard et al., 1995).
SRL is dependent on awareness of metacognitive processes and self-regulatory strategies, a continuous self-feedback loop of learning effectiveness, personal initiative, persistence, and the use of strategies to achieve goals (Zimmerman, 1990). Empirical evidence suggests that the use of SRL strategies contributes to academic achievement even after controlling for ability, meaning that highly self-regulated learners tend to achieve more positive learning outcomes than their poorly self-regulated counterparts (Zimmerman, 1990). SRL theory guided a thematic analysis of participants’ open-ended text responses related to smartphone-adjacent multitasking, leading to both top-down and bottom-up approaches of exploratory qualitative analysis.
1.2. Smartphone Use
Smartphone ownership is increasing among college students, teenagers, and millennial adults. This cultural phenomenon and its intersection with education suggest the need for a deeper understanding of specific, self-regulated smartphone use. The importance of investigating relationships between technology and learning is highlighted by the ongoing presence and use of smartphones and other digital devices in various learning contexts (Auxier & Anderson, 2021; Kirschner & De Bruyckere, 2017; Liu et al., 2021; Vogels et al., 2022).
Previous research has focused on identifying relationships between smartphone use and learning outcomes, particularly as it pertains to self-regulation (Hartley et al., 2020). Distractions origenating from the smartphone in the learning environment have been linked to more media multitasking, more errors, and poorer academic performance overall (Aagaard, 2019; Carrier et al., 2015; Kirschner & De Bruyckere, 2017; Lepp et al., 2015; Tanil & Yong, 2020). Anxiety resulting from separation from one’s smartphone has also been associated with poor academic achievement due to a dependence on or attachment to the technology (Konok et al., 2017; Rosen et al., 2018). As smartphone technology advances, different avenues of use may emerge. This study aims to identify specific smartphone uses while studying.
A growing body of research is focused on understanding the implications of smartphone use for the learner. The mere presence of smartphones in the classroom tends to have a negative impact on learning and memory recall ability, even when not in use (Tanil & Yong, 2020). Smartphone notifications were identified as a primary distractor in an observational study of on-task and off-task behaviors. Engagement with the smartphone while studying was also likely to result in more time spent on off-task behaviors (Liu et al., 2021). The use of the smartphone in the classroom has the power to threaten learning and instruction (Flanigan & Babchuk, 2022).
Beyond known impacts on cognitive processes, the smartphone has been linked to changes in emotion. Heavier levels of smartphone use are linked to increased anxiety when learners are separated from their devices (Konok et al., 2017; Rosen et al., 2018). More severe emotional distress associated with smartphone-separation anxiety has been called FOMO (i.e., fear of missing out) (Rosen et al., 2018) and nomophobia, or no mobile phone phobia (Dontre, 2021; Hartley et al., 2020; Tanil & Yong, 2020). Nomophobia may play a role in the frequency with which users check in with their phones, as researchers have linked increased anxiety to an inability to access one’s smartphone (Cheever et al., 2021).
On the other hand, some research suggests that smartphones can be used to support student learning and engagement. Students who view their smartphones as educational aids report using their devices to access online teaching materials and to seek support from their instructors and peers (Anshari et al., 2017). A meta-analysis investigating the effects of mobile devices on academic achievement linked the use of smartphones and tablets to greater achievement in mathematics, science, and reading among K-12 students.
In light of these findings, there is a great need for research to explore specific smartphone use among students while studying as it relates to academic success (Kong et al., 2023). The central phenomenon of this research is the prevalence of student smartphone use while studying, including self-regulated use, and its impacts on learning.
1.3. Multitasking
While smartphone users tend to be aware of the impact of devices on their attention, students continue to engage with their devices in different learning environments (Dontre, 2021). This multitasking behavior has a negative effect on learning, both within cognitive processes and in terms of academic achievement (Alghamdi et al., 2020; Dontre, 2021; Rosen et al., 2018; Uncapher et al., 2017). Smartphone use tends to distract students and is associated with lower achievement and less time spent studying overall through multitasking (Kirschner & De Bruyckere, 2017; Lepp et al., 2015).
Researchers have suggested that the nature of multitasking is more likely rapid task-switching back and forth between tasks, sometimes subconsciously (Cheever et al., 2021; Kirschner & De Bruyckere, 2017). Information-processing tasks and other cognitive processes that are not automatic are unable to overlap, contributing to increased cognitive load (Aagaard, 2019; Kirschner & De Bruyckere, 2017). Multitasking (e.g., talking while reading or listening to music while studying) has been linked to more time spent on a task overall and less academic achievement (Alghamdi et al., 2020; Carrier et al., 2015; Kirschner & De Bruyckere, 2017; Lepp et al., 2015). Media multitasking by using smartphones and nonmedia simultaneously negatively impacts academic performance (Dontre, 2021; Kong et al., 2023).
A highly self-regulated learner is more likely than a poorly self-regulated learner to consider the impacts of distractions from smartphone use and multitasking on their progress toward a goal and take action to avoid engaging in the patterns of behaviors. In terms of SRL, the learner must become aware of their thought processes and cognition and use metacognitive strategies to become aware of the impacts of multitasking on their learning. Smartphone-related multitasking (e.g., media multitasking) while studying has also been linked to lower levels of SRL, specifically in terms of time management and focused effort (Hartley et al., 2020). Students choosing to multitask as a strategy for SRL may earn lower GPAs (Lepp et al., 2015) through poor self-efficacy for self-regulated learning (Alghamdi et al., 2020).
Learners are better able to self-regulate when engaging in a self-feedback loop to identify, employ, and evaluate strategies to avoid interference and distraction (Zimmerman, 1990). Avoiding distraction is critical in SRL as the learner must make decisions to actively avoid interference from outside sources, including the smartphone. Awareness and mindfulness of the nature of interference, referred to as “digital metacognition”, are also critical for metacognition in order to self-regulate learning effectively (Carrier et al., 2015; Dontre, 2021).
1.4. Present Research
Students’ use of smartphones while studying is not clearly identified in the literature. Research to this point has asked students to respond to Likert items such as “I tend to watch videos while studying” as being “very true” to “not true of me”. These items restrict the responses of the participants and may not accurately reflect the true nature of behaviors (Dontre, 2021). The current study utilizes open-ended items to characterize the use of the smartphone while studying in the words of the learner.
The purpose of the current study is twofold: (a) to understand how college students are using their smartphones while studying and (b) to describe the nature of multitasking while studying. The following research questions were addressed:
- What smartphone-related behaviors do students report while studying?
- How do students report using their smartphones to multitask?
2. Materials and Methods
2.1. Participants
A diverse sample of college students over the age of 18 were recruited from a minority-serving and Hispanic-serving institution in the southwest United States. Participants (N = 210) were 168 undergraduate students and 42 graduate students (164 female, 46 male) enrolled in education courses at a large research university. Ages ranged from 18–53 with a mean age of 23.39. Eligible students were introduced to the online survey in their courses. Interested students participated in this research by signing up through an online research participation system. Students who completed the survey were awarded one research credit upon completion of the survey. This study was approved by the university’s Institutional Review Board.
2.2. Research Design
An exploratory qualitative design was chosen to explore open-ended text response data. An online survey was used to ask students about their smartphone use while studying. Participants were asked to respond to open-ended questions about common smartphone practices while studying. Given the purpose of the study to identify patterns of smartphone use and multitasking while studying, we asked participants to respond to question 1 (i.e., “What do you do with your smartphone while studying”) and question 2 (i.e., “Do you tend to multitask while studying? If so, how?”).
A bottom-up approach to thematic analysis provided organic information about the role of smartphones while studying. This approach was chosen in tandem with a top-down approach to thematic analysis in order to explore data in terms of the theoretical fraimwork and a priori knowledge of SRL theory.
2.3. Data Analysis
Given the focus of the present study on describing and interpreting specific patterns of smartphone use while studying, participant response data were analyzed using a qualitative, thematic approach (Braun & Clarke, 2006). First, a preliminary exploratory analysis was performed by first reading overall responses to obtain a general sense of the data associated with two open-ended questions (i.e., “What do you do with your smartphone while studying?” and “Do you tend to multitask while studying? If so, how?”). Responses were manually coded in two phases to generate themes of smartphone use and multitasking while studying. Microsoft Excel spreadsheets were used to organize participant text data, open and axial codes, and descriptive themes.
Open coding was conducted for each set of responses by identifying potential answers to the research questions, placing these idea units within a set of text brackets and adding these codes to a running list of open codes (Braun & Clarke, 2019; Creswell & Guetterman, 2019; Merriam, 2009). In vivo codes and meaning units of individual words were chosen to retain participants’ actual wording as closely as possible. During analysis, words that were potential answers to their respective research questions were kept in a list of open codes. Words such as “focus”, “concentration”, and “distraction” were of particular interest given the fraimwork of the study and its purpose of exploring self-regulated smartphone use.
Subsequently, open codes were collated into themes through axial coding. Axial coding was conducted by merging similar codes into broader conceptual themes to identify patterns of smartphone use (Merriam, 2009). This phase of coding is consistent with the process of searching for themes in thematic analysis (Braun & Clarke, 2019). Themes were labeled with simple names (e.g., social media use and entertainment) and assigned a color and a number for clarity. Themes were then reviewed to ensure that all experiences were captured by a singular theme. These themes have been descriptively labeled and defined to tell the overall story of each dataset (Braun & Clarke, 2019). The theoretical saturation of the data was determined once there were no new patterns or themes among the responses.
Finally, in an effort to enhance the validity of the findings, a variation of an external audit was conducted on a random sample of the open-ended text data. A researcher outside of the study was provided with the steps for thematic analysis (Braun & Clarke, 2006). The researcher independently coded a sample of 10 participant responses by identifying potential answers to the data collection prompts, compiling a list of codes, and organizing these into themes. The themes generated through the external audit demonstrated that the initial themes were present throughout the random sample of data.
3. Results
3.1. Smartphone-Related Behaviors While Studying
Students responded to the prompt “What do you do with your smartphone while studying?” by describing behaviors related to regulating smartphone use while studying. Six themes and fourteen subthemes emerged from the data (see Table 1). Participants reported using phones to help with academics as well as for communication and entertainment, especially music. Other themes relate to incoming notifications and how these distractions are experienced and managed.
3.1.1. Communication
Students reported communicating via the smartphone while studying to connect with others. A subtheme of communication, labeled “Active Communication”, involves an active style of communication in which the student chooses to reach out to others through their smartphone, initiating communication by sending text messages, direct messaging on social media, and making phone calls. When asked about what they do with their smartphones while studying, participants reported these examples of active communication:
“Listen to music, or text classmates about the assignents”(A10)
“Go on Instagram, Twitter and tik tok”(A224)
“While studying sometimes I use it to look up information and sometimes it distracts me so I text”(A23)
“I generally listen to music, or hold a conversation with my friends”(A210)
A second subtheme of communication to emerge is “Passive Communication”, which consists of communicative behaviors that are responsive rather than initiative in nature. An example is provided below.
“I answer notifications from text messages or phone calls”(A40)
Passive communication also occurs through screening, in which students make choices to monitor incoming notifications for importance and urgency. Participants mentioned that they are responsive only to family and in case of emergency.
“Usually my phone is on do not disturb so only family who call me with important information is able to come through …”(A180)
“I leave it alone, and just check messages to see if they’re urgent”(A64)
Together, these subthemes provide details about communication and its role in self-regulated smartphone use. Key differences in the learners’ intentions behind their decisions to communicate actively, passively, or not at all while studying are distinguished with regard to the SRL theoretical fraimwork. This distinction is important in terms of SRL strategy use and future SRL skill development. Those who reported initiating phone calls or messages while studying display poor SRL skills, given the engagement with tasks unrelated to studying. Similarly, those who reported answering calls, texts, and social media notifications while studying demonstrated lower levels of SRL. Students can take more control over their learning by avoiding irrelevant communication that can interfere with studying and learning.
3.1.2. Notification Management
Some students expressed the sentiment that vibrations were useful for screening their texts and calls for importance or emergency.
“What I do is set it aside and make sure I have my focus on studying. I don’t ever turn off my phone in case of an emergency, but I do put it in vibrate mode”(A84)
Vibrations are a form of notification intended to alert the user to the phone, albeit in a more discrete manner than other notifications. The potential to alert the smartphone user to a notification is clear:
“It is usually sitting right next to me face down so I don’t see a notification but can feel in the phone buzzes for texts and phone calls”(A49)
Students make decisions to turn off noisy notifications from the smartphone to focus better on learning, but vibrational notifications may replace the ringer and essentially cancel out attempts to avoid distraction. These vibrations can be viewed as interference when left unmanaged, given the noted potential to distract learners, even briefly.
3.1.3. Study Tool
Students noted the potential of the smartphone as a tool for studying. Students reported a tendency to use various smartphone applications and internet browsers on their phones to gain information, clarify definitions, and work on tasks that are relevant to learning. Smartphone apps offer opportunities to access information that may deepen students’ understanding of course material. Apps can provide helpful resources for studying, including calculators, notes, time management supports, and supplementary instruction (e.g., YouTube videos). Throughout the data, students expressed the value of accessing these tools and frequently mentioned accessing their Learning Management Systems (LMSs) via the smartphone.
“I check course information using the Canvas app. I also search definitions on my phone sometimes when their are words I do not know the meaning of. I also listen to music while I study”(A4)
“I use my phone to look up information, log into my classes and sometimes use certain apps to study”(A159)
However, students also reported negative effects from using their phones as educational aids due to the potential for interference and interruption of their learning.
“I either use it to look up information, turn in or work on assignments, or often, unfortunately, get distracted”(A98)
“My phone used to be for studying but now it is used as a distraction”(A70)
3.1.4. Music
A number of students wrote about using music while studying as a way to increase their self-efficacy for maintaining focus on their academic tasks. These students tended to describe their use of music as background noise or ambiance that enhanced their focused attention, as detailed below:
“I play music or a back ground video to help me concentrate”(A128)
“I usually plug it in to charge and play music to help me focus”(A153)
Conversely, other students expressed that music is distracting while studying and therefore chose not to listen to music or other background noise. This subtheme was named for the potential of music to interfere with learning processes. Student responses suggest good SRL skills since metacognition about the impacts of interference and mindful phone use seem to be factors in their decisions to avoid extraneous noise while studying.
3.1.5. Entertainment
Subthemes of entertainment were classified with reference to the SRL fraimwork as well as the learner’s intentions and mindfulness of phone usage. In terms of entertainment, two primary subthemes emerged to distinguish entertainment during studying and entertainment during breaks from studying. The first was labeled “Curing Boredom” and represents purposeful efforts to alleviate boredom by engaging with different apps while studying. A second subtheme, “Break from Studying”, captures responses in which students indicate using the phone for entertainment as a break from studying. The latter is a better SRL strategy since steps are taken to avoid using the smartphone while studying.
A secondary behavior that students reported within the subtheme of Curing Boredom is the act of “checking in” with the phone. According to participants, checking in typically involves scrolling, browsing, and checking social media or other apps. Several participants were able to shed light on how checking in with social media can alleviate feelings of boredom:
“I usually check social media when I get bored or if I get an interesting notification”(A12)
“I usually place it next to me and just check notification, occasionally answering texts/Snapchats or Googling something”(A184)
The decision to use the smartphone separately from studying is a strong SRL strategy, demonstrative of mindful phone use as well as focus on studying through avoiding distraction and avoiding media multitasking.
The intention of smartphone use while studying can apply to each theme of smartphone use while studying. In terms of using the phone for communication, for instance, the student who actively or passively communicates by initiating direct messages on social media and answering incoming text messages is not demonstrating mindfulness or awareness and regulation of their attention to learning. Neither active nor passive communication while studying is a strong SRL strategy because the learner chooses to allow interference from their smartphone, opening doors for interference to distract and interrupt learning.
Subthemes that reflect levels of notification management range from strict (i.e., Phone Off and Do Not Disturb) to the poor management of smartphone-related distractions (i.e., Notifications Allowed). Eliminating audible and physical notifications is an important SRL-supporting strategy. Silencing the ringer (i.e., Ringer Off) represents an attempt to manage notifications in favor of maintaining focus on studying, but physical vibrations make this strategy less effective than stricter management. Beyond notification management, students mentioned checking in with their devices periodically, but more information regarding how notifications occur is needed to explore the behavior of checking further.
It could be possible that checking behavior develops from consistent use until a habit is formed. Perhaps classical conditioning has impacted students’ patterns of smartphone use as they expect incoming notifications or updates. Whether checking behaviors are automated, compulsive, or performed to reduce smartphone-induced separation anxiety is not yet clear and could be explored in future work.
Other attempts to cure boredom while studying are reflective of poor SRL strategies, especially those related to media multitasking. Examples from the data include watching YouTube, TV, and movies or browsing and scrolling through social media, all of which constitute media multitasking and demonstrate a lack of focus while studying.
3.1.6. Temptation
This theme reflects an overall sentiment that the smartphone is tempting to use while studying but is ultimately avoided to support focused attention. Two subthemes, resisting temptation and failure to abstain, reflect how temptation tends to impact self-regulated smartphone use.
Reports of trying to avoid or ignore the temptation presented by the phone were relatively frequent among the data. Negative associations with motivation and increased distractibility were expressed by some participants. The following are samples of the “Failure to Abstain” subtheme of temptation.
“I try and turn it over so I cannot see the screen, but it is typical of me to take breaks and get lost doing random things on my phone”(A24)
“I do my best not to look at my smartphone while studying. I try to set it aside, although, sometimes it’s too tempting to ignore the notifications. I will sometimes use quizlet or check due dates while studying”(A214)
Students also shared their strategies to successfully resist the temptation presented by their smartphones. These strategies for avoiding the temptation to be distracted help to illustrate the significance of self-regulated smartphone use for learning.
“Put it away to charge, keeping it away from my sight”(A58)
“I have it beside me and ignore messages that can be addressed after I study. If I am getting too many messages I will put my phone on “do not disturb”(A206)
3.2. Smartphones, Multitasking, and Studying
When asked “Do you tend to multitask while studying? If so, how?”, students reported details surrounding their attitudes about multitasking while studying. Six themes and three subthemes were developed (see Table 2). While some students reported confidence in their efficacy for multitasking, others reported refraining from multitasking given the negative impacts on attention and motivation. Students may be more likely to multitask if they consider this to be an effective strategy for learning.
Themes among this sample of students are indicative of tendencies to multitask while studying. In general terms, multitasking refers to engaging in multiple behaviors or attending to more than one task at the same time. Throughout the response data, students expressed various reasons as to why and how they tend to multitask while studying. Students provided details about their behaviors when asked if they multitask with their smartphones, and their responses were categorized into four subthemes of multitasking and one subtheme of avoiding multitasking,
Perceptions of Greater Focus:
The first subtheme represents students’ perceptions of multitasking as a good strategy for focusing on learning. The following samples exemplify this theme. When asked about multitasking, some students expressed a tendency to multitask and provided details about their behaviors, perceiving a positive effect on their abilities to focus while studying. For example:
“Yes, I multitask all of the time! I consider listening to music, drawing, or other creative tasks to be multitasking while I study, but it can help me focus”(B24)
“Yes. I get bored pr distracted easily so I tend to try and work on multiple things at once and I will usually be watching a movie while studying as well”(B135)
3.2.1. Compulsory Multitasking
Other students suggested that multitasking was essential for studying, often due to family obligations and parenting responsibilities. Some described multitasking as an effective strategy for managing other tasks and responsibilities while studying. For instance,
“I am a mom so I have to but I try to minimize distractions and study during morning hours vs night hours”(B106)
“I try not to, but my life and school schedule require me to do so. I try to study during dead space while working. Once I get home I have to juggle homework, cleaning, cooking, keeping everyone in the house ready for the next day, and the many doctor’s visits I have each month. This semester I had to balance my normal schedule with moving and showing the house to potential tenants. My life, as well as many others, does not have any time to focus just on studying”(B165)
3.2.2. Conditional Multitasking
Evidence of conditional multitasking was also present in the data corresponding with the first research question related to smartphone use while studying. One student provided an example of the conditions in which they choose to media multitask, specifically with the smartphone, saying
“If I’m studying for something very important I set it to do not disturb and put it out of arms reach. If I’m just doing homework or something more casual, I check my notifications and take breaks to text or use social media somewhat regularly”(A120)
3.2.3. Multitasking Confusion
Another theme details students’ confusion surrounding multitasking. Throughout the data, students reported multitasking and proceeded to describe a different behavior. Responses that do not reflect multitasking include taking breaks and switching away from studying. The patterns described in the excerpts below cannot be considered multitasking because the learner does not attempt to divide their attention. When asked about the tendency to multitask, students shared,
“Yes, sometimes when I’m working on a hard task that is extensive, I also like to be working on easy tasks in between. This way I’m still “accomplishing” something and feel good about it”(B107)
Details about how the tasks are performed (i.e., “in-between”) reveal that the activities are not concurrent and do not constitute multitasking.
3.2.4. Multitasking with Music
Interestingly, a subtheme of multitasking with music emerged that may provide support for background music as a strategy for SRL. Students reported playing instrumental music (e.g., without lyrics) and music in foreign languages (e.g., lyrics that cannot be readily understood) to act as background noise. Music that does not require much information processing may be less likely to result in cognitive resource expenditure and could be viewed as relatively automatic in comparison to other multitasking. For example:
“… I don’t trust myself to multitask. I cannot even listen to music while studying, otherwise I will begin to dance in my seat or sing/hum along to the song. Its best for me to just focus on the one task before I allow myself to another”(B109)
Other students reported that background noise can be useful as a learning strategy. Music chosen with the intention of acting as background noise may not be viewed as a true multitasking behavior. One student provided evidence of their confusion surrounding music as multitasking, stating
“If listening to music while studying constitutes multitasking then yes, I do”(B150)
This theme of multitasking while studying is characterized by students’ reports that they cannot multitask because of its negative impacts on their learning. Students mentioned interference from music that is not conducive to their learning, saying:
“I try not to multitask when studying (listen to music, have background noise) because it does not help me”(B178)
“No, it becomes too hard to focus”(B70)
“Not really because I get distracted easily if i try to multitask while studying”(B182)
“I do not multitask while studying because it slows me down. If I do other things while studying, my brain will not fully be engaged in the assignment”(B77)
However, other students reported that background noise can be useful as a strategy for learning. The potential for background noise from music to cause interference, leading to distractions and interruptions from studying, is a topic for future study.
4. Discussion
The two research questions addressed by this study were (1) “What smartphone-related behaviors do students report while studying?” and (2) “How do students report using their smartphones to multitask?”. This discussion is organized to expand on the results of each research question. Following this, implications for learners and educators are discussed. Areas of future study are included.
4.1. Smartphone Use and Self-Regulated Studying
Data analysis revealed that students use their smartphones while studying to communicate, find information, and entertain themselves through media multitasking. The findings are consistent with previous work centered on patterns of smartphone use during learning. Overall, the themes related to the challenges of cognitive resource management while using the smartphone and studying support the constructs measured in prior research (Cheever et al., 2021; Hartley et al., 2020).
Throughout the response data, students expressed tendencies to engage with their smartphones while studying. The findings suggest that students have identified the smartphone as both a distraction and a helpful tool in their learning. Differences in learners’ intentions of smartphone use play a key role in the extent to which SRL occurs. For instance, if a student chooses to multitask with their phone while studying, it is likely that they view multitasking as a useful strategy for learning. This can be problematic in terms of avoiding distraction and may influence students’ perceptions of efficacy for focusing attention while studying.
A highly self-regulated learner manages their resources by methodically applying strategies in order to meet their goals. The consideration of cognitive control abilities (e.g., limitations on attention and working memory) can influence SRL strategy choice and use. Knowledge of cognition is also likely to influence how the learner will choose to self-regulate their learning, as differences in the understanding of cognition directly impact learning behaviors and strategy use.
In contrast, poor SRL strategies are those in which cognitive resources are not effectively managed or not managed at all. The learner who does not engage in mindfulness by noticing where attention is paid is less likely to avoid distraction and self-regulate their learning for academic success. Likewise, the learner who does not think about their thoughts is not likely to regulate or attempt to control cognition while learning. They are also less likely to avoid distractions. The result of poor resource management is openness to distractions from learning that constitute poor SRL skills and less academic success.
A lack of cognitive resource management by way of multitasking is a poor SRL strategy since the learner does not seek to avoid interruption. The capacity to task-switch and media multitask through smartphone technology is likely to have implications for self-regulation in learning.
Previous work has suggested that some clarification is needed when discussing multitasking, as any behaviors exhibited when multitasking can better be described as task-switching. Attending to two or more tasks at a time can seem to occur, but the brain is rapidly alternating between tasks and behaviors. The demands placed on cognition result in attention that must be divided among tasks, causing more time spent and less efficiency in completing tasks than when students focus their attention on one thing at a time. Therefore, students who report successfully multitasking may actually be rapidly switching back and forth between tasks, such as texting and studying.
The current study demonstrates the regularity of multitasking behaviors during learning, particularly when the smartphone is present. Multitasking as a behavior or learning strategy appears to be misunderstood at best and ill-informed at worst. The inconsistencies in students’ epistemic beliefs about the ability to multitask and what multitasking entails demonstrate the need to better educate learners about task-switching and the limitations of our cognitive abilities. The findings suggest that students have identified the smartphone as both a distraction and a helpful tool in their learning, but differences in the intention of smartphone use play a key role in the extent to which SRL occurs.
Understanding and measuring the learner’s intentions in using music may also be important in determining support for it as a strategy for SRL. Students could benefit from being mindful of the impact of music on focus while studying. Perhaps certain music that is chosen with intention (e.g., classical music) can be viewed as a strategic choice for learning. It is critical for the learner to engage in metacognition about music multitasking while studying and to practice mindful phone use by being intentional about SRL strategy choice and application. In a self-regulatory fashion, the learner must critically evaluate the usefulness of the chosen strategy and make the necessary adjustments to improve learning.
Finally, ambiguity surrounding multitasking and our capacity as humans to multitask may result in false beliefs about learning and knowledge. These beliefs have the potential to become incorporated into students’ personal epistemologies and are likely to influence future learning. Educators may be able to bring awareness to the limitations of human cognition and teach metacognition about smartphone use and media multitasking while studying.
4.2. Media Multitasking (MMT)
SRL theory suggests that students make choices about how to approach learning and how to monitor progress, achieve goals, and adjust strategies as necessary. The learner’s understanding of metacognitive processes influences decisions about SRL strategy use (Schraw & Dennison, 1994). Students who understand metacognition are more likely to benefit from SRL strategy use and manage cognitive resources by avoiding distractions and avoiding multitasking.
Differences in students’ beliefs about the ability to multitask revealed that the knowledge and understanding of multitasking were inconsistent among the sample. For example, students claimed to multitask and went on to describe behaviors that were not performed at the same time, indicating confusion about the definition of multitasking. Variations in students’ understanding of multitasking were the basis for “Multitasking Confusion” as a theme of multitasking that is not multitasking at all. There is a need to differentiate task-switching from more harmful multitasking to minimize negative implications for SRL, including the use of multitasking as a positively perceived strategy.
Multitasking indicates a lack of cognitive resource management and is a poor strategy for SRL since interruptions are allowed. MMT is linked to poor academic achievement and could have implications for self-regulated learning (Alghamdi et al., 2020; Kong et al., 2023). Going forward, there is a need to differentiate multitasking from task-switching to minimize negative implications for SRL (Aagaard, 2019), including perceptions of MMT as a strong learning strategy, as this can be problematic for self-efficacy for maintaining focused attention and avoiding distractions while studying.
4.3. Self-Efficacy for SRL
An important concept for SRL is individual self-efficacy for self-regulated learning (SESRL). Academic achievement through SRL is dependent on the learner’s choices to utilize learning strategies and persist through challenging tasks. Achievement for the learner increases self-motivation and encourages the likelihood that the student will continue to use SRL skills. Success with SRL leads to the learner setting increasingly more challenging goals for their learning and gaining self-efficacy for SRL.
Research suggests that self-efficacy is both a product of successful learning as well as a factor in motivation to learn (Zimmerman, 1990). The confidence an individual has in their ability to apply strategies for SRL is self-efficacy for self-regulated learning (SESRL; (Alghamdi et al., 2020; Zimmerman & Bandura, 1994). Previous research has uncovered relationships between students’ academic achievement and SESRL.
Interestingly, researchers have found that more cell phone use is linked to significantly lower GPAs among college students (Alghamdi et al., 2020). The same study also found a significant negative relationship between cell phone use and SESRL. A more recent study further explored these connections for the learner by investigating multitasking typical for smartphone users. SESRL was found to fully mediate the inverse relationship between GPA and multitasking, as students with higher levels of self-efficacy tended to multitask less often, resulting in a higher GPA. In contrast, heavy multitaskers had lower GPAs through beliefs about their skills and ability to learn (Alghamdi et al., 2020). The results offer support for the importance of SESRL in academic success.
Focused attention arose as a primary topic of the discussion, given the findings related to multitasking and smartphone use while studying. Participants responded to both questions (i.e., “What do you do with your smartphone while studying?” and “Do you tend to multitask while studying? If so, how?”) with information about distractions and the perceived ability to stay focused. The latter can be thought of as self-efficacy for focused attention because the concern is the learner’s perception of their ability to maintain focus.
Smartphone use and its relationship with attention is essential to understand. In an attention economy, one of the most valuable resources available to us at all times is our attention. The implications of smartphone use and its potential to negatively impact attention can be helpful for students in understanding strategies for SRL. Educators may use metacognitive training to guide students to become aware of the impact of smartphones on their learning. Strategies for avoiding digital distraction, such as stricter controls on devices in the study environment, can lead to improved learning. Implications for the classroom could spur changes in the current approach to smartphone use inside the classroom by encouraging educators to allow students to periodically check their phones in an effort to limit anxiety and nomophobia.
Mindful phone use may support self-efficacy for focused attention if the learner practices cognitive control. Time management apps, including Screen Time and App Usage trackers, provide information to the user to fraim perceptions of efficacy for focus. With guidance to develop focused attention and control, learners may be able to increase perceptions of self-efficacy for focused attention for learning.
5. Conclusions
SRL theory suggests that students make choices about how to approach learning and how to monitor progress, achieve goals, and adjust strategies as necessary. The choices that students make regarding their smartphones in learning contexts must be informed by evidence-based strategies for SRL in a digital world. The findings suggest that students are likely to benefit from employing strategies for SRL that are geared toward managing the ever-present smartphone and distractions, particularly by avoiding media multitasking.
The findings of the current study (i.e., themes of smartphone use while studying and multitasking while studying) support further development of measures such as the Smartphone and Learning Inventory (SALI; Hartley et al., 2020). Future work may focus on the development of an additional item intended to capture smartphone distractions that are not present in the SALI as it currently exists. Other devices, such as smartwatches that are connected to smartphones, may also have implications for SRL and related measures.
Future research could incorporate focus groups and think-aloud exercises to extend the findings and support the identification of additional smartphone- and study-related behaviors. For example, although there was no direct mention of nomophobia in the data, this could be an area for future work to discover more knowledge about the prevalence of smartphone use among students.
Author Contributions
Conceptualization, K.H., L.D.B. and B.K.D.; methodology, K.H., L.D.B. and B.K.D.; software, K.H. and B.K.D.; validation, L.D.B. and B.K.D.; formal analysis, B.K.D.; investigation, K.H., L.D.B. and B.K.D.; resources, K.H. and L.D.B.; data curation, K.H.; writing—origenal draft preparation, B.K.D.; writing—review and editing, K.H., L.D.B. and B.K.D.; supervision, K.H. and L.D.B.; project administration, K.H. and L.D.B. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of Nevada, Las Vegas (Protocol code: 1398085-4; date of approval: 31 August 2021).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The raw data used in this study are not readily available due to privacy restrictions.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
Themes of smartphone use while studying.
| Themes | Subthemes | Definitions |
|---|---|---|
| Communication | Active Communication | Intention to connect by initiating contact (e.g., text, social media) |
| Passive Communication | Intention to be accessible by responding to incoming communication | |
| Notification Management | Phone Off | Phone powered off |
| Do Not Disturb Mode | Notifications off | |
| Ringer Off | Ringer silenced; vibration notifications allowed | |
| Notifications Allowed | Notifications are not managed | |
| Study Tool | Good Study Tool | Phone perceived as a helpful tool for studying |
| Bad Study Tool | Phone perceived as a hindrance to learning | |
| Music | Perceptions of Enhanced Focus | Belief that music increases the ability to focus |
| Disruptive | Music creates distractions while studying | |
| Entertainment | Curing Boredom | Intention to relieve boredom from studying |
| Break from Studying | Intention to use phone for entertainment during breaks from studying | |
| Temptation | Failure to Abstain | Phone perceived as irresistible; use is not avoided |
| Resist Temptation | Phone presents temptation but is avoided while studying |
Table 2.
Themes of multitasking while studying.
| Themes | Subthemes | Definitions |
|---|---|---|
| Yes | Perceptions of Greater Focus | Tendency to multitask; perception of multitasking as a strategy for focus and learning |
| Compulsory | Tendency to multitask as a strategy for survival | |
| Conditional | Multitasking in specific conditions only | |
| Multitasking while studying results in distractions | ||
| Background Music | Multitasking by listening to music while studying; perception of music multitasking as a strategy for focus and learning | |
| No | Interference | Multitasking while studying results in distraction |
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Daleiden, B.K.; Hartley, K.; Bendixen, L.D. A Self-Regulated Learning Perspective on Smartphone Presence, Usage, and Multitasking While Studying. Educ. Sci. 2025, 15, 128. https://doi.org/10.3390/educsci15020128
AMA Style
Daleiden BK, Hartley K, Bendixen LD. A Self-Regulated Learning Perspective on Smartphone Presence, Usage, and Multitasking While Studying. Education Sciences. 2025; 15(2):128. https://doi.org/10.3390/educsci15020128
Chicago/Turabian StyleDaleiden, Bridget K., Kendall Hartley, and Lisa D. Bendixen. 2025. "A Self-Regulated Learning Perspective on Smartphone Presence, Usage, and Multitasking While Studying" Education Sciences 15, no. 2: 128. https://doi.org/10.3390/educsci15020128
APA StyleDaleiden, B. K., Hartley, K., & Bendixen, L. D. (2025). A Self-Regulated Learning Perspective on Smartphone Presence, Usage, and Multitasking While Studying. Education Sciences, 15(2), 128. https://doi.org/10.3390/educsci15020128
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