1 Introduction

The omnipresence of digital devices, particularly smartphones, has made them the main source of distraction during focused learning, which gives rise to a growing research interest in digital distraction in the learning environment (e.g., Brady et al., 2022; Chen et al., 2020; Flanigan & Babchuk, 2021; McCoy, 2020). Digital distraction often serves as a precursor to multitasking, a process that involves the deviation from the intended goals of primary tasks and switching to other secondary tasks (Cardoso-Leite et al., 2015; Rosen & Samuel, 2015). Admittedly, multitasking behavior manifests in different forms and types. Following the work of Kraushaar and Novak (2010), our study focused on “distractive multitasking,” which involves cognitive engagement with tasks not directly related to course material. A wealth of studies has indicated that distractive multitasking during learning is often induced by digital devices, which has an adverse impact on students’ learning process and academic success (e.g., Demirbilek & Talan, 2018; McCoy, 2020; Mendoza et al., 2018; Rosen et al., 2013; Zhang, 2015). Thus, it is critical to understand how students deal with digital distractions and refrain from distractive multitasking in the course of learning. In this respect, another line of work on self-regulated learning is concerned with how students persist with their learning and attain the intended goal (Pintrich, 2000; Zimmerman, 1989). It seems that distractive multitasking and self-regulated learning are two interconnected processes, yet there has been a scarcity of studies that combine these two perspectives.

This study seeks to bridge the perspectives of self-regulated learning and multitasking in academic settings in an effort to illuminate how distraction induces multitasking and how students deal with distraction through the implementation of self-regulated learning strategies. A framework was developed that recognizes self-regulation and distractive multitasking as inter-connected sub-processes underlying in-class engagement. On the one hand, students need to deploy regulative strategies to persist on-task in pursuit of learning goals; on the other hand, a range of distractions may divert their attention to other unrelated tasks. In our study, distractive multitasking is conceptualized as distraction-induced switching from the intended learning goals/tasks of an ongoing class to other student-defined tasks unrelated to the class at hand.

At the heart of this study lies the belief that the exploration into the educational use of technologies warrants attention to the associated problems and challenges that accompany their use. Distraction and multitasking are among the most serious and prevalent issues at the nexus of technology and education. By zooming in the challenges of distraction and multitasking, this study offers a nuanced understanding of smartphone distraction and self-regulation strategies that influence students’ learning experience and engagement. It can potentially inform learners, educational practitioners, and researchers on how to mitigate the digital distraction and foster engagement in learning.

2 Literature review

2.1 Distraction and multitasking

With digital technologies becoming omnipresent, the use of digital devices for non-academic purposes has become pervasive inside university classrooms (Derounian, 2020; Deng, 2020). A collection of studies has explored the drivers for distractive multitasking in educational settings, and the results can be classified as internal and external distractions (Deng, 2020; Wu, 2017; Zhou & Deng, 2022). Internal distractions are associated with motivation/emotions, cognitive states, and personal habits, while external distractions stem from physical and social contexts, with digital devices being the most prominent one.

First, research has consistently shown that motivation plays an influential role in determining students’ engagement and multitasking during learning. For example, Ralph et al. (2021) reported that individuals with a strong motivation to complete tasks were less inclined to engage in distractive multitasking. In learning contexts, students’ motivation is often associated with their interest and perceptions of learning tasks. A considerable number of studies have identified the important role of tasks in determining students’ engagement or distraction during learning (Deng, 2020; Gupta & Irwin, 2016). For example, le Roux and Parry (2021) revealed that decisions to multitask were influenced by the perceived values of and interest in academic activities. Moreover, internal distractions such as emotions have been found to play a role in multitasking. Individuals tend to multitask more frequently when they experience negative emotional states such as boredom (Aagaard, 2015; Derounian, 2020), frustration, or mental exhaustion (Adler & Benbunan-Fich, 2013; Calderwood et al., 2014).

The second type of internal distraction is students’ cognitive states. In light of flow theory, Adler and Benbunan-Fich (2013) denoted that multitasking can be induced by the feeling of being stuck if a learning task is perceived as cognitively too challenging or the feeling of boredom if a task is not challenging enough. In other words, students are likely to switch attention when encountering tasks that are either too difficult or too easy. Lastly, multitasking with phones has been regarded as a habitual, impulsive, and automatic behavior (Chen et al., 2020; Fu et al., 2021; Heitmayer & Lahlou, 2021). The daily dependency on smartphones is also identified as a significant predictor of multitasking with digital devices (Qian & Li, 2017).

Furthermore, multitasking behavior is subject to the influence of external conditions. Zhang and Zhang (2012) pinpointed three components, including physical (e.g., bedroom, public space), social (alone or with others), and technological environment (computers and devices in use), that might induce multitasking. Within classroom settings, multitasking can be contiguous as fellow students engaged in off-task multitasking are regarded as a significant distractor during class (Fried, 2008; Sana et al., 2013). In particular, digital distraction has become prominent in educational settings as a growing body of studies has pinpointed laptops and mobile phones as the primary sources of distraction inside the classroom (e.g., Deng et al., 2022a). Previous studies involving Hong Kong students singled out smartphones as the main threat to students’ attention during learning (Deng, 2020). Among a wide range of activities afforded by digital devices, scholars have identified online chatting (e.g., McCoy, 2020) as the most common distraction. Admittedly, pushed alerts or notifications on smartphones are inherently interruptive as they were designed to draw people’s attention (Benbunan-Fich et al., 2011).

To sum up, a collection of scholarly works concerning the drive for multitasking has revealed a set of internal and external distractions that might diverge students’ engagement in ongoing tasks. However, there is a limited understanding of students’ perceptions and strategies for dealing with distraction, especially digital distraction (Neiterman & Zaza, 2019). In this respect, another line of scholarly work on self-regulated learning can shed some light.

2.2 Self-regulated learning

Self-regulation has gained widespread recognition as a vital ability associated with learning performance and engagement (Zimmerman, 1989) in school-based and lifelong learning contexts (Broadbent et al., 2020; Vosniadou et al., 2021). Individual learners vary in their use of self-regulation, which involves setting goals, planning, and employing strategies to achieve learning objectives. Effective self-regulation entails continuous monitoring, evaluation, and adjustment of these strategies to improve progress toward achieving learning goals (Zimmerman, 1986). A successful self-regulated learner typically focuses on learning objectives, persists through challenges, manages time effectively, and seeks help when necessary (Pintrich et al., 1993). Research has shown that self-regulated learning strategies are positively linked to academic outcomes across various education settings, including primary, secondary, and higher education, as well as in online learning environments (Dignath & Büttner, 2008; Richardson et al., 2012; Schneider & Preckel, 2017; Broadbent & Poon, 2015).

Pintrich et al. (2000; 2004) identified four areas, namely, cognition, motivation, behavior, and context, that are important for regulating learning goals. The regulation of cognition involves setting goals, planning, monitoring, selecting, and adapting learning strategies. The regulation of motivation concerns various motivational beliefs such as goal orientation, self-efficacy, and interest. The regulation of behaviors includes time and effort management and seeking help (Pintrich, 2000; Zumbrunn et al., 2011). Lastly, the regulation of context concerns changing environmental conditions (e.g., arranging physical settings) to eliminating distractions (Pintrich, 2004; Zimmerman, 1989). One criticism of these self-regulation models is that they reveal little about sub-optimal situations such as difficulties, distraction, and disinterest (Boekaerts & Corno, 2005). That is to say, students may face cognitive, motivational, behavioral, and contextual difficulties during learning. In such situations, students either use adaptive strategies to overcome obstacles or maladaptive strategies that may lead to switching attention or deviation from the original learning tasks.

2.3 Multitasking and self-regulated learning

Multitasking in educational settings has previously been considered a failure to regulate one’s behaviors to achieve the intended outcome. For example, le Roux and Parry (2021) argued that when students engage in unrelated media use during learning, it reflects a failure of self-regulation. Wei et al. (2012) also found a negative association between students’ self-regulated learning and multitasking during class. Positively, self-regulated learning has been found to help students exercise deliberation regarding whether and when to switch attention when facing distraction (Iqbal & Horvitz, 2007) and employ strategies such as delaying their responses to or even ignoring their phone notifications (Deng, 2020). Such delays in response to interruptions show the “interruption lag” between distraction and task switching (Trafton et al., 2003), which has been viewed as the manifestation of self-regulation (Deng, 2020).

Another small body of studies focuses on self-regulated learning strategies for dealing with distractions associated with digital devices. Wu (2017) differentiated two types of self-regulated learning strategies university students use to prevent being distracted: behavioral strategies involving behavioral control of device use and outcome appraisal that put a positive or negative outcome or emotion into perspective. Barron and Kaye (2020) examined university students’ smartphone regulation and revealed individual, environmental, and behavioral factors that co-influence phone use during self-study. Specifically, they delineated the environmental factors, including task urgency, the proximity of smartphones, as well as the location and people the students studied with.

Overall, the existing work on multitasking in academic settings mostly centers on multitasking behaviors and their effects on student learning (Zhou & Deng, 2022). There is a paucity of studies on how students deal with distraction (Neiterman & Zaza, 2019) and regulate device use during the learning process (Barron & Kaye, 2020). More recently, there has been burgeoning work centering on digital distraction and self-regulated learning (e.g., Brady et al., 2022; Wang et al., 2022); yet these papers are review or conceptual papers without empirical data. A handful of empirical work in this area centers on the control of digital devices, such as logging out of social media, putting phones away, and avoiding responding to notifications (Deng, 2020; Heitmayer & Lahlou, 2021). These works only focus on behavioral regulation without attending to other aspects of self-regulated learning. As such, a framework was proposed to bring together a self-regulated learning perspective and research on multitasking.

3 Conceptual framework

The literature on self-regulated learning (Pintrich, 2004; Zimmerman, 1989) suggests that cognition, motivation/affect, and behavior are the internal conditions that need to be regulated in pursuit of learning.

These three dimensions dovetail with the internal source of distractions from the literature concerning multitasking (e.g., Aagaard, 2015; Deng, 2020). Furthermore, external context is also an important consideration for self-regulated learning (Pintrich et al., 2000; 2004), thus being included in the framework. Aligning the scholarly work on multitasking with digital devices, three types of external factors are identified, including physical (e.g., location of learning), social (e.g., peers), and technological (e.g., presence of digital devices) (Barron & Kaye, 2020; Zhang & Zhang, 2012). That is to say, distractive multitasking might be induced by distractions from physical settings, peers, and digital devices at present. To persist in learning, self-regulation strategies can be called for to manage these distractions.

Put together, the framework shown in Fig. 1 offers a comprehensive depiction of internal and external sources of distractions that may lead to distractive multitasking and highlights the need for self-regulated learning strategies to manage these distractions. It directed our investigation into how university students regulate their behavior and cognitive and motivational states as well as external contexts (physical, social, technological) or switch tasks due to internal and external distractions. Based on this framework, two research questions were put forward to guide our investigation:

  • How and under what conditions do university students engage in distractive multitasking during class?

  • How and under what conditions do university students regulate their learning during class?

Fig. 1
figure 1

Source of distraction and dimensions for self-regulated learning

Full size image

4 Methods

4.1 Research design and participants

A mixed-method design was adopted in the current study to examine the inter-connected processes of multitasking and self-regulated learning inside a classroom. Questionnaire data (n = 385) was first collected from three comprehensive universities located in Northern, Eastern, and Southeast China. The participants of the study were from a variety of disciplines such as electronic information engineering, environmental engineering, pre-school education, management, chemistry. The quantitative data provided an initial understanding of students’ multitasking behavior and self-regulated learning strategies, as well as informed the qualitative data collection. Subsequently, semi-structured interviews were conducted with 15 participants purposefully chosen from the quantitative sampling pool to gain a deep understanding of how students engage in multitasking and implement self-regulated learning strategies. Due to space constraints, this paper primarily focuses on qualitative data as it provides a detailed account of student’s behavior as a dynamic process. Still, we believe the quantitative phase played an important role in guiding the research design and informing the selection of participants for the qualitative phase. While lesser in volume, the quantitative data complements the rich qualitative insights and offers a holistic view of the research topic.

4.2 Quantitative data collection and analysis

The online questionnaire was hosted by Qualtrics to collect quantitative data about in-class multitasking behaviors (e.g., frequency, preference, duration, devices) and self-regulated learning ability. The questionnaire was divided into three sections: (1) Demographics, (2) Multitasking frequency, and (3) Self-regulated learning.

Demographics

The first section asked general demographic questions such as gender, year level, major, and GPA.

Multitasking behavior

Students’ multitasking behaviors were measured through two questions regarding the frequency of multitasking and types of activities designed based on the related literature (e.g., Judd, 2015; Zhang & Zhang, 2012). Students were also asked to report the time they generally spent on different types of multitasking activities during a 45-minute class.

Self-regulated learning (SRL)

Items for self-regulated learning ability were adapted from subscales of persistence (Jansen et al., 2017) and Self-regulation Strategies (SRS) developed by Wu (2017). Each subscale included five items that were rated on a 4-point Likert scale (1 = very untrue of me, 4 = very true of me). The phrasing was modified to fit the current research context. For example, the item “When I notice that I am browsing unrelated sites or playing computer games, I will feel guilty.” was changed to “When I notice that I multitask on activities not related to the class, I will feel guilty.”

As the questionnaire was translated into Chinese from English, the translated questionnaire was initially piloted with 63 Chinese university students to receive feedback about language, comprehensibility, and clarity. The final pool of respondents was a balanced mix of genders, consisting of 198 (51%) female students and 187 (49%) male students. The distribution across various undergraduate year levels was balanced, with slightly more juniors (123, 31.9%), followed by sophomores (99, 25.7%), freshmen (83, 21.6%), and seniors (79, 20.5%). More than half reported having an ‘average’ GPA ranking, while 137 participants (35.6%) considered themselves ‘high-achieving’ students (top 25%). Only 9.4% of the participants (36) self-reported having a ‘low’ GPA (bottom 25%). The questionnaire data were analyzed using SPSS Statistics 26 software to provide a general picture of the demographic information and multitasking behaviors. Specifically, the continuous and categorical data were presented in the form of mean, sum, standard deviation (SD), maximum, minimum, and percentage.

4.3 Qualitative data collection and analysis

The quantitative data not only provided a general picture of students’ behaviors and self-regulative strategies but also informed the selection of participation for the qualitative interviews. The respondents to the questionnaire were given the opportunity to volunteer for interviews and15 students were purposefully selected based on their questionnaire responses. The selection, guided by the maximum variation sampling technique (Patton, 1990), took into account the criteria including the frequency of multitasking, levels of self-regulation, and gender. The frequency of multitasking was adopted as the main criteria and the descriptive data of multitasking frequency, ranging from “seldom” to “always”, guided the determination of the number of interviewees in each category. For example, there were six interviewees in the category of “sometimes” since nearly half of questionnaire respondents fell into this category. Then within each category, efforts were made to identify students with varying levels of self-regulation and different genders based on questionnaire data. The level of self-regulation was categorized into three levels (low, average, and high) according to the mean and standard deviation. As shown in Table 1, the recruited students had mixed genders, varying levels of self-regulation, and the frequency of multitasking.

The design of semi-structured interviews was guided by our proposed framework of distraction and self-regulated learning (see Fig. 1). The interview protocol consisted of questions that related to how internal (cognitive, motivational/emotional, and behavior) and external (physical, social, and technology) sources of distraction might induce multitasking and how they implement self-regulated learning strategies to combat these distractions. The participants were also asked specific questions based on their responses to the questionnaire. For example, if the respondents reported a high frequency of multitasking during classes, the prompting questions were designed to elicit more details. The interview protocol was piloted with three Chinese University students who proposed suggestions for improvement. All interviews were conducted and recorded through Zoom, with each interview session lasting for approximately one hour. Most of the interviews were conducted jointly by the first and third author. Immediately after each interview, a preliminary analysis was performed with initial codes and themes being noted down. After conducting ten interviews, there were very few new themes emerged in the responses, indicating that data saturation was approaching. Interviews with five additional students were then conducted to ensure comprehensive coverage.

The interview transcripts were transcribed and analyzed jointly by the first and second authors. The coding process involved both bottom-up open coding of transcripts and top-down classification of data using related literature (Blair, 2015). The two researchers first coded two interviews on their own, then compared and discussed the codes to reach an agreement on code structure. The rest of the interviews were coded jointly by the two researchers. The constant comparison technique was used to construct and refine the hierarchical node structure inside Nvivo (Leech & Onwuegbuzie, 2011). Furthermore, to ensure the trustworthiness of the qualitative data, an expert researcher who specialized in qualitative research and mobile learning reviewed the interview protocols and emerging themes.

Table 1 Demographic information of interviewees
Full size table

5 Results

5.1 Multitasking behaviors

In the questionnaire, the students reported the frequency of multitasking during class time using a 5-point Likert scale with options including “always”, “often”, “sometimes”, “seldom”, and “never”. Most respondents acknowledged the occurrences of multitasking during class time, with around half reporting that the frequency of off-task activities as “sometimes” (49.6%, n = 191), 14.3% (n = 55) as “often”, and around a quarter of respondents as “seldom” (26.8%, n = 103). Only a small portion reported the state as “always” (4.4%, n = 14) or no multitasking (4.9%, n = 19). Smartphones are acknowledged as the main device for distractive multitasking in class (99%, n = 381), followed by laptops (47.9%, n = 184) and tablets (41.8%, n = 161). There were four main types of secondary tasks involved in multitasking, namely social (e.g., social media and chatting online), information-seeking (e.g., searching and browsing websites for learning-related materials), entertainment (e.g., playing games and watching videos), and learning-related tasks (e.g., completing assignments from other subjects). More than 80% of the participants reported engaging in social, information-seeking, and learning-related tasks during class. However, the majority of respondents (66.5%, n = 256) reported the frequency of entertainment as never (32.2%, n = 124) or occasional (34.3%, n = 132), while a smaller portion of students acknowledged sometimes (23.9%, n = 92) or always (9.6%, n = 37). During a typical 45-minute instructional session, the students, on average, spent 12.8 min on activities not related to the classes, with 20% of respondents reporting between 15 and 30 min and more than 70% reporting no more than one-third of class time. It is also noted that the tasks that the students multitasked on might be related to learning. Around half of the students (53%, n = 204) reported spending no more than 15 min on these activities, with another one-third (32.8%, n = 127) reporting 15 to 30 min.

5.2 Variations in multitasking

During interviews, students’ multitasking behaviors were examined in relation to the perceived interest and importance of the class. The students were prompted to compare their multitasking behaviors in three types of courses: (1) important and interesting courses, (2) important yet not interesting, and (3) neither important nor interesting courses. Unanimously, the students acknowledged that they would show drastically different behaviors and engagement in different types of classes. SS3 described the variance vividly: “For the courses I am interested in, I will only check my phone during break. I would not think about playing on the phone during class time. For the important courses that I am not interested in, I might play on the phone for a little bit, but not long. For the courses that are not important, I will not listen for very long.” It is apparent that the students drew a clear line between courses of different importance and behaved differently.

For the courses that were neither important nor interesting, the students candidly stated they would not pay much attention but work on other tasks. Some interviewees reported a higher level of off-task multitasking with smartphones, such as being “immersed [d] in doing other things for the entire class” (SS8). SS10 confessed that he would hold his smartphone when courses were neither important nor interesting, while for important courses, he would deliberately put the phone out of sight. The interviewees also reported that they worked on unrelated learning tasks out of the desire “not to waste time” (SS1). Many students reported instances of working on the assignments of other courses, as SS10 shared: “Since there is a learning atmosphere inside the classroom, I would like to make good use of the time to work on learning-related tasks.” In such scenarios, some students made deliberate plans on specific assignments or tasks to work on beforehand. In contrast, off-task multitasking (such as checking on social media feeds and chatting) was often unplanned and spontaneous.

5.3 External distraction and multitasking

During interviews, internal (cognitive, motivational/emotional, and behavior) and external (physical, social, and technology) sources of distraction that induced multitasking behaviors were explored. First, consistent with the questionnaire results, many students were well aware of the influence of smartphones and acknowledged smartphones as the main source of distraction. For example, SS9 and SS10 said they were usually quite focused in class until disturbed by smartphone notifications. For SS13, what was especially disturbing were the notifications related to urgent matters that required an immediate response. Other students (e.g., SS11, SS14) seemed more susceptible to external social influence and attributed the main distraction to peers who did not want to study. SS11 shared his tendency to get distracted by multitasking peers, such as: “If the classmates around me are checking on their phone, I might follow them.” SS3 and SS6 shared that they would be distracted if they saw other classmates engaged in online shopping or watching videos. Having their phone physically present also led to distractions, with SS1 commenting: “It would be fine if I keep my hands off my phone. Once I touch it, I find it hard to stop.”

5.4 Internal distractions and multitasking

Internally, phone multitasking was often described as a habitual behavior and “subconscious” process when students felt bored or stuck in class, as SS1 remarked: “Whenever I feel there is nothing to do, I will pick up the phone.” SS8 provided a vivid description as such: “I feel it was subconscious behavior. After a while, I suddenly realized that I was off-task, then I would consciously control myself.” On the other hand, some students highlighted the inner conditions, such as motivation and willingness to be engaged in class, that contributed to their susceptibility to distraction. SS3 remarked that she would be prone to distraction when feeling mental fatigue “after concentrating for about 20 minutes”. SS6, who often multitasked, acknowledged the biggest distraction as “whether I want to study or not.” Another observation was that for students with lower academic capability, distraction is more likely to stem from cognitive disengagement. SS4, a student with low levels of GPA and self-regulated learning, shared the scenario of slipping into multitasking as such: “I could not understand the lecture, then I will turn to something else.” SS7 and SS2 with average GPA also reckoned the difficulty in following the lecture as the main threat to concentration. Additionally, the students’ multitasking is also subject to the influence of bad mood and anxious feelings (SS6), stress and sense of pressure (SS9), and mind wandering (SS3).

5.5 Self-regulated learning strategies

The questionnaire data indicates a considerable range in the levels of self-regulated learning (Minimum = 1.0, Maximum = 4.0) with a moderate level of self-regulation (M = 2.95, SD = 0.514) on a 4-point Likert scale. Moreover, the correlation analysis revealed that there is a significant negative correlation between self-regulated learning and distractive multitasking (r=-0.311, p < 0.01), indicating that those with higher levels of self-regulated learning tended to multitask less on class-unrelated tasks. However, the interview data offers a more nuanced insight into the implementation of these strategies. A recurring theme from the interview data is that the students did not always utilize self-regulated learning strategies. In those courses that were neither important nor interesting, the respondents often felt no need for self-regulated learning. In contrast, for courses they attached great importance to, the students felt a heightened need to use a wide range of self-regulated learning strategies to eliminate distraction and remain on-task. Especially for the important courses with low interest, the interviewees shared different ways to “force themselves to focus” (SS3, SS9, SS10). The upcoming sections will elaborate on a range of strategies the students used for combating internal as well as external distractions.

5.6 Regulation of internal distraction

First, our data showed strong evidence that the students made decisions about whether or to what extent they would need to stay focused before a class. SS11 described his inner thoughts as follows: “I usually remind myself before the class that this course is important. Even though I am not really interested in it, I should concentrate and listen to it.” Similarly, SS4 shared: “If you plan to pay attention in class before it starts, then you are definitely not going to do something unrelated. But if you don’t plan to listen attentively in class beforehand, you might unconsciously do something else.” Conversely, in the courses they found little interest and value, they would not pay much attention. As SS12 commented: “I would fully immerse in doing my own things.”

Most often, the regulation of cognitive states and motivation involves psychological suggestion through inner talk. The most mentioned strategy for cognitive regulation is “psychological suggestions,” prompting them to “be attentive” during class. For instance, SS10 shared such a self-nudge: “This course is important. I have to study! I have to be focused! Even though it’s boring, I need to listen and work hard.” Some students thought of the adverse consequences of not paying attention to the class as a deterrent for multitasking. Examples of such self-talk are: “I might fail this course if I play now” (SS1) and “It occurred to me that I might not be able to find a good job if I don’t study hard” (SS3). The students show evidence of intentionally inhibiting the impulse of multitasking as S11 shared: “I would remind myself before the class that this course is important…. Sometimes when I picked up my phone then I would immediately put it back”. SS4 described his state in courses with high importance as such: “If you keep giving yourself psychological nudge, you would only get off-task for a bit, then you will feel it was enough. Then you would return to class.”

Emotions also stimulated the use of internal SRL strategies. For example, the feelings of “guilt” reinforced motivational regulation as SS3 remarked: “If I am not paying attention, I would have a sense of guilt because it is not a good thing if you don’t study and pay attention during class.” Similarly, SS1 confessed her feeling of “disrespect for lecturers” when she played on her phone during class, and SS8 shared the feeling of “shame” when thinking of his parents, who supported his studies. SS2 described vividly how such guilty feeling boosted motivation: “When I switched attention back to class with this feeling of guilt, I would not feel the class that boring anymore. Then I would become more attentive”. Even though phone use is widespread during class, most students still hold the view that such behavior is not appropriate in the face of teachers during class time.

Other than negative affect, some students also resort to positive emotions to foster attentiveness in class. SS6 shared his strategy of talking himself into being better focused by instilling positive emotions. He stated: “I would keep telling myself that this course is actually interesting. It worked sometimes. I would listen attentively, and as I understood more, I became more interested in it. That will drive me to be focused.” SS11 shared a similar strategy of “trying to find interesting content from the textbook,” which could help to boost his motivation and engagement.

In addition to regulating mental activities, the students also deployed behavioral regulation strategies to keep them on task, such as note-taking while listening to lectures (SS3, SS13, and SS14). SS13 also took photos of slides, while SS3 and SS14 also reported that they maintained focus by annotating textbooks. In addition, the students reported diverse brain break activities, such as “looking out of windows” (SS8), “going out for a walk” (SS9), and “closing eyes and meditating” (SS5), that helped them to be better focused afterward. Some students (SS11, SS9, SS7) shared a strategy for preparing for class by reviewing textbooks and homework. Others regarded multitasking as a brain break that could help “relax the brain” and provide “renewed mental energy” (SS13). For instance, SS9 remarked that he could be “more focused with a refreshed brain” after playing on the phone. The brief multitasking with the phone could also help relieve the pressure and “resistance for learning” (SS14).

5.7 Regulation of external distractions

In addition to the control of self, the students also resort to various strategies for managing the context. Most interviewees showed pre-planning by deciding where to sit based on the importance level of the course. Many students shared the experience of “fighting for the front rows in classrooms” for those high-stakes courses. The sitting location of the classroom is also associated with the social context, as SS11 commented: “The students who chose to sit in the front are usually attentive in class; those who took the back seats are not engaged in class.” Most students recognized the adverse influence of their multitasking peers during class time and hence sought to avoid such distractions by sitting in the front. When surrounded by focused peers, they did not want to “spoil the learning atmosphere” (SS10). SS14 shared his inner thoughts as such: “If classmates around me were all attentive, I would feel guilty if I use my phone for unrelated tasks.” On the other hand, some students deliberately create a social environment conducive to learning. For instance, SS3 and SS11 mentioned that she would choose to sit with good friends who were attentive in class so as to keep herself on task. SS6 shared a similar instance: “My good friend and I would put our phones in the same place” so they could monitor and remind each other. SS8 also deliberately sat with peers with high ability whom she could turn to for asking questions and discussion.

Instructors play an important role in keeping students focused in the classroom setting. Many students acknowledged that instructors’ physical proximity could help them stay focused. SS11 remarked that the teacher’s presence could interrupt his phone use and pull him back to the ongoing class. SS10 described that the instructors played a supervisory role and exerted a “deterrent effect” (SS10). Additionally, the students reported various instructional strategies that helped pull their attention back to the class. For example, instructors could give a direct reminder for better engagement, ask questions, call students to answer (SS15) and put stress on important content (SS14).

As the questionnaire revealed smartphones as the leading distraction, special attention was paid to how students cope with smartphone distraction in the interviews, which further revealed various SRL strategies students used to stay focused. The commonly used strategies include setting the phone to “Do not disturb,” putting the phone facing down or leaving the phone in a place out of sight (e.g., SS6, SS8). SS6 also mentioned that he would disconnect the Internet from his phone. SS1 commented that she would force herself not to touch the phone. SS13 used the app “Forest” to help him focus, while SS14 used time management timers that forbid phone use for a certain period of time. In addition to the avoidance strategy, the students exercised conscious pre-planning on whether and how to deal with phone notifications during class. For example, SS8 reported that he only read the messages in class without responding; SS3 only responded to the messages that “required only little mental effort,” while SS7 refrained from responding at all. SS13 also shared the inner deliberation regarding whether he should switch attention from the class depending on the urgency of the matter involved and the importance of the ongoing class. SS11 described a similar pattern as such: “For those important courses that I found little interest [in], the location of my phone matters. If I put it away inside my bag, I would be better focused, whereas if left in sight, I would not be so focused.”

Furthermore, some students reported monitoring their engagement level and phone use in class. During interviews, students were asked to reflect on whether they were immersed in multitasking or kept monitoring what was going on inside the classroom. Many students acknowledged that they still kept vigilance and could immediately return focus to the class when attention was needed. Such vigilance is described as “leaving one ear to the class” (SS10) or frequent switching between distractive activities and the class at hand. SS1 offers a vivid description of such a state: “After spending a few minutes on my phone, I would switch back to class to check whether anything important is covered. If not, I would switch back to the phone”. Moreover, SS3 denoted that she would feel ill at ease if she couldn’t hear the instructors’ voice, and SS13 noted that the feeling of worry that “lecture might cover something important” directed his attention back to class. Various scenarios that could drive their attention back to class were shared, such as “instructor called my name” (SS10, SS11), “asked questions” (SS3), “talked about in-class exercise” (SS13), “emphasized something” (SS9), or “moving on to a new topic” (SS12).

6 Discussion

The study centers on university students’ in-class engagement by examining two intertwined processes: distractive multitasking and self-regulated learning. It has explored (1) how distractions from both internal (cognitive, emotional, behavior) and contextual (physical, social, and technological) dimensions might induce distractive multitasking and (2) how students remain on-task through regulating their internal states as well as external contexts. Overall, the data depicts a tug-of-war between multitasking induced by various distractions and self-regulated learning to remain or switch back to learning tasks.

6.1 Multitasking as complex and changing behaviors

Our data points to distractive multitasking as a common and complex phenomenon among Chinese university students. First, our work joined the previous work (e.g., Deng, 2020; Kornhauser et al., 2016) in highlighting the magnitude and pervasiveness of distractive smartphone multitasking in learning settings. The quantitative data set reveals that, on average, the respondents spent nearly 30% of class time on distractive multitasking. More than 80% of them were engaged in various distractive activities, including checking social media, chatting, and seeking information with smartphones. As such, our findings corroborate with a large collection of work that identified social-oriented and Internet-enabled activities as the main distractive activities students engaged with (e.g., Derounian, 2020). However, the Chinese university students in our study also worked on other learning tasks unrelated to the ongoing class quite often, which provides additional evidence to what was previously identified as off-task yet learning-related multitasking (Deng et al., 2022a). The students tended to switch to other unrelated learning tasks deemed more urgent, important, and rewarding.

The in-depth analysis of interview data confirmed our framework that depicts the source of distraction and self-regulated learning strategies. It showed that distractive multitasking could be triggered by a complex mixture of internal emotion, motivation, and external factors, including physical settings and social milieu. Consistent with prior studies (e.g., Aagaard, 2021; Chen et al., 2020), smartphones were the main source of distraction and the main devices for multitasking, particularly in low-importance and low-interest classes. The lack of motivation or cognitive disengagement is another main trigger for abandoning the class at hand, especially for those academically weaker students.

Furthermore, our data indicates that smartphone distraction, as the main threat to students’ attention, is associated with both internal and external distraction, hence residing across the border of internal and external dichotomy (See Fig. 2). On the internal side, since smartphone use has been an ingrained habit (Aagaard, 2021; Chen et al., 2020; Wilmer et al., 2017), the students showed stronger vulnerability to distraction and propensity to multitasking with smartphones when in the state of cognitive disengagement, lack of motivation or negative emotion. On the external side, the presence of smartphones and pop-up notifications could become an external source of distraction (Benbunan-Fich et al., 2011; Deng, 2020; Heitmayer & Lahlou, 2021). Our data also revealed that the visibility of their phone, sitting location inside a classroom, and peers in proximity could influence distractibility and ensuing multitasking.

Another striking theme that emerged from the data is multitasking as a context-specific behavior, as previously reported (e.g., Lin, 2013; Deng et al., 2022b). Our data extend the previous work by providing a more detailed account of students’ multitasking in different types of classes. In courses students perceived as unimportant and/or uninteresting, they would be more likely to engage in phone-related tasks (e.g., chatting). On the other hand, if a course was deemed important, students were more likely to activate SRL strategies to keep them on task. Another interesting phenomenon noted in Chinese university students is their engagement in unrelated learning tasks (e.g., working on assignments of another course) in some low-interest and low-importance courses.

Fig. 2
figure 2

Smartphone distraction in relation to internal and external distractions

Full size image

Lastly, while some past work (e.g., Aagaard, 2021; Chen et al., 2020) attribute multitasking more as habitual and impulsive behavior, our findings show more evidence that students deliberately decided to switch their attention after weighing the cost of such a switch. Aligned with the findings by le Roux and Parry (2021), multitasking in our study was the result of conscious choice that involved active decision-making. However, we disagree with le Roux and Parry (2021), who described multitasking as simply the lack of self-regulation or failure of self-regulated learning. We argue that distractive multitasking does not reside on the opposite end of the self-regulated learning spectrum. In a classroom setting, students made deliberate decisions regarding whether they would draw on a self-regulated learning strategy through evaluation and comparison of the importance, urgency, and values of the in-class learning tasks and other self-defined tasks. As such, our finding lent empirical support to McCombs and Marzano’s (1990) proposition that highlighted students’ will or desire as essential for self-regulated learning.

6.2 Self-regulated learning as a multi-dimensional process

The second aim of the study was to elucidate the self-regulation process that involves creating favorable conditions for learning and dealing with internal and external distractions. The literature concerning self-regulated learning often describes it as a process consisting of forethought, planning, activation before learning sessions, monitoring and controlling during learning, and reflection afterward (Pintrich, 2000). Our data provides a rich description of a range of self-regulated learning strategies students employed before and during class time. Figure 3 summarizes the main regulative strategies in use in the stages of forethought, planning, and activation, followed by the stage of monitoring and controlling.

Prior to the start of important classes, the students made deliberate plans and assigned cognitive and mental resources as the cognitive regulation mechanism according to the importance of the class on hand. The students also prepare themselves mentally and emotionally, usually through self-talk (e.g., reminding themselves that the class is important). Besides, the strategy of avoiding distraction is employed, such as planning where they would sit in the class (e.g., the front row to avoid distractions) and where they would put their phone (e.g., out of reach). While our data supports the findings of Sana et al. (2013) that classroom peers can contribute to distraction and multitasking, it also highlights a positive impact where peers can help classmates sustain their attention. Smartphone distractions have been put into the spotlight in our study. For important courses, the students manifested a range of regulative strategies to avoid digital distractions and curb off-task phone use. Our findings corroborate previous work (e.g., Heitmayer & Lahlou, 2021; Wu, 2017) in illustrating a range of avoidance strategies, such as moving smartphones out of reach or sight and turning off notifications. As pointed out by Hartley et al. (2020), pre-planned distraction avoidance could be viewed as a proactive measure for avoiding distractive multitasking.

Despite the preparation, the students might experience unplanned distractions such as mental exhaustion, which might lead to off-task multitasking. While there is limited literature on how students monitor and control multitasking behavior by employing self-regulated learning, our study illuminates in-the-moment strategies to help regulate this behavior (see Fig. 3). First, students would monitor the classroom for cues indicating the need to switch back to the learning task. Johannes et al. (2018) used the term “smartphone vigilance” to describe the state of ongoing alertness and readiness to respond to cues associated with smartphones. Our study found a similar state of vigilance for the cues from the classroom when they are in the course of multitasking. Moreover, some students in the study seemed to be more mindful of their device use so that they could quickly resume class once they had finished phone-related tasks.

Second, emotions could motivate students to reduce multitasking. Consistent with a study by le Roux and Parry (2021), our findings indicate that feelings of guilt could help curb off-task smartphone use. However, this sense of guilt could be greatly reduced or even dissipated in the courses with low interest or importance. Last but not least, our study gives rise to self-regulatory strategies for managing smartphone distractions. The students utilized strategies to combat unexpected pop-up notifications, such as delaying responses until a more appropriate time. Such a lag between distraction and task switching as an SRL strategy has been reported in earlier works (e.g., Deng, 2020; Iqbal & Horvitz, 2007; Trafton et al. (2003). In addition to controlling phone use, students also reported other behavioral strategies (e.g., reviewing, taking notes, taking a short break) to reinforce on-task activities or recharge energy.

Fig. 3
figure 3

Self-regulation strategies and process

Full size image

6.3 Multitasking and self-regulated learning as dynamic processes

One of the salient themes that emerged from our data is that students’ multitasking behaviors and implementation of self-regulated learning strategies for multitasking are not fixed but malleable depending on task motivation associated with perceived importance, values, and interest. In this sense, our study confirms the notion that self-regulated learning strategies for multitasking are situational and underlines the value component that concerns students’ perceived importance and interest in learning tasks (le Roux & Parry, 2021; Pintrich & De Groot, 1990; Popławska et al., 2021; Zimmerman, 2002). For courses perceived as valuable and essential, the students call into action a range of self-regulated learning strategies to avoid and combat digital distraction. In contrast, for those courses that they felt little importance or value, they felt less need to execute self-regulated learning strategies. Hence, students would indulge in distractive multitasking or unrelated learning tasks that could provide more stimulation and fulfillment. Smartphone distraction as the main threat to students’ attention will be amplified when students feel no need for self-regulation. They put phones in sight and within reach, leaving notifications more conspicuous. Therefore, our study suggests that motivation of the primary learning tasks governs the allocation of attention, susceptibility to distraction, multitasking behavior, and the execution of self-regulated learning strategies. As such, this paper echoes the proposition of attending to the internal states of students in understanding distractibility and multitasking (Brady et al., 2022; Heitmayer & Lahlou, 2021).

7 Conclusion

This study has examined in-class distractive multitasking and self-regulated learning situated within internal cognitive, emotional, and behavioral conditions, as well as external factors concerning physical, social, and technological environments. The findings pinpoint smartphones as the main source of distraction and highlight student’s motivation and interest as catalysts for activating self-regulated learning strategies. The study provides a more nuanced account of how students execute self-regulated learning in classes with varying motivation levels to mitigate smartphone distraction. It makes four main contributions that are worth noting. First, a framework has been proposed depicting the internal and external sources of distraction as precursors for multitasking and self-regulated learning strategies that could be called for combating these distractions. This framework provides a valuable roadmap for exploring how self-regulated learning comes into play and how off-task multitasking occurs as a result of internal and external antecedents. It also encourages a more holistic perspective on digital distraction, which could guide future exploitation in this area. Second, the findings contribute to a better understanding of the complex characteristics of smartphone distraction that is associated with various internal and external factors. Cognitive disengagement, lack of motivation, and phone use as habits constitute internal distractions that will infuse smartphone distraction in determining off-task multitasking. The external distraction from physical proximity, co-present peers, and, in particular, their smartphones will be amplified when students feel no need for self-regulation. Third, our study provides a nuanced account of self-regulation strategies that involved proactive measures to minimize distraction as a forethought and planning phase, as well as monitoring and dealing with distraction and distractive multitasking during class. Two recent reviews by Barron and Kaye (2020) and Wang et al. (2022) also highlighted forethought, planning, and activation as the prelude to coping with digital distraction, and our study contributed much-needed empirical data on this. Lastly, our data elucidate the complex interplay of distractive multitasking and self-regulation, which offers a fine-grained understanding of how students multitask or deploy self-regulated learning strategies in different types of classroom settings. The integration of the perspectives of self-regulated learning and multitasking yields a comprehensive understanding of the dynamic process that underlies students’ engagement or disengagement in class.

Our study, inevitably, has several limitations that are worth mentioning. First, although both quantitative and qualitative data were collected, they were all self-reported. Sometimes, the students could not precisely describe their in-class behaviors and cognitive states. It is plausible that when the students are engrossed in class, they pay little attention to their behaviors. Moreover, the students shared rich data through interviews regarding the self-regulated learning strategies they used, but little has been revealed on whether or to what extent they adhere to these strategies. Second, the study did not include instructors’ behavior, teaching strategies, or instructional methods that could influence students’ motivation and subsequent engagement. Arguably, instructors play an important role in attracting, sustaining, and regaining attention and engagement in classroom settings.

Notwithstanding its limitations, the present study yields several noteworthy implications for researchers, students, and educational practitioners. For researchers, our finding points to a need to examine the execution of self-regulated learning in relation to multitasking in different learning contexts. Because of the complex and context-bound characteristics of digital distraction and self-regulated learning, we strongly urge researchers to adopt mixed research methods to capture student behaviors across various educational settings. Future work can further explore self-regulated learning in relation to digital distraction in different phases of the SRL model, including planning, monitoring, and reflection (see Pintrich, 2000). For students, the findings could heighten their awareness of digital distraction and inform them of the self-regulation strategies to help mitigate such distraction. This ability of regulating their digital devices and maintain learning engagement is especially critical in an era where technology permeates every aspect of daily life.

For educational practitioners, the findings point to the central role of student motivation in the activation of self-regulated learning. This implies that more attention should be paid to boosting students’ motivation and the perceived values of learning content so as to reduce internal distractions. Training can be implemented to equip students with both the will and skill of self-regulated learning, thereby combatting distraction and enhancing learning engagement. On the whole, this study offers a nuanced understanding of smartphone distraction and self-regulation strategies that can foster learning experience and engagement. It can inform educational practitioners on how to create more engaging learning environments and effective educational practices in the digital age. Furthermore, this issue concerning smartphone distraction and multitasking extends far beyond the university classroom, affecting self-directed study, informal educational settings, and lifelong learning contexts as well. Hence the findings of the study might have a much broader implications for learners and educators in diverse contexts.