COVID-19 has forced educational institutions to promptly respond to this emergency and take a big step forward from traditional face-to-face learning to online learning to ensure that education continues (Gumede and Badriparsad, 2022). Previous studies have pointed out the advantages of online learning, including time and cost effectiveness (Elida et al., 2012; Almahasees et al., 2021), flexible use of time and space (Lemay et al., 2021; Maqableh and Alia, 2021; Turan et al., 2022), and the repetitive learning features of online resources (Bdair, 2021). These online resources are considered especially important for students’ self-directed learning, but research also points out that students’ online self-directed learning is challenging because students report that they are easily distracted and have limited attention (Lemay et al., 2021; Maqableh and Alia, 2021), which in turn affects their academic performance (Kim et al., 2022).

Distraction and inattention may be exacerbated by learners’ multitasking behavior (MB; Cebollero-Salinas et al., 2022). However, time-limited multitasking seems to be different from the current online learning ethos that emphasizes flexibility and self-direction. The recent education reform in Taiwan requires the cultivation of interdisciplinary, independent, and autonomous talent, prompting self-directed online learning to become the focus of education in Taiwan and emphasizing students’ personalized learning processes rather than learning within a given time (Chen and Li, 2021). Given the lack of research on MB in online learning, one of the study’s purposes is to explore MBs in online learning and students’ perceived learning performance.

Multitasking behavior is closely related to polychronicity. Polychronicity is an individual’s natural tendency or preference for constructing time (Sanderson, 2012; Capdeferro et al., 2014), specifically defined as an individual’s preference to participate in two or more tasks or events simultaneously and the belief that one’s preference is the best way to do things (Bluedorn et al., 1999). Although studies of polychronicity are rare in educational settings, previous studies have shown differences in multitasking behavior among students at different levels of education (e.g., Jeong et al., 2010). However, polychronicity needs to be discussed more in online education. Based on the relationship between multitasking behavior and learning outcomes, as well as the relationship between multitasking behavior and polychronicity, this study explores the relationship between polychronicity, multitasking behavior, and learning outcomes by considering differences in educational stages.

In the predictive relationships of the entire sample, “scheduling preference” is an important predictor of “multitasking behavior” and perceived learning performance. This result is consistent with the general intuitive assumption that when learners prefer to do multiple things in a given time period and believe that this method is the best way to do things, they are more likely to adopt “multitasking behavior” (König et al., 2010); furthermore, a multitasking learning environment is in line with their personal interests and needs, which increases their satisfaction (Sanderson, 2012; Chuang et al., 2016). In addition, “time tangibility” can also predict “multitasking behavior” and perceived learning performance. That is, people who prefer to work in a timely, timesaving, and schedule-based manner actually participate in more tasks simultaneously and experience higher satisfaction in the process of using digital technology to learn. Luo et al. (2021) and Nonis et al. (2005) pointed out that “time tangibility,” which is closely related to time management, enables people to complete tasks on time or even deal with more tasks, and it is in line with the online learning environment that requires learners to perform learning tasks in a polychronic manner (Lee and Perry, 2001; Capdeferro et al., 2014; Lepp et al., 2019). Finally, the “involvement with people” aspect of polychronicity in online learning did not predict “knowledge acquisition.” It is possible that although emerging IT has multiple interpersonal interaction functions, Taiwanese students still seldom use these interactive functions in IT-supported learning, possibly because obedience and silence in classroom learning have long been aspects of the learning culture of Chinese students (Luo and Yang, 2018), which may extend to virtual online learning environments. Therefore, “involvement with people” may not reflect knowledge acquisition in the online learning environment.

Jeong et al. (2010) found that the frequency of media multitasking behaviors differed between high school students and college students because of the differences in their daily life schedules. This study aligns with a previous study (Jeong et al., 2010) and further finds that college students’ frequency of “multitasking behavior,” “scheduling preference” and “time tangibility” and their “learning satisfaction” in multitasking online learning environments were significantly higher than those of high school students. This may be because college students have more experience in using technology to learn than high school students (Jeong et al., 2010), which is evidenced in studies revealing more time overall in internet usage (Taiwan Network Information Center, 2018) and a higher percentage of internet addiction than high school students (Writer, 2022). Personal possession of technological media increases individuals’ use of technological media. Accessibility provides individuals with more opportunities for media MBs, thereby improving their self-efficacy in the use of technological media and increasing their preference for “multitasking behavior” in technological media (Srivastava et al., 2016).

Furthermore, this study found that, for students at different educational stages, different aspects of polychronicity had different predictive relationships on online learning “multitasking behavior” and perceived learning performance. “Scheduling preference” positively predicted “multitasking behavior” and perceived learning performance for both high school and college students. This result seems not surprising. As mentioned above, students who prefer and believe that multitasking is an ideal method are more likely to prefer multitasking behavior (König et al., 2010) and are more satisfied with the online learning environment, which may be potentially full of multitasking (Sanderson, 2012; Chuang et al., 2016). The statistical results of different education stages further explain that this phenomenon applies to many young people.

“Time tangibility” had a significant impact on the learning performance (including “knowledge acquisition” and “learning satisfaction”) of college students but not on that of high school students. Especially in “time tangibility” and “learning satisfaction,” the path coefficients of the two samples were significantly different. This may be because students with “time tangibility” feel that they can complete tasks on time or even complete more tasks through time management, resulting in higher academic performance (Nonis et al., 2005; Luo et al., 2021). In addition, “time tangibility,” which is related to time management, relates to students’ successful self-regulated learning. Liu et al. (2014) showed that time management was associated with self-regulated learning and that both positively predicted learning engagement. Wu (2017) found that students’ media multitasking indirectly predicted course grades through perceived attention and self-management strategies. Students’ learning performance on media multitasking can be improved by using attention regulation strategies, while students at higher stages of education have a clearer understanding of their learning patterns and are better able to regulate their learning in specific situations (Song and Vermunt, 2021).

High school students were more likely to be involved with people and had a higher frequency of multitasking behaviors, suggesting that media multitaskers were more likely to value online socializing (Zhong et al., 2011). However, this relationship did not apply to college students, possibly due to the aforementioned regulation strategies. College students can master multitasking more effectively than high school students through self-regulation strategies without being affected by interpersonal relationships. The positive effect of IP on “learning satisfaction” was also shown among high school students but not among college students, highlighting the importance of online socialization for high school students’ learning. For high school students who are primarily in adolescence, a desire to “fit in” with peers is characteristic (Wang and Chen, 2018), and “involvement with people” satisfies their desires, leading to positive “learning satisfaction.” However, “involvement with people” did not significantly predict “knowledge acquisition” in either college or high school students. Previous research has shown that interpersonal engagement in the classroom, such as collaborative learning and classroom dialog, must be a part of effective teacher scaffolding to produce good learning benefits (Muhonen et al., 2016; Chan, 2020). Therefore, online “involvement with people” may lack scaffolding guidance and effective collaboration and dialog, leading to reduced “knowledge acquisition.”

Interpretation of our findings should be made with caution due to the following limitations. First, due to individual subjectivity, students’ self-reports may not reflect their actual multitasking behaviors. Second, although we found that polychronicity is related to multitasking behavior and perceived learning performance, these variables are possibly not causally related in a practical sense. To clarify this issue, other research methods, such as experiments or observation methods, should be used in future research to deeply examine polychronicity, multitasking behavior and learning performance in an online learning environment. Finally, the “knowledge acquisition” investigated in this study is the result of students’ subjective perceptions, lacking objective data on academic knowledge and academic performance. Future research should consider collecting multiple academic achievements. Although there are limitations in this study, it is still helpful for developing a preliminary understanding of the polychronicity, multitasking and learning performance of online learning, as well as the differences across educational stages, and can be regarded as a basis for subsequent research development.

Media multitasking behavior has become a response to the application of digital technology, which also reflects that online learning multitasking behavior suitable for digital media ecology results in higher learning performance for both high school and college students. However, students’ mastery of self-regulation strategies at different educational stages caused by learning experiences may influence digital learning multitasking behavior and learning performance. In addition, the quality of involvement with people may be one of the antecedent factors affecting students’ online learning outcomes. Therefore, it is suggested that future research consider the moderating effects of self-regulation strategies and the quality of cooperative scaffolding and classroom dialog on polychronicity, multitasking behavior and learning performance in an online learning environment.

Future studies can also further examine what motivates heavy polychronic, or molychronic, learners to perform online multitasking behaviors in their learning engagement and how those learners activate and regulate cognitive capacity in their processing and consuming different media tasks at hand (cognitive/attentional control, executive functioning, media type attention, superficial/deep level of processing) and their cognitive/affective/physiological emotions (positive feelings accomplishments or negative stress) associated with their online multitasking and polychronicity activities (the perceived feeling of control, cognitive overload, perceived ability to process all information). Finally, future studies can further examine behavioral and neural as well as inhibiting or stimulating indicators related to the cognitive/affective/social mechanism of students’ engagement in polychronicity and multitasking behavior in an online learning environment.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

YL and SY contributed to conception and design of the study. CL collected the data and organized the database. YL performed the statistical analysis and wrote the first draft of the manuscript. YL, SK, SY, and CL wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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