The smartphone can introduce substantial burdens on a learner’s cognitive resources. As it is currently conceived, the smartphone is distracting and particularly detrimental to learners attempting to study (Aharony & Zion, 2019; May & Elder, 2018). The core functionality and use of the smartphone is based largely on the notification. This can be a beep, vibration, or visual cue. The notification is the cue for the user to make a decision. Regardless of the decision, the distraction has now occurred. The focused attention of the learner is disrupted and progress on the initial task will suffer (Chen & Yan, 2016; Terry, Mishra, & Roseth, 2016).
While notifications and distractions represent one challenge to the learner, the propensity to use the smartphone to support multitasking is another significant obstacle (Kirschner & De Bruyckere, 2017). The multimedia capacity of the smartphone encourages nearly continual consumption of multiple media streams. A recent study reported the mean number of apps used by college students while doing homework was 4.9 (Patterson, 2017). The same study reported that higher levels of multitasking while studying were associated with lower levels of exam performance. The consumption of multiple media is expanding as the use of multiple devices increases. The learner is often surrounded by an array of technological devices vying for attention (Chen & Koufaris, 2020).
In a review of media multitasking and academic performance research May and Elder (2018) observed:
Laptops and mobile phones are particularly distracting while studying or doing course-work outside of class, as students can easily access alternate media sources such as email, Facebook, or Instant Messaging (IM) on them. Much of the research to date primarily assessed the impact of media multitasking on in-class activities, such as test performance. Few studies have examined the role of media multitasking on assignments outside of class, such as homework or studying. (p. 8).
This research targeted the smartphone related habits of learners while studying and the consequent impact on achievement.
Self-regulated learning
Student awareness of the cognitive challenges presented by the smartphone is unclear. Is the deleterious use of the smartphone a result of unawareness or apathy? In other words, do learners understand the negative impact on the learning and choose to continue the behavior regardless? Or, do they believe smartphone use while learning does not adversely impact productivity (i.e., learning)?
Self-regulated learning (SRL) theory is used as a framework to address these questions. Broadly conceived, SRL incorporates learner motivation, metacognitive awareness, cognitive skills and beliefs about learning (Muis, 2007; Schraw & Dennison, 1994; Usher & Schunk, 2018). A description of SRL from Usher and Schunk (2018) provides relevant context:
The process of systematically organizing one’s thoughts, feelings, and actions to attain one’s goals is now commonly referred to as self-regulation. In this information-rich, fast-paced world, individuals are presented with many possible paths of thought and behavior, which can sometimes feel overwhelming. (p. 32).
The well self-regulated learner recognizes limits on cognitive capacity and the necessity to be strategic in the deployment of these resources (Schraw & Dennison, 1994; Zimmerman & Schunk, 2011). This knowledge generally is revealed with increased effort, time management, and focused attention (Mrazek et al., 2018; Pintrich & De Groot, 1990; Zimmerman & Kitsantas, 2014). This becomes key in an environment that seems to encourage cyberloafing (Durak, 2020). In an analysis of variables related to classroom multitasking, self-regulation has been identified as a key influence on multitasking (Zhang, 2015). Similarly, students who report low multitasking behaviors (or high focus) while studying exhibit higher levels of self-regulated learning behaviors such as time management and focused effort Hartley, Bendixen, Shreve, et al., 2020).
This research pays particular attention to the resource management component of SRL as it has demonstrated clear impacts on academic achievement (Pintrich & De Groot, 1990; Pintrich, Smith, Garcia, & Mckeachie, 1991) and is strongly related to smartphone usage while studying (Hartley, Bendixen, Shreve, et al., 2020). Resource management includes activities such as exerting increased effort towards difficult content and establishing a study environment conducive to focused attention.
Changing behavior
Ultimately, the importance of clarifying the relationships between smartphone usage, SRL, and learning is in the service of identifying strategies to improve learning outcomes. Addressing self-regulatory skills is a natural conduit for improving academic performance (Mrazek et al., 2018). Given the relationships between smartphone usage, SRL, and achievement, modifications to how the smartphone is used are also appropriate (Dalvi-Esfahani et al., 2020). In addition, given the ubiquity of the smartphone, it is prudent to utilize it in any SRL intervention (e.g, as a support for cue-based interventions; van Merriënboer & de Bruin, 2019).
Like any instructional goal, substantial changes in behavior are best supported by activities that are well-planned, long-term, time-intensive, and highly engaging. Unfortunately, the opportunities to implement these activities with the current population (new college students) are limited. The existing curriculum is already viewed as impacted. Thus, adding additional components on top of the standard curriculum risks information and activity fatigue for both instructors and students alike. The goal is to identify an intervention that does not detract from the main goals of a course, is seamlessly integrated into the student experience, and requires minimal instructor support.
Brief interventions have garnered substantial interest in the area of implicit theories of intelligence. These interventions involve little more than introductory instructions that frame the activity in a different light or modifications to the type of feedback provided (Walton, 2014). Researchers have found that inducing a growth mindset (or incremental theory of intelligence) can be achieved with young children through simple adjustments to task feedback by complementing learners on hard work rather than praising intelligence (Dweck, 2008). Others have had mixed results with brief task instructions and college students (Bråten, Lien, & Nietfeld, 2017).
More substantive interventions have provided more reliable outcomes with young adults. Researchers studying college students have had success with a 2-h online training that required limited instructor intervention (Bernacki, Vosicka, & Utz, 2019). There is evidence that a semester-long intervention that incorporates instructor modeling can improve beliefs about learning and the use of SRL strategies such as elaboration (Muis & Duffy, 2013). While this intervention was introduced over a larger time frame, the additional time required was minimal in that it replaced one interaction and questioning approach (traditional/control) with another (epistemic beliefs/intervention).
Morisano, Hirsh, Peterson, Pihl, and Shore (2010) investigated the impact of an online, personal goal-setting intervention among self-nominated, academically struggling college students (n = 85) at a four-year research institution. Participants were randomly assigned to a goal group or control group; each group completed a 2.5-h guided online program on their own time, outside of a course, with no instructor engagement. The goal group saw two major benefits in the semester post-intervention: GPA improvement and a greater likelihood of maintaining at least a nine-credit course load.
In a recent large-scale study examining lower-achieving high school students, Yeager et al. (2019) utilized a scalable, online growth mindset intervention consisting of two brief sessions that averaged 25 min each session with most institutions distributing the sessions around 3 weeks apart. The intervention required no additional teaching from instructors. The results suggested notable gains in enrolling in advanced-math courses, and for the schools where peer norming aligned with the intervention content, grades were improved.
In summary, research indicates that well-conceived short-term interventions can have a positive influence on academic achievement and related behaviors. This study aimed to extend these findings to smartphone usage as it relates to learning.
Present research
The current study aimed to address two questions. What is the relationship between limited smartphone usage while studying (LSU), SRL (as measured by resource management), and academic achievement? Can a brief intervention positively impact smartphone usage and self-regulation over the course of a semester?
Relationship between SRL, limited smartphone usage while studying, and achievement
The model described in Fig. 1 represents the proposed relationship between SRL as measured by resource management, limited smartphone usage (LSU) while studying and achievement while controlling for prior achievement. Prior research has demonstrated a positive relationship between resource management and LSU while studying (Hartley, Bendixen, Shreve, et al., 2020). See arrow A in Fig. 1. Similarly, prior research has demonstrated a positive relationship between resource management and achievement (Pintrich & De Groot, 1990; Robbins et al., 2004) or arrow C. While overall smartphone usage has a deleterious effect on achievement (Lepp et al., 2015) the relationship between LSU while studying and achievement (arrow B) has not been directly addressed. In addition, the indirect impact of resource management on achievement through LSU while studying has not been investigated.
The proposed model (Fig. 1) presents resource management as a trait that temporally precedes LSU while studying. In other words, it suggests that the learner’s resource management traits influence smartphone use. Subsequently, smartphone usage will influence the first-semester GPA.
Can a brief intervention positively impact SRL and limited smartphone usage while learning?
The exercises were inspired by interventions described by (Morisano et al., 2010) and Yeager et al. (2019). However, the intervention utilized here was shorter in duration and intentionally designed to have minimal impact on instructor and student resources. Participants were guided through one of three brief exercises on (1) career planning (CP, control), (2) academic planning (AP), or (3) attention and awareness (AA).
Hypotheses:
-
1.
Limited smartphone usage while studying, SRL, and Achievement
-
a.
When controlling for prior achievement, SRL will have a positive influence on the first semester GPA.
-
b.
When controlling for prior achievement, SRL mediated by limited smartphone usage will have a positive influence on the first semester GPA.
-
c.
When controlling for prior achievement, limited smartphone usage will have a positive influence on the first semester GPA.
-
2.
Instructional Intervention
-
a.
Intervention participants will earn higher grades than control participants.
-
b.
Intervention participants will report less smartphone use while studying smartphone when compared to control participants.
-
c.
Intervention participants will report increased SRL when compared to control participants.