University students’ use of motivational regulation during one semester

Highlights

• Autonomous motivation and motivation regulation showed nonlinear rates of change.

• Trait-like aspects of motivation mirrored one another over time.

• State-like autonomous motivation reduced future motivation regulation.

• Educational interventions must consider both trait and state motivation.

Abstract

This study examined the interplay between university students' trajectories of motivational regulation and autonomous motivation across one semester, exploring both between and within person components. Participants (N = 193) from one large class reported motivation in two-week intervals over the course of one semester. Bivariate latent curve models with structured residuals revealed rates of change in motivational regulation and autonomous motivation were not linear, declining across the first ten weeks of the semester then bouncing back in the final month. Between-person effects of individual change demonstrated mirroring relationships of latent intercepts and slopes across the semester. Within-person findings revealed that autonomous motivation was a negative predictor of future motivation regulation. Students' grade point average only predicted students’ beginning level of motivational regulation. It appears that students with higher states of autonomous motivation view motivation regulation as unnecessary or even a potential threat to their learning pleasure and satisfaction.

Introduction

Students often experience barriers to initiating and sustaining learning motivation in their daily academic lives, such as exposure to content or instructors that fail to spark their interest, or to challenging assignments that are part of a steeper learning curve than anticipated (Järvelä, Järvenoja, & Malmberg, 2012; Kim, Brady, & Wolters, 2018; Wolters, 1998; Zimmerman & Schunk, 2008). The manner in which students address such motivational barriers can result in a very wide range of outcomes, ranging from advanced academic achievement to dropping out of school (Robbins, Oh, Le, & Button, 2009). One characteristic of high achieving students is the ability to self-regulate different aspects of their learning experiences (Pintrich, 2004; Zimmerman & Schunk, 2008). A key assumption in self-regulated learning (SRL) theoretical frameworks is that students take an active role in monitoring, planning, managing, and reflecting on their own learning experiences. Thus, students who are proficient at self-regulating their learning are better able to detect barriers when they arise and to proactively adopt strategies to minimize or overcome them. Conversely, students less proficient at self-regulating their learning often fail to detect, or address learning barriers effectively (Wolters, 2011).

SRL is especially salient for students attending colleges and universities (Cohen, 2012; Wolters, 1998). University students often face the juxtaposition of increased academic demands and responsibilities with greater freedom to make choices in and out of the classroom. Should I stay home to study or go out with my friends? Should I wake up early to go to class or sleep in? Should I pay attention to this boring lecture or read social media feeds? SRL may be especially difficult to achieve when other options appear more appealing and the learning context provides minimal external accountability. Will I really be missed if I don't attend a lecture with 300 other students? I'm sure that my friend will let me know if I missed anything important, won't he? Furthermore, university classes often place greater emphasis on independent learning outside of the classroom (Pintrich & Zusho, 2007). These contextual structures increase the stakes for university students so that misjudgments in planning, monitoring, and managing learning strategies can more easily lead to severe academic problems than ever before. Difficulties in a single class can disrupt university students' academic progress (Goudas & Boylan, 2012). Despite the emphasis placed on SRL in university settings, previous research suggests that many university students use SRL skills infrequently (Lan, 2005).

The goal of this study is to investigate university students' use of motivation regulation over the course of one semester including seven waves of data. Specifically, we investigate the joint within-and between-person interplay between students’ use of motivation regulation and autonomous motivation. In the following paragraphs, we start by explaining the concept of motivation regulation and its importance in university settings. Next, we examine autonomous motivation including its potential links with motivational regulation. Finally, we outline the specific research questions that guided this study and outline how it addresses gaps in the current literature.

Examination of motivational regulation as a unique and substantive component of SRL is gaining momentum in educational research (Järvelä & Järvenoja, 2011; Kim et al., 2018; Miele & Scholer, 2018; Schwinger & Stiensmeier-Pelster, 2012; Smit, Barbander, Boekaerts, & Martens, 2017; Wolters & Hussain, 2015). Wolters (2003) defines motivational regulation as the thoughts, actions, and behaviors students use to influence their choices, effort, and persistence toward academic work. Wolters and Benzon (2013) identified six major motivational regulation strategies used by university students: Regulation of value, regulation of performance goals, self-consequating, environmental structuring, regulation of situational interest, and regulation of mastery goals. Regulation of value occurs when students engage in strategies that makes understanding course content more interesting, useful, and important. For example, students focus on how course knowledge will be useful in their future careers. Regulation of performance goals occurs when students engage in strategies that emphasize the importance of achievement outcomes. For example, students focus on the benefits associated with getting high marks in the course. Self-consequating occurs when students engage in self-reinforcing strategies such as rewarding themselves with their favorite type of food after studying. Environmental structuring occurs when students’ purposefully exert control over contextual factors in order to enhance motivation. For example, students find a place to study where interruptions are unlikely. Regulation of situational interest occurs when students engage in strategies that makes course content and activities more enjoyment. For example, students create different games when studying for an exam. Finally, regulation of mastery goals occurs when students engage in strategies that underscore the importance of learning as much as possible. For example, students challenge themselves to learn as much as possible about course topics.

Schwinger Steinmayr, and Spinath (2009) extended Wolters’ work, identifying eight commonly used motivational regulation strategies organized into interest-based strategies and goal-based strategies. Interest-based strategies include enhancing situational interest, enhancement of personal significance, and self-consequating. Goal-based strategies include proximal goal setting, mastery self-talk, performance approach self-talk, and performance avoidance self-talk. The final motivational regulation strategy, environmental control, is the only strategy that does not fit into either goal or interest categories. Proximal goal setting and performance avoidance self-talk are the two new strategies that do not closely overlap with those proposed by Wolters and Benzon (2013). Proximal goal setting occurs when students set short-term goals to enhance motivation for long-term or complex tasks. For example, university students may set short-term goals for each exam in a course in order to accomplish the long-term task of getting a high final grade. Performance avoidance self-talk occurs when students focus their thinking on the avoidance of normative incompetence such as doing worse than fellow classmates on assignments or exams. It is important to note that Schwinger, Steinmayr, and Spinath (2009) developed these strategies with secondary students, not university students.

More recently, Kim et al. (2018) developed a general measure of university students' motivational regulation, called the Brief Regulation of Motivation Scale (BRoMS). The BRoMS measured students' beliefs about engagement in motivational regulation rather than the use of different strategies. Kim et al. (2018, p. 261) report that the BRoMS evaluates students’ “overall tendency to respond to cued motivational challenges in a way meant to sustain or improve their motivation.” The BRoMS produced sound psychometric scores for the motivational regulation factor, which consists of eight items, in a sample of approximately 400 university students. The addition of the BRoMS provides researchers with opportunities to explore a global factor of motivational regulation, rather than having to rely on a longer measure focusing on specific regulation strategies.

Similar to other types of SRL (Pintrich, 2000; Zimmerman, 2000), adaptive motivational regulation implementation requires basic elements including meta-motivational knowledge, self-monitoring of motivational states, and managing motivational regulation strategies (Wolters, 2011). First, students must possess knowledge about their own academic motivation including the topics, types of learning activities, and classroom interactions they find interesting, boring, or frustrating. Students must also consciously explore various strategies to use under different motivational conditions (e.g., if these math problems are boring, I should try to make a game out of solving them). Second, students must consistently monitor their current states of motivation. Developing self-awareness about motivational states through monitoring helps students become more responsive to dealing with periods of low motivation when effort, persistence, and engagement are likely to suffer. Finally, students must put their meta-motivational knowledge into action by implementing and managing their use of motivational regulation strategies successfully.

Wolters (2003) suggests that motivation regulation is closely related but distinct from students' motivation. Specifically, he delineates the contrast between active and subjective control. With motivation regulation, students actively monitor and manipulate the energy and direction underlying behavior whereas motivation is a more subtle process that drives the energy and direction of behavior through perceptions and beliefs. From a self-determination theory perspective, Deci and Ryan (2000) theorize that the underlying reasons of behavior creates a continuum of motivation. This perceived locus of causality ranges from reasons that are completely intrinsic (e.g., pursuing one's interest) to completely extrinsic (e.g., avoiding punishment) in nature (Sheldon, Osin, Gordeeva, Suchkov, & Sychev, 2017). Intrinsic motivation is the healthiest type of motivation and occurs when individuals feel fully autonomous in their actions. In essence, behavioral engagement in and of itself represents the reward that stimulates behavior. For example, a student studies because she finds course content interesting. However, self-determination can still occur through extrinsic regulation when the underlying reasons for behavior are internalized as important and valuable to one's goal pursuits. Deci and Ryan (2000) describe this type of motivation as identified regulation. For example, a student studies because she believes that learning course content will help her achieve future professional goals.

Autonomous motivation is the combination of intrinsic and identified regulation, representing actions powered by an internal locus of causality (Sheldon & Elliot, 1999). In other words, autonomous motivation reflects one's self-determination to engage in behavior. There is extensive evidence that across all levels of education, students' autonomous motivation helps explain adaptive behavioral, cognitive, and affective academic outcomes (Ryan & Deci, 2017). For example, previous longitudinal studies underscore how autonomous forms of motivation predict course grades (Burton, Lydon, D’Alessandro, & Koestner, 2006) and grade point average (Baker, 2003) while controlling for university students' previous levels of achievement. Guay, Ratelle, Roy, and Litalien (2010) also found that autonomous motivation predicted future academic achievement after controlling for previous achievement in secondary students. These studies have generally revealed a small positive effect with some heterogeneity according to a meta-analysis study (Taylor et al., 2014).

Researchers have also explored relations between autonomous motivation and different aspects of SRL. For example, studies have investigated relationships between autonomous motivation and self-control (Converse, Juarez, & Hennecke, 2019) as well as goal pursuits (Sheldon & Elliot, 1999). Converse et al. (2019) conclusions from six studies suggest that individuals with higher levels of self-control were more likely to experience autonomous motivation across a variety of contexts including university educational settings. In sports settings, Jordalen, Lemyre, Durand-Bush, and Ivaarsson (2020) demonstrated cross-lagged relationships between intrinsic motivation and trait self-control over time in elite university athletes, with the link between trait self-control to future autonomous motivation being more robust. Self-control appears to have conceptual similarities to motivation regulation because it relies on active control in the face of challenges, temptation, or fatigue (Baumeister, Vohs, & Tice, 2007).

Reeve, Ryan, Deci, and Jang (2008) report that self-determination theory helps explain why students regulate different types of behavior whereas most self-regulation theories help explain how students regulate different types of behavior. Wolters (2003) provides a similar perspective, theorizing that motivational regulation guides students' adaption of motivation. Thus, it seems plausible that motivational regulation strategies represent students’ attempts to exert control over their self-determination, especially when faced with academic challenges that may undermine it. Reeve et al. (2018) hypothesize that students with insufficient levels of autonomous motivation are unlikely to engage in SRL skills consistently or effectively.

No studies that we are aware of have directly examined the interplay of motivational regulation and autonomous motivation. Studies to date explore connections between university students’ motivational regulation and motivation constructs such as achievement goals, effort regulation, self-efficacy, and subjective value (Kim et al., 2018; Schwinger & Otterpohl, 2017; Wolters & Benzon, 2013). Some of these studies investigate how global motivational regulation relates to motivation constructs (e.g., Kim et al., 2018; Schwinger & Stiensmeier-Pelster, 2012) while other studies examine how each motivational regulation strategy relates to motivation constructs (Schwinger & Otterpohl, 2017; Wolters, 1999; Wolters & Benzon, 2013) in both secondary and university students. Findings from studies focusing on global motivational regulation highlight consistent positive, small-to-moderate relations between motivational regulation and effort regulation in secondary students (Schwinger & Stiensmeier-Pelster, 2012) as well as mastery goals (Kim et al., 2018), and self-efficacy (Kim et al., 2018) in university students. Interestingly, Kim et al. (2018) revealed small, negative relations with performance-avoidance achievement goals and no correlation with performance approach goals, which are extrinsic-oriented aspects of motivation (Elliot, 1999; Ryan & Deci, 2017).

These findings reflect similarities with motivational regulation studies that examine each strategy with motivation constructs. Specifically, regulation of mastery goals is the motivation regulation strategy most closely related to intrinsic-oriented aspects of motivation such as effort regulation while regulation of performance goals relates to extrinsic-oriented motivation constructs such as performance goal pursuit in both secondary and university students (Schwinger & Otterpohl, 2017; Wolters, 1999; Wolters & Benzon, 2013). However, Schwinger and his colleagues argue that there are many advantages to examining global motivational regulation including providing an authentic, big picture perspective on how students regulate motivation and minimizing potential multicollinearity issues due to strong correlations among the strategies. Global motivation regulation includes diverse intrinsic- and extrinsic-oriented strategies students use to control their motivation. Similarly, autonomous motivation is also a composite of intrinsic and extrinsic forms of motivation (Sheldon et al., 2017). Thus, there appear to be meaningful underlying processes that connect motivational regulation and autonomous motivation.

While there seems to be consensus that motivational regulation and autonomous motivation are related, many questions remain unclarified. It is important to note that a majority of the studies noted above focusing on motivational regulation and motivation rely on cross-sectional research designs (e.g., Kim et al., 2018; Wolters, 1999; Wolters & Benzon, 2013) while longitudinal studies only test the motivational regulation to motivation temporal pathway (Schwinger & Otterpohl, 2017; Schwinger & Stiensmeier-Pelster, 2012). Reeve et al. (2008) suggest autonomous motivation may be a key antecedent to self-regulation skills. Thus, there is a clear need to gather evidence on the autonomous motivation – motivation sequence because arguments to date remain theoretical in nature. Gaining better understanding of this sequence can provide guidance for future educational interventions that address multiple aspects of student motivation.

In the present study, we examine the links between motivation regulation and autonomous motivation from an intra-individual process perspective (Hamaker, 2012; Schmitz, 2006). Specifically, our investigation addresses how motivation regulation and autonomous motivation unfold within students over time rather than associations between motivation regulation and autonomous motivation occurring across students (Hamaker, 2012). In doing so, we explore the interplay of trait-like (i.e., one's usual level of motivation) and state-like (i.e., one's momentary deviation from the usual level of motivation) aspects of motivation regulation and autonomous motivation. This intra-individual process perspective assumes that learning phenomena such as motivation (Heemskerk & Malmberg, 2020; Malmberg & Martin, 2019), interest (Fastrich & Murayama, 2020), and self-regulation (Schmitz & Wiese, 2006) are dynamic rather than static in nature, with students consistently experiencing variations in these phenomena based on various situational factors. This intra-individual process perspective places emphasis on understanding developmental sequences by repeatedly measuring states over short intervals (Schmitz, 2006). Thus, this approach is well suited to explore the temporal sequencing of autonomous motivation and motivation regulation as it plays out for university students over the course of one semester.

For example, Patall et al. (2018) used intra-individual process analysis and noted that secondary students’ autonomous motivation toward a science class tended to vary on a daily basis based on whether or not students believed that their teacher provided them with meaningful choices in the class. Similarly, Fastrich and Murayama (2020) used intra-individual process analysis to investigate the interplay between situational interest and knowledge acquisition when learning about a novel topic. Specifically, they demonstrated reciprocal nonlinear sequences whereby interest and knowledge acquisition reinforced one another during the early stages of learning. These are just two examples of how taking an intra-individual process approach can help illuminate developmental aspects of learning phenomena. Because research focusing on motivation regulation relies extensively on learning how it relates to students in the aggregate, applying a process-oriented perspective can add additional understanding to its dynamic characteristics within students (Hamaker, 2012).

Associations between motivational regulation strategies, antecedents and outcomes have started to emerge in the research literature on academic motivation. However, this emerging area of research remains in its early stages, with rigorous longitudinal investigations just beginning. In this study, we address substantive gaps in this area of research, aiming to advance understanding of the interplay between university students' motivational regulation and autonomous motivation. We use the bivariate latent curve model with structured residuals to disentangle the interplay between students' trait-like (i.e., between person) and state-like (within person) motivational regulation and autonomous motivation rates of change (Curran, Howard, Bainter, Lane, & McGinley, 2014). Examining whether students with higher reports of autonomous motivation also report higher levels of motivation regulation over time highlights trait-like interplay while examining whether higher levels of autonomous motivation relative to one's trait like level at a specific time point predicts higher reports of motivation regulation relative to the individual at a subsequent time point highlights state-like interplay. While the between person effects provide important information on how autonomous motivation and motivation regulation develop over time, within person effects underscore the possible existence of clear temporal sequencing including potential reciprocal effects between autonomous motivation and motivation regulation (Curran et al., 2014).

Relations between students' motivation and motivational regulation has often been examined as a snapshot in time, whereas the present study was specifically designed to examine their interplay over time. Indeed, previous studies have generally examined effort regulation as an indicator of student motivation (Schwinger et al., 2009). Adopting this perspective, we hypothesize close links between students’ autonomous motivation and their use of motivational regulation strategies. Specifically, we posit that self-determined students should have greater awareness of their changing motivational states and willingness to address motivational barriers as they occur (Wolters, 2013; Wolters & Rosenthal, 2000). Although highly motivated university students may need to rely on motivational regulation strategies less frequently (Wolters & Benzon, 2013), research using effort regulation as a proxy for motivation suggests a positive, linear relationship between these two constructs (Schwinger & Otterpohl, 2017). Interestingly, autonomous motivation (Black & Deci, 2000), just like motivational regulation (Schwinger et al., 2009; Wolters & Benzon, 2013), incorporate intrinsic and extrinsic (i.e., identified regulation) components. During a given semester, university students are likely to rely on both types of motivation within a given course. For example, different topics provide students with different levels of interest and challenge.

Finally, theorists typically position academic performance as a distal outcome of motivational regulation, occurring via students' effort regulation (Schwinger et al., 2009; Schwinger & Stiensmeier-Pelster, 2012) or procrastination (Grunschel, Schwinger, Steinmayr, & Fries, 2016). In the present study, however, our main goal is to examine how university students' autonomous motivation and use of motivational regulation relate across time. In this context, we consider students' grade point average (GPA) at the beginning of the semester as a fixed predictor of their trajectories of motivational regulation and autonomous motivation over the course of the semester. We also explore the role of students’ sex and first generation status as fixed predictors of motivational regulation.

In sum, the purpose of this study was to investigate the joint within- and between-person interplay between students’ use of motivation regulation and autonomous motivation. We address the following research questions (RQ):

RQ1: How do students’ autonomous motivation and use of motivational regulation toward a specific class evolve over the course of one University semester?

RQ2: To what extent does students’ trait-like autonomous motivation and motivational regulation trajectories covary over the course of the semester?

RQ3: What are the temporal relationships in students’ state-like autonomous motivation and motivational regulation?

RQ4: To what extent does students' demographic characteristics and GPA at the beginning of the semester predict students’ motivational regulation and autonomous motivation trajectories?