Academic Exchange Quarterly      Winter   2008    ISSN 1096-1453    Volume  12, Issue  4

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A Conceptual Model of Self-Regulation Online


Anthony R. Artino, Uniformed Services University of the Health Sciences, Bethesda, MD


Dr. Artino is a Lieutenant Commander in the U.S. Navy and an Assistant Professor in the Department of Preventive Medicine and Biometrics.



Recently, several educational psychologists have suggested that students require considerable motivation and self-regulation to initiate and sustain their learning in online settings. This article presents a conceptual model of academic self-regulation in the context of online learning and provides theoretical and empirical support for the concepts and relationships described in the model. Ultimately, this article is meant to highlight the importance of self-regulated learning in online settings and to encourage instructors to consider these processes when teaching online.



Distance learning is hardly a new phenomenon. For example, in the United States, correspondence courses have provided distance learning to students around the country since the creation of the postal service in the 19th century (Phipps & Merisotis, 1999). This trend continued well into the 20th century with the advent of television and radio—media technologies that allowed for expanded opportunities to learn at a distance (Moore & Kearsley, 2005). Today, computer-mediated communications and the Internet have resulted in a rapid and explosive interest in distance education (Larreamendy-Joerns & Leinhardt, 2006).


As online distance learning has grown, so too has interest in self-regulated learning (Boekaerts, Pintrich, & Zeidner, 2000). Self-regulated learning (SRL) has been defined as “an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained by their goals and the contextual features of the environment” (Pintrich, 2000, p. 453). Self-regulated learners are described as active students who efficiently control their own learning experiences in many different ways, including establishing a conducive work environment and using resources effectively; employing various cognitive and metacognitive learning strategies to help comprehend information to be learned; regulating their emotions during academic tasks; and adopting positive motivational beliefs about their capabilities, the value of learning, and the factors that improve learning (Schunk & Zimmerman, 2008).


In the last 10 years, several educational psychologists (e.g., Dabbagh & Kitsantas, 2004; Hartley & Bendixen, 2001; Schunk & Zimmerman, 1998) have suggested that students require considerable motivation and self-regulation to stay engaged, guide their cognition, and regulate their effort in online situations. This suggestion stems from the belief that learning on the Web tends to be much more autonomous and self-directed (Allen & Seaman, 2007). The highly independent nature of online learning is thought to be due, in part, to the lack of structure and guidance that normally comes from face-to-face, social interactions with an instructor and other students (Moore & Kearsley, 2005).


Theoretical Framework

Self-regulated learning refers to “learning that occurs largely from the influence of students’ self-generated thoughts, feelings, strategies, and behaviors, which are oriented toward the attainment of goals” (Schunk & Zimmerman, 1998, p. viii). Also referred to as academic self-regulation, SRL has been studied in traditional classrooms as a means of understanding how successful students adapt their thoughts, feelings, and actions to improve learning. In general, investigations have consistently found that students with adaptive self-regulatory beliefs, emotions, and behaviors outperform their less-adaptive counterparts (for a review, see Pintrich, 1999).


Although various conceptualizations of academic self-regulation exist (for a review, see Boekaerts et al., 2000), several scholars have found social cognitive models to be particularly useful in analyzing student success in online contexts (e.g., Artino, 2007b; Hodges, 2005; Militiadou & Savenye, 2003). Social cognitive models of self-regulation distinguish themselves from purely cognitive theories in that they focus on the links between students’ personal perceptions and their use of self-generated learning strategies (Pintrich, 1999; Zimmerman, 2000). Moreover, social cognitive models are concerned with explaining how these personal perceptions and associated behaviors are ultimately influenced by contextual features of the learning environment (Pintrich, 2000; Zimmerman, 2000).


Conceptual Model

The conceptual model proposed here uses social cognitive self-regulation as its theoretical foundation. At the macro level, the model includes four interacting components: (a) contextual features of the online learning environment, (b) personal perceptions, (c) personal behaviors, and (d) various academic outcomes that result, in part, from these personal perceptions and behaviors. The following section provides an in-depth description of the concepts and processes contained within these four interacting components, with special emphasis on contextual features of the environment and personal perceptions. Included in this section is theoretical and empirical evidence for the concepts and relationships described in the model.


Contextual Features of the Online Environment

In the classic model of social cognitive theory, as conceptualized by Bandura (1986), contextual features of the learning environment are considered one of three determinants of human behavior. In particular, human functioning purportedly results from the triadic, dynamic, and reciprocal interaction of personal factors, behaviors, and the environment (Bandura, 1986). That is, personal factors (beliefs, expectations, attitudes, and prior knowledge), the social and physical environment (resources, consequences of actions, other people, and physical settings), and behaviors (individual actions, choices, and verbal statements) interact as determinants of one another.


The model presented here is based on Bandura’s (1986) original model; however, it differs slightly in that personal factors (beliefs and emotions) and academic behaviors (use of learning strategies) are embedded within, and ultimately influenced by, the learning environment. This difference is meant to highlight the importance of contextual features of the learning environment (classroom, online, or otherwise) and their ultimate affect—for better or worse—on the other learner-centered aspects of academic self-regulation (Boekaerts & Cascallar, 2006). Moreover, consistent with social cognitive theories, the model presented here assumes that instructional contexts are perceived and evaluated by students. That is, the same objective environment may be perceived (or appraised) differently by different students. As such, it is the subjective environment that ultimately shapes students’ beliefs, emotions, and academic behaviors (Roeser, Marachi, & Gehlbach, 2002). This is not to say, however, that subjective perceptions of the environment are fixed. Instead, these perceptions, as well as the objective environment itself, can also change as students’ thoughts, feelings, and actions vary over time (Bandura, 1997). Thus, SRL can be thought of as an interactive phenomenon, the product of a dynamic interchange between personal, behavioral, and environmental influences (Pajares, 2002).


The importance of the environment and its influence on personal factors, as proposed in the present model, is in keeping with social cognitive views of self-regulation (Pintrich, 2000; Zimmerman, 2000). For example, in their discussion of an instrument designed to measure various aspects of SRL, Duncan and McKeachie (2005) argued that personal components of self-regulation are not static traits of the learner, but rather that “motivation is dynamic and contextually bound and that learning strategies can be learned and brought under the control of the student” (p. 117). Stated another way, students’ motivations and emotions change from course to course (e.g., depending on their interest in the course, self-efficacy for performing in the course, etc.). Therefore, the extent to which students use adaptive self-regulatory behaviors may vary as well, depending on the nature of the online course and how that course relates to them personally (Boekaerts & Cascallar, 2006).


Examples of how contextual features of the online environment might influence students’ self-regulatory perceptions and behaviors are not difficult to imagine. For instance, a student majoring in education and completing an online course in learning theories might value the course more than another, non-education major. As a result, the education major might also be more apt to utilized adaptive learning strategies, such as elaboration (i.e., actively linking new information to prior knowledge) and metacognition (i.e., planning, goal setting, and comprehension monitoring). Artino (2008d) found just such an effect in his study of 481 service academy undergraduates learning about aviation physiology in an online course. Specifically, students who reported that they were planning to become aviators upon graduation from the academy also reported higher mean scores on measures of task value and metacognition than their non-aviator counterparts. Effect sizes for the differences were moderate (Cohen’s d = 0.60 and 0.56 for task value and metacognition, respectively; Cohen, 1988).


Features unique to the online learning environment may also influence personal perceptions and behaviors. In a descriptive case study of six graduate students in an online technology course, Whipp and Chiarelli (2004) found that various components of the online environment (e.g., instructor support, peer support, and course design) influenced students’ SRL strategy use. For instance, students stated that the constant presence of the teacher and peers in the online discussion forums added incentive for continued participation in the discussions. Additionally, results revealed some variations of traditional help-seeking and peer-assistance behaviors that seemed to result, in part, from the unique affordances of the online context. For example, several students discussed how they regularly used their peers’ online discussion posts to plan and shape their own work (Whipp & Chiarelli, 2004).


Consistent with these empirical findings, the conceptual model presented here highlights contextual features of the online environment and their relationship to important aspects of academic self-regulation. Moreover, the association between the environment and behaviors are thought to be reciprocal. That is, not only does the environment influence students’ behaviors, but students’ behaviors can actually influence aspects of the environment (e.g., students can choose to find a quiet area to study online materials, and they can make use of online tools, such as electronic grade books and communication tools, to assist with self-regulation; Dabbagh & Kitsantas, 2005). In short, the importance of the online instructional context, and its influence on components of self-regulation, is critical to an understanding of how students learn and perform online (Whipp & Chiarelli, 2004).


Personal Perceptions: Motivational Beliefs

Social cognitive theories of self-regulation (Pintrich, 2000; Zimmerman, 2000) stress the importance of students’ motivational beliefs in all aspects of SRL. Knowledge of effective learning strategies is not enough to promote and sustain learning; students must also be motivated to employ those strategies when needed (Pintrich & De Groot, 1990). The model proposed here identifies two specific motivational beliefs that researchers have found to be important in both traditional and online contexts: students’ self-efficacy for learning (Bandura, 1997) and the extent to which students value learning tasks (i.e., their task value beliefs; Eccles & Wigfield, 2002).


Self-efficacy beliefs. Bandura (1986) defined self-efficacy as “people’s judgments of their capabilities to organize and execute courses of action required to attain designated types of performances” (p. 391). In general, highly self-regulated students tend to have greater self-efficacy for learning than those with less-adaptive self-regulatory skills (Schunk, 2005). With this in mind, several investigations have studied self-efficacy and its relations to other important variables in online contexts. Overall, results have revealed that when compared to their counterparts with lower perceived self-efficacy, efficacious students report fewer negative achievement emotions (Artino & Stephens, 2007), greater use of SRL learning strategies (Artino & Stephens, 2006, 2008; Joo, Bong, & Choi, 2000), more satisfaction with their learning experience (Artino, 2007a, 2008b; Lim, 2001), increased likelihood of enrolling in future online courses (i.e., improved continuing motivation; Artino, 2007a; Lim 2001), and superior learning and performance (Joo et al., 2000; Wang & Newlin, 2002). Taken together, these empirical findings support the theoretical links between students’ self-efficacy beliefs and their achievement emotions, SRL behaviors, and academic outcomes (Bandura, 1997; Pekrun, 2006), as suggested in the conceptual model.


Task value beliefs. Eccles and Wigfield (2002) defined task value as the extent to which students find a task interesting, important, and/or useful. Like perceived self-efficacy, task value beliefs are hypothesized to positively impact students’ learning and performance. Moreover, Schunk (2005) concluded that “students with greater personal interest in a topic and those who view the activity as important or useful are more likely to use adaptive self-regulatory strategies” (p. 87). Over the past decade, a few researchers have used task value as a predictor of adaptive outcomes in online settings. In fact, findings from several studies have shown task value to be negatively related to students’ negative achievement emotions (Artino & Stephens, 2007), and positively related to their use of cognitive and metacognitive learning strategies (Artino & Stephens, 2006, 2008), overall satisfaction (Artino, 2008b; Miltiadou & Savenye, 2003), and continuing motivation (Artino, 2007a). Overall, these findings support the theoretical relations between students’ task value beliefs and their achievement emotions, self-regulatory behaviors, and academic outcomes (Eccles & Wigfield, 2002), as presented in the conceptual model.


It is worth noting that the links between students’ motivational beliefs (i.e., self-efficacy and task value), achievement emotions, and academic behaviors and outcomes are thought to be reciprocal (Bandura, 1997; Pekrun, 2006; Zimmerman, 2000). For example, several studies (Artino & Stephens, 2006, 2008; Joo et al., 2000) have found students’ self-efficacy to be related to adaptive academic behaviors, such as students’ use of cognitive and metacognitive strategies during online learning. In turn, by using adaptive learning strategies that result in “deeper and more elaborated processing of the information” (Schunk, Pintrich, & Meece, 2008, p. 226), students are likely to experience greater academic success in the form of improved learning and better grades. These behaviors and the positive outcomes that result then feed back into the system, conveying to students that they are “capable of learning and performing well, which enhances their self-efficacy for further learning (Schunk et al., 2008, p. 127).


Personal Perceptions: Achievement Emotions

In recent years, motivation researchers (e.g., Linnenbrink & Pintrich, 2004; Pekrun, Goetz, Titz, & Perry, 2002) have acknowledged the importance of discrete academic emotions and their influence on cognitive engagement and learning. For instance, Pekrun (2006) has developed a control-value theory of achievement emotions that outlines hypothesized linkages between students’ motivational beliefs, their emotions, and, ultimately, their learning and performance. According to Pekrun’s theory, positive achievement emotions, such as enjoyment and hope, and negative emotions, such as boredom and frustration, are determined, in part, by students’ motivational beliefs, also known as their cognitive appraisals. Furthermore, the effects of emotions on learning and performance are thought to be mediated, in part, by several cognitive and motivational mechanisms, such as students’ use of learning strategies, effort allocation, and persistence (Pekrun et al., 2002).


Among the many categories of motivational beliefs that are thought to be important antecedents of achievement emotions, Pekrun (2006) has suggested that two appraisals are critical in achievement contexts: the perceived controllability of achievement activities, as indicated by competence perceptions (e.g., self-efficacy), and the subjective value of those activities (e.g., task value). Moreover, the relationship between motivational beliefs and emotions are thought to be bidirectional. That is, “control and value appraisals are posited to be antecedents of emotions, but emotions can reciprocally affect these appraisals” (Pekrun, 2006, p. 327). For example, not only does self-efficacy for learning impact achievement emotions, but negative feelings, such as test anxiety, can also influence future self-efficacy beliefs. In fact, according to Bandura (1997), information conveyed by emotions is cognitively assessed by an individual and can positively or negatively influence self-efficacy beliefs, depending on the level of arousal and the person’s cognitive appraisal.


Using control-value theory as a framework, a small number of studies with university students in traditional classrooms have found that achievement emotions were related, as predicted, to students’ use of learning strategies and various measures of academic performance (Pekrun et al., 2002). For instance, negative achievement emotions (e.g., boredom and anger) were negatively related to motivational variables (e.g., interest and effort) and measures of learning strategies use (e.g., elaboration and metacognition); whereas positive emotions (e.g., enjoyment, hope, and pride) were positively related to these same outcomes.


Findings in online settings, although limited, are similar to the results described above. For example, in a study of two samples of service academy undergraduates (total N = 783), Artino (2008a) found that online learners’ emotions were related to several adaptive outcomes. In particular, findings from several multiple regressions revealed that students’ boredom and frustration were statistically significant predictors of metacognition, with boredom emerging as a negative predictor and frustration unexpectedly emerging as a positive predictor. On the other hand, enjoyment emerged as a positive predictor of both elaboration and metacognition.


Although inconsistent with the empirical work of Pekrun (e.g., Pekrun et al., 2002), the finding that frustration was positively related to metacognition corroborates the theoretical suggestion that certain negative emotions “may well facilitate the use of specific kinds of learning strategies, even if such effects do not appear in more consistent ways when self-report measures of learning strategies are used” (Pekrun et al., 2002, p. 99). If nothing else, this novel finding is further evidence of the multifaceted, dynamic interplay between cognition, affect, and behavior, as described by Linnenbrink and Pintrich (2004). Nonetheless, the results reported by Artino (2008a) generally support the tenets of control-value theory (Pekrun, 2006), indicating that students’ achievement emotions are related, in important ways, to their use of SRL strategies and their overall online success.



The purpose of this article was to present a conceptual model of social cognitive self-regulation as it relates to students learning in online contexts. To achieve this objective, the article included a narrative description of academic self-regulation, as well as theoretical and empirical support for the concepts and relationships described in the model.


Altogether, results from studies of online learners largely support the assumptions of social cognitive theory and findings from research with traditional, classroom students. In particular, it seems that adaptive self-regulatory beliefs, emotions, and behaviors are important, if not essential, for effective learning and performance in online settings (Artino, 2007b; Hodges, 2005; Miltiadou & Savenye, 2003).


Using the conceptual model presented in this article, online teachers are encouraged to consider and explicitly address their students’ academic motivation, achievement emotions, and self-regulation as they strive to provide effective and engaging online instruction. For detailed accounts of specific instructional techniques intended to support motivation, emotion, and self-regulation in both traditional and online classrooms, interested readers are directed to consult Ley and Young (2001) and Artino (2008c). 


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