self-regulated learning 1 running head: literature review ... · 2/1/2011 · self-regulated...

Self-Regulated Learning 1

Running head: LITERATURE REVIEW OF SELF-REGULATED LEARNING

Literature Review of Self-Regulated Learning & Learner Readiness

in Online Learning Environments

Buncha Samruayruen

University of North Texas

December 2010


Literature Review of Self-Regulated Learning & Learner Readiness

in Online Learning Environments

Nowadays, Internet-based learning or online education is finding increased use wildly,

and may prove effective in facilitating advanced study coursework for both urban learners and

rural area students. For instance, online learning can provide effective strategies for offering

courses and field experiences in special education teacher preparation programs (Collins,

Schuster, Ludlow, & Duff, 2002). Organizations apply online learning to employ a Web-based

training in their training employees program (Gravill & Compeau, 2008). Majority of

universities in the US are adding asynchronous Web-based instruction to their undergraduate

degree programs (Bell & Akroyd, 2006; Chen, 2002; Lynch & Dembo, 2004; Miller & Lu,

2003). The use of Internet-based technology is embedded in most learning activities today.

According to the US Department of Education as of the 2006/2007 academic year, 97% of all

public two year degree granting institutions and 88% of public four year degree granting

institutions offered college level distance classes (National Center for Educational Statistics, U.S.

Department of Education, 2002). In a related finding Picciano and Seaman (2007) estimated that

in the next few academic years, approximately one million K-12 students took an online class

(Picciano & Seaman, 2007). Thus, it is important for students to learn new skills as technology

changes or is introduced in their learning environments (Perry, Phillips, & Hutchinson, 2006).

Learners are increasingly expected to assess and manage their own learning needs. Wageman

(2001) stated that self-management is a disciplinary skill that offers benefits, and learners have to

learn this particular skill. Also, Vonderwell and Savery (2004) suggested that students, especially

in online learning environments, need to be prepared for changing demands related to online


situations with respect to technology, learning management, pedagogical practice and social

roles.

Online learning environments are platforms where educational courses are delivered

through the Internet, or using Web-based instructional systems either in real-time

(synchronously) or asynchronously. Reid (2005) stated that a web-based instructional system or

online learning is easy and inexpensive compared to traditional learning methods. Conformed to

Moore and Kearsley (2005), web-based instructional can make extensive use of network

technologies to incorporate a variety of organizational, administrative, instructional, and

technological components, in offering flexibility concerning the new methodology of learning.

Online learning is self-managed when an instructor provides the software programs and

resources to transfer new skills while the learner controls the process to achieve their own

objective to acquire those new skills (Gravill & Copeau, 2008). Therefore, the process of online

learning is going to be implemented by the learner, and the learner will become an active

controller instead of being a passive learner, which has been the norm in the past. Online learners

need to understand the dynamics in an online setting (Voderwell & Savery, 2004). Learners need

to know how online learning works, interactions, relations, perceptions, and role of learners. But

are they ready for online learning environments?

Learner readiness influences most institutions in terms of their curricular development

and pedagogies to entire academic divisions dedicated to Web-specific delivery (Blankenship &

Atkinson, 2010). According to Hung and others (2010), online learner readiness involved in five

dimensions; self-directed, motivation, self-efficacy in computer/Internet, self-control, and online

communication self-efficacy (Hung, Chou, Chen, and Own, 2010). In particular, learners have

realize their responsibility for guiding and directing their own learning for time management, for


keeping up with the class, for completing the work on time, and for being active contributors to

instruction. Most of those activities are the important part of self-regulated learning, which

become the answer for preparing novice online students to be successful learners in online

learning environments. Self-regulated learning becomes a central topic in facilitating learning in

online learning environments. Self-regulated learning strategies have been identified (Boekaerts

& Corno, 2005; Dweck, 2002; Perry et al., 2006). Self-regulated learning is a learning behavior

that is guided by 1) metacognition or thinking about one's thinking include planning, monitoring,

and regulating activities; 2) strategic action such as organizing, time management, and evaluating

personal progress against a standard; and 3) motivation to learn such as self-believe, goal setting,

and task value (Boekaerts & Corno, 2005). Learners choose the best approach to learn the

material and gain the skills they need, and then become their habits. These processes are called

self-regulated learning strategies (Boekaerts & Corno, 2005; Dweck, 2002; Perry et al., 2006).

To manage these self-regulated learning experiences effectively, individuals have to make self-

directed choices of the actions they will undertake or the strategies they will invoke to meet their

goals. Then, its will become learning habits when learners often use them as their learning

strategies. Self-regulated learning strategies have the potential of becoming study skills and

habits through repetitive use and behaviors. Individuals who are self-regulated learners believe

that opportunities to take on challenging tasks, practice their learning, develop a deep

understanding of subject matter, and exert effort will give rise to academic success (Perry et al.,

2006). Given the background information toward self-regulated learning strategies in online

learning environments, teachers could adapt self-regulation strategies to match their teaching

styles; instructors might apply these strategies to develop their course activities effectively;

researchers will use this review to be part of their secondary data and indicate them to find the


issues that need to be researched; and finally learners could apply the findings from research into

their own learning strategies in order to improve their learning skills and the effectiveness of

their online learning environments.

The purpose of this review is to provide educational researchers, instructors,

practitioners, and online learners with an understanding of extant research and theories on

academic self-regulated learning and its influence on learner success in online education. This

review will address the applicability of employing a theoretical framework of self-regulation for

understanding learner success in an online learning environment, including gaps in the literature

and suggestions for future inquiry.

What is Self-Regulated Learning (SRL), and how is it useful?

The term self-regulated learning became popular in the 1980’s because it emphasized the

emerging autonomy and responsibility of students to take charge of their own learning (Bandura,

1986). In Pintrich’s study, 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-regulation

occurs when individual uses personal (self) processes to strategically motivate, monitor, and

control his or her behavior and the environment. Furthermore, even if an individual is already

exhibiting self-regulation, these processes can be enhanced to better support learning,

motivation, and performance (Pintrich, 2000). Zimmerman and Pons (1988) defined SRL as

actions directed at acquiring information or skill that involves agency, purpose, goals, and

instrumentality self-perceptions by a learner. They also pointed out that SRL seeks to explain

student differences in motivation and achievement based on a common set of processes. In


particular, self-regulated learners are cognizant of their academic strengths and weaknesses, and

they have a repertoire of strategies they appropriately apply to tackle the challenges of academic

tasks. These learners hold incremental beliefs about intelligence and attribute their successes or

failures to factors, such as effort expended on a task, and effective use of particular SRL

strategies within their control (Artino, 2007; Dweck, 2002; Zimmerman, 1989).

According to Barry Zimmerman (2002), self-regulated learning involves the regulation of

three general aspects of academic learning. First, self-regulation of cognition involves the control

of various cognitive strategies for learning, such as the use of deep processing strategies;

planning, monitoring, and regulating, which refer to awareness, knowledge, and control of

cognition that result in better learning and performance. Second, self-regulation of behavior

involves the active control of the various resources students have available to them, such as their

time, their study environment, the place in which they study, and their use of others help seeking

such as peers and teachers to help them learn effectively. Third and finally, self-regulation of

motivation and affect involves controlling and changing motivational beliefs such as self-

efficacy, task value, control beliefs, and goal orientation, so that students can adapt to the

demands of a course. In addition, students can learn how to control their emotions and affect of

anxiety in the ways that improve their learning. Many researchers have agreed with the

importance of self-regulated learning for students at all academic levels, and self-regulation can

be taught, learned and controlled (Pintrich & Garcia, 1991; Pintrich, Smith, Garcia, &

McKeachie, 1993). In fact, Zimmerman (1989, 1990), an expert in this area, has found evidence

of many different types of self-regulation that are explained in the following section. In

Zimmerman's studies, successful students reported that the use of self-regulated learning

strategies accounted for most of their success in school.


In short, SRL is regarded as a valuable term because it emphasizes how the “Self” was

the agent in establishing learning goals and tactics and how each individual’s perceptions of the

self and task influenced the quality of learning that ensued. Thus, Self-regulated learners are

generally characterized as active participants who efficiently control their own learning

experiences in many different ways, including establishing a productive work environment and

using resources effectively; organizing and rehearsing information to be learned; maintaining

positive emotions during academic tasks; and holding positive motivational beliefs about their

capabilities, the value of learning, and the factors that influence learning (Schunk & Zimmerman,

1998).

How does SRL involve teachers and learners?

What is important for teachers and learners is that self-regulated learning (SRL) can help

describe the ways that teachers and learners approach problems, apply strategies, monitor their

performance, and interpret the outcomes of their efforts. From this perspective, three

characteristics of SRL can be identified; (a) awareness of thinking, (b) use of strategies, and (c)

sustained motivation (Paris & Winograd, 1990).

Awareness of thinking: Part of becoming self-regulated involves awareness of effective

thinking and analyses of one’s own thinking habits. This is known as metacognition, or thinking

about thinking, that Brown & Campione (1990) and Flavell (1978) first described. They showed

that children from 5-16 years of age become increasingly aware of their own personal knowledge

states, the characteristics of tasks that influence learning, and their own strategies for monitoring

learning. Paris and Winograd (1990) summarized these aspects of metacognition as children’s

developing competencies for self-appraisal and self-management and discussed how these

aspects of knowledge can help direct students’ efforts as they learn. They tried to emphasize that


the educational goal was not simply to make children think about their own thinking but, instead,

to use metacognitive knowledge to guide the plans they make, the strategies they select, and the

interpretations of their performance so that awareness leads to effective problem-solving. This

approach is consistent with Bandura (1986) who emphasized that self-regulation involves three

interrelated processes; self-observation, self-evaluation, and self-reaction. Understanding these

processes and using them deliberately is the metacognitive part of SRL.

Use of strategies: A second part of SRL involves a person’s growing repertoire of

strategies; for learning, studying, controlling emotions, pursuing goals, and so forth. However,

this review wants to emphasize that the concern is with “being strategic” rather than “having” a

strategy. It is one thing to know what a strategy is and quite a different thing to be inclined to

use, to modify it as task conditions change, and to be able to discuss it and teach it. According to

Paris and Winograd (1990), there are three important aspects of strategies, often referred to as

declarative strategy (what the strategy is), procedural strategy (how the strategy operates), and

conditional strategy (when and why a strategy should be applied). Knowing these characteristics

of strategies can help students to discriminate productive from counterproductive tactics and then

to apply appropriate strategies. When students are strategic, they consider options before

choosing tactics to solve problems and then they invest effort in using the strategy. These choices

embody SRL because they are the result of cognitive analyses of alternative routes to problem-

solving.

Sustained motivation: The third aspect of SRL is motivation because learning requires

effort and choices. SRL involves motivational decisions about the goal of an activity, the

perceived difficulty and value of the task, the self-perceptions of the learner’s ability to

accomplish the task, and the potential benefit of success or liability of failure. Awareness and


reflection can lead to a variety of actions depending on the motivation of the person. Zimmerman

(2002) has characterized SRL as a positive set of attitudes, strategies, and motivations for

enhancing thoughtful engagement with tasks. In class, teachers need to understand students’

motivation in order to understand how they learn, what tasks they choose, and why they may

display persistence and effort or, conversely, avoidance and apathy. Because teachers need to be

diagnostic about their students’ learning styles and orientations, it is helpful to analyze students’

matacognitive, use of strategies, and their motivations.

How do SRL strategies support learner readiness?

Research found that self-regulation is an important aspect of learning and achievement in

academic contexts (Puzziferro, 2008; Whipp & Chiarelli, 2004). Vonderwell and Savery (2004)

stated that successful online learners who are learner readiness need to be self-regulated or in the

process of learning how to become self-regulated learners. Self-regulated learners know how to

learn, how they learn, how to reflect on their learning, how to initiate learning and how to use

time management skills efficiently. Students who are self-regulating are much more likely to be

successful in school, to learn more, and to achieve at higher levels. Self-regulated learning will

result in student achievement and scores as presented in many standardized tests (Puzziferro,

2008). Although many studies have been written about self-regulated learning (SRL) in

traditional classroom, there have been few that researched how to improve self-regulated

learning skills in online environments (Terry & Doolittle, 2006). There are, however, studies

emerging that begin to examine the impact of SRL in distance and distributed learning

environments; specifically, whether SRL strategies should be implemented in a similar fashion to

those that are implemented within traditional classroom environments, and whether there is a


need to develop and recommend additional SRL strategies (Whipp & Chiarelli, 2004; Kitsantas

& Dabbagh, 2004). Those studies begin to provide general evidence SRL can be facilitated in

online learning environments. They also begin to provide guidance on general web-based

pedagogical tools that can facilitate such learning outcomes. Whipp & Chiarelli (2004) describe

findings from case study research that investigated the general question of how SRL strategies

could be translated to online environments and attempted to identify whether SRL strategies

recommended for traditional classroom instruction could be applied to online learning

environments or if different strategies were needed. They concluded that some traditional SRL

strategies were directly applicable to the online learning environments. Specifically, students

cited the need for careful time management, utilizing traditional methods such as calendars and

goal setting.

Much of the research on self-regulation in online education assumed that effective self-

regulation depends on students’ confidence in their ability to attain designated types of

performances (Bandura, 1986; Zimmerman, 2002). According to Schunk and Zimmerman

(1998), “self-regulated learners are more self-efficacious for learning than are students with

poorer self-regulatory skills; the former believe that they can use their self-regulatory skills to

help them learn” (p. 87). As such, researchers interested in using a social cognitive view of self-

regulation to understand student performance in online settings have studied, more than any

other construct, self-efficacy and its relations to other variables. Overall, results have revealed

that when compared to their counterparts with lower perceived self-efficacy, efficacious students

report more use of learning strategies (Artino & Stephens, 2006; Joo, Bong, & Choi, 2000; Bell

& Akroyd, 2006); greater satisfaction with their learning experience (Artino, 2007; Lim, 2001);

increased likelihood of enrolling in future online courses (Artino, 2007; Lim 2001); and superior


academic performance (Bell & Akroyd, 2006; Hsu, 1997; Joo et al., 2000; Lee, 2002; Lynch &

Dembo, 2004; Wang & Newlin, 2002). For example, in one of the more comprehensive studies

of self-efficacy and its relationship to academic performance, Bell and Akroyd (2006) surveyed

201 undergraduates enrolled in a variety of asynchronous online courses and found that students’

self-efficacy for learning and performance was among the three most powerful predictors of final

course grade (β = 1.65, p < .001). Moreover, the researchers found that an interaction term

(college grade point average [GPA] X self-efficacy) was also a significant individual predictor of

final course grade (β = -2.35, p < .001), indicating that self-efficacy beliefs had a greater effect

on course grade for students with lower GPAs (Bell & Akroyd, 2006).

Although investigators have given some attention to the relationships between several

motivational components of self-regulation and various academic outcomes (e.g., satisfaction,

academic performance, and choice behaviors), very little research has been conducted on how

these motivational components relate to students’ academic behaviors, such as their use of

cognitive and metacognitive learning strategies. Two exceptions are the studies conducted by

Artino and Stephens (2006) and Joo et al. (2000). For example, using path analytic techniques,

Joo et al. (2000) found that academic self-efficacy and self-efficacy for SRL both significantly

and positively predicted students’ self-reported use of cognitive and metacognitive learning

strategies. However, contrary to expectations, neither cognitive nor metacognitive strategy use

was related significantly to performance outcomes. Thus, the researchers failed to confirm their

hypothesis that learning strategy use mediates the relationship between self-efficacy and student

performance. Based on these results, the authors questioned the usefulness of self-reports of

strategy use. Furthermore, they recommended that future studies employ more direct, behavioral

indicators of learning strategy use to help clarify how students’ motivational characteristics relate


to their capacity to apply learning strategies in online environments.

From the research findings, correlational studies indicating that students’ motivational

beliefs about a learning task are related to positive academic outcomes. The existing research in

this area, however, suffers from several limitations. For instance, results are strictly correlational

in nature; therefore, one cannot infer causality from the observed relationships. Although,

overall, the results suggest moderate to strong relations between motivational components and

adaptive outcomes, the direction of influence between the variables is sometimes ambiguous. For

example, although many of the study designs imply that academic performance results from

students’ motivational beliefs, these causal relations could be reversed. Hence, additional

research is needed before the exact direction of operation of these social cognitive components

can be fully understood.

What did learners do in applying SRL to their learning strategy?

Children become students when they move into formal schooling but throughout their

careers in education, they gain other identities. Those identities are sometimes evident in labels

and sometimes they are more covert, evident only by participation in prescribed activities of the

group whether that is consistent with teachers’ educational goals or not. This means that learners

(students) use SRL for different ends, depending on their identities. If they believe that getting

good grades is inappropriate for their group, they may avoid effective SRL techniques such as

doing homework with plan fully. If their identity is consistent with a college-bound or

intellectually curious person, then they may engage in positive aspects of SRL appropriately

(Lave & Wegner, 1994).

In online environments, some researchers have examined how student differences and

how characteristics of the online environment interact with each other to influence learning. In


many ways, those investigations reflect the Aptitude-Treatment Interaction (ATI) studies, which

have been conducted by Cronbach and Snow since 1977, and were designed to determine which

instructional strategies are more or less effective for particular individuals with specific abilities

(Cronbach & Snow, 1977). As a theoretical framework, ATI posits that optimal learning results

when the instruction is closely matched to the aptitudes of the learner. Using an ATI framework,

Eom and Reiser (2000) examined the effects of SRL strategies use on achievement and

motivation in 37 middle school students taking a computer-based course. Essentially, the authors

were trying to determine how varying the amount of learner control within the computer-based

course might effect the achievement and motivation of students who rated themselves as either

high or low in SRL skills. Using a self-report instrument, students were classified as being either

high or low self-regulated learners and then were randomly assigned to either a learner-

controlled or program-controlled version of a computer-based course. Results revealed that,

regardless of how students rated their SRL skills, “learners in the program-controlled condition

scored significantly higher on a posttest than did learners in the learner-controlled condition”

(Eom & Reiser, 2000, p. 247). Additionally, the researchers found that poorer performance in the

learner-controlled condition was particularly evident in the students who rated themselves as low

self-regulated learners. In fact, students who rated themselves as low in SRL skills scored higher

on the posttest (approximately 76.4% higher) when taking the program-controlled condition as

compared to the learner-controlled condition. The trend supported the researchers’ hypothesis

that students with low SRL skills are not as able to learn from computer-based courses that

provide high quantities of learner control as students with high self-regulating skills.

Another ATI-type investigation, McManus (2000) attempted to determine what

combinations of online course non-linearity and the use of advance organizers would work best


for 119 undergraduates reporting different levels of self-regulation. Students’ declarative

knowledge was measured by a 12-item, multiple-choice test, and their procedural knowledge was

measured by a 20-item, performance assessment. Although the researcher found no significant

main effects or interactions, results revealed a near significant interaction between non-linearity

and self-regulation (p = .054). According to McManus (2000), “these results suggest that highly

self-regulating learners learn poorly in mostly linear Web-based hypermedia learning

environments, where they have very few choices, while medium self-regulating learners learn

poorly in highly non-linear environments where they are given too many choices” (p. 219).

Despite the non-significance of this interaction, the results are promising in that they suggest the

ATI framework may be a useful approach that allows researchers to study how individual learner

differences and features of the online environment interact with each other to influence learning

and performance.

However, both studies of Eom and Reiser (2000) and McManus (2000) reviewed here

attempted to study online instruction by utilizing instruments, ATI, developed for traditional

classrooms since 1977. Although some measurement instruments may work equally well in

classroom and online environments, considering the differences between the two learning

environments, the instrument that works well in the classroom may not be valid in online or

computer-based learning situations.

How can instructors apply SRL to online learning environments?

Experts in SRL believe that online learning environments require the learner to assume

greater responsibility for the learning process (Artino, 2007; Dabbagh & Kitsantas, 2005; Schunk

& Zimmerman, 1998). Furthermore, many of these same experts argue that self-regulatory skills

are essential for success in these highly autonomous learning situations and that the development


of these skills can be supported by Web-based pedagogical tools (WBPT) (Azevedo, Cromley, &

Seibert, 2004; Dabbagh & Kitsantas, 2005; Kramarski & Gutman, 2006). Accordingly, several

researchers have attempted to determine the characteristics of effective WBPT, as well as the

extent to which various self-regulatory skills might be supported or enhanced by these tools. For

example, Kramarski and Gutman (2006) randomly assigned 65 ninth graders to one of two

online learning environments designed to teach mathematics: one with self-regulatory support

(SRS) in the form of metacognitive questioning and the other without explicit support for self-

regulation. Results showed that when pre- and post-test scores were compared, students in the

SRS group significantly outperformed their counterparts in the non-supported group on all

outcome measures, including performance on mathematical explanations, procedural and transfer

tasks, and use of SRL strategies. In terms of effect sizes, post-test improvements in the SRS

group were moderate on SRL strategies use (d = .45) to large on mathematical explanations (d =

2.24).

What does this mean for instructors? Often teachers are unprepared to work with students

who have backgrounds substantially different from their own. They need to consider how

students’ differences influence the likelihood that they will be responsive to teaching about SRL.

For example, instructors who are sensitive to multicultural values and non-academically oriented

families may understand why some students actively avoid deep engagement in virtual classroom

discussion while others embrace it. These experiences will help novice instructors understand

how their peers as well as their students might resist new learning strategies and motivational

appeals but might work diligently for other types of SRL that are consistent with their

characteristics, groups, and aspirations.

Other results from many research have been promising and suggest the following


practical and theoretical implications for instructors to apply using in online learning

environments: (1) Web-Based Pedagogical Tools (WBPT) can be an effective way to support

and/or enhance students’ self-regulatory skills (Azevedo et al., 2004; Dabbagh & Kitsantas,

2005; Kramarski & Gutman, 2006; Niemi, Nevgi, & Virtanen, 2003); (2) adaptive scaffolding

appears to be more effective in supporting students self-regulatory processes and academic

performance than fixed or no scaffolding (Azevedo et al., 2004); (3) different types of WBPTs

support different self-regulatory processes (Dabbagh & Kitsantas, 2005; Kramarski & Gutman,

2006); and (4) WBPTs may be more effective for novice learners with under-developed self-

regulatory skills than for veteran learners with more advanced SRL skills (Niemi et al., 2003). In

terms of research quality, these self-regulatory scaffolding studies tended to use superior

research methods when compared to the empirical work on self-regulation in online education.

For example, in the majority of WBPT studies, researchers randomly assigned participants to

treatment and control/comparison groups, thereby enhancing the internal validity of their

experiments and improving their ability to establish causal relationships (Shadish, Cook, &

Campbell, 2002). Additionally, all of the investigators used multiple outcome measures and

employed both qualitative and quantitative methods to analyze their data. Taken together, these

studies have taken a positive step toward improving the methodological quality of research in

online distance education (Abrami & Bernard, 2006; Bernard et al., 2004).

Conclusions

Obviously, online learning environments afford greater opportunities for

individualization and flexibility, thereby creating and increased demand for SRL. The variety of

tools now contained within most Learning Management Systems (LMS) provides instructors and

learners in online environments with a wide array of options for facilitating SRL skills in order to


develop and improve an online learner readiness. From group posting areas to collaborative tools

and beyond, learners can be engaged in rich instructional experiences in online learning

environments that provide them with the scaffolds and experiences needed to their learning

strategies and become self-regulated learners. Also, some research in online learning

environments seems to indicate that providing students with self-regulatory scaffolding can be

effective instructional method. Additionally, It appears that highly self-regulated learners may

have more success in learner-controlled their personal thinking, behaviors, and environments

than their peers with poorer self-regulatory skills.

Instructors who create and facilitate online need to be aware that traditional courses do

not necessarily prepare students for the level of interdependence, but an online course always

requires the independent learning. He or she should provide an active learning environment in

which learners take ownership for their learning. Preparing learner readiness is an important

factor in helping learners achieve their goals, increase skill development, and produce higher

learning satisfaction.

Learners need to learn to become active learners and seek active learning strategies in

their learning. Learner autonomy as well as collaborative strategies needs to be negotiated for the

effectiveness of learning. Recognition of online learning tools’ capabilities and limitations, self-

efficacy, and effective communication can help to develop an active learning and learning

readiness in online learning environments.

Several types of measure tools for self-regulation and motivation have been used in both

traditional and online environments. Most of them that used for measure SRL in online

environments, however, have been adapted from the measure tools of traditional classrooms.

Hence, future research should continue to create and invent an appropriate tool for measuring the


level of SRL of learners in online environments. Despite the limitations of research

methodologies, most of the studies reviewed here seem to support the linkages between students’

self-regulatory skills about metacognition, strategies used, and motivation. More well-designed

research is needed on self-regulated learning and its influence on student success in online

learning environments.


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