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Learner Self-Regulation inDistance Education: A Cross-Cultural StudyAisha S. Al-Harthi aa Sultan Qaboos University , OmanPublished online: 25 Aug 2010.

To cite this article: Aisha S. Al-Harthi (2010) Learner Self-Regulation in DistanceEducation: A Cross-Cultural Study, American Journal of Distance Education, 24:3,135-150, DOI: 10.1080/08923647.2010.498232

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The Amer. Jrnl. of Distance Education, 24:135–150, 2010Copyright © Taylor & Francis Group, LLCISSN 0892-3647 print / 1538-9286 onlineDOI: 10.1080/08923647.2010.498232

Learner Self-Regulation in Distance Education:A Cross-Cultural Study

Aisha S. Al-HarthiSultan Qaboos University, Oman

Abstract: This study investigated cultural variations between two samples of Araband American distance learners (N = 190). The overarching purpose was to chart theunderlying relationships between learner self-regulation and cultural orientation withindistance education environments using structural equation modeling. The study foundsignificant differences between Arab and American students on all but one variable. Inthe best-fitting model, only future orientation, a cultural variable, explained variancesin learner self-regulation.

Cross-cultural variations are superficially addressed in the distance educationliterature, and the majority of the available literature lacks empirical research.With the expansion of distance education through information technology, thebody of learners is becoming more diverse and multiple cultural contexts areinvolved yet not represented or even fully understood.

At first glance, the process of learning at a distance appears to be simi-lar for many learners around the world. It is minimally viewed as a function ofthe distance education system, which stresses self-directed learning and learnerautonomy by moving the bulk of the responsibility for learning to the learner.However, not all learners are able, willing, or prepared to handle this burden(Moore and Kearsley 2005), which results in dropping out of the system orsilently struggling to regulate one’s learning process. In fact, Young (1996) pro-vides evidence that learners with low self-regulation or self-direction performpoorly when given control over their learning in relation to choice, sequence,and pace of learning events (structural component of transactional distance),whereas their counterparts with high levels of self-direction or self-regulationperform equally well regardless of the type of control given. This may be moreso when cultural views of learning are in direct conflict with the philosophical

Correspondence should be sent to Aisha S. Al-Harthi, P. O. Box 138, PC 123, Al-Khoudh, Oman. E-mail: alharthi22@yahoo.com

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assumptions made by instructors and instructional designers regarding learnerautonomy and personal control of learning.

Learning at a distance cannot be entirely autonomous (Candy 1991). Whatstudents will learn is largely predetermined by social agents represented in dis-tance teaching institutions. Even in their absence, teachers play the role ofsocial agents by organizing and designing student learning and consequentlyaffecting student cognitive processing. Self-learners only appear to be alonewhen in fact their “thinking is determined by many diverse social inputs andwith additional socially mediated help not far away if it is needed” (Pressley1995, 211).

The learning process cannot be conceptualized without the socioculturalcontext. Bandura (2001) believes that individuals are producers as well asproducts of the social system. Their “internal mechanisms are orchestrated byenvironmental events” (4) and organized through their active efforts to coordi-nate their behaviors with the dominant cultural systems of practice (Kitayama2002). To understand what first seems to be an autonomous process of learning,we need to frame research questions within a cultural context. Pointing to thecomplexity of distance education in its various phases of formation, adaptation,and application, Saba (2003) further expands this argument to include global,social, economic, and technological factors. He suggests these factors shouldbe viewed as nested hierarchical subsystems that are affecting and affected byeach other. Therefore, the whole distance education system is dynamic not onlybecause of the changing factors and their relationship but also because of theinternal feedback loop based on the changing nature of transactional distance.

SIGNIFICANCE OF THE STUDY

Generally, studying self-regulation serves a twin goal of providing both theo-retical understanding of learning and practical information for designing bettereducational environments to support learner self-regulation (Pintrich 2004;Vohs and Baumeister 2004). Little evidence exists in explaining how distancelearners regulate their learning at a distance. It is expected that distance learn-ers would have higher self-regulation compared with regular students as aresult of the absence of the instructor and the increased responsibility on thelearner. According to M. C. Zimmerman (2002), learner self-regulation is thevariable that best explains both student attrition and persistence in Web-basedcourses. Previous research shows that self-regulation is also a valid predictor ofacademic achievement (B. J. Zimmerman and Bandura 1994). Thus, studyingself-regulation should be of particular interest to distance educators; however,only a few guidelines exist on how to incorporate self-regulation processes inWeb-based learning environments (Dabbagh and Kitsantas 2004).

In addition, within the global market of distance education, understandingcultural expectations provides a competitive edge for distance education

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providers (Bentley, Tinney, and Chia 2005). Based on previous research(Bagozzi, Verbeke, and Gavino 2003), this study hypothesizes that the effectof self-regulation on performance may be mediated through culture. Learnersuse standards and reference points from culturally based notions of what isconsidered an acceptable behavior in their societies (Jackson, MacKenzie, andHobfoll 2005). These result in differences in the way they behave in achieve-ment settings. For example, in a comparative study between Singaporean andIsraeli students on the effect of self-regulation in choosing task difficulty,Singaporeans behaved in a self-handicapping manner trying to be cautious andavoid risky tasks by choosing easier tasks with lower points (Kurman 2001).Research studies conducted on the Chinese culture revealed a number of cul-tural constructs and variables that are specific to this culture (Chan 2002; Smithand Smith 1999; Tu 2001). For example, renqing (social interaction), face, andharmony are three Eastern constructs found to be significantly related to tutors’teaching effectiveness at Hong Kong Open University (Chan 2002). Still, moreempirical research is needed to investigate individual and cultural factors indistance education (Lim 2001).

METHOD

Study Population and Sample

The target population for this study was distance learners at The Arab OpenUniversity and World Campus of The Pennsylvania State University. The ArabOpen University (AOU) was formally launched in June of 2001 in its head-quarters office at the State of Kuwait. Later, six other branches were added inJordan, Lebanon, Saudi Arabia, Bahrain, Egypt, and Oman (The Arab OpenUniversity 2006). World Campus was established in 1998, reflecting the longdistance education history at The Pennsylvania State University, United Statesof America. World Campus offers a wide range of graduate and undergrad-uate degrees in addition to numerous certificates. It has students from morethan forty countries and all seven continents (Penn State World Campus 2005).In this study, only students from Kuwait and Bahrain campuses were selectedfrom the AOU.

After deleting cases with missing data, ninety-five cases were used fromeach university (N = 190). Ninety-five cases constitute a “small” sample forstructural equation modeling (Kline 2005), which is considered one of the lim-itations of this study. To assure sample equivalence, similar programs weretargeted at the two universities. In the American sample, thirty-nine (41.1%)were males and fifty-six (58.9%) were females. In the Arab sample, forty-nine(51.6%) were males and forty-six (48.4%) were females. Whereas more thanhalf of the American sample were graduate students (53.7%), only 6% of theArab sample were graduate students. In the Arab sample, most respondents

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were in studies leading to a bachelor’s degree in information technology andcomputing (43.1%) and a bachelor’s degree in business studies (40.0%). Inthe American sample, more than half of the sample (53.7%) were graduatestudents in business and education. The remaining came from Bachelor ofArts in Letters, Arts, and Sciences (22.1%); Associate in Science in BusinessAdministration (16.8%); Associate in Information Sciences and Technology(7.4%); and Bachelor of Science in Nursing (RN to B.S.) (1.1%). Therefore,the two samples were not fully comparable in academic specialization andeducational levels, which is another sample limitation.

Study Instruments

One of the difficulties this study faced was the lack of sound measures for cul-tural variations, especially when measured at the individual level. Eventuallyonly two cultural variables were measured: time orientation (future time ori-entation), and relational orientation (group interdependence). The first wasmeasured through Zimbardo’s future-orientation scale (Zimbardo and Boyd1999), and the second was measured through Singelis’s (1994) Self-ConstrualScale.

Learner self-regulation was measured through six variables represent-ing metacognitive and motivational aspects of self-regulation. These variableswere planning, self-monitoring, effort, self-efficacy, help-seeking, and timeand environment management. The first three variables were measured usinga modified Herl et al. (1999) trait self-regulation scale. Self-efficacy, help-seeking, and time and environment management scales were modified from theMotivated Strategies for Learning Questionnaire (MSLQ), which was devel-oped by Pintrich and his colleagues in the 1990s at the University of Michigan(Pintrich et al. 1994).

Data were collected through an online survey for which students wereinvited to participate through an e-mail message sent to them through gate-keepers at their institution. The survey was pilot tested with a sample fromboth Arab and American students. According to the pilot test results, somesurvey items were deleted or adjusted.

Measurement Reliability and Cross-Cultural Validation

Measures developed in one culture cannot just be enforced or extended tobe used in another culture with little regard for cultural differences and con-struct biases. Therefore, according to Vijver and Leung (1997), validity ofcross-cultural research comparisons requires establishing the equivalence at thelevels of constructs, methods, and items. Vijver and Leung noted that “equiv-alence is usually unknown in empirical studies. Therefore, equivalence cannot

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Table 1. Cronbach’s α for All Study Variables for the Equivalent Final Samples Usedin the Analysis

Dimension Items in scale Arab (n = 95) American (n = 95)

Group interdependence 6 .721 .707Future orientation 6 .711 .617Planning 6 .845 .862Monitoring 5 .737 .764Effort 6 .764 .766Time and environment

management8 .706 .780

Help 5 .615 .718Self-efficacy 6 .891 .872

be assumed but should be established” (10). In this study, it was established asexplained here.

Construct equivalence. Construct equivalence was established at many lev-els. During instrument development, back-translation was used to assure thatconstructs in the questionnaire have cross-cultural equivalence using four com-petent bilinguals—with Arabic as a mother tongue and a master’s degree inteaching English as a second language. If the concept survives back-translation,then the item is considered an “etic,” meaning that the concept has the samemeaning in the two cultures; if it does not, then it must be an “emic,” meaningit is different in the two cultures (Brislin 1986). Another level of constructequivalence was conducted after data were collected by establishing mea-surement reliability and measurement invariance across the two groups. First,score reliability for each group was separately calculated using internal con-sistency reliability coefficients of Cronbach’s alpha for all scales (see Table 1).Establishing reliability for the observed scores of the scales is more likely toresult in reliable across-group comparisons. Then, confirmatory factor analysiswas conducted for each scale (Cozby 2001; Vijver and Leung 1997) usingLISREL software. Models were estimated using maximum likelihood andthe Satorra-Bentler correction for nonnormality. An overall model fit as wellas component fit were evaluated to make sure that each model significantlyexplained the relationships among factors and indicators for each group sepa-rately. Modifications were made accordingly. Then, the validity of the factorstructure for multiple group analyses was tested. In addition to constructequivalence, the variance and covariance structure of the overall structuralmodel was analyzed using structural equation modeling for each group toestablish structural equivalence of the whole model. Because of space lim-itation, the results of the individual factor analysis are not reported in thisarticle.

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Method equivalence. Method bias is the result of the instrument itself and itsadministration. This includes issues such as differential response style andextremity ratings of some cultures, which has been documented by Hui andTriandis (1989). To maintain survey consistency, the researcher used a four-point Likert scale for all the self-regulation scales and maintained the originalrating scales of all other scales. Differential language problems were dealtwith through back-translation. The survey was conducted in Arabic for Arabparticipants and English for American participants.

Item equivalence. Although item bias may suggest cultural differences, it couldalso be the result of measurement inconsistency at the item level. Reasons forthat include poor translations and complex wording. In addition, some itemsmay invoke additional traits or abilities for some cultural groups. In this case,item bias analysis can be conducted using item-response theory to identify andremove biased items (Vijver and Leung 1997). However, in this study, theseitems were easily detected though multiple sample confirmatory factor anal-ysis because their factor loadings were significant for one group and not theother. All differentially functioning items were deleted from further analysis toestablish cross-cultural equivalence of constructs. This was done according tomodel trimming strategies as the model without the nonsignificant paths fit thedata for both groups reasonably well (Kline 2005).

RESEARCH QUESTIONS AND FINDINGS

Research Questions

More specifically, the study aimed to address the following questions:

1. Are there any differences in self-regulation (planning, effort, self-efficacy,self-checking, help-seeking, and time and study environment management)between Arab and American distance learners?

2. Are there any differences in cultural orientation (future time perspective andgroup interdependence) between Arab and American distance learners?

3. What is the best model (variance and covariance structure) to explain therelationship between learner self-regulation and cultural orientation?

Multivariate analysis of variance (MANOVA) was used to test the differ-ence between Arabs and Americans when they are compared simultaneouslyon the six self-regulation variables. The analysis revealed a significant multi-variate effect for group with Wilks’s lambda = .612 and p < .000. In otherwords, in the population, Arabs and Americans are not likely equal on the cri-terion self-regulation variables. To check for the impact of the main effect of

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Table 2. Multivariate Analysis of Variance of Self-Regulation Variables

Independentvariable Dependent variable Univariate F d.f. p

Group Self-efficacy 14.129 1/188 .000Planning 12.309 1/188 .001Time and environment

management22.905 1/188 .000

Monitoring 51.713 1/188 .000Effort 7.33 1/188 .007Help 31.26 1/188 .000

group (Arab or American) on the individual six self-regulation variables, uni-variate analysis of variance (ANOVA) was examined for each variable. Resultsof this analysis are reported in Table 2.

Since each of these ANOVAs is an independent test, an experiment-wiseType I error was adjusted for using Bonferroni adjustment, so for α-level of0.05, the adjusted alpha for each test is 0.008. As you can see from the univari-ate ANOVAs, significant differences were found between the two groups forall self-regulation variables. Group means of the American sample was signif-icantly higher than the Arab sample for the variables of planning, monitoring,effort, time and environment management, and self-efficacy, whereas the meanfor Arabs was higher on the measure for help. This result is within the study’sexpectation. Since Americans are considered more independent than Arabs,they were expected to generally have higher self-regulation. Help-seeking isthe only self-regulation variable that was found to be higher for Arabs thanAmericans; further discussion about this is presented.

Similarly, MANOVA analysis was conducted to test the differencebetween Arabs and Americans when they are compared simultaneously on thetwo cultural variables. It revealed a significant multivariate effect for group,Wilks’s lambda = .931 and p < .000. In other words, in the population Arabsand Americans are not equal on all the criterion cultural variables. All univari-ate ANOVAs were significant. In this analysis, please note that the adjustedalpha for each test is 0.025 for the univariate ANOVAs after adjusting forthe experiment-wise Type I error, for the α-level of 0.05. The group mean ofthe American sample was significantly higher than the Arab sample for bothgroup interdependence and future orientation. Although it was expected thatAmericans would be more future oriented than Arabs, it was surprising thatthey were also more group interdependent (see Table 3).

Before exploring a model that can possibly explain the relationshipbetween self-regulation and cultural orientation, the author first explored thebest model for self-regulation because there are many ways one can specifythis concept. The initial model for self-regulation was a first-order confirmatoryfactor analysis in which self-regulation was measured through planning,

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Table 3. Univariate Analysis of Variance of Cultural Variables

Independent variable Dependent variable Univariate F d.f. p

Group Group interdependence 5.657 1/188 .018Future orientation 10.198 1/188 .002

monitoring, time and environment management, help, and self-efficacy. Thismodel was fitted for both groups simultaneously to establish measurementinvariance across the two groups. The overall model did not fit the data well(chi-square = 38.18, p = .0037; RMSEA = 0.11). Therefore, a two-factorself-regulation model was fitted. Hong and O’Neil (2001) suggested a second-order model for self-regulation with metacognition consisting of planning andmonitoring and motivation consisting of self-efficacy and effort. Help and timeand environment management were not in the work of Hong and O’Neil; theywere added by the researcher. In the MSLQ, they were categorized underlearning strategies scales (Duncan and McKeachie 2005; Pintrich et al. 1994).Therefore, in this study, they were fitted under metacognition. When fittingthis model, metacognition was scaled by setting the loading of planning to1.00 and motivation was scaled by setting the loading of effort to 1.00. Helpwas found to be insignificant for Americans. Because of space limitations, fur-ther tests and results are not reported in this article. In order to have a modelthat will work for both groups, a decision was made to delete “help” from themodel. The model without help was found to fit the data adequately with achi-square value of 18.22 (p = .02). Although the Root Mean Square Errorof Approximation (RMSEA = .12) indicated poor fit for the model, other fitindices indicated acceptable fit (NNFI = 0.93; CFI = 0.97). Given the limita-tions of the study, it can be cautiously concluded that this model was found tobe measurement invariant across the two groups. Measurement invariance wastested through a chi-square difference test between the model with equal fac-tor loadings (constrained model) and the model without equal factor loadings(unconstrained model).

Once the measurement invariance was established for self-regulation fac-tors (metacognition and motivation), cultural factors were added to the modelas suggested by the model of culture fit (Kanungo and Jaeger 1990) in order todetermine their effect on self-regulation, as shown in Figure 1. Please note thatcultural variables were fitted as observed exogenous variables, which in pathanalysis are assumed to be measured without error (Kline 2005).

This model was fitted simultaneously for both groups without anyconstraints. It indicated a good fit with chi-square = 28.79 (p = .051),RMSEA = 0.080, NNFI = 0.95, and CFI = 0.98. Group interdependence wasfound to be insignificant in predicting both metacognition and motivation forboth Arabs (λ = 0.055, ns, for metacognition and −0.00098, ns, for motivation)

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CROSS-CULTURAL STUDY 143

Arabs: R2 = 0.45

Americans: R2 = 0.29

1

1

Arabs: R2 = 0.34

Americans: R2 = 0.21

Metacognition

Motivation

Group

Interdependence

Future

Orientation

Planning

Monitoring

Time and

Environment

Management

Effort

Self-Efficacy

Figure 1. Model for self-regulation and cultural orientation.

and Americans (λ = 0.011, ns, for metacognition and −0.056, ns, for moti-vation). Accordingly, group interdependence was deleted from the model.Therefore, the next model was fitted with future orientation only predictingmetacognition and motivation. In this model only the paths from future ori-entation to metacognition and motivation were freely estimated. As found inthe previous step, the model with metacognition and motivation was invari-ant across the two groups, so factor loadings were constrained to be equal inthis test. This model fit well for both groups. Fit indices were χ2(16) = 20.55,p = .20, NNFI = 0.96, CFI = 0.99, and RMSEA = 0.055 with 90% confidenceinterval 0.00 and 0.12. Future orientation significantly predicted both metacog-nition and motivation for both Arabs (λ = 0.44, SE = 0.06 for metacognitionand λ = 0.34, SE = 0.06 for motivation) and Americans (λ = 0.46, SE = 0.09for metacognition and λ = 0.31, SE = 0.09 for motivation). When the unstan-dardized direct effects of future orientation were constrained to be equal acrossthe two groups, the model still fit well. Chi-square difference test indicated thismodel with equality constraints did not fit the data appreciably worse than theunconstrained model with a chi-square difference of 0.33 (p = .8479) for twodegrees of freedom difference. For the constrained model, fit indices all indi-cated a close model fit: 2(18) = 20.88, p = .25, NNFI = 0.99, CFI = 0.99,and RMSEA = 0.041 with 90% confidence interval 0.00 and 0.10. Futureorientation significantly predicted both metacognition and motivation. Futureorientation had greater predictive power for Arabs than Americans becausethe proportions of explained variance for metacognition and motivation were,respectively, 44% and 35% for Arabs and 27% and 21% for the Americans.Figures 2 and 3 show the within-group completely standardized solutions forthe models in both groups.

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1.33Future

Orientation

MetacognitionR2

= 0.44

MotivationR2

= 0.350.60

0.67

0.39

0.65

0.56

Figure 2. Final model for Arabs.

0.67Future

Orientation

MetacognitionR2

= 0.27

MotivationR2

= 0.210.46

0.52

0.66

0.79

0.73

Figure 3. Final model for Americans.

DISCUSSION

The main finding in this study was establishing a conceptual model forself-regulation in distance education and a cultural effect of future orienta-tion on learner self-regulation. As a construct, self-regulation was found tobe equivalent in the two cultures, confirming that the final model was foundto be of an “etic” nature; in other words, the concept has the same meaning inthe two cultures (Brislin 1986). The only “emic,” culture-specific componentof the hypothesized self-regulation construct was help-seeking, which was partof the Arab, not American, students’ conception of self-regulation. There weresignificant differences between Arab and American distance learners in groupinterdependence and future orientation.

Group Interdependence

In this study, Americans were found to be more group interdependent thanArabs. This was surprising, since group interdependence was conceptualized

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as an indicator of collectivism. Arabs were expected to be more group interde-pendent than Americans. In this study, group interdependence was measuredthrough a modified version of Singelis’s (1994) Self-Construal Scale withitems focusing on relational orientation with other members in a group reflect-ing norms of group work. Group work by nature requires collective thinking.Individuals from more collectivistic societies show more cooperative behaviorin group work whereas individuals from more individualistic societies showmore competitive behavior (Cox, Lobel, and McLeod 1991). Despite their pref-erence for individualism, it is possible that American students displayed morecollective behavior to essentially serve their self-interest of striving for success,especially in that their program required group work for course assignmentsand projects. Although students may not prefer it, they may still be dedicatedto it. This was confirmed in students’ open comments: “Given a good syllabus,I would prefer to work at my own pace and not have to rely on other students.That is the most difficult part of distance education for me, working with otherstudents.”

Future Orientation

In this study, future orientation significantly predicted both metacognitionand motivation for both groups. It was found to be significantly higher forAmericans than Arabs. These results on future orientation between Arabstudents (in Kuwait and Bahrain in this study) and the American students(United States) were consistent with the results from the Global Leadershipand Organizational Behavior Effectiveness (GLOBE) Research Project, whichcovered sixty-two cultures through the collaboration of 170 researchers (Houseet al. 2004). In this research, Kuwait was categorized among the countries withthe least future orientation whereas the United States was in the second-highestcategory of countries with the highest future orientation (Ashkanasy et al.2004). Educational psychologists agree that perceptions of the future pro-foundly affect student motivation and self-regulation (Kauffman 2004). Froma social cognitive perspective, “future orientation is embedded in the notionof perceived instrumentality” (Greene and DeBacker 2004, 113). This isbest explained by Miller and Brickman (2004): “Personally valued futuregoals influence proximal self-regulation through their impact on the develop-ment of proximal subgoals leading to future goal attainment” (9). So, theysuggest in order to attain future goals, one needs to develop a system ofsubgoals/proximal motivation and self-regulation that will be perceived asinstrumental to the achievement of future goals. As they summarize fromthe work of Bandura (1986) and Corno (1989) on social cognitive theory,simply desiring something without action will not result in obtaining it; anoutcome can be obtained only through action incorporated into one’s largerself-regulatory system (Miller and Brickman 2004).

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The metacognitive and motivational processes of planning, monitoring,time and environment management, effort, and self-efficacy can be used todevise proximal self-regulation strategies and tactics. Through these processes,students create proximal subgoals to eventually achieve their personally valuedgoals. For example, students should realize the instrumentality of completingand submitting an assignment in an online course on time to the achievementof their subgoal of passing the course successfully. This in itself is instrumentalto achieving their personally valued future goal of gaining a college degree. Inorder to do so, they will have to use proximal self-regulation and motivationprocesses. In this study, it was apparent that both Arab and American stu-dents realize the importance of self-regulation processes; however, Americansreported significantly higher use of these processes while working on courserequirements in their distance education programs than Arabs. They seem tobe at two different points in the continuum of independence. Whereas Arabstudents are still at the low end of the continuum trying to learn how to adaptto the distance education system, American students seem to be much furtherin the continuum of independent learning. Although AOU tried to provide itsstudents with independent learning skills (Hashim 2007), some Arab studentsindicated in their open comments that they still feel they lack independentlearning skills. One indicated she left the system because she could not adjust.Others indicated they learned how to work independently exclusively throughtheir personal experiences.

FUTURE RESEARCH

This study was confined by a number of limitations including sample size andcharacteristics and measurement issues. It is recommended that future researchreevaluate the suggested model of cultural effect on learner’s self-regulationpresented in this study with a larger sample size. This study was able toestablish only one link in the model: the link from future orientation tometacognition and motivation. Other cultural measures were either found tobe insignificant (group interdependence) or unreliable (power distance anduncertainty avoidance) and were not used in the final model. Furthermore, thismodel needs to be validated in more cultures. Future research can explore otherways to measure cultural variables such as power distance and uncertaintyavoidance and then examine their effect on learner self-regulation. Furtherdevelopment of the model is another consideration. For example, the modelcan be reconceptualized to include a connection between future orientation,proximal self-regulation processes, and personally valued goals. Based onMiller and Brickman (2004), this connection assumes that self-regulation pro-cesses mediate the effect of future orientation on personally valued goals.In addition to quantitative methods, qualitative research methodology can beused to provide better understanding of the nature and sources of learner

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differences. Furthermore, future research can examine other dimensions ofself-regulation. The author echoes Kauffman’s (2004) call for researchers tocontinue exploring strategies to enhance students’ self-regulated learning inWeb-based environments. Alternative data collection can make use of think-aloud, error detection, and trace methodologies, which could reflect moreaccurate reports of self-regulation (Winne and Perry 2005).

In conclusion, self-regulation in distance education should be viewed fromboth a metacognitive and a motivational perspective as this study found bothwere explained by future orientation. One implication of this finding is thatlearners, especially in distance education, need to be more future oriented inorder to maintain their self-regulation effort. One way in which distance edu-cation institutions can help learners do so is through proximal future orientationand proximal self-regulation.

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