Online Mathematics Achievement: Effectsof Learning Strategies and Self-Efficacy

By Leigh M. Wadsworth, Jenefer Husman, Mary Anne Duggan, and M. Nan Pennington

As students transitionfrom high school to college,frequently they are asked tobe more responsible for theirown learning.

Leigh M. WadsworthLecturerVanderbilt UniversityBox 330 GPCNashville, TN 37203Doctoral CandidateArizona State Universityleight.wadsworth@vanderbilt.edu

Jenefer HusmanAssistant ProfessorPsychology in Education

Mary Ann DugganDoctoral CandidatePsychology in Education

M.Nan PenningtonDoctoral CandidatePsychology in Education

Mary Lou Fulton Coiiege of EducationArizona State UniversityBox o6nTempe, AZ 85287-0611

ABSTRACT: A flutd and flexible teaming Strat-egies repertoire and self-efficacy have beendocumented as important factors for learningand achievement. However, there has been lit-tle research examining the effects of these samefactors on achievement in an online leamingenvironment. The current research investigatesthe strategies used by and self-efficacy demon-strated by successful college students in an on-line developmental mathematics course. Thisarticle provides evidence of the relationship be-tween leaming strategies, motivation, self-effi-cacy, and student achievement in this environ-ment. Participants were 89 students enrolled inan online developmental mathematics course.Results indicate four types of learning strate-gies—motivation, concentration, informationprocessing, and self-testing—along with self-ef-ficacy predicting 42% (r=o.65) of the variancein grade achievement.

As tnost faculty are painfully aware, successin a college course is dependent on more thanjust exposure to content knowledge. Students'success in a course depends largely on whatthey do with that content knowledge in order topass exams and to be successful in subsequentcourses within a domain. As education researchhas demonstrated, to be successful students haveto be motivated to put effort into their studiesand use learning strategies and skills that sup-port meaningful learning (Weinstein, Husman,& Dierking, 2000}. In an extensive review ofcurrent research concerning reading and learn-ing strategies curricula, Simpson and colleagues(2004) have argued that high-risk undergradu-ate students would best be served through acomprehensive cognitive-strategies curricula.Simpson et al.'s review of the literature on learn-ing strategies within developmental educationcontexts focuses, as most do, on traditionalrather than Web-hased or online learning con-texts. The goal of the current study is to examinethe relationship between specific cognitive andmotivational strategies and high-risk studentsuccess in online/Web-based courses.

The need for this research stems from agrowing trend in American universities to tran-sition course delivery to Web-based teaching

through online instruction. According to theUnited States Department of Education, the en-rollment in online classes doubled between 1995and 1998, and the rapid growth is expected tocontinue (Wood, 2001). Universities continue tooffer more courses online and students are thusin need of skills that will heip them succeed inthese new learning environments. Some institu-tions are transitioning their developmental edu-cation courses onto the Web to provide greateraccess for students and to reduce teaching loads.Although the popularity of Web-based instruc-tion Is growing, very little research has been con-ducted that examines the use of learning strate-gies and their effects on student learning andachievement in Web-based courses. The currentresearch seeks to better understand the impactof students' motivation and learning strategieson their performance in an online developmen-tal mathematics course. Understanding howlearning strategies influence learning in onlinecourses may help instructors and course design-ers to provide tailored, and therefore more ef-fective, course-specific instruction in these vitallearning strategies.

Learning StrategiesAs students transition from high school to col-lege, frequently they are asked to be more re-sponsible for their own learning and more ac-tive in their learning environment. Postsecond-ary education research has shown that studentsare more likely to make this transition success-fully if they have mastered a strong repertoire oflearning strategies (Alexander, Murphy, Woods,& Duhon, 1997). Learning strategies include"any thoughts, behaviors, beliefs, or emotionsthat facilitate the acquisition, understanding,or later transfer of new knowledge and skills"(Weinstein et al., 2000, p. 727).

It is important for teachers to know whatstrategies are most effective in ensuring successin any course and how to incorporate those intotheir curricula. This information may be partic-ularly important when thinking about teachingpostsecondary developmental education cours-es because students enrolled in developmentaicourses have encountered the information pre-

JOURNAL 0/DEVELOPMENTAL EDUCATION

viously and have not retained it, possibly due toa lack of appropriate learning strategies.

Colleges and universities have implementedvarious forms of academic assistance for un-derperforming students who lack the strategiesneeded for success (Hofer, Yu, & Pintrich, 1998;Simpson, Hynd, Nist. & Burrell, 1997; Wein-stein et al., 2000). This assistance can take manyforms from stand-alone courses to instructionembedded within regular coursework. A goalof strategy instruction has been to help studentsbecome "good strategy users" or "good thinkers"(Pressley, Borkowski, & Schneider, 1987; Press-ley & McCormick, 1995). Likewise, hypermediaclassrooms must contain strategy instructionneeded to teach students strategies that are ef-fective specifically in the online classroom (Aze-vedo, 2005; Zimmerman & Tsikalas, 2005).

Weinstein's model of strategic learning hasemerged as a useful way to identify and organizethe skills necessary for academic success (Wein-stein et ai., 2000). The model has the learner atits core, focusing on self-concept, individual dif-ference factors, and learning history. Surround-ing this core are three broad components of skill,will, and self-regulation, which are interactive;an overlap between the components and withinihe subcomponents occurs as a result.

The skill component involves the ability touse cognitive strategies effectively and the pos-session of knowledge about the self as a learner{Weinstein et al., 2000). The will componentconsists of a student's perception of self-efficacyand ability to set goals, maintain motivation,and sustain a positive attitude toward learning(Pintrich, 2003). The self-regulation componentrequires skills such as time management, self-testing strategies, and comprehension monitor-ing (Zimmerman & Schunk, 2001).

One tool widely used to assess the level ofskill, will, and self-regulation among studentsis the Learning and Study Strategies Inventory(LASSI; Weinstein, Schulte. & Palmer, 1987). TheLASSI has been a central assessment tool usedto inventory the multitude of areas of strategiclearning (Braten & Olaussen, 2000; Deming,Valeri-Gold, & Idleman, 1994; Kovach & Wil-gosh, 1999; Nist, Mealey, Simpson, & Kroc, 1990;Oiejnik & Nist, 1992; Schumacker, Sayler, &Bembry, 1995; Yip & Chung, 2005). The invento-ry measures 10 aspects of skill, will, and self-reg-ulation: attitude, motivation, time management,anxiety, concentration, information processing,selecting main ideas, use of support techniquesand materials, self-testing, and the use of test-taking strategies. Students respond to 77 items,indicating responses on a 5-point Likert scalefrom "not at all typical of me" to "very typicalof me." The results are reported as standardizedscores with national norms of relative strengths

and weaknesses in each learning strategy. TheLASSI provides diagnostic and prescriptive in-formation for students wishing to expand theirstrategy knowledge base.

Web-Based LearningAlthough most of the research on learning strat-egies and achievement outcomes has focused ona traditional classroom setting, the relatively newarena of online course delivery suggests researchmay have to revisit learning strategies researchand consider which types of strategies are mostuseful in this new learning environment. Educa-tors teaching courses presented solely in an on-line format can benefit from better understand-ing what strategies are most effective and howstudents use strategies in this environment.

Strategic learning for students in traditionaleducational settings requires students to be self-regulated in their thoughts and actions in orderto attain personal goals. Students' levels of self-

The increased autonomyof an online or Web-basedlearning environmentmay support students'motivation for learning.

regulation have been measured by examiningthe ways they set goals, access resources to ac-complish those goals, and manage the distrac-tions and roadblocks that occur on the path toreaching those goals (Zimmerman & Schunk,2001). Students enrolled in Web-based coursesas a part of a traditional curriculum face thesame responsibilities as students in traditionalclassrooms; yet the resources available to themand distractions they face may be quite different.Specifically, Web-based learners must becomeaccustomed to a structured learning environ-ment without direct instruction.

Research has shown that students enrolledin Web-based courses utilize some ofthe sameself-directed learning strategies as traditionalstudents while also incorporating many uniquetactics specific to the Web-based learning envi-ronment (Kauffman, 2004; Whipp & Chiarelli,2004). For example, Chang (2005) found thatstudents in a Web-based course that includedself-regulated learning strategy instruction hadpositive motivational orientations and weremore self-directed as learners than those with-out learning strategy instruction.

Skills and StrategiesIn both traditional and Web-based learningenvironments, effective learners are those whohave developed a wide array of reliable learningstrategies and use them flexibly and efficiently.In research conducted in traditional classrooms,student success has been related to having alarge, fluid, and ffexible repertoire of learningstrategies (Weinstein et al., 2000). Even thoughuniversity students have previously used effec-tive learning strategies in a traditional class-room, they may not be able to transfer theseskills into their new learning environment. Oneimportant aspect of all learning strategy use,which is likewise emphasized in online learning,is the motivation of students to learn the mate-rial and enact appropriate strategies to succeed.Motivation has been previously noted as a keycomponent to student success in the traditionalclassroom (Robbins, Lauver, Le, Davis, Langley,& Carlstrom, 2004), and thus would likewise beexpected to be influential on success in an on-line course.

Motivation and Student LearningEffective learning strategies increase students'self-efficacy, which in turn increases motiva-tion and willingness to engage and persist inchallenging tasks (Pajares, 1996). Although theincreased autonomy of an online or Web-basedlearning environment may support students'motivation for learning, it also may create asituation in which motivation is even more im-portant to students' success than in a traditionalclassroom setting. One aspect of motivationparticularly critical in an online setting—whichmay be particularly lacking in the developmen-tal students these settings are attempting toserve—is self-efficacy.

Self-EfficacySelf-efficacy is known as the belief in ones ca-pabilities to organize and execute the resourcesrequired to manage prospective situations (Ban-dura, 1997). Zimmerman (2000) and Brophy(2004) discuss the role of self-efficacy in influ-encing motivation through activity choice, ef-fort, and persistence. Bandura and colleaguesshow that students with higher self-efficacychoose more challenging tasks, persist longerin the face of a challenge, and put forth moreeffort (Bandura & Schunk, 1981). Research hasshown that a students' self-efficacy is influencedby the feedback they receive and the attributionsthey make regarding that feedback (Bandura,1993, Schunk, & Gutin, 1986). In the ideal on-line classroom, students encounter many feed-back tools that are available to them to helpregulate their progress. However, without the

VOLUME 30, ISSUE 3 • SPRING 2007

face-to-face interaction that many students areaccustomed to, there may be significant chal-lenges in interpreting and/or using the feedbackgiven. The effects of feedback on self-efficacy inthe traditional cla.ssroom has been established(Zimmerman, 2000), but it has yet to be firmlydetermined in the online course environment.Because of the different forms of feedback, it isimportant to determine the effect that self-effi-cacy has on student performance in an onlineenvironment.

PurposeIn the preceding text we have argued that under-standing the skills, motivation, and self-regula-tory strategies developmental students bring toonline or Web-based learning environments isimportant. However, little is known about whichstrategies have the greatest impact on studentlearning in the online environment. The currentresearch is designed to investigate the learningstrategies used by students who are successfulin the online course environment. Although thecurrent study is based on previous research re-lated to learning and motivational strategy usein traditional course settings, it is crucial to ap-ply this research within online course dynamicsdue to the increased number of postsecondarydevelopmental education courses offered on-line. The research question guiding this studyis, "Which ofthe learning, motivation, and self-regulatory strategies, examined by educationalresearchers as critical to the success of develop-mental education students, affect student suc-cess when instruction is provided in an onlinelearning environment?" By identifying strate-gies that affect student learning in an onlinecourse, our goal is to help instructors becomemore efficient, allowing them to streamlinetheir strategy instruction.

MethodParticipantsThe students who participated in this studywere part of an online developmental math-ematics course at a large Southeastern publicuniversity. For the spring semester, 511 studentswere placed in a developmental mathematicscourse based on their performance on a place-ment exam required of all incoming freshmen.Incoming freshman take a battery of placementexams prior to the beginning of the semester.These exams are intended to place students inability-appropriate courses. Freshman studentsentering this university are required to takea mathematics placement exam if they scorelower than 29 on the ACT math, tower than 640on the SAT math, or have not completed highschool calculus with a C- or higher. Students

who score 0-189 out of 600 on the placementexam are required to take a remedial mathemat-ics course in order to master algebraic skills thatwere not previously mastered.

Of the 511 students enrolled, 252 studentswere enrolled in the course for the first time;all others in the course had taken the course atleast once and had received no credit. In the de-velopmental mathematics course, students whoearned a grade lower than a C or did not com-plete the required assignment received a grade of"no credit" and were required to continue takingthis course until they earned a passing grade (Cor above). Since this study focuses on the effectsof learning strategies and motivational strate-gies on student learning, we were concernedthat those who had already taken the courseand failed may have had a different motivationalprofile and be affected differently than thosetaking it for the first time. Although all studentsenrolled in the class were asked to complete thesurveys, only 89 completed all three ofthe sur-

This setting provided aunique opportunity toexamine the learningstrategies students utilizedin order to managelearning in this new onlineenvironment.

veys to be included in the current study.Ofthe 89 participants included in the analy-

sis, 61% ofthe participants were female, which isrepresentative of the overall distribution of thestudents enrolled in the mathematics course.Eighteen percent ofthe students earned an A fortheir final grade, 36% earned a B, 34% earned aC, and 12% earned No Credit (NC) for takingthe course. These grade distributions were simi-lar to those ofthe entire class: In the sample forthe current study, there was an 88% pass rate,whereas there was a 79% pass rate for the entireclass. Similarly, university records for math-ematics students enrolled in the online coursesince its inception revealed achievement levelssimilar to the semester of interest in the currentstudy. Pass rates ranged from 70% to 81% overthe 2000-2002 time period for the entire class.So, although the sample for the current studyhad a higher pass rate, there was not a major dis-crepancy between pass rates of participants andtheir fellow classmates from other semesters.

SettingThe project focused on a large undergraduatedevelopmental math course. Students who didpoorly on mathematics placement exams wererequired to pass a basic algebra course beforeadvancing on to the compulsory math coursesfor their major. As basic algebra is part of all K-12 academic curricula in the states served by thisuniversity, most students who placed into basicalgebra had already encountered and failed tolearn the curriculum at least once.

What made this basic algebra class unique,however, was that the content was entirely de-livered online through the use of a hyperme-dia-based math instruction package. The soff-ware provided students with lectures (througha video "teacher" who lectured on the content),practice exams, homework (which was gradedautomatically, providing immediate feedback),and major chapter exams. The class presentedstudents with the opportunity for self-pacedinstruction, which also meant it required themto regulate their time and their environment inways not necessary in their other classes. Thus,this setting provided a unique opportunity toexamine the learning strategies students utilizedin order to manage learning in this new onlineenvironment.

ProcedurePeriodically throughout the semester, studentsresponded to online surveys at several points aspart of a larger program evaluation. The math-ematics department was evaluating the onlinedevelopmental math course and its effective-ness, and the surveys used for the current re-search were part of this larger set of surveys.Students were given extra credit to completethe surveys and were allowed to do so duringtheir required lab hours. With the exception ofthe LASSI (Weinstein et al., 1987), which wasadministered by the researchers in a group set-ting in the mathematics laboratory, the studentscompleted the surveys on their own during theappointed week for each survey. At the begin-ning of the semester, students were told of theopportunity to participate in research for extracredit over the semester. As surveys becameavailable, the students were emailed a reminderto participate. Students responded to question-naires described in this article during the finalweek ofthe semester.

InstrumentsLearning Strategies Inventory, Students'

learning strategies were assessed using the LAS-SI (Weinstein & Palmer, 2002). The LASSI is adiagnostic measure of learning strategies and

CONTINUED ON PAGE lO

JOURNAL 0/DEVELOPMENTAL EDUCATION

CONTINUED FROM PAGE 8

skills that provides standardized scores (percen-tlle score equivalents) and national norms for lodifferent scales: attitude, motivation, time man-agement, anxiety, concentration, informationprocessing, selecting the main idea, use of sup-port/materials, self-testing, and lest strategies.

Self-efficacy. As recommended by Pajaresand Graham (1999) and Bandura (2001), self-ef-ficacy was measured by asking the students toindicate, on a scale from o-io, how confidentthey were about their capability to successfullycomplete specific types of mathematics prob-lems. The mathematics problems were createdby an expert in the mathematics department.These mathematics problems were similar to theproblems students encountered on the place-ment exam and the material covered in thecourse curriculum.

Achievement. Student achievement wasoperationally defined and measured as the to-tal number of points the student received forthe online developmental mathematics courseduring the semester. Points were accumulatedthrough quiz and test performance and timespent in the mathematics lab (4 hours per weekwere required). Extra credit was given for com-pletion of the surveys.

ResultsA gender analysis of the 89 participants revealedfew differences between male and female stu-dents taking tbe online developmental math-ematics course with respect to their final gradereceived and their scores on the 10 subscales ofthe LASSI (see Table 1). Sixty-one percent ofthe sample is female and 39% male. Female andmale students were significantly different onlyon their motivation, ((87) = 3.17, p = .002, withfemale students scoring higher on all three mea-sures. These fmdings suggest there is very littledifference between male and female studentswhen examining the final grade received andtheir subscale scores on the LASSI.

A multiple regression analysis was conductedto examine the relationship between self-efficacy,learning strategies, and final grade. The analysisshowed four learning strategies and self-efficacysignificantly predicted the students' final grade,R'=.42.F(n,88)=5.oi,p<.oo;{seeTable2). Self-efficacy, motivation, concentration, informationprocessing, and self-testing strategies were sig-nificant in predicting final grade. Student atti-tude, time management skills, anxiety, selectingthe main idea, use of support materials, and test-ing strategies did not contribute significantly tothe overall model. An interesting finding was anegative relationship between self-testing and

Motivation/StrategyMeasure

achievement. In thisstudy, the results in-dicated that the moreparticipants used self-testing strategies, thelower their achieve-ment scores.

We divided par-ticipants into groupsbased on fheirachievement levelsas evidenced by thegrade they received inthe course in order toexamine the relation-ship between levelsof performance andlearning strategy use.The LASSI providedus with standardized,student percentilescores; therefore wehave some informa-tion about the studentsrelative to each otherand to a larger nation-al sample of students.It is apparent thatstudents who earn agrade of a C or below,on average, scoredbelow the 50th per-centile on 7 of the 10LASSI subscales (seeTable 3, page 12). V̂ efurther examined therelationship betweenperformance level andthe motivational andlearning strategiesdetermined to pre-dict student achieve-ment in the first half

of this study by conducting a MANOVA usingthe grade the student earned as the independentvariable and their learning and motivationalstrategies as the dependent measures. The goalof this analysis was to determine if the grade thestudents received reflected differences in theirstrategy use. The multivariate tests revealed thefull model was significant: F(3.73) - 3.21, p<.05.When broken into groups by final grade, a uni-variate analysis revealed the students were sig-nificantly different in their self-efficacy, f(3, 73)= 7.6,/»<.ooi; motivation, ^(3,73) = 5.84,^^.001,and concentration, F(3,73) = 5.05, p=.0O3. Stu-dents in different grade groups were not signifi-cantly different in their information processing,K3.73) = 2.14, p=.io and self-testing strategies,^(3.73) = i-io, p=.36. Table 3 also provides in-

Table 1Students'Mean Scores on Motivational and StrategyMeasures Reported by Gender

Motivation/StrategyMeasure

AttitudeMotivationTime managementAnxietyConcentrationInformation processingSelecting main ideaUse of supporting materialsTesting strategiesSelf-testing

Female X (n=54)M

32.0053.8767.1145.9159.7065.8559.9138.4650.0650.70

SD

29.9131.0325.0825.7329.1527.0228.1727.2129.4530.31

MaleX(n=3S)M

29.2934.0961.9752.2052.1462.1447.8930.5741.7742.31

SD

24.9924.7118.7029.8323.1525.4631.8222.3126.7823.57

Table 2Standard and Unstandardized Coefficients for RegressionEquations Predicting Grade with Self-Efficacy and LearningStrategies

Self-efficacyAttitudeMotivationTime managementAnxietyConcentrationInformation processingSelecting main ideaUse of supporting materialsTesting strategiesSelf-testing

1.08-0.031.01

-2.020.631.360.97

-0.07-0.27-0.80-1.35

0.270.010.29

-0.040.160.350.24

-0.02-0.07-0^2-0.36

Note: Model 1—Predicting Grade with Self-efficacy and Learning Strate-gies: R^ = .42, F(l l , 88)=5.01, p<.001 *p< .05 **p< .01

formation concerning the mean differencesbetween the grade groups and the results of theLeast Squares Difference post hoc analysis.

DiscussionThis study has examined students' learning strat-egies and motivation in the context of an onlinedevelopmental mathematics course. There is alarge body of literature indicating both learn-ing strategies (Simpson et al., 2004) and studentself-efficacy are critical to students' success {Pa-jares, 1996: Schunk, 1996), and learning strategyinstruction has been shown to be of particularbenefit to underprepared students {Weinstein

CONTINUED ON PAGE 12

10 JOURNAL o/DEVELOPMENTAL EDUCATION

Table 3Mean Student Scores on Motivation and Strategy MeasuresReported by Grade Group

Grade

Motivation/StrategyMeasure

Self-efficacyAttitudeMotivationTime managementAnxietyConcentrationInformation processingSelecting main ideaUse of supporting materialsTesting strategiesSelf-testing

A{n=16)

152.50"42.1968.3875.1360.6375.88'73.9471.5638.8163.7550.69

B(n=32)

135.60"32.9449.5964.5954.9759.47''67.6352.5037.4445.7844.56

C{n=30)

118.27'17.0034.9765.6340.7050.83* '̂57.9748.5727.2341.5346.03

NC(n=10)

120.20''24.9030.2046.5034.6031.70'57.9055.0044.6037.4050.60

Note: Key motivation and strategy measures in bold. Means with differ-ent superscripts are significantly different p<.01.

is not surprising in thecontext of this math-ematics course thatselecting the mainidea would not signifi-cantly predict studentperformance. Withinthe sample, attitude,time management,anxiety, use of supportmaterials, and testingstrategies all followedexpected patterns be-tween groups withstudents receiving Asgetting higher scoresthan those receivinglower grades. Furtherresearch is needed toshed light on the im-portance of each in-dividual strategy and

CONTtNUED FROM PAGE 10

et al., 2ooo). Although research examining theeffectiveness of specific learning strategies forsupporting deveiopmentai student learning inonline or Web-based instructional contexts isscarce, research has shown benefits from usingmetacognitive tools integrated into online soft-ware to keep students on track and remind themto use strategies such as note taking and reflec-tion (Chang 2005, Quintana, Zhang, & Drajcik,2005). As colleges and universities are beingpressured to move toward providing computer-ized instruction, it is critical to understand whataspects of student learning need to be supportedto ensure student success in these contexts. Inthis study, we found students' study strategy andself-efficacy applied to a Web-based setting isnot substantially different from student learningin traditional developmental courses. Findingsindicated that students in our sample reportingbelow-average study strategy use also receivedlower grades than those reporting above-aver-age strategy use, thereby supporting previousresearch (Nist et al., 1990).

The regression analysis indicated students'reports of some learning and motivational strat-egy use at the end of the semester was highlypredictive of their final performance in thecourse. Specifically students* percentile scoreson the motivation, concentration, informationprocessing, and self-testing strategy scales sig-nificantly predicted final grade; whereas, stu-dent scores on the attitude, time managementskills, anxiety, selecting the main idea, use ofsupport materials, and testing strategies did notcontribute significantly to the overall model. It

The good news is directinstruction can affect eachof these factors.

how each strategy can most effectively be usedin the Web-based classroom.

Within the study sample, all of the studentsscored below the 50th percentile on the attitudeand use of support strategies scales. As studentsin this class have experienced some academicfailure in the past, it is not too surprising that, asa group, they would exhibit some skill deficien-cies. These findings provide evidence of the needfor learning skills and strategies instruction tobe part of online or Web-based developmentaleducation courses.

An interesting finding was the negative rela-tionship between self-testing and achievement.Ahhough tbere has been no previous research toexamine exactly how the strategies students em-ploy translate from a traditional to online learn-ing environment, this negative finding suggests acall for such research. Thus, there appears to be aneed for a learning strategies inventory designedspecifically for an online learning environment.An examination of how traditional learningstrategies should be adapted and used in an on-line course and what strategies are unique to theonline classroom is warranted.

LimitationsThere are a few limitations of the current studythat need to be addressed and considered forfuture research. Tbe first area of concern would

be the sample size. With a sample size of 89 stu-dents, the current study does not provide strongevidence for generalizing the findings. Anotherlimitation of the current study is the timing ofthe survey from which the data was collected;Using a survey that examines motivationalstrategies at the end of a semester may provideskewed results. A future study should either sur-vey the students at a more neutral point in thesemester, sucb as the middle, or collect data attwo points during the semester to look for cohe-sion.

The motivation of students may have likewisebeen affected by the type of course they weretaking. Because these students were requiredto take the developmental mathematics course,they may not have heen as motivated as stu-dents who take developmental courses becausethey want to or know they need the additionaleducation. As the course for the current researchwas only offered online, the students may havenot been as motivated to come to the computerlab to complete their work instead of complet-ing the work in their dorm rooms. This mighthave affected their grade as fhey were requiredto spend 4 hours a week in the computer tab forthis course. Thus, some students may have had ahigher grade if tbey simply had been motivatedto do their work in the computer lab or if thegrading procedure was changed to decrease thenumber of required hours in the lab.

Further, the fact that only 89 of the 511 stu-dents enrolled in the course completed all sur-veys for the study may indicate the sample wasskewed to include those more motivated to suc-ceed in tbe course. Students self-selected themselves by taking the time to complete all surveysand receive the extra credit. Thus, findings werelimited to only those who chose to do the extrawork to receive the extra credit.

Implications for Practice andFuture Research

The findings of the current study suggest suc-cess in an online deveiopmentai mathematicscourse is dependent, in part, upon the learningstrategies and self-eificacy of the students. Morespecifically, it is the students' self-efficacy, mo-tivation, concentration, information processing,and self-testing skills that are of most impor-tance when predicting grade achievement. Thegood news is direct instruction can affect each ofthese factors (Weinstein et al., 2000). Providingreal examples and practice to students on howto transfer strategies from a traditional to an on-line classroom can give students tools to ensuresuccess in the online classroom. In an onlinecourse, this may be a little difficult to do as stu-dents often have little to no face-to-face inter-

12 JOURNAL of DEVELOPMENTAL EDUCATION

action with an instructor. Instructors may con-sider setting up meetings with students enrolledin the online course during which they focus onlearning strategies and overtly teach studentshow to apply strategies in their online course.This study identifies strategies most importantto success in an online mathematics course andprevious research has identified direct methodsto teach these strategies. The combination canhelp improve academic success even in onlinemathematics courses (Hofer, Yu, & Pintrich,1998; Weinstein et al., 2000).

The ctirrent study is a foundational pointfrom which future research may take severaldirections. First, future research should focuson teaching the strategies that are important toonline learning success. By examining the dif-ferences between students who do and do notreceive training on how to learn in an onlinecourse, research can help establish causality andlook more closely at the direct influence of theiearning strategies on student achievement.

A second recommendation for future re-search would be to compare success rates indevelopmental courses offered via a traditional

versus an online classroom. If the same coursematerial is covered in each class, this compari-son would provide interesting data to discussthe effects of tiie classroom environment. Like-wise, a comparison study between students whohave or have not taken the course previouslymay provide evidence for strategy use and moti-vation of students taking the course a first timeas compared to those who have previously takenthe course and not succeeded.

Additionally, universities might considerscreening who is and is not allowed to take anonline course. The current research suggeststhat students with specific skills are more likelyto succeed in the online classroom than studentswho lack those skills. Therefore, inventories tomeasure specific study skills could be part of aplacement system. This may suggest the needfor programs to perform some sort of learning-strategy screening for students wanting to enrollin online courses in order to offer students themost optimal learning environment for theirown skill set.

ConclusionAs the use of online education continues to in-crease, there is a strong sense of urgency to un-derstand student learning in this new environ-ment (Picciano, 2002). Research has providedevidence for the importance of learning strate-gies in the traditional classroom but has yet toexamine what strategies are important and use-ful in the online learning classroom (Weinsteinet al., 2000). Although students in online cours-es are implementing many of the same strategiesas their counterparts in traditional classrooms,there has been little evidence to show whatstrategies are most useful in this new environ-ment and how some strategies may translate toa new learning environment (Kauffman, 2004).Understanding what is important for success inonline educational environments is vitally im-portant to the success of online education.

The current research implies that with strat-egy education, success rates for students in on-line developmental courses will increase. Withthe inclusion of specific learning strategy educa-tion imbedded within the course work, students

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can better use these powerful tools to improvetheir learning. The results of the current studyprovide insight into the importance of under-standing learning strategy use and education inthe developmental online education setting toensure student success.

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Duhon, K. E. (1997). College instruction andconcomitant changes in students' knov '̂ledge,interest, and strategy use: A study of domainlearning. Contemporary Educational Psychol-ogy, 22(2). 125-146.

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