readiness. behavior, and foundational mathematics course success

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  • Readiness. Behavior, and FoundationalMathematics Course SuccessBy Kevin Li, Richard Zelenka, Larry Buonaguidi, Robert Beckman, Alex Casillas, Jill Crouse, Jeff Allen,Mary Ann Hanson, Tara Acton, and Steve Robbins

    ABSTRACT: This study examines the effects ofmath readiness and student course behavior (e.g.,attendance, participation, homework comple-tion) on knowledge gain and course success usingtwo samples of students enrolled in foundationalskills (noncredit-bearing) mathematics courses.As hypothesized, entering student mathematicsreadiness and course behavior predicted posttestmathematics knowledge. Posttest knowledge andcourse behaviorpredicted course success (i.e.,pass-ingthe course). Results highlight the importance ofmathematics readiness and student behavior forunderstanding mathematics knowledge gains andcourse success. Implications for institutional policyand practice using effective diagnostic testing andbehavioral monitoring are discussed.

    Few studies have looked atthe impact of specific andobservable course behaviors[on academic performance].

    Kevin LiDean of Instructionkli@ccc.edu

    Richard ZelenkaChair of Foundational Studies Department

    First-year student retention and successful coursecompletion is a challenge for postsecondaryinstitu-tions, particularly community colleges. Accordingto the latest national figures, approximately 45%of degree- or certificate-seeking community col-lege students fail to maintain enrollment or earna credential (certificate or degree) within the first2 years of enrollment (Provasnik & Planty, 2008).The longer-term retention and degree-attainmentrates are even more concerning: Only 28% ofcommunity college students received any type ofcertificate or degree within 6 years (Provasnik &Planty, 2008). Important factors contributing to lowdegree attainment are lack of academic preparationfor college-level coursework (Strayhorn, 2011) anda lack of student motivation (Rosenbaum, Redline,& Stephan, 2007). For example, based on the ACTCollege Readiness Benchmarks, which are tied toa 50% likelihood of earning a B or better in credit-bearing college general education courses, only 24%ofhigh school graduates were academicallypreparedfor college in all four core subject areas (ACT, 2010).When considering the ACT Mathematics Bench-mark alone, 43% of high school graduates are readyto succeed in college-level mathematics courses.Students who do not have the necessary prerequi-site skills and knowledge, or who may not be fullycommitted to attaining a degree, are less likely tosucceed in college courses or to return for a secondyear (Robbins, Allen, Casillas, Peterson, &Le, 2006).

    Larry BuonaguidiQuality Assurance Coordinator

    Robert BeckmanDirector ofTesting

    Wilbur Wright College4300 N. Narragansett Ave.Chicago, IL60634

    Alex CasillasSenior Research Associatealex.casillas@act.org

    Jill CrouseJeff AllenMary Ann HansonTara Acton

    Research Division, ACT,Inc.500 ACT Drive, P.O.Box 168Iowa City, IA52243

    Steve RobbinsDirector of Research InnovationsEducational Testing Service (ETS)Rosedale Road MS 18-EPrinceton, NJ 08541

    14

    The primary method for helping students whoare underprepared for postsecondary courseworkprogress toward successful degree attainment isdevelopmentalinstruction (see Attewell, Lavin,Domina, & Levey, 2006). Due to their openenrollment policies, much of the remediationchallenge has fallen to community colleges.Between 2000 and 2008, the percentage of two-year college students taking at least one devel-opmental course to improve their foundationalskills rose from 39% to 44% (NCES, 2010).The estimated percentage of students needingremediation tends to be greater in mathematics(70%) than in English (34%; Biswas, 2007).

    Course placement is used to put students intocourses in which they are able to master materialdesigned to prepare them for eventual successfulcompletion of college algebra (Smith & Michael,1998). The dilemma involved in setting rigorouscourse placement standards has been highlightedby Jacobsen (2006), who has pointed outthat plac-ing students in noncredit-bearing developmentalmathematics courses increased their mastery ofthe foundational skills needed for college algebra.However, it also has resulted in reduced programcompletion due to high attrition rates. Bahr(2010) reinforces this point by highlighting thedisparities in course completion rates at differinglevels ofinitial math skills. Within developmentalmathematics, Bahr's results suggest that studentpersistence behavior is critical for understandingcourse completion and success as the math "skillgaps" themselves.

    The effectiveness of developmental instruc-tion has been a topic of research for approxi-mately 2 decades (e.g., Bailey, Jeong, & Cho, 2010;Boylan, 2009; Conley, 2007). Specific to devel-opmental math, researchers have tried to betterunderstand which approaches and strategieswork best to strengthen adult students' mathskills to help them progress into college-levelcourses (Golfin, Jordan, Hull, & Ruffin, 2005).Promising practices include the use of technol-ogyto assess and accurately place students intoappropriate-level math courses and tailoredinstruction as opposed to traditional lecture andlab format.

    JOURNAL a/DEVELOPMENTAL EDUCATION

  • The potential of the tailored instructionapproach was demonstrated by Waycaster (2001),who successfully used individualized computer-aided instruction in developmental math coursesat several two-year institutions. Similarly,Quinn (2003) studied a college that used a com-puter-based placement test to route studentsinto the appropriate courses. Students who didnot meet a particular cutoff score were assignedcourseware modules to improve their skills and- en allowed to take the placement test again. Aseneral conclusion ofthis line of work has been-- t effective course placement is essential to

    cess.In a separate line of research, a variety of

    r - ary studies and meta-analyses have demon-ed the importance of psychosocial constructs,uding personality, study skills, and other

    academic behaviors (e.g., time management) toacademic performance and persistence in postsec-ondary settings (Crede &Kuncel, 200S; O'Connor

    Paunonen, 2007; Poropat, 2009; Robbins et al.,2004). However, few studies have looked at theimpact of specific and observable course behaviorsrelated to psychosocial skills, such as attendanceand participation. For example, Dollinger, Matyja,and Huber (200S) examined the extent to whichcontrollable factors such as attendance and studytime contribute to college academic performanceafter controlling for past performance, aptitude,and personality characteristics. Although the bestpredictors of performance were ability and pastperformance, study time and attendance wererelated to performance and explained an additional6-10% of the variance explained. Similarly, stud-iesby Conard (2006) and Farsides and Woodfield_003) found that attendance, as an indicator of

    "application and effort" (Farsides & Woodfield,2003, p. 1236) predicted academic performanceGPA) above and beyond the effects ofintellectual

    ility and personality characteristics such as con-- .entiousness. Thus, based on available research,.::our e behavior appears to be an important factor

    course success.Taken together the preceding lines of researchest that placing students in courses in which

    - are likely to succeed depends on both enter-(J skill levels and student motivation or effort.

    :examining these two issues can help shed lightthe best ways to improve the success rates of

    mmmunity college students. The premise of thecurrent study is that the combination of effectivecourse placement and behavioral effort factors isessential to successful completion of developmentalraarhematics courses. We applied the best practices. individualized delivery of developmental matheducation along with our knowledge of academicoehavior (cf. ACT, 2012, Robbins et al., 2004;Robbins, Oh, Le, & Button, 2009) to better under-stand developmental mathematics course success.

    OlUME 37, ISSUE 1 FALL 2013

    The model tested in this study (shown in Figure1, p. 16) combines academic skills (entering mathskills) and course behavior (instructor-reportedeffort ratings) measures to predict course suc-cess. The model proposes that entering students'mathematics readiness (i.e., initial knowledge)directly predicts later math knowledge (measuredvia a posttest) which, in turn, is predictive of coursesuccess (completing with a passing grade). Thismodel also allows for examination of the extentto which precourse academic skills and studentbehaviors influence course success separately aswell as together. Specifically, math readiness isexpected to show a strong direct effect on posttestmath knowledge and a strong indirect effect oncourse success. Course behavior is expected to havedirect effects on both math knowledge and coursesuccess. Students who expend higher levels of effortare expected to demonstrate higher score gains andsuccess rates than those students who expend lesseffort.

    MethodDemographics

    Institution. The study institution is an openadmissions two-year public college in a large Mid-western U.S. city serving more than 14,000 students.Based on institutional enrollment data, approxi-mately70% of the students are part time and 5S%are

    female. A majority of students (47%) are Hispanic,33% are White, 9% are African American, and S%are Asian/Pacific Islander. Nearly all participantsreside in state, and 47% are age 24 or younger.

    Participants. A total of 1,254 students wereenrolled in developmental mathematics coursesat the study institution during the Fall200S (N =S19) and Spring 2009 (N = 435) semesters. Thesestudents were enrolled in developmental coursesbecause they did not have the ski

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