journal of college student developmentsnyder/reslife/differentbydesign.pdf · tution. student...

MAY/JUNE 2003 ! VOL 44 NO 3 335

Different by Design: An Examination of StudentOutcomes Among Participants in Three Types ofLiving-Learning ProgramsKaren Kurotsuchi Inkelas Jennifer L. Weisman

This study examines college environmentsand outcomes among students in threedifferent types of living-learning programscompared with a control sample at oneuniversity. Results reveal that living-learningstudents exhibit higher levels of engagementin college activities with stronger academicoutcomes, and experiences that varied byprogram type.

Undergraduate education at Americanresearch universities has been criticized forits lack of integrated and focused studentlearning (Boyer Commission on EducatingUndergraduates in the Research University,1998; Edgerton, 1999; Wingspread Group onHigher Education, 1993). In response tothese critiques, several institutions haveestablished learning communities in anattempt to improve their undergraduateeducational endeavors (Gabelnick, Mac-Gregor, Matthews, & Smith, 1990). Broadlyconstrued, learning communities link to-gether learning opportunities—whether theybe courses, cocurricular activities, specialtopics, or interactions and conversations withfaculty and peers— to help students integrateand obtain a deeper understanding of theirknowledge (Gabelnick et al.; Lenning &Ebbers, 1999). Shapiro and Levine (1999)identified 4 major types of learning com-munities: (a) paired or clustered courses;(b) cohorts in large courses or first-year

Karen Kurotsuchi Inkelas is Assistant Professor of Counseling & Personnel Services, University ofMaryland, College Park. Jennifer L. Weisman is a doctoral student in Counseling & Personnel Servicesat University of Maryland, College Park.

interest groups (FIGs); (c) team-taughtcourses; and (d) residence-based programs,also known as living-learning programs. Thefirst 3 types of communities are morecurriculum-focused, and have been exa-mined by two national studies (Pascarella,Nora, et al., 1996; Snider & Venable, 2000);however, there have been fewer studiesconducted on the fourth type of learningcommunity: living-learning programs.

The critical difference between living-learning programs and other types of learn-ing communities is that the participants notonly partake in coordinated curricularactivities, but also live together in a specificresidence hall where they are provided withacademic programming and services. Theseprograms and services may include academiccourses taught in the residence facility, in-hall tutoring, academic advising, ongoinglecture series, etc. (Shapiro & Levine, 1999).Living-learning programs were created as ameans to integrate students’ in-class and out-of-class experiences by providing a com-munity that fosters greater faculty and peerinteraction, increased opportunities forcoordinated learning activities, and an aca-demically and socially supportive livingenvironment (Gabelnick et al., 1990; Len-ning & Ebbers, 1999); thus, living andlearning are combined seamlessly in stu-dents’ college experience.

Since the 1960s, several different incar-

public.press.jhu.edu

336 Journal of College Student Development

Inkelas & Weisman

nations of living-learning programs havebeen introduced on campuses around thecountry. Indeed, living-learning programshave become very popular in the past fewdecades, especially at institutions with largeenrollments where the faculty and admini-stration are attempting to make the campusmore intimate and personalized. The foci ofliving-learning programs can vary widely,from one-year programs meant to improvethe academic achievement of at-risk stu-dents, to four-year programs aimed atproviding more challenging academicenvironments for high-talent students, toprograms open to students of any class yearthat are designed to broaden students’ socialand cultural perspectives.

In fact, one institution may offer studentsthe opportunity to participate in a host ofdifferent types of living-learning programs.The institution in this study boasts sevendifferent living-learning programs—each ofthem with a different focus—which can beclustered into three broad thematic groups.The first group, Transition Programs,typically enroll first-year students and focuson facilitating a successful transition fromhome to college by providing academicsupport, skill development training, andprograms and classes designed to create amore intimate learning environment. Thesecond group, Academic Honors Programs,provides a rigorous academic experience topreselected high-talent students throughspecialized classes taught by affiliatedfaculty and concentrated coursework in col-laborative and creative endeavors. The thirdgroup, Curriculum-Based Programs, focuseson specific topics of study or research, suchas programs devoted to a foreign language,or for female science and mathematicsmajors, or for students interested in workingon a professor’s research project.

This study compares students’ experi-ences and outcomes across the three differentliving-learning program groups at thisinstitution. The results of this study illustratehow living-learning environments mayimpact the participants differently, evenwhen the programs are at the same insti-tution. Student development personnel atuniversities with more than one type ofliving-learning program can learn from thisstudy how living-learning participants indifferent programs may interact with theircollege environments in disparate ways. Thefindings from this study may also help thoseconsidering the introduction of one or moretypes of living-learning programs at theirinstitutions appreciate how different types ofprograms with varying goals and objectiveshave an impact on the students.

Indeed, the inclusion of several differenttypes of living-learning programs in onestudy addresses one of the major limitationsin the empirical literature, namely that muchof the previous research on living-learningimpact either assesses only the effects ofparticipation in a single program on studentoutcomes (e.g., Arminio, 1994; Kanoy &Bruhn, 1996; Pascarella & Terenzini, 1980,1981; Rice & Lightsey, 2001) or aggregatesall types of participation in living-learningprograms into one category (e.g., Pike,1999). This study not only compares theoutcomes of living-learning students tononparticipating students but also examinesoutcomes among students in three differenttypes of living-learning programs to investi-gate whether participation in these programsfosters the student engagement and outcomescommonly associated with the programobjectives. The three outcomes examined inthis study parallel the goals of the three typesof programs:• Transition Program goal: to facilitate a

MAY/JUNE 2003 ! VOL 44 NO 3 337

Living-Learning Programs

smooth academic transition for first-yearstudents;

• Academic Honors Program goal: tosupport students in their pursuit andenjoyment of challenging academicendeavors;

• Curriculum-Based Program goal: toprovide stimuli that broaden students’social and cultural perspectives andhorizons.

Three distinct questions guide thisinquiry:1. Are living-learning students more in-

volved than their nonparticipant counter-parts in college activities designed to becritical aspects of the living-learningexperience? Do living-learning studentsperceive their college environmentsmore positively?

2. Do living-learning students exhibit morepositive outcomes than nonparticipantsin three types of outcomes: (a) theiracademic transition to college; (b) theirpreference for and enjoyment of chal-lenging academic pursuits; and (c) theirinterest in and openness to learning newand different perspectives? Additionally,do living-learning students in programsdesigned to achieve a specific objectiveshow more positive outcomes for thisobjective than students in other living-learning programs?

3. Finally, do key college activities andenvironments provided by living-learn-ing programs influence the outcomes ofthe students who participate in them?And how do the different types ofprograms influence the participantsdifferently?At first blush, the answer to these

questions might appear to be self-evident.

Of course, one would assume that living-learning students would be more involvedin activities and environments designed tobe key components of their program, andwould exhibit outcomes that mirror theprogram goals and objectives at higher ratesthan their counterparts; however, partici-pation in a living-learning program is notrequired to engage in coursework usingcritical thinking skills, to study in groups,to interact with faculty and peers, or toperceive the academic environment assupportive. Students who are not in living-learning programs may nevertheless engagein these activities at similar rates to living-learning participants. In addition, whileprograms are designed to provide certaincontexts in the hope of facilitating specificoutcomes, there is no guarantee that theseresults occur. Indeed, because admission tothe institution at which this study took placeis highly competitive, there may be littledifference in the level of engagement andacademic outcomes of living-learningstudents versus students in the generalenrollment. This study examines potentialdifferences that may be attributable to living-learning participation and further investi-gates whether participation in varying typesof living-learning programs elicits similar ordistinct patterns of engagement and studentoutcomes.

A commonly noted criticism in priorresearch on outcomes associated withparticipation in residential learning com-munities posits that positive student out-comes among this population may be lessrelated to college or program impact andmore related to the innate abilities andpreferences of the students who elect toparticipate in living-learning programs.Embedded in this study’s conceptual frame-work are several measures that attempt to


Inkelas & Weisman

capture preexisting differences in intellectualability, engagement, and curiosity thatprecede the college experience. The additionof these variables in this study, as well asthe use of a college impact framework(Astin, 1993) in the study’s causal analyses,will help to mitigate the vulnerability of therelationship between living-learning partici-pation and higher outcomes as affected by athird variable: the precollege characteristicsof a strong and talented self-selected poolof students who participate in living-learningprograms.

REVIEW OF LITERATURE

Participation in living-learning programs hasbeen associated with several positive studentoutcomes in the research literature. Based ona review of single-institution studies pub-lished prior to the early 1990s, Pascarella,Terenzini, and Blimling (1994) concludedthat students in living-learning programswere more likely to persist, exhibit strongeracademic achievement, interact with faculty,and engage in a more intellectual residencehall atmosphere than students in conven-tional residence halls. In their review of edu-cational research, Pascarella and Terenzini(1991) reported that:

A small but reasonably consistent bodyof research indicates that residence ina living-learning center (LLC) haspositive and significant effects ofstudents’ gains in autonomy and person-al independence, intellectual disposi-tions and orientations, and general-ized personal development, as well ason declines in authoritarianism anddogmatism. (p. 261)

Other studies have shown positiveoutcomes in levels of involvement andinteraction with faculty and peers, integra-

tion, learning, and intellectual development(Pike, 1999; Pike, Schroeder, & Berry, 1997)and institutional commitment (Pike et al.).

Study findings from institutions such asstate universities in Maryland, Michigan,Missouri, and Wisconsin similarly reportedthat students in residential learning com-munities were significantly more likely thanstudents in traditional residence halls: (a) tobe more involved with campus activities andinteract with instructors and peers (Inkelas,1999; Pike, 1999); (b) to show greater gainsin or higher levels of intellectual devel-opment (Inkelas; Pike); (c) to use campusresources, seek assistance from peers,faculty, and staff (Brower, 1997) and toexperience a more smooth transition tocollege (Inkelas); and (d) to report theirresidence hall communities to be aca-demically and socially supportive (Inkelas;Scholnick, 1996).

How do living-learning programs influ-ence the positive student outcomes high-lighted above? Part of the explanation maybe inferred from the higher educationliterature. Over the past few decades, highereducation researchers studying the impact ofcollege on students have argued that onephenomenon distinguishes between thosestudents who benefit from the collegeexperience and those who do not. Thisphenomenon has been defined using dif-ferent labels, including the concepts ofinvolvement (Astin, 1984), integration(Tinto, 1993), engagement (Kuh, Schuh,Whitt, & Associates, 1991), and quality ofeffort (Pace, 1984); however, the premiseremains similar: student outcomes are relatedto the amount of effort—both physical andemotional—that students put into theircollege experiences. The more effort studentsput into their experiences, or the moreinvolved or engaged students are with their

MAY/JUNE 2003 ! VOL 44 NO 3 339


college environments, the more likely theyare to exhibit positive cognitive and affectivedevelopment. After 40 years of research oncollege students across the United States,Astin (1996) concluded that the three typesof student involvement with their collegeenvironments that are most influential ontheir academic outcomes are: (a) involve-ment with academics (e.g., time spentstudying, etc.), (b) involvement with faculty,and (c) involvement with student peergroups.

The concept of engagement and the threekey types of involvement cited by Astin(1996) lie at the foundation of the living-learning program philosophy. At their core,nearly all living-learning programs empha-size the above three elements in theirprogramming to facilitate student persis-tence, academic performance, and otherbeneficial academic outcomes. These ele-ments are manifested through academicservices (such as tutoring, advising, andstudy groups), greater opportunities tointeract with faculty on an informal basis,easier access to faculty (including officesfrequently found inside the residence hall),and structured programming that promotessustained interaction with peers (e.g.,cultural outings, community service, in-hallprograms, etc.) (Shapiro & Levine, 1999).

Among living-learning participants, thepositive effects of interaction with facultymembers and peers on student outcomeshave been noted in several studies. In onestudy, after controlling for the effects ofpreenrollment characteristics, peer-groupinteractions and interactions with facultywere both significant predictors of intellec-tual development and personal development(Pascarella & Terenzini, 1980). Pike (1999)found that interaction with peers and inte-gration of course material positively influ-

enced gains in general education. The latterof the two predictors, namely integration ofcourse material, infers that the emphasis ofcritical thinking skills in living-learningprograms has a direct impact on positiveintellectual outcomes.

Two other components of living-learningcommunities have been identified as criticalto student learning and development: work-ing in groups and students’ perceptions thattheir living environment is supportive oftheir endeavors. Lenning and Ebbers (1999)recommended that successful learningcommunities incorporate small-group workprojects that promote collaborative andcooperative learning. Indeed, learningcommunities that emphasize collaborativelearning have been associated with improvedgrades, better retention, and increased satis-faction for undergraduates (see Gabelnicket al., 1990). Although there has been nodirect empirical link between students’perceptions of a supportive living envi-ronment and improved academic outcomes,prior research has found that students’overall conceptions of a supportive campusclimate influenced their intellectual devel-opment (Nora & Cabrera, 1996). Inkelas(1999) found that living-learning studentsperceived their residential environments tobe significantly more supportive, bothacademically and socially, than studentsliving in traditional residence halls. Thiscurrent study examines the potential influ-ence of students’ perceptions of theirresidence environments on their outcomes.

Living-learning students are not solelyinfluenced by their participation in theirprograms. In fact, a large body of researchhas shown that students’ academic transitionto college and level of intellectual engage-ment can be shaped by their precollegecharacteristics and collegiate experiences


Inkelas & Weisman

outside the living-learning program. Severalstudies have shown that student backgroundcharacteristics influence their quality ofthinking. For example, the following demo-graphics were found to be significant tointellectual development and openness tonew perspectives: gender, race/ethnicity,parents’ educational attainment, and pre-college academic ability (Pascarella, Edi-son, Nora, Hagedorn, & Terenzini, 1996;Terenzini, Springer, Pascarella, & Nora,1995).

Of course, the college impact literaturehas also demonstrated the influence ofcurricular and co-curricular environmentsoutside living-learning programs on stu-dents’ academic outcomes, such as thestudents’ success of transition to college andlevel of intellectual engagement. Curricularinfluences found to predict intellectualoutcomes are the student’s major andacademic class level (Mentkowski & Strait,1983, as cited in Pascarella & Terenzini,1991; Walsh & Hardy, 1999). Research oncocurricular influences has been morevaried; for example, Inman and Pascarella(1998) found that extracurricular involve-ment in student clubs and organizations hada significant positive effect on criticalthinking scores. In addition, a multi-insti-tutional study of students during the firstyear of college found several significantdifferences between male students who areGreek-letter organization members and thosewho are nonmembers on several cognitiveoutcomes (Pascarella, Edison, & Whitt,1996). Finally, positive cocurricular influ-ences on openness to diverse perspectivesduring the first year of college include hoursworked per week, student acquaintances, andconversations with other students; whileparticipation in Greek life had a negativeimpact on the same outcome (Pascarella,

Edison, Nora, et al., 1996).

CONCEPTUAL FRAMEWORK ANDVARIABLES IN THE ANALYSIS

The conceptual framework for this studycombines many of the constructs describedin the Review of Literature section into onemodel by using an inputs-environments-outcomes (I-E-O) college impact model(Astin, 1993) as its foundation. In Astin’smodel, outcomes, or student characteristicsafter exposure to college, are thought to beinfluenced by both inputs, student charac-teristics before and at time of entry tocollege, and environments, various pro-grams, policies, faculty, peers, and educa-tional experiences that students interact withwhile in college. Astin argued that researchexamining how the college environment mayinfluence student change or developmentwill always be biased unless measures aretaken to control for as many student inputsas possible. Thus, research that drawsconclusions on the impact of living-learningprogram participation on student outcomes,but fails to take into account the inherentdistinctions among students before they evenenter college, will most likely overestimatethe effects of living-learning programs onstudents’ lives and achievements. Similarly,assessment that attempts to capture theimpact of a specific program (such as aliving-learning program or set of programs)on student outcomes may overestimate thesignificance of the program if it does notaccount for other types of activities andcollege environments in which students inthe program may participate.

The inputs selected for the study reflectnoncollegiate variables that were found tobe significant predictors of students’ transi-tion to college and intellectual development

MAY/JUNE 2003 ! VOL 44 NO 3 341


in previous research. Demographic inputmeasures include gender, race/ethnicity, andparents’ educational attainment; and highschool academic aptitude measures includeaverage high school grades and SAT scores(Pascarella, Edison, Nora, et al., 1996;Terenzini et al., 1995). The second set ofinput measures reflects students’ anticipatedcollege experiences and can be consideredas pretest variables for the outcome measuresfor this study. These variables includedcomposite measures representing students’anticipated ease of transition to college andthe importance they assigned to intellectualself-discovery during their college years.

Similar to other college impact models(e.g., Tinto, 1993; Weidman, 1989), bothcurricular and cocurricular aspects of thecollege environment were incorporated intothis study’s framework. Curricular charac-teristics included students’ current class leveland their majors, which were grouped into4 categories: undecided major (the referentcategory), science or mathematics, liberalarts, and professional or technical fields.Cocurricular environments included some ofthe most common types of out-of-classroomactivities that students participate in: studentclubs and organizations, social fraternitiesand sororities, community-service activities,and employment either on or off campus.

The remaining environmental measuresin the framework are related to the specialactivities or experiences that living-learningprograms strive to create for their parti-cipants: exposure to critical thinking incoursework, opportunities to study in groups,interaction with faculty and peers, and asupportive residence environment. Thecritical thinking composite measure includessingle items, such as: indication whether ornot students were required as part of theircourse assignments to compare or contrast

different discussion topics; arguing for oragainst a particular point of view; andpointing out the strengths and weaknessesof a specific argument or perspective. Typesof faculty interaction included the extent towhich students discussed academic issueswith their instructors outside of class or metinstructors on social occasions (such asgoing to a cultural event or having dinner ata faculty member’s home). Types of studentinteraction included the extent to whichstudents discussed with their peers academicissues (e.g., assignments) and socioculturalissues (e.g., human rights, politics, multi-culturalism). The final set of environmentmeasures in the framework includes stu-dents’ perceptions of their residence hallenvironments. The first perceptual climateindex measures students’ opinions that theirresidence hall environment is academicallysupportive (e.g., conducive for studying).The second index measures students’ per-ceptions that their residence hall environ-ment is socially supportive (e.g., sociallytolerant). For a description of the singleitems that comprise the composite mea-sures in the conceptual framework, seeAppendix A.

The three outcome measures, as men-tioned previously, mirror the goals andobjectives of the types of living-learningprograms highlighted in this study. Transi-tion Programs seek to facilitate a smoothtransition to college for first-year students,and this measure is comprised of students’perceptions of the relative ease or difficultyin the first year regarding forming studygroups, communicating with instructorsoutside of class, and seeking academic orpersonal help when needed. AcademicHonors Programs aim to support students intheir pursuit and enjoyment of challengingintellectual endeavors, and this composite


Inkelas & Weisman

measure includes individual items such as:questioning professors’ statements andarguments in readings; figuring things outfor oneself; exploring the meaning andinterpretations of new ideas; and organizingand interpreting ideas instead of memorizingfacts or details. Finally, Curriculum-BasedPrograms, through a particular disciplinaryfocus or theme, seek to provide stimuli tobroaden students’ sociocultural perspectivesand horizons. This outcome measure iscomprised of items such as: enjoyment oftalking with people with different values;enjoyment of discussing issues with peoplewho do not hold the same opinion; andenjoyment of taking courses that challengeexisting beliefs and values. (Again, forinformation on the composite outcomemeasures, see Appendix A.)

METHODSample and ProcedureThis study utilized a stratified randomsample of 4,269 students living in theresidence hall system of a large, highlycompetitive public research university in theMidwest. Admission to this university isvery competitive; entering undergraduateshave an average high school grade pointaverage (GPA) of 3.8 and cumulative SATscores of 1269 (Princeton Review, 2002). Tounderstand the effect of living-learningenvironments on student outcomes, thesample selected needed to include both thosestudents who were participating in living-learning communities and those who werenot; thus, all of the 1,531 living-learningparticipants on the campus were selected forthe sample population, and a control sampleof 2,738 nonparticipants consisted of stu-dents who lived in one of the university’sresidence halls but who were not part of a

living-learning program. The control samplewas stratified by gender, race or ethnicity,academic class, and residence hall so that itwould roughly mirror the subjects in theliving-learning sample.

In January 2001, resident advisors (RAs)of all of the undergraduate residence hallsat the university were asked to distributelabeled envelopes to the residents who wereselected to participate in the study. Indi-vidually coded surveys inside the envelopeswere matched to the name of the resident onthe label of the envelope. The surveys werecoded so that respondents’ data could belinked to institutional records. Of the 4,629students surveyed, 2,833 residents filled outand returned their surveys (a 61.2% responserate). Living-learning participants respondedto the survey at a slightly higher rate (63.4%)than nonparticipants (60.1%).

The final sample is diverse by severaldemographic factors; however, the living-learning sample varies from the controlsample in several respects. For example,although the control sample selected wasstratified to match the gender and race/ethnicity distribution of the living-learningsample, nonresponse created a genderdistribution among the control sample(54.9% female) that was slightly less biasedtoward females than the living-learningsample (58.9% female). In addition, a greaterpercentage of students in the living-learningsample (48.7%) had parents with graduatedegrees than students in the control sample(38.2%), which suggests that the socio-economic status of students in living-learning programs may be higher thanstudents in the general student body. Finally,despite the fact that there was no statisticaldifference in the self-reported average highschool grades of students in the living-learning and control samples (no doubt due

MAY/JUNE 2003 ! VOL 44 NO 3 343


to the competitive nature of this institution),living-learning students did report higherSAT or converted ACT composite scores.While 33.3% of the living-learning respon-dents reported an SAT composite score of1370 or higher, only 20.2% of the controlsample reported SAT scores in the samerange. (See Appendixes B and C for moredetails.)

InstrumentThe survey instrument contains 44 mostlymultiple-choice questions and item setsasking respondents about their precollegedisposition toward the collegiate experience,their ease or difficulty with their adjustmentto college, the types of activities they haveparticipated in while in college, theirperceptions of the academic and socialclimate in their residence hall, and theirpreferences for or against varying aspects ofintellectual engagement and curiosity. Theinstrument also includes several demo-graphic and academic background questions.An initial version of the instrument wasgiven to institutional living-learning direc-tors and administrators for their feedback onquestion clarity and appropriateness. Afterrevisions stemming from the feedback, theinstrument was used in a pilot test onapproximately 100 students living in oneresidence hall in October 2000. Studentswere also asked to comment on questionitems that were confusing or difficult toanswer; these vulnerabilities were addressedin the final instrument. Alpha reliability testswere conducted on all of the item setsincluded on the pilot instrument, resultingin scale reliabilities ranging from .65 to .87.

QUANTITATIVE ANALYSES

Before the analyses of the data were con-

ducted, the data set underwent severalpreliminary treatments. First, outliers andinconsistent responses were removed fromthe data set. Second, to preserve samplesizes, especially by individual living-learning program type, all missing inde-pendent variables that did not relate todemographics were replaced with meansubstitutions; it is important to note that thegreatest proportion of missing data for anysingle variable in the study was only 8.5%.Finally, to utilize a manageable set ofvariables for the study, data reductiontechniques were used to create compositemeasures. The composite measures werecreated via exploratory factor analysis withorthogonal rotation; the reliability (modelalpha) of the composite measures createdwas confirmed. The complete list of thecomposite measures utilized in this report,including reliability measures and individualitem factor loadings, can be found inAppendix A.

Several analyses were conducted tostudy the effect of living-learning parti-cipation on students’ experiences andoutcomes. First, using analysis of variancetests (ANOVA), students’ involvement withand perceptions of their college environ-ments were compared among the threeliving-learning groups and the controlsample in order to ascertain if living-learningparticipants are engaging more often in keycollegiate experiences and perceiving moresupportive living atmospheres. Second,another set of ANOVAs were conducted todiscern significant differences in the threeoutcomes among the three types of living-learning programs versus the control group.For all ANOVA analyses, Tukey’s post hoctests were utilized to discern among thegroup differences.

Finally, multiple regression analyses


Inkelas & Weisman

were conducted to examine the influence ofthe key living-learning environment mea-sures in the study’s conceptual frameworkon students’ outcomes. Forced-entry hier-archical regression analysis was chosen toenter the input and environment measuresin discrete blocks, which follow the orderof the variables in the conceptual framework.The key living-learning environment mea-sures were the final block entered in eachregression equation to examine the inde-pendent impact of this block on the outcomesafter having controlled for student inputs andother college environments outside living-learning programs. For each dependentmeasure, the regression analyses were runseparately with 4 different samples: studentsin the Transition Program, students in theAcademic Honors Program, students in theCurriculum-Based Program, and students inthe control sample.

RESULTS

The data in Table 1 represent the meandifferences in college experiences andperceptions among students in the threedifferent types of living-learning programs,as compared to the students in the controlgroup. All of the results discussed in thissection are of statistically significantfindings. As Table 1 shows, generallyspeaking, students in the living-learningprograms were more engaged in key living-learning activities and perceived theirenvironments more positively than non-participants. More specifically, Transitionand Academic Honors Program participantsmore often used critical thinking skills inclass assignments, met socially with a facultymember outside of class, and discussedsociocultural issues outside of class. For allthree of the above activities, Curriculum-

Based Program participants were less-oftenengaged or were not significantly differentin their level of engagement than studentsin the control group. This is surprising, giventhat Curriculum-Based Programs explicitlypromote curricular links; but the lack ofdifference may be related to the fact that oneof these programs caters primarily to first-and second-year mathematics and sciencemajors, who may be taking introductorycourses in their disciplines that emphasizecontent mastery over critical thinking skillsand tend not to discuss sociocultural issuesat length. It is important to note, however,that participants in the Curriculum-BasedPrograms did take advantage of one featureof their programs: they tended to utilizestudy groups more frequently than studentsin any of the other samples.

Interestingly, students in Transition andAcademic Honors Programs tended to go todifferent sources to discuss academic issues.Students in Transition Programs most oftendiscussed academic issues outside of classwith a faculty member, while students in theAcademic Honors Programs were signifi-cantly more likely to discuss academic issuesoutside of class with their peers. While allliving-learning programs strive to promotegreater faculty and peer interaction, thesefindings can be construed as somewhatconsistent with the intellectual developmentof students in these two programs. Parti-cipants in the Transition Programs areprimarily first-year students, who might beassumed to be in earlier stages of intellectualdevelopment and therefore more reliant onthe academic advice of authority figures (seeKitchener & King, 1994; Baxter Magolda,1992; Perry, 1970). Participants in theAcademic Honors Programs, on the otherhand, have already been identified as highlytalented and include students across all four

MAY/JUNE 2003 ! VOL 44 NO 3 345


TAB

LE 1

.D

iffer

ence

s in

Col

lege

Env

ironm

ent I

nter

actio

ns a

nd P

erce

ptio

ns A

mon

g D

iffer

ent T

ypes

of L

ivin

g-Le

arni

ng P

rogr

am P

artic

ipan

ts

Acad

emic

Curri

culu

m-

Tran

sitio

nHo

nors

Base

dCo

ntro

lPr

ogra

mPr

ogra

mPr

ogra

mSa

mpl

e(n

= 3

18)

(n =

378

)(n

= 1

87)

(n =

1,2

77)

Sign

ifica

nce

Tuke

y’s p

ost h

oc te

sts

Use

d cr

itica

l thi

nkin

g sk

ills

in c

lass

ass

ignm

ents

2.88

3.05

2.51

2.76

F =

26.4

5; d

f = 3

; p <

.001

1, 2

> 4

; 3 <

4

Dis

cuss

ed a

cade

mic

issu

es o

utsi

deof

cla

ss w

ith fa

culty

mem

ber

2.52

2.32

2.38

2.33

F =

8.82

; df =

3; p

< .0

011

> 4

Met

soc

ially

out

side

of c

lass

with

facu

lty m

embe

r1.

611.

501.

391.

41F

= 11

.57;

df =

3; p

< .0

011,

2 >

4

Dis

cuss

ed a

cade

mic

issu

esou

tsid

e of

cla

ss w

ith p

eers

3.14

3.30

3.13

3.12

F =

10.0

7; d

f = 3

; p <

.001

2 >

4

Dis

cuss

ed s

ocio

cultu

ral i

ssue

sou

tsid

e of

cla

ss w

ith p

eers

2.86

2.95

2.75

2.68

F =

17.4

3; d

f = 3

; p <

.001

1, 2

> 4

Stud

ied

in g

roup

s2.

872.

633.

222.

90F

= 19

.95;

df =

3; p

< .0

013

> 4;

2 <

4

Res

iden

ce e

nviro

nmen

t is

acad

emic

ally

sup

porti

ve2.

792.

612.

912.

62F

= 24

.43;

df =

3; p

< .0

011,

3 >

4

Res

iden

ce e

nviro

nmen

t is

soci

ally

sup

porti

ve2.

852.

962.

892.

78F

= 12

.74;

df =

3; p

< .0

012,

3 >

4


Inkelas & Weisman

years of their college careers; thus, they maybe more comfortable in using their peers asacademic sounding boards. Ironically,Academic Honors Programs participantsindicated that they studied in groups lessfrequently than students in any other pro-gram or in the control group; these studentsmay converse with one another on academicissues such as class assignments or a topicdiscussed in class, but tend to study moreindependently.

Finally, living-learning students tendedto find their residence environment to bemore supportive than nonparticipants.Transition and Curriculum-Based Programparticipants were significantly more likelyto find their residence environment to beacademically supportive, and AcademicHonors and Curriculum-Based Programparticipants were more likely to find theirresidence environments to be sociallysupportive. It is interesting to note thatstudents in the Curriculum-Based Programswere satisfied with both the academic andsocial support they received in their resi-dence environments; thus, they appear to besustaining the healthiest living atmosphereof the participants in the three programs andin comparison to the control sample.

Table 2 shows that living-learningparticipants, more often than their controlgroup counterparts, enjoy: (a) a smoothtransition to college during their first year;(b) challenging academic pursuits; and(c) learning new or different perspectives. Inaddition, the students in the programs withobjectives that mirror the outcomes in thisstudy were generally found to exhibit thehighest outcomes among their classmates.For example, participants in TransitionPrograms were the most likely to perceive asmooth transition to college, and signi-ficantly more so than Academic Honors

Program participants and the control groupstudents. Similarly, participants in AcademicHonors Programs indicated more often thatthey enjoyed challenging academic pursuits,more than students in the other two living-learning programs and the control group.Participants in the Curriculum-Based Pro-grams were surprisingly not the most likelyto indicate that they enjoyed learning newor different perspectives; instead, studentsin the other two living-learning programs—Transition and Academic Honors—hadhigher mean scores.

Some may question whether the differ-ences in outcomes in Table 2 are actually theresult of differences in ability or aptitudeamong living-learning students versus thecontrol sample. After all, students in theliving-learning sample did have higherstandardized test scores (although there wasno significant difference in high school GPAamong the two samples). So, perhaps thereason why living-learning students have aneasier transition to college and find that theyprefer intellectually engaging activities ismore a by-product of a student populationthat is more academically inclined andtalented.

To address this limitation, mean differ-ences in the three outcome measures werere-analyzed, using only those respondents inthe living-learning programs and the controlsample whose SAT composite scores wereover 1310 (the highest third of the sample).The results of these analyses revealed thatliving-learning program participants, as awhole as well as by program type, hadsignificantly higher mean scores among thethree outcomes than nonparticipants.

The final research question asks whetherthe key living-learning college environmentsidentified for this study are pivotal ininfluencing the three outcomes of the

MAY/JUNE 2003 ! VOL 44 NO 3 347

Living-Learning ProgramsTA

BLE

2.

Diff

eren

ces

in O

utco

mes

Am

ong

Par

ticip

ants

by

Type

of L

ivin

g-Le

arni

ng P

rogr

am

Livi

ng-L

earn

ing

Prog

ram

Par

ticip

ant

Cont

rol S

ampl

e(n

= 8

83)

(n =

1,2

77)

Sign

ifica

nce

Sm

ooth

aca

dem

ic tr

ansi

tion

durin

g fir

st y

ear

2.68

2.62

F =

6.61

; df =

1; p

< .0

1

Enj

oy c

halle

ngin

g ac

adem

icpu

rsui

ts2.

932.

80F

= 37

.55;

df =

1; p

< .0

01

Enj

oy le

arni

ng n

ew o

r diff

eren

tpe

rspe

ctiv

es3.

012.

86F

= 44

.45;

df =

1; p

< .0

01

Acad

emic

Curri

culu

m-

Tran

sitio

nHo

nors

Base

dCo

ntro

lPr

ogra

mPr

ogra

mPr

ogra

mSa

mpl

e(n

= 3

18)

(n =

378

)(n

= 1

87)

(n =

1,2

77)

Sign

ifica

nce

Tuke

y’s p

ost h

oc te

sts

Sm

ooth

aca

dem

ic tr

ansi

tion

durin

g fir

st y

ear

2.76

2.63

2.66

2.62

F =

5.32

; df =

3; p

< .0

011

> 2,

4

Enj

oy c

halle

ngin

g ac

adem

icpu

rsui

ts2.

893.

022.

812.

80F

= 22

.13;

df =

3; p

< .0

011

> 4;

2 >

1, 3

, 4

Enj

oy le

arni

ng n

ew o

r diff

eren

tpe

rspe

ctiv

es3.

003.

062.

902.

86F

= 19

.07;

df =

3; p

< .0

011

> 4;

2 >

3, 4


Inkelas & Weisman

students in the living-learning programsanalyzed. The data in Tables 3, 4, and 5depict both the total variance explained (totalR 2) as well as the percent of varianceexplained by the final block consisting of thekey living-learning environments (R2 ex-plained by the final block) for the threeoutcomes examined in this study, broken outby the three different living-learning samplesand the control group. In addition, these threetables also show the partial regressioncoefficients that predict each outcome,respectively. The remainder of this sectionwill highlight the significance of the keyliving-learning environments in predictingthe academic outcomes of this study forliving-learning students, and will concludewith the input and environmental measuresoutside the living-learning programs thatwere also found to predict the three out-comes.

As shown in Table 3, the total R2 for thefirst outcome—smooth academic transitionduring the first year—appears to indicate thatthe conceptual framework developed for thisstudy is a more effective predictor oftransition for the students in the Curriculum-Based Programs than the Transition Pro-grams and control sample. This is surprising,given that one might assume that theframework would be most effective amongthe Transition Program group, given theclose relationship of this program’s objec-tives with this particular outcome. It isinteresting to note that the key living-learning environments entered in the lastblock were the most useful in predicting aneasy transition for students in the TransitionPrograms (R2 change = .15); thus, the keyliving-learning environments are importantin understanding the factors that foster aneasy transition to college for TransitionProgram students. Furthermore, the key

living-learning environments are also fairlysignificant predictors for the other living-learning program samples as well with theproportion of variance left to be explainedby the final block has already accounted forthe variance consumed by the controlmeasures.

Through the examination of the partialregression coefficients in Table 3, we candiscern that several of the key living-learningenvironments were significant predictors ofstudents’ easy transition to college. Forexample, among students in any group, thosewho discussed academic issues with facultymembers and studied in groups were morelikely to indicate a smooth transition tocollege. For the living-learning students,however, a mix of academic and socialinteractions seems to have influenced theirtransition experiences. On the other hand,among the students in the control sample,all of the significant predictors involvedacademically focused activities that are moretraditional.

The negative relationship betweensociocultural peer discussions and thetransition-to-college dependent measureamong Academic Honors Program studentsis curious. Perhaps academically talentedstudents, such as those recruited for theAcademic Honors Programs, may be morewilling to tackle controversial and complexcontemporary issues, and these interactionsmay become impassioned to the point whereparticipants may be uncomfortable. Thisdiscomfort may then undermine students’sense of belonging to the campus, which isa component of students’ confidence in theirtransition to college. Indeed, other results inthe Table 3 show that Academic HonorsPrograms students who perceive theirresidence hall environment to be sociallysupportive are more likely to express a

MAY/JUNE 2003 ! VOL 44 NO 3 349


smooth transition to college; so the per-ception of social support by one’s peers maybe both directly and indirectly linked to aneasy academic transition for these talentedstudents.

For Transition Program students, per-ceptions of the residence hall environmentplay an even more critical role in theirtransitions, given that Transition Programstudents who perceive their residence hallsto be both academically and socially sup-portive showed a significant positive rela-tionship to a smooth transition to college.Similarly, one of the only other significantpredictors of a smooth transition to collegefor Curriculum-Based students (other thanacademic discussions with faculty members)was their perception that their residence hallwas supportive of their academic endeavors.For students in the control sample, the moretraditional academic environments aredirectly related to the dependent measure:discussing academic issues with both facultyand peers, studying in groups, and perceivingan academically supportive residence hallwere all positively associated with a smoothtransition to college. This may indicate thatstudents who are not socialized into a living-learning environment tend to rely on themore traditional outlets for academic sup-port, while living-learning participantsemploy both these traditional outlets andbuilt-in social networks as well.

For the second outcome measure—enjoyment of challenging academic pursuits—the conceptual framework is a strongerpredictive model for all three living-learningsamples, particularly for Academic HonorsProgram participants, than the control group(see Table 4). The last block of key living-learning environments were effective inpredicting approximately 10% of the vari-ance for all three living-learning program

samples. Yet, for the three living-learningsamples, the percent of variance explainedin the final block was shaped primarily fromone measure, although this measure changedfrom living-learning sample to sample.Among Transition Program students, dis-cussing academic issues with faculty mem-bers was the only key living-learningenvironmental measure that significantlyinfluenced their preference for challengingacademic pursuits. In addition to discussingacademic issues with faculty members, theother significant predictor of preference forchallenging academic pursuits among Aca-demic Honors Program students was dis-cussing sociocultural issues with peers,which was also the case for participants inCurriculum-Based Programs.

Thus, discussions with peers of socio-cultural issues are a strong positive predictorof this form of intellectual engagementamong nearly all of the different groups.(The beta for the Transition Programs samplewas marginally significant at p = .065.)Interestingly, discussing academic issueswith peers was not a significant predictoramong any of the samples. Thus, it wouldappear that the type of discussions thatstudents partake in with their peers isinfluential in shaping their preference forengagement in challenging academic pur-suits, and the type of discussions thatpositively predict this outcome are thoseconcerning cultural differences and majorsocial problems.

Finally, having coursework requiringcritical thinking skills was only influentialin predicting engagement in challengingacademics for students in the control sample.The nonsignificance of critical thinkingactivities among the living-learning samplesmay be due to the fact that many suchprograms emphasize critical thinking exer-


Inkelas & WeismanTA

BLE

3.

Pre

dict

ors

of S

tude

nts’

Per

cept

ions

of a

Sm

ooth

Aca

dem

ic T

rans

ition

to C

olle

ge b

y Ty

pe o

f Liv

ing-

Lear

ning

Pro

gram

Tran

sitio

nA

cade

mic

Cur

ricu

lum

-Bas

edPr

ogra

mH

onor

s Pr

ogra

mPr

ogra

mC

ontr

ol S

ampl

e(n

= 3

23)

(n =

382

)(n

= 1

95)

(n =

1,2

92)

Step

/Var

iabl

eB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

g

Dem

ogra

phic

cha

ract

eris

tics

and

HS

apt

itude

Gen

der (

Fem

ale)

–.16

–.04

–.19

–.05

–.58

–.15

.00

.00

Whi

te.1

0.0

3.4

0.1

1–.

25–.

07.1

8.0

5A

frica

n A

mer

ican

.45

.08

.91

.13*

–.96

–.19

–.05

–.01

Latin

o–.

01.0

01.

04.1

3*–1

.10

–.11

–.03

.00

Asi

an A

mer

ican

.08

.02

.28

.05

–.82

–.18

–.05

–.01

Edu

catio

nal a

ttain

men

tof

par

ents

.01

.01

.06

.10*

–.03

.06

.00

.01

Hig

h sc

hool

GPA

–.34

–.12

*.1

0.0

4.1

2.0

4–.

07–.

03S

AT s

core

.00

.06

.00

.04

.00

–.12

.00

.01

Pre

test Con

fiden

ce o

f eas

ytra

nsiti

on to

col

lege

.06

.12*

.10

.21*

**.1

0.2

0**

.11

.23*

**P

redi

spos

ition

to le

arni

ngne

w p

ersp

ectiv

es.0

4.0

6.0

0.0

0.0

0–.

01.0

2.0

4

Cur

ricul

ar e

nviro

nmen

tsA

cade

mic

cla

ss le

vel

–.43

–.12

*–.

16–.

06–.

25–.

09–.

11–.

04U

ndec

ided

maj

or (R

efer

ent)

Sci

ence

or M

athe

mat

ics

maj

or–.

30–.

07–.

08–.

02–.

42–.

12.0

9.0

2Li

bera

l Arts

maj

or–.

28–.

07.0

6.0

2–.

59–.

13.1

8.0

5P

rofe

ssio

nal o

r Tec

hnic

al m

ajor

–.29

–.07

.01

.00

.05

.01

.29

.06

tabl

e co

ntin

ues

MAY/JUNE 2003 ! VOL 44 NO 3 351


BLE

3.

cont

inue

d

Pre

dict

ors

of S

tude

nts’

Per

cept

ions

of a

Sm

ooth

Aca

dem

ic T

rans

ition

to C

olle

ge b

y Ty

pe o

f Liv

ing-

Lear

ning

Pro

gram

Tran

sitio

nA

cade

mic

Cur

ricu

lum

-Bas

edPr

ogra

mH

onor

s Pr

ogra

mPr

ogra

mC

ontr

ol S

ampl

e(n

= 3

23)

(n =

382

)(n

= 1

95)

(n =

1,2

92)

Step

/Var

iabl

eB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

g

Coc

urric

ular

env

ironm

ents

Stud

ent c

lubs

& o

rgan

izat

ions

–.02

–.04

–.02

–.03

–.09

–.17

.00

.00

Gre

ek-le

tter o

rgan

izat

ions

–.06

–.06

–.03

–.02

.10

.07

–.01

–.01

Com

mun

ity s

ervi

ce a

ctiv

ities

–.05

–.05

.05

.05

.09

.09

–.04

–.04

Wor

k on

or o

ff ca

mpu

s.0

2.0

1.0

6.0

4–.

08–.

06–.

05–.

04

Key

livi

ng-le

arni

ng e

nviro

nmen

ts a

nd p

erce

ptio

nsH

ad c

ours

ewor

k re

quiri

ngcr

itica

l thi

nkin

g.0

2.0

3–.

02–.

02.0

2.0

2–.

01–.

02D

iscu

ssed

aca

dem

ic is

sues

with

facu

lty m

embe

r.1

4.1

9**

.17

.23*

**.1

3.2

0*.0

9.1

3***

Met

soc

ially

with

facu

lty m

embe

r–.

01–.

02–.

02–.

03.1

0.1

3.0

2.0

2D

iscu

ssed

aca

dem

ic is

sues

with

pee

rs.0

5.0

5.1

7.1

7*.1

4.1

4.0

9.0

9*D

iscu

ssed

soc

iocu

ltura

l iss

ues

with

pee

rs–.

01–.

01–.

10–.

16**

–.05

–.08

–.02

–.04

Stud

ied

in g

roup

s.2

5.1

2*.1

9.1

0*–.

07–.

04.2

5.1

3***

Per

ceiv

ed re

side

nce

hall

asac

adem

ical

ly s

uppo

rtive

.10

.16*

.05

.06

.12

.17*

.12

.17*

**P

erce

ived

resi

denc

e ha

llas

soc

ially

sup

porti

ve.0

9.1

7**

.11

.17*

**.0

4.0

6–.

02–.

03

R2 e

xpla

ined

by

final

blo

ck.1

5.1

2.1

3.0

8To

tal R

2.2

4.2

6.2

9.1

9F

3.64

***

4.81

***

2.63

***

11.7

1***

Not

e.B

etas

repr

esen

t the

sta

ndar

dize

d va

lues

in th

e fin

al b

lock

for e

ach

sam

ple.

* p

< .0

5.**

p <

.01.

***

p <

.001

.


Inkelas & WeismanTA

BLE

4.

Pre

dict

ors

of S

tude

nts’

Pre

fere

nce

for o

r Enj

oym

ent o

f Cha

lleng

ing

Aca

dem

ic P

ursu

its b

y Ty

pe o

f Liv

ing-

Lear

ning

Pro

gram

Tran

sitio

nA

cade

mic

Cur

ricu

lum

-Bas

edPr

ogra

mH

onor

s Pr

ogra

mPr

ogra

mC

ontr

ol S

ampl

e(n

= 3

23)

(n =

382

)(n

= 1

95)

(n =

1,2

92)

Step

/Var

iabl

eB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

g

Dem

ogra

phic

cha

ract

eris

tics

and

HS

apt

itude

Gen

der (

Fem

ale)

–.27

–.06

–.43

–.09

–.99

–.17

*–.

53–.

11**

*

Whi

te.3

0.0

6–.

35–.

07.3

3.0

7.1

5.0

3A

frica

n A

mer

ican

.10

.01

–.94

–.10

–.74

–.09

.20

.03

Latin

o.0

5.0

1–.

59–.

06–.

30–.

02.1

9.0

2A

sian

Am

eric

an.0

1.0

0–1

.74

–.24

***

–.84

–.13

–.41

–.06

Edu

catio

nal a

ttain

men

t of p

aren

ts–.

05–.

05–.

04–.

05–.

04–.

05.0

0.0

0H

igh

scho

ol G

PA.0

8.0

2–.

07–.

02–.

22–.

05–.

03–.

01S

AT s

core

.00

.14*

.00

.16*

*.0

0.0

7.0

0.1

9***

Pre

test Con

fiden

ce o

f eas

y tra

nsiti

onto

col

lege

.05

.08

.08

.13*

*.0

6.0

9.0

5.0

9***

Pre

disp

ositi

on to

lear

ning

new

per

spec

tives

.11

.13*

.17

.21*

**.1

4.1

7*.1

3.1

6***

Cur

ricul

ar e

nviro

nmen

tsA

cade

mic

cla

ss le

vel

.10

.02

–.08

–.02

.38

.10

.03

.01

Und

ecid

ed m

ajor

(Ref

eren

t)S

cien

ce o

r Mat

hem

atic

s m

ajor

–.26

–.05

–.14

–.02

.78

.15

–.11

–.02

Libe

ral A

rts m

ajor

.76

.15*

–.04

–.01

.64

.10

.40

.08*

Pro

fess

iona

l or T

echn

ical

maj

or–.

05–.

01–.

28–.

041.

19.1

1.4

2.0

7*

tabl

e co

ntin

ues

MAY/JUNE 2003 ! VOL 44 NO 3 353


BLE

4.

cont

inue

d

Pre

dict

ors

of S

tude

nts’

Pre

fere

nce

for o

r Enj

oym

ent o

f Cha

lleng

ing

Aca

dem

ic P

ursu

its b

y Ty

pe o

f Liv

ing-

Lear

ning

Pro

gram

Tran

sitio

nA

cade

mic

Cur

ricu

lum

-Bas

edPr

ogra

mH

onor

s Pr

ogra

mPr

ogra

mC

ontr

ol S

ampl

e(n

= 3

23)

(n =

382

)(n

= 1

95)

(n =

1,2

92)

Step

/Var

iabl

eB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

g

Coc

urric

ular

env

ironm

ents

Stud

ent c

lubs

& o

rgan

izat

ions

.07

.09

.00

.01

.07

.10

.02

.03

Gre

ek-le

tter o

rgan

izat

ions

–.07

–.05

–.05

–.02

.09

.05

–.14

–.09

***

Com

mun

ity s

ervi

ce a

ctiv

ities

–.07

–.05

.01

.01

.23

.15

.02

.02

Wor

k on

or o

ff ca

mpu

s.0

8.0

4.0

6.0

3–.

06–.

03.0

0.0

0K

ey li

ving

-lear

ning

env

ironm

ents

and

per

cept

ions

Had

cou

rsew

ork

requ

iring

criti

cal t

hink

ing

–.01

–.01

.11

.09

.06

.06

.10

.10*

*D

iscu

ssed

aca

dem

ic is

sues

with

facu

lty m

embe

r.1

6.1

7**

.14

.14*

–.07

–.08

.06

.06*

Met

soc

ially

with

facu

lty m

embe

r.0

3.0

3.0

3.0

3.0

9.0

8–.

03–.

03D

iscu

ssed

aca

dem

ic is

sues

with

pee

rs.1

6.1

2.1

2.0

9.1

9.1

2.0

6.0

5D

iscu

ssed

soc

iocu

ltura

l iss

ues

with

pee

rs.1

1.1

3.1

4.1

5**

.20

.22*

*.1

3.1

5***

Stud

ied

in g

roup

s.1

2.0

5–.

19–.

07–.

25–.

09.0

5.0

2P

erce

ived

resi

denc

e ha

ll as

acad

emic

ally

sup

porti

ve.1

0.1

2–.

03–.

03–.

12–.

12.0

1.0

1P

erce

ived

resi

denc

e ha

llas

soc

ially

sup

porti

ve–.

04–.

06.0

1.0

2.0

3.0

3–.

06–.

09R

2 exp

lain

ed b

y fin

al b

lock

.09

.09

.09

.05

Tota

l R2

.29

.34

.32

.22

F4.

56**

*6.

97**

*2.

97**

*14

.08*

**

Not

e.B

etas

repr

esen

t the

sta

ndar

dize

d va

lues

in th

e fin

al b

lock

for e

ach

sam

ple.

* p

< .0

5.**

p <

.01.

***

p <

.001

.


Inkelas & WeismanTA

BLE

5.

Pre

dict

ors

of S

tude

nts’

Pre

fere

nce

for o

r Enj

oym

ent o

f Lea

rnin

g N

ew o

r Diff

eren

t Per

spec

tives

by

Type

of L

ivin

g-Le

arni

ng P

rogr

am

Tran

sitio

nA

cade

mic

Cur

ricu

lum

-Bas

edPr

ogra

mH

onor

s Pr

ogra

mPr

ogra

mC

ontr

ol S

ampl

e(n

= 3

23)

(n =

382

)(n

= 1

95)

(n =

1,2

92)

Step

/Var

iabl

eB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

g

Dem

ogra

phic

cha

ract

eris

tics

and

HS

apt

itude

Gen

der (

Fem

ale)

-.32

-.11

-.06

-.02

-.30

-.09

-.20

-.06*

Whi

te.0

4.0

1-.1

6-.0

5.2

5.0

8.0

0.0

0A

frica

n A

mer

ican

.14

.03

-.28

-.04

.29

.06

-.40

-.07*

Latin

o-.4

4-.0

6-.0

3.0

01.

80.2

0**

-.06

-.01

Asi

an A

mer

ican

.09

.02

-.42

-.09

.35

.09

-.12

-.03

Edu

catio

nal a

ttain

men

t of p

aren

ts-.0

2-.0

4.0

5.0

8.0

1.0

1.0

2.0

3H

igh

scho

ol G

PA-.0

2-.0

1-.0

3-.0

1-.2

5-.0

9-.1

6-.0

6*S

AT s

core

.00

.09

.00

.11*

.00

.06

.00

.04

Pre

test Con

fiden

ce o

f eas

y tra

nsiti

onto

col

lege

.00

-.01

.05

.11*

.08

.17*

*.0

4.1

0***

Pre

disp

ositi

on to

lear

ning

new

per

spec

tives

.09

.16*

*.2

0.3

6***

.12

.23*

*.1

4.2

6***

Cur

ricul

ar e

nviro

nmen

tsA

cade

mic

cla

ss le

vel

-.29

-.10

-.09

-.04

-.24

-.10

-.07

-.03

Und

ecid

ed m

ajor

(Ref

eren

t)S

cien

ce o

r Mat

hem

atic

s m

ajor

-.16

-.05

.16

.04

-.15

-.05

-.11

-.03

Libe

ral a

rts m

ajor

.11

.03

.33

.10

.05

.01

.01

.00

Pro

fess

iona

l or T

echn

ical

maj

or-.1

5-.0

4.3

4.0

7-.2

8-.0

4.0

8.0

2

tabl

e co

ntin

ues

MAY/JUNE 2003 ! VOL 44 NO 3 355


BLE

5.

cont

inue

d

Pred

ictor

s of S

tuden

ts’ P

refer

ence

for o

r Enjo

ymen

t of L

earn

ing N

ew o

r Diffe

rent

Persp

ectiv

es b

y Typ

e of

Living

-Lea

rning

Pro

gram

Tran

sitio

nA

cade

mic

Cur

ricu

lum

-Bas

edPr

ogra

mH

onor

s Pr

ogra

mPr

ogra

mC

ontr

ol S

ampl

e(n

= 3

23)

(n =

382

)(n

= 1

95)

(n =

1,2

92)

Step

/Var

iabl

eB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

gB

eta

!!!! ! Si

g

Coc

urric

ular

env

ironm

ents

Stud

ent c

lubs

& o

rgan

izat

ions

.03

.07

-.03

-.06

.00

-.01

-.02

-.03

Gre

ek-le

tter o

rgan

izat

ions

-.02

-.02

.11

.07

-.01

-.01

-.04

-.03

Com

mun

ity s

ervi

ce a

ctiv

ities

.15

.18*

**.0

0.0

0.2

0.2

1**

.03

.03

Wor

k on

or o

ff ca

mpu

s-.1

0-.0

8-.0

3-.0

2.1

5.1

1.0

2.0

2

Key

livi

ng-le

arni

ng e

nviro

nmen

ts a

nd p

erce

ptio

nsH

ad c

ours

ewor

k re

quiri

ngcr

itica

l thi

nkin

g-.0

4-.0

6.0

8.1

1.0

0.0

1.0

5.0

8*D

iscu

ssed

aca

dem

ic is

sues

with

facu

lty m

embe

r-.0

4-.0

7.0

4.0

7-.0

1-.0

2-.0

2-.0

3M

et s

ocia

lly w

ith fa

culty

mem

ber

-.07

-.01

-.03

.00

-.09

-.12

.02

.02

Dis

cuss

ed a

cade

mic

issu

esw

ith p

eers

.03

.04

-.08

-.09

-.03

-.03

.03

.03

Dis

cuss

ed s

ocio

cultu

ral i

ssue

sw

ith p

eers

.15

.30*

**.1

1.1

8***

.19

.34*

**.1

2.2

2***

Stud

ied

in g

roup

s.1

4.0

9-.0

1-.0

1-.1

0-.0

6-.0

2-.0

1P

erce

ived

resi

denc

e ha

ll as

acad

emic

ally

sup

porti

ve.1

0.1

8**

-.03

-.04

-.03

-.05

.01

.01

Per

ceiv

ed re

side

nce

hall

asso

cial

ly s

uppo

rtive

.02

.06

.04

.07

.01

.01

.02

.04

R2 e

xpla

ined

by

final

blo

ck.1

3.0

4.0

9.0

6

Tota

l R2

.32

.31

.39

.25

F5.

37**

*6.

12**

*4.

15**

*16

.58*

**

Not

e.B

etas

repr

esen

t the

sta

ndar

dize

d va

lues

in th

e fin

al b

lock

for e

ach

sam

ple.

* p

< .0

5. *

* p

< .0

1. *

** p

< .0

01.


Inkelas & Weisman

cises so often in their courses that studentsno longer draw any significance from them,while students outside the programs parti-cipate in critical thinking activities with lessfrequency and thus draw more meaning fromthem.

As shown in Table 5, the conceptualframework was especially effective inprediction for the third outcome measure—enjoyment of learning new or differentsociocultural perspectives—among studentsin Curriculum-Based Programs (39% vari-ance explained). Although the final living-learning environmental block was effectivein influencing between 4% and 13% of thevariance for the living-learning samples,there appears to be only one key environ-mental measure that is a strong predictor ofstudents’ preference for learning newperspectives across all of the differentsamples: discussing sociocultural issues withpeers. In other words, students who fre-quently discuss sociocultural issues withtheir peers are more likely to be open to andinterested in learning new or differentsociocultural perspectives. So, unlike theprevious two outcome measures, peerinteraction around social issues is thestrongest influence on students’ predilectionto this form of intellectual engagement. Thisfinding is consistent with a recent argumentput forth by defenders of diversity as acompelling educational interest (Gurin,1999; Hurtado et al., 1999). These authorsargued that interaction with diverse peers isvital to learning new perspectives, andlearning new perspectives is vital forappreciating cultural differences, a crucialaspect of a healthy and diverse democracy.

In addition to the key living-learningenvironments, there were several inputcharacteristics and environmental charac-teristics outside the living-learning programs

that significantly influenced students’academic outcomes. For example, in Table3, African American and Latino students inthe Academic Honors Programs were posi-tively associated with a smooth academictransition in their first year. In addition,students with highly educated parents weremore associated with a smooth transition.While it is to be expected that students withhighly educated parents would be moreconfident in their academic transition tocollege than students with less educatedparents, it is positive news that AfricanAmerican and Latino students in the Aca-demic Honors Programs felt confident intheir transition. Surprisingly, lower highschool GPAs were significantly related to asmooth academic transition in the first yearfor students in the Transition Programs.Students with lower high school grades whofelt at-risk with their academic transition tocollege may have decided to participate in atransition-focused living-learning programand as a result made the greatest gains in theperceptions of their actual transition.

Also in Table 3, first-year students inTransition Programs were found to be signi-ficantly more likely to express an easy tran-sition than juniors and seniors. It is curiousthat first-year students would be more likelyto find their transitions easier than upperdivision students, but this may be becausejuniors and seniors might look back upontheir first year through a perspective thatincludes several more years of experienceonly to realize that it was more difficult thanthey initially thought, while first-yearstudents do not have this perspective.

In Table 4, it is interesting to note thatwomen in the Curriculum-Based Programsand the control sample and Asian Americanstudents in Academic Honors Programs wereless likely to prefer engaging in challenging

MAY/JUNE 2003 ! VOL 44 NO 3 357


academic pursuits. This finding may besomewhat related to the hesitancy of studentsin these two groups to challenge authority,especially in light of the knowledge that thesample in this study is predominantlycomposed of first-year and sophomorestudents. Given that women and AsianAmericans are not always negatively asso-ciated with this trend across the entiresample, these results may be idiosyncraticto the women and Asian Americans in certainprograms. Indeed, such culturally basedstereotypes should always be used withcaution when examining a diverse samplesuch as the one in this study. As expected,students with higher SAT scores across mostother demographic groups were more likelyto show a greater inclination toward chal-lenging academic pursuits than students withlower SAT scores.

Two college environments outside theliving-learning programs were associatedwith engagement with challenging academicwork (see Table 4). For Transition Programstudents and students in the control sample,those who were majoring in liberal artsdisciplines were more likely to express apreference for engaging in intellectualpursuits. Among students in the controlsample, those involved with Greek-letterorganizations were less likely to express aninterest in challenging academic pursuitsthan those who were not. The Greek-letteraffiliation finding has been validated inseveral previous studies (see Pascarella,Edison, & Whitt, 1996). For this study, theliberal arts category includes students whosemajors are clustered in the humanities, socialsciences, and fine arts. Given that themajority of the sample is composed of first-year students and sophomores, perhaps thosewho declare these types of majors so earlyin their college careers are predisposed to

the study of broad issues that challengeassumptions and question authority.

Finally, among the input characteristicsin Table 5, women and African Americanstudents in the control sample were nega-tively associated with a preference forlearning new or different social or culturalperspectives. This relationship may besimilarly related to the conjecture in the prioranalysis, in which it was hypothesized thatwomen and minority groups may be lessinclined to challenge the status quo. How-ever, given that this finding was onlyprevalent in the control sample, it may besuggesting that living-learning programsmitigate this negative association for womenand African Americans. Indeed, amongliving-learning participants, Latino studentsin Curriculum-Based Programs are posi-tively associated with an openness to newor different perspectives; nevertheless, thisrelationship should be further investigated.One final surprising relationship among thecontrol group sample is the finding thatstudents with lower high school GPAs weremore likely to enjoy learning new or dif-ferent perspectives than students with higherGPAs.

For students in Transition Programs andCurriculum-Based Programs, performingcommunity service activities were positivelyassociated with an interest in learning newor different sociocultural perspectives. Thisfinding seems logical: students who parti-cipate in service learning are probably thetypes of students who wish to expand theirhorizons and learn about social differences.It is interesting to note that, among the threeliving-learning program types, the greatestproportion of students participating incommunity service activities were TransitionProgram students. This is primarily becauseone of the living-learning programs in the


Inkelas & Weisman

Transition Program grouping requires itsstudents to participate in a service learningopportunity. On the other hand, the lowestinvolvement in community service wasamong Curriculum-Based Program parti-cipants. This may suggest that those studentswho partake in community service, no matterthe service orientation of their surroundings,appear to derive positive intellectual out-comes from their involvement. Perhaps thisis a call to include a service learning oppor-tunity into all living-learning programs.

DISCUSSION AND IMPLICATIONSFOR RESEARCH

This study examined three research ques-tions associated with participation in dif-ferent types of living-learning programs andfound that participants in living-learningprograms are more involved than non-participants in college activities designed tobe critical aspects of the living-learningexperience. Moreover, not only did living-learning students exhibit stronger outcomeson all dependent measures than students inthe control group, but also participants inspecific types of living-learning programstended to exhibit the strongest outcomesamong the dependent measures that mostclosely mirrored the objectives of thoseprograms. These findings suggest that living-learning programs are effective in theirmissions; it is surprising, however, that theoutcomes of participants in Curriculum-Based Programs were not significantlydifferent than those of students in the controlsample. Perhaps it is important for staffmembers in these programs to follow up withtheir students to understand why the programis not facilitating the expected outcomes.

Several findings from this study areinstructive for practitioners and researchers

interested in living-learning programs.Practitioners can observe that peer academicsupport occurs in different forms in thedifferent types of living-learning programs.In this study, Academic Honors Programstudents tended to be more likely to discussacademic issues with their peers, but theydid not often hold these discussions in groupstudy sessions. On the other hand, studentsin Curriculum-Based Programs tended tostudy more often in groups and found theirresidence environments to be both aca-demically and socially supportive, but theydid not indicate that they often discussedacademic issues with their peers. Living-learning staff members should be careful todraw conclusions about peer interaction intheir programs: although students in theseprograms may be participating in someacademically oriented peer activities (e.g.,studying in groups), they may not befulfilling other academic needs (e.g., holdingdiscussions about academic issues). Instead,program staff should continually assessseveral different types of academic inter-actions that participants are or should beinvolved with to ensure that target inter-actions and program objectives are beingserved.

Campus administrators and programpractitioners should also be cognizant ofsocially focused activities and environmentalclimates as well. The results from this studyindicate that socially oriented activities aremost influential in shaping a preference fornew sociocultural perspectives. It is signi-ficant to note that openness to and tolerancefor diversity is associated most strongly withpeer interactions and not academic activities.Thus, those wishing to influence theirstudents to be more open to new ideas andpoints of view should not only providedifferent perspectives in their curricula, but

MAY/JUNE 2003 ! VOL 44 NO 3 359


also offer students opportunities to interactwith and discuss social issues with theirpeers. In addition, perceptions of supportiveresidence environments were influential ineasing the transition to college for studentsin all three types of living-learning programs.An academically minded and culturallyinclusive living atmosphere can be inferredto be vital in two important institutionalgoals: retention and tolerance for diversity.

Differences found between the controlsample and the living-learning samples inthis study may indicate areas in whichcampus-level decision makers could infusesome elements of living-learning programsinto the college experience of all students.The regression analyses in this study haveshown that the types of environments thatliving-learning programs strive to enhancefor their students are important in influencingpositive academic outcomes. These envi-ronments include increased contact withfaculty members, frequent peer discussionsinvolving both academic and social issues,and a supportive residence environment.Since the ANOVA data revealed that studentsnot participating in living-learning programswere significantly less likely to engage inall of these types of environments than theirliving-learning peers, and because re-searchers have shown how critical thesetypes of environments are to student success,campus leaders should create ways forstudents who are not in living-learningprograms to access these environmentswithout a program structure.

On campuses where more living-learn-ing programs cannot be instituted, it may notbe necessary to create more living-learningprogramming to facilitate successful studentengagement in these critical environments;instead, learning communities (without theresidential component), such as cluster

courses and interest groups, should beconsidered. Or, these critical environmentsmay be augmented with cocurricular pro-gramming, such as faculty-student lunchesand cultural outings or student tutoringprograms. With increasingly diverse studentpopulations, these types of alternativeprograms may be more beneficial forcommuting students (including nontradi-tional-aged and low-income students, amongothers who do not or cannot live on campus).

There are several lingering questionsfrom this study to guide future research inthis area. Several of these questions arerelated to the methodological limitations ofthis study.

First, this inquiry was conducted at onlyone institution, so the representativeness ofthe findings may not be wholly transferableto all institutional types. Second, severalmeasures in the study were self-reported bythe respondents, which could have intro-duced bias or been affected by students’wishes to give socially desirable responses.The next generation of research shouldcompare and contrast the impact of differenttypes of programs in different institutionalcontexts and should link students’ surveydata to their institutional records to mitigatethe effects of potentially skewed responses.

Another area for future investigationinvolves the surprising findings related towomen and minority groups. Why arewomen and students of certain racial orethnic backgrounds more or less likely tomanifest intellectual outcomes than others?And why is this relationship only evident inspecific types of living-learning programs?Addressing these questions could determinewhether these findings are idiosyncratic towomen and minority students in thesespecific programs, or if there is a moregeneral pattern to be discerned.


Inkelas & Weisman

Finally, living-learning research hasalways been hampered by the selectioneffect: it is difficult to draw conclusionsabout the impact of these programs when thetypes of students who elect to participatepossess strong characteristics for collegesuccess. Yet, by using a hierarchical block-entry form of regression analysis, and byperforming ancillary analyses by SAT scores,this study attempted to show that the living-

learning effect is tangible and that it doesadd something unique and special to thecollege experiences of students who chooseto participate.

Correspondence concerning this article should beaddressed to Karen Kurotsuchi Inkelas, Departmentof Counseling and Personnel Services, College ofEducation, 3214 Benjamin Building, College Park,MD 20742; [email protected]

APPENDIX A.Summary Statistics for Factor Scales in the Conceptual Framework

Factor AlphaScale description and individual items loadings reliabilities

Precollege characteristicsPretest: Confidence of easy transition to college .77

Feeling like I belong at this institution .68Feeling confident about my academic success .63Feeling comfortable in large classes .60Becoming friends with students whose views are different from my own .59Interacting with professors outside of class .55Overcoming homesickness .49Becoming part of extracurricular activities .48

Pretest: Predisposition to learning new perspectives .79Learning about cultures different from my own .77Getting to know people from backgrounds different from my own .76Learning more about myself .58Gaining a broad education and appreciation of ideas .50Discussing ideas and intellectual topics with friends and other students .43

Appendix continues

MAY/JUNE 2003 ! VOL 44 NO 3 361


Key living-learning environments and perceptionsHad coursework requiring critical thinking .80

Compare or contrast different topics presented in the course .82Point out the strengths and weaknesses of a particular argumentor point of view .79Argue for or against a particular point of view .67

Discussed academic issues with faculty member .76Made an appointment to meet with an instructor in his/her office .70Visited informally with an instructor before or after class .66Asked instructor for information related to a course .60Communicated with instructor using E-mail .60

Met socially with faculty member .82Discussed personal problems or concerns with an instructor .73Visited informally with an instructor during a social occasion(e.g., lunch, coffee, home visit) .73Went to a cultural event (e.g., concert or play) with an instructor .71Discussed career plans and ambitions with an instructor .63

Discussed academic issues with peers .73Discussed something learned in class .75Held discussions with students whose personal values werevery different from my own .71Shared concerns about classes and assignments .50

Discussed sociocultural issues with peers .85Talked about different lifestyles and customs .73Discussed views about multiculturalism and diversity .71Discussed major social problems such as world peace, human rights,equality, and justice .66Held discussions with students whose political opinions werevery different from my own .64

Perceived residence hall as academically supportive .73I think staff in my residence environment spend a great deal of timehelping students succeed academically .65I think it’s easy for students to form study groups atmy residence environment .59My residence environment clearly supports my academic achievement .51I can find adequate quiet study space available in my residenceenvironment .50I think students in my residence environment are well awareof the campus academic support services available to them .41

Appendix continues

APPENDIX A. continued

Summary Statistics for Factor Scales in the Conceptual Framework



Inkelas & Weisman

Perceived residence hall as socially supportive .83I find students in my residence environment have an appreciationfor people of different religions .68I find students in my residence environment have an appreciationfor people of different races .67Students in my residence environment are concerned with helpingand supporting one another .60

I would recommend this residence environment to a friend .57I see students with differing backgrounds having a lot of interactionwith one another in my residence hall .50I have enough peer support in my residence environment todo well in college .49

Dependent measuresPerceptions of easy academic transition in first year .63

Forming study groups .57Communicating with instructors outside of class .52Seeking academic or personal help when needed .47

Preference for or enjoyment of challenging academic pursuits .68In college, I frequently question or challenge professors’ statementsand ideas before I accept them as “right” .54I’d rather figure something out for myself than simply have itexplained to me .53I prefer courses requiring me to organize and interpret ideasover courses that ask me only to remember facts or information .48I try to explore the meaning and interpretations of the factswhen I am introduced to a new idea .47There have been times when I have disagreed with theauthor of a book or article that I was reading .40

Preference for or enjoyment of learning new or different perspectives .60I enjoy talking with people who have values different from minebecause it helps me understand myself and my values better .53I enjoy discussing issues with people who don’t agree with me .48I enjoy taking courses that challenge my beliefs and values .34

APPENDIX A. continued

Summary Statistics for Factor Scales in the Conceptual Framework


MAY/JUNE 2003 ! VOL 44 NO 3 363


APPENDIX B.Means and Standard Deviations of the Measures in the Conceptual Framework

(N = 4,622)

Measure M SD Description

Precollege characteristicsGender (Female) 1.56 0.50 Coded 1 = male; 2 = femaleWhite 0.59 0.49 Coded 0 = no; 1 = yesAfrican American 0.10 0.31 Coded 0 = no; 1 = yesLatino 0.04 0.22 Coded 0 = no; 1 = yesAsian American 0.14 0.35 Coded 0 = no; 1 = yesParents’ educational attainment 7.78 2.83 Composite of mother’s and father’s education

level; Individual items coded 1 = high schoolor less to 6 = doctoral degree

High school GPA 5.48 0.63 Coded from 1 = D+ or lower to 6 = A+ or ASAT score or converted ACT score 1266.87 136.97 Continuous math + verbal measure from 420-

1600Pretest: Confidence of easy transition to college 20.12 3.78 Scale index from 7 to 28, high value indicating

high confidencePretest: Predisposition to learning new perspectives 15.53 3.08 Scale index from 5 to 20, high value indicating

high importanceCurricular and cocurricular environments

Academic class level 11.36 0.66 Coded 11 = freshman; 12 = sophomore;13 = junior; 14 = senior

Science or Mathematics major 0.33 0.47 Coded 0 = no; 1 = yesLiberal Arts major 0.18 0.39 Coded 0 = no; 1 = yesProfessional or Technical major 0.17 0.38 Coded 0 = no; 1 = yesStudent clubs and organizations 10.16 3.07 Scale index from 8 to 32, high value indicating

participation in several clubsGreek-letter organizations 2.78 1.46 Scale index from 2 to 8, high value indicating

significant participationCommunity service activities 3.49 1.77 Scale index from 2 to 8, high value indicating

significant participationWork on or off campus 1.76 1.15 Coded 1 = not at all involved to 4 = very involved

Key living-learning environments and perceptionsHad course work requiring critical thinking 8.38 2.38 Scale index from 3 to 12, high value indicating

frequent requirementDiscussed academic issues with faculty member 9.43 2.57 Scale index from 4 to 16, high value indicating

frequent contactMet socially with faculty member 5.80 2.35 Scale index from 4 to 16, high value indicating

frequent contactDiscussed academic issues with peers 9.43 1.77 Scale index from 3 to 12, high value indicating

frequent contactDiscussed sociocultural issues with peers 10.98 2.87 Scale index from 4 to 16, high value indicating

frequent contact

Appendix continues


Inkelas & Weisman

Key living-learning environments and perceptions (continued)

Studied in groups 2.89 0.92 Coded 1 = Never to 4 = Once or more a weekPerceived residence hall as academically supportive 13.34 2.60 Scale index from 5 to 20, high value indicating

strong agreementPerceived residence hall as socially supportive 16.91 3.16 Scale index from 6 to 24, high value indicating

strong agreementDependent measures

Perceptions of easy academic transition in first year 7.90 1.75 Scale index from 3 to 12, high value indicatingvery easy

Preference for or enjoyment of challengingacademic pursuits 14.20 2.43 Scale index from 5 to 20, high value indicating

strong agreementPreference for or enjoyment of learning new ordifferent perspectives 8.71 1.58 Scale index from 3 to 12, high value indicating

strong agreement

APPENDIX B. continued

Means and Standard Deviations of the Measures in the Conceptual Framework(N = 4,622)

Measure M SD Description

MAY/JUNE 2003 ! VOL 44 NO 3 365


APPENDIX C.Demographics by Percentage of Respondents by Living-Learning Program

and Control Samples

Academic Curriculum-Transition Honors Based ControlProgram Program Program Sample(n = 302) (n = 346) (n = 169) (n = 1,243) """"" 2

Appendix continues

GenderMale 37.9 33.7 25.1 37.2 "# = 12.93; df = 3, p = .005Female 62.1 66.3 74.9 62.8

Race or EthnicityWhite 58.3 65.8 55.1 61.9 "# = 24.28; df = 18, p = .146African American 11.1 6.5 5.0 10.0Latino 5.0 5.0 2.9 4.1Asian American 15.2 11.2 17.4 13.9Native American 0.3 0.5 1.4 0.4Race not included 10.2 11.0 10.1 9.4None indicated 0.0 0.0 0.0 0.3

Educational level of Father or male guardianDon’t know 2.7 1.8 2.5 3.1 "# = 57.10; df = 18, p = .000High school or less 6.8 7.3 8.8 9.2Some college 8.6 7.3 12.3 11.4Associate’s degree 1.8 2.8 3.9 3.6Bachelor’s degree 21.9 20.7 26.0 26.6Master’s degree 28.7 25.2 25.5 26.2Doctoral or Professional degree 29.6 35.0 21.1 19.8

Educational level of Mother or female guardianDon’t know 1.8 1.8 1.0 2.2 "# = 52.44; df = 18, p = .000High school or less 7.1 8.5 10.2 12.1Some college 12.5 11.6 13.1 15.9Associate’s degree 5.0 4.8 8.3 6.5Bachelor’s degree 33.8 30.2 32.5 34.2Master’s degree 28.8 29.1 27.7 22.7Doctoral or Professional degree 11.0 14.1 7.3 6.5

Average high school gradesA+ or A 43.7 62.0 66.5 54.5 "# = 45.36; df = 12, p = .000A– or B+ 49.9 33.7 31.1 38.6B 5.3 3.5 2.4 6.4B– or C+ 1.2 0.8 0.0 0.5C or C– 0.0 0.0 0.0 0.1

SAT score (in quartiles)1180 or lower 18.2 12.2 22.3 24.9 "# = 133.50; df = 9, p = .0001190 to 1270 31.8 21.3 24.9 33.81280 to 1350 25.5 18.9 26.4 21.71360 or higher 24.5 47.6 26.4 19.7


Inkelas & Weisman

APPENDIX C. continued

Demographics by Percentage of Respondents by Living-Learning Programand Control Samples

Academic Curriculum-Transition Honors Based ControlProgram Program Program Sample(n = 302) (n = 346) (n = 169) (n = 1,243) """"" 2

Confidence in transition to collegeNot at all confident 0.0 0.0 0.0 0.4 "# = 15.82; df = 9, p = .071Somewhat confident 6.5 9.1 6.1 6.8Confident 51.8 59.6 61.9 58.7Very confident 41.7 31.3 32.0 34.2

Importance of learning new perspectivesNot at all important 52.9 79.7 78.9 69.7 "# = 97.80; df = 9, p = .000Somewhat important 2.9 1.0 1.5 2.7Important 14.7 7.3 8.3 14.5Very important 29.4 12.0 11.3 13.1

Academic class levelFreshman 88.0 63.1 76.0 70.8 "# = 69.91; df = 9, p = .000Sophomore 8.8 28.7 16.2 22.8Junior 2.1 7.5 6.9 4.9Senior 1.2 0.7 1.0 1.5

Academic majorUndecided 25.2 19.6 13.3 17.9 "# = 192.92; df = 9, p = .000Science or Mathematics 22.7 16.1 62.2 34.0Liberal Arts 28.9 52.1 18.4 30.7Professional or Technical 23.3 12.2 6.1 17.4

On-campus clubs and organizationsNot at all involved 35.4 25.7 38.9 42.3 "# = 40.45; df = 9, p = .000Somewhat involved 60.1 70.2 56.5 54.6Involved 3.2 3.4 3.6 2.8Very involved 1.3 0.8 1.0 0.3

Greek-letter organizationsNot at all involved 0.0 0.0 0.0 0.0 "# = 21.75; df = 6, p = .001Somewhat involved 30.8 59.4 54.1 47.9Involved 49.7 33.3 29.7 39.2Very involved 19.6 7.2 16.2 12.9

Community service activitiesNot at all involved 27.2 41.2 50.3 52.8 "# = 90.25; df = 9, p = .001Somewhat involved 28.2 26.2 25.6 23.9Involved 34.4 22.6 19.0 17.1Very involved 10.0 10.0 5.1 6.2

Work on or off campusNot at all involved 70.8 61.1 63.3 67.6 "# = 18.64; df = 9, p = .028Somewhat involved 5.2 5.2 5.0 6.7Involved 10.5 17.5 15.1 11.2Very involved 13.5 16.2 16.6 14.5

MAY/JUNE 2003 ! VOL 44 NO 3 367


Arminio, J. W. (1994). Living-learning centers: Offeringcollege students an enhanced college experience. Journalof College and University Student Housing, 24(1), 12-17.

Astin, A. W. (1984). Student involvement: A developmentaltheory for higher education. Journal of College StudentPersonnel, 24, 297-308.

Astin, A. W. (1993). What matters in college? Four criticalyears revisited. San Francisco: Jossey-Bass.

Astin, A. W. (1996). Involvement in learning revisited:Lessons we have learned. Journal of College StudentDevelopment, 37, 123-133.

Baxter Magolda, M. (1992). Knowing and reasoning incollege: Gender-related patterns in students’ intellectualdevelopment. San Francisco: Jossey-Bass.

Boyer Commission on Educating Undergraduates in theResearch University. (1998). Reinventing undergraduateeducation: A blueprint for America’s research uni-versities. Stony Brook: State University of New York atStony Brook.

Brower, A. (1997). End-of-year evaluation on the Bradleylearning community. Unpublished report, University ofWisconsin–Madison.

Edgerton, R. (1999). Education white paper. RetrievedSeptember 5, 2000, from http://www.pewtrusts.com/programs/Edu/edwp1.cfm

Gabelnick, F., MacGregor, J., Matthews, R. S., & Smith,R. L. (1990). Learning communities: Creating connec-tions among students, faculty, and disciplines. NewDirections for Teaching and Learning, No. 41. SanFrancisco: Jossey-Bass.

Gurin, P. (1999). Expert report of Patricia Gurin. In Thecompelling need for diversity in higher education. Gratzet al. v. Bollinger et al. No. 97-75231; Grutter et al. v.Bollinger et al. No. 97-75928 [On-line]. Available: http://www.umich.edu/~urel/admissions/legal/expert/

Hurtado, S., Milem, J. F., Clayton-Pedersen, A. R., Allen,W. R., ERIC Clearinghouse on Higher Education, &Association for the Study of Higher Education. (1999).Enacting diverse learning environments: Improving theclimate for racial/ethnic diversity in higher education.Washington, DC: Graduate School of Education andHuman Development, The George Washington University.

Inkelas, K. K. (1999). The tide on which all boats rise: Theeffects of living-learning participation on undergraduateoutcomes at the University of Michigan. Ann Arbor, MI:University Housing.

Inman, P., & Pascarella, E. T. (1998). The impact of collegeresidence on the development of critical thinking skillsin college freshmen. Journal of College Student Devel-opment, 39, 557-568.

Kanoy, K. W., & Bruhn, J. W. (1996). Effects of a first-year living and learning residence hall on retention andacademic performance. Journal of the Freshman YearExperience & Students in Transition, 8(1), 7-23.

Kitchener, K. S., & King, P.M. (1994). Developing reflectivejudgment: Understanding and promoting intellectualgrowth and critical thinking in adolescents and adults.San Francisco: Jossey-Bass.

Kuh, G. D., Schuh, J. H., Whitt, E. J., & Associates. (1991).Involving colleges: Successful approaches to fosteringstudent learning and development outside the classroom.San Francisco: Jossey-Bass.

Lenning, O. T., & Ebbers, L. H. (1999). The powerfulpotential of learning communities: Improving educationfor the future (ASHE-ERIC Higher Education ReportVolume 26, No. 6). Washington, DC: The GeorgeWashington University, Graduate School of Educationand Human Development.

Nora, A., & Cabrera, A. F. (1996). The role of perceptionsof prejudice and discrimination on the adjustment ofminority students to college. Journal of Higher Education,67, 119-148.

Pace, C. R. (1984). Measuring the quality of college studentexperiences: An account of the development and use ofthe College Student Experiences Questionnaire. LosAngeles: Higher Education Research Institute, GraduateSchool of Education, University of California.

Pascarella, E. T., Edison, M., Nora, A., Hagedorn, L. S., &Terenzini, P. T. (1996). Influences on students’ opennessto diversity and challenge in the first year of college.Journal of Higher Education, 67, 174-195.

Pascarella, E. T., Edison, M. I., & Whitt, E. J. (1996).Cognitive effects of Greek affiliation during the first yearof college. NASPA Journal, 33, 242-259.

Pascarella, E. T., Nora, A., Terenzini, P. T., Whitt, E. J.,Edison, M. I., & Hagedorn, L. S. (1996). What have welearned from the first year of the National Study onStudent Learning? Journal of College Student Devel-opment, 37, 182-192.

Pascarella, E. T., & Terenzini, P. T. (1980). Student-facultyand student-peer relationships as mediators of thestructural effects of undergraduate residence arrangement.Journal of Educational Research, 73, 344-353.

Pascarella, E. T., & Terenzini, P. T. (1981). Residencearrangement, student/faculty relationships, and freshman-year educational outcomes. Journal of College StudentPersonnel, 22, 147-156.

Pascarella, E. T., & Terenzini, P. T. (1991). How collegeaffects students: Finding and insights from twenty yearsof research. San Francisco: Jossey-Bass.

Pascarella, E. T., Terenzini, P. T., & Blimling, G. S. (1994).The impact of residential life on students. In C. C.Schroeder, P. Mable & Associates (Eds.), Realizing theeducational potential of residence halls (pp. 23-32). SanFrancisco: Jossey-Bass.

Perry, W. G., Jr. (1970). Forms of intellectual and ethicaldevelopment in the college years: A scheme. New York:Holt, Rinehart, & Winston.

Pike, G. R. (1999). The effects of residential learningcommunities and traditional residential living arrange-ments on educational gains during the first year ofcollege. Journal of College Student Development, 40,269-283.

REFERENCES


Inkelas & Weisman

Pike, G. R., Schroeder, C. C., & Berry, T. R. (1997).Enhancing the educational impact of residence halls: Therelationship between residential learning communities andfirst-year college experiences and persistence. Journalof College Student Development, 38, 609-621.

Princeton Review. (2002). [Name of institution]. RetrievedSeptember 13, 2002, from http://www.princetonreview.com/college/research/profiles/admissions.asp?listing=1023092&LTID=1

Rice, N. D., & Lightsey, O. R. (2001). Freshman livinglearning community: Relationship to academic successand affective development. Journal of College andUniversity Student Housing, 30(1), 11-17.

Scholnick, E. K. (1996). A two-year longitudinal study ofscience and math students in the College Park Scholarsprogram. Unpublished report, University of Maryland,College Park.

Shapiro, N. S., & Levine, J. H. (1999). Creating learningcommunities: A practical guide to winning support,organizing for change, and implementing programs. SanFrancisco: Jossey-Bass.

Snider, K. J. G., & Venable, A. M. (2000). Assessing learningcommunity effectiveness: A multi-campus approach. Paperpresented at the Annual Meeting of the Association forInstitutional Research, Cincinnati, OH.

Terenzini, P. T., Springer, L., Pascarella, E. T., & Nora, A.(1995). Influences affecting the development of students’critical thinking skills. Research in Higher Education,36(1), 23-39.

Tinto, V. (1993). Leaving college: Rethinking the causes andcures of student attrition (2nd ed.). Chicago: Universityof Chicago Press.

Walsh, C. M., & Hardy, R. C. (1999). Dispositionaldifferences in critical thinking related to gender andacademic major. Journal of Nursing Education, 38(4),149-155.

Weidman, J. C. (1989). Undergraduate socialization: Aconceptual approach. In J. Smart (Ed.), Higher education:Handbook of theory and research (5th ed.). New York:Agathon.

Wingspread Group on Higher Education. (1993). AnAmerican imperative: Higher expectations for highereducation. Racine, WI: The Johnson Foundation.

journal of college student developmentsnyder/reslife/differentbydesign.pdf · tution. student...

Documents