'Moving Into Students' Spaces: The Impact of Location of AcademicAdvising on Student Engagement among Undecided Students

Janet Heiss Arms, Montana State LlniversityAlberto F Cabrera, University of MarylandAaron M. Brawen University of Wisconsin at Madison

University stakeholders recognize the impor-tance of exposing all students to academic advis-ing as a means to enhance their engagement withthe institution, Living-learning communities areof particular prumise. In this study, conducted at amidwestern land grant university in the 2A04-05academic yean advisees in living-learning com-muniti e s re port e d s ignifi c ant ly higher engagementin their educational experiences than advisees withaccess only to a central advising ffice, and the dif-

ferences in levels of engagement with advisors weresignificant as other predictors of engagement weretaken into account.

KEY WORDS: advising centers, advising deliveryliving-learning communities, outcomes of educa-tion, residence hall advising, student expectationsof advising

Relative Emphasis: practice, research, theory

University stakeholders recognize the potentialthat academic advising affords to all students and aretherefore devoting greater resources to make aca-demic advising programs available to students in avariety of settings (Frost, l99l;Young, 1989). Thedevelopment of personal relationships between advi-sors and students has been emphasized when craft-ing advising programs (Gallagher & Allen, 2000;Kelley & Lynch, l99l; Love & Tinto, 1995; Schnell,1998; Shapiro & Levine, 1999; Winston & Sandor,1984; Zhao & Kuh, 2004 ). The importance placedon the value of personal relationships is well-grounded in research, which shows a link betweendeveloping a personal relationship with an academicadvisor and students'satisfaction, success, and per-sistence (Schnell, 1998; Winston & Sandor, 1984).

Universities have sought to make advising moreaccessible by offering academic advising servicesin residence halls and living-learning communities(Schein, Biggers, & Reese, 1986). Schein (1995)noted that residential halls are uniquely located toprovide easy access to academic advising.Consequently, he postulated that such proximitywould enhance the probabiliry of students devel-

8

oping meaningful connections with academic advi-sors. Love and Tinto (1995) called attention tolearning communities as another mechanism toincrease engagement with academic advising. Theyargued that academic advisors are the individualson campus best equipped to construct course clus-ters, assist with the recruitment of students intolearning communities, and participate as membersof the learning community.

Although there has been a 2O-year discussionabout the role academic advising can play in stu-dents' spaces, particularly in learning communities,little previous literature is backed with empiricalfindings. In this study, we address this deficiencyby examining the following research question: Towhat extent does the location of academic advisingimpact the engagement of undecided students? Toanalyze this research question, we compared advis-ing provided in three different locations: conven-tional residence halls, living-learning communities,and a central advising office. The literature on liv-ing-learning communities, student engagement,and academic advising suggests that academicadvising provided in living-learning communitieswill have the greatest impact on student engage-ment, while students will be impacted least whenthey have access only to a central advising office.The structures and events in living-learning com-munities provide more opportunities for advisors toget involved in the community and to publicizeacademic advising services (Love & Tinto, 1995;Zhao & Kuh, 2004).

As universities devote more resources (e.g.,salary time, and office space) to making academicadvising services available in a variety of students'living spaces, thasignificance regarding effective-ness of service location is heightened. While per-sonal relationships bet'ween students and academicadvisors are known to matter (see Schnell, 1998;Winston & Sandor, 1984), the question remains:Does the physical location of such services matter?

Literature Review

Student engagement has long been recognizedby the major student personnel organizations as a

iVAC,IDA Journal Volume 28 (l) Spring 2008

valid criterion in judging the likelihood of studentlearning. As cited in Learning Reconsidered: ACampus-lfide Focus on the Student Experience(Keeling, 2004), both the National Association ofStudent Personnel Adminisrators and the AmericanCollege Personnel Association recognized a con-nection between student engagement and personaldevelopment that manifests itself in learning. In aprevious publication, the American CollegePersonnel Association ( 1996) stressed the impor-tance of creating a seamless environment that linksstudents' in-class and out-of-class experiences andfocuses on academic success and student learning.

Such emphasis on student learning to appraisethe quality of academic services is well-groundedin research, which shows that engagement mattersfor learning, persisting to graduation, and experi-encing greater success after graduation (Astin,1984, 1993; Kuh, 2003; Pascarella & Terenzini,2005; Pike, 20}4;Tinto, 1997). While some researchshows that students' backgrounds and demographicshave a significant impact on their levels of engage-ment (Hu & Kuh, 2002; Pascarella & Terenzini,2005), other research shows that institutions candevelop programs that increase students' engage-ment (Astin, 1993; Kuh, 1996;Terenzini, Springer,Pascarella, & Nora, 1995; Tinto, 1997).

Learning communities can be a tool universitiesuse for increasing student engagement and persis-tence (Tinto, 1997 Zhao & Kuh, 2004). Most oftheeffect that learning communities have on engage-ment appears to be channeled through the socialrelationships they foster (Kuh, 2003; Pike, 2000;Pike, Schroeder, & Berry 1997; Stassen, 2003;Tinto, 1997; Zhao & Kuh, 2004). Because of thissocial connection, learning communities have beenrecognized as potential programmatic areas to fos-ter seamless and sustained relationships with aca-demic advisors (Tinto, 1997;' Zhao & Kuh, 2004).

As research on learning communities hasfocused on faculty-student and student-to-studentrelationships, the role of academic advising inincreasing student engagement within learningcommunities has not been examined. Some evi-dence suggests that administrators (specificallyacademic and student affairs professionals) mayhave a positive impact on student learning anddevelopment (Love, 1995; Schroeder & Mable,1994). Kuh, Schuh, and Whitt ( l99l ), in particu-lar, hypothesized that academic advisors may playan important role: They noted that student affairspersonnel are more likely than college professorsto be present when learning opportunities takeplace outside the classroom. In a similar vein,

Advising Spaces

Evenbeck and Williams (1999) argued that famil-iarity with and accessibility to academic advisorsare two strengths associated with learning com-munities. Based on learning community evalua-tions, Evenbeck and Williams found that studentsreported being comfortable with their advisor, ask-ing questions as they arise, and not waiting forscheduled appointments to see their advisor. Theyfurther speculated that the advisor-student con-nection often continues long after the learning-community experience has ended.

Methodology

Research DesignWe used a longirudinal research design to answer

the research question: Does the location of advis-ing services impact student engagement? Duringsummer orientation, we surveyed students to col-lect information on students'expectations for aca-demic advising and predispositions to engage incollege. A follow-up survey was conducted at theend of the students' first college year to assess stu-dents'levels of engagement with advising and edu-cational experiences. Demographic data andacademic ability measures were obtained from stu-dent records.

Target PopulationThe target population is comprised of first-time,

fint-year undecided students attending a majoq mid-westem, land gnnt univenity during the 2004-05 aca-demic year.The university under study is a highlyselective suburban institution with over 40,000 under-graduate and graduate students. Undecided studentswere identified during summer orientation andassigned to a noncollege-based academic-advisingunit. Approximately 1,500 first-time first-year stu-dents are assigned to the noncollege-based advisingoffice serving undecided or exploring, students.Academic advising for undecided freshmen is pro-vided in three different locations: conventional res-idence halls, living-learning communities, and acentral advising office. The conventional residencehalls in this study have academic advising officeslocated.on the first floor. The living-learning com-munities are housed within university-owned resi-dence halls that house academic advising officeson the first floor. Srudents must specifically apply tolive in the two available living-learning communitiesand pay additional fees ($300 per academic year) forthe learning community programs and services. Thestudents who comprise the control group only haveaccess to academic advising through the centraladvising office; they do not have advising offices

I{,4CADA Journal Volume 28 (l) Spring 2008

Arms et al.

* within their living environments.The selection of undecided first-year students

was based on several considerations. First-year stu-dents represent the majority (80%) of students wholive in university residence halls. They also con-stitute the majority of undecided students at thefocus institution. Only stafffrom the advising unitsthat serve undecided students work in residence halland learning community offices. Moreover, theemphasis on first-year students is consistent withthe literature that characterizes the first year ofcollege as critical to student success and persistence(Barefoot, 2000; Upcraft & Gardner, 1989).

SampleA total of 714 first-time first-year students who

received advising from the advising office for trnde-cided students during the 4 weeks under study atsunmer orientation consented to participate andcompleted the initial survey. Five participants pro-vided false or unidentifiable student identificationnumbers and two withdrew from the universityprior to the start of the fall semester. As a result, thesample population consistedof 707 first-time first-year undecided students. The participants were allcontacted in the spring semester and asked to com-plete the follow-up survey. The follow-up surveyswere linked to students'initial surveys based on stu-dent identification numbers. The use of numbers,rather than names, provided more accuracy andanonymity. In attempting to achieve a higherresponse rate, we created several incentives includ-ing entry in a raffle drawing for six gift cards, fiveE-mail reminders for nonresponders, and a mailedpaper survey to all students who had not respondedby the third E-mail contact.

Of the original sample of 707 students, 333 ofthem (47 .t%) completed the follow-up survey.Thirty-four of the respondents had declared a majorand were therefore no longer assigned to the advis-ing office for the undecided. In addition, academicadvising at this university is optional, and 42 stu-dents reported that they had never utilized advising.The declared and nonadvised students were elim-inated from the analysis. Of the remaining 257 stu-dents, 108 received academic advising in aconventional residence-hall setting, 36 studentsreceived academic advising in a living-learningconrmuniry and I 13 students received academicadvising at the central campus advising office.

Comparisons between the original sample of707 students and the final sample of 257 studentsrevealed that the final sample population is skewedtoward females (X = 19.8; df = l;p < .0001 ), those

t 0

with higher academic performance in high school(t = 3.968 ; df : 7 A5 ; p < .000 I ), and higher reportedhigh-school academic engagement (r = 2.341; df =701; p < .05) than the original sample. No differ-ences were noted between the original and finalsample based on the extent of social engagement inhigh school (r = .224; df = 701; p : .823), post-secondary degree expectations (f - 1.026; df:705;p = .305); or ethnicity (K = 3.55; df = 2; p: .17).We were unable to compare the original and finalsamples based on first-generation status as thatinformation was collected at the follow-up survey.

Surtey DevelopmentBoth the initial and follow-up surveys comprised

questions derived from the National Surrq of Living-Learning Prcgrams (NSLLP) (Inkelas & Brower,2004), the N atio n al Surt q, for S nd ent E nga gem e nt(NSSE) (National Survey of Student Engagement,2007), and a nonpublished first-yeiu expectationsquestionnaire used at Arcadia University (D.Gallagher, personal communication, April 24, 20M).Two different focus goups were conducted to reviewthe survey questions. The first focus group includedeight academic advisors who work in the advisingoffice involved with this research as well as twomembers of the university's orientation committee.The second focus group was comprised of fiveundergraduates who had utilized academic advisingat the university.

The focus group of academic advisors brain-stormed on their beliefs about the outcomes ofeffective advising as well as provided feedback onthe logistics of collecting survey information fromincoming students at summer orientation. The groupbelieved that a short survey would be importantbecause new students are focused on picking classesand would not have a long attention span at thebeginning of the orientation day. A small numberof advisors provided specific feedback on howquestions should be worded for better clarity. Thestudent focus group memben specifically addressedsome terminology they did not understand andthought should be reworded for their peers. Theyalso suggested new elements to make the surveyquicker and easier to navigate. All feedback wasconsidered and used to redesign and reword ques-tions in the final survey.

VariablesDependent variables. Two variables were used

to measure engagement: engagement with enrich-ing educational experiences and student-advisorengagement. The items comprising these two scales

NAC.4DA Journal Volume 28 (1) Spring 2008

were drawn frorn the NSLLP (Inkelas & Brower,2004), the NSSE, ild from themes emerging fromthe focus group meetings with academic advisorsand students who gauged the nature of academicadvising at different settings within the university.This approach in scale development is consistentwith Pike's (2006) recommendation that factorstructures should be reliable indicators of studentexperiences at a particular institution.

Items comprising each scale were identifiedafter a series of exploratory factor analyses basedon a principal components option with varimaxrotation. The Enriching Educational Experiencesscale is made up of eight items used to appraise thecontribution of the university in learning and per-sonal development. Five of the eight items aredrawn from the NSSE and three are drawn from theNSLLP.

The Student-Advisor Engagement scale is madeup of five items used to appraise the quality ofrelationships with academic advisors and the extentto which advising was conducive to defining careerand personal goals. These items were designedbased on a series of themes students identified ascharacteristic of the most successful advisors.

Independent variables. The independent variablesare the three locations where academic advising isprovided for the students in this sample: conven-tional residence halls, living-learning communi-ties, and a central advising office. For the purposesof this research, a conventional residence hall is con-sidered a university-owned building where under-graduates live together without a building-widelearning community program to organize them.Shapiro and Levine (1999,p. 36) defined a living-learning community as a "student living space withintentional academic programming and services,such as in-hall tutoring, ongoing lecture series, andacademic advising." The central advising officeserves sfudents who only have access to academicadvising through it; therefore, it serves as the con-trol variable. Trvo dummy variables were used toidentiff whether the student received academicadvising at a conventional residence hall or at a liv-ing- learning community.

Confoundingvariables. In an ideal research sit-uation, the effect of location could be ascertainedby random assignment of students to the differentlocations of academic advising (see Kirk, l98l). Inthe academic advising environmenf random assigr-ment to treatments can rarely be accomplished(Conrad & Serlin, 2006). Students self-select dif-ferent living locations where academic advising isundertaken. and academic advisors at the focus

Advising Spaces

institution are assigned by location. Therefore, onecould argue that learning through enriching edu-cational experiences and engaganant with one's aca-demic advisor is more likely associated withstudents' personal characteristics than the locationof advising.The engagement behaviors may be theproduct of previous involvement in academic andsocial activities in high school, students' educa-tional goals, and students'predispositions to engagewith academic advisors. Gender, degree aspira-tions, academic ability, and even being raised by par-ents without a college education could affect thischoice of location and predispositions to interactwith academic advisors. Therefore, we sought tocapture information on three constructs: a) predis-positions to engagement (degree aspiration, self-reported social engagement in high school, andself-reported academic engagement in high school),b) expectations for academic advising (a: .689),and c) students'expectations for engagement incollege academic, social, and residential life.

Results

Demographics and Scale ReliabilitiesThe Student-Advisor scale yielded factor load-

ings ranging from .54 to .85 (cr = .836).The factorloadings for the eight items in the EnrichingEducational Experiences scale ranged from .54 to.74 (a: .835).Table I displays selective descrip-tive statistics and reliabilities for the variables andscales used in the study.

Mean Dffirences between Location andEngagement

Mixed support was found for the hypothesisabout the connection between location of advisingand engagement. ANOVA analyses reveal no sig-nificant differences in engaging with academicadvisors across the three advising locations. Inother rvords, students in living-learning communi-ties reported similar levels of engagement withacademic advisors as their counterparts at con-ventional residential halls and students with accessonly to the cenffal office, F = (30, 257) - 27 .536;p: .979. While location does not appear to matterfor engaging with one's academic advisor, the loca-tion where students received academic advisinghad an impact on students'enriching educationalexperiences. Students in living-learning cornmu-nities reported higher levels of enriching educationalexperiences than did those students who wereadvised at the central office (F: [34,2571= 1.857;p < .01). Because under an ANOVA analysis theexperimental conditions are presumed to be ran-

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Arms et al.

'Table I Descri ive statistics for final sample

CharacteristicDemographics

Location of advising

Prior academicachievement

Engagement variables

Expectations foradvising

Predispositionsto engage (self reported)

Variables

FemaleWhiteFirst-generationCentral officeLiving-learning communityResidential hallHigh school GPA percentileACT compositeE nri ch i n g-e duc ati onal

experiences (scale 1-8)Student-Advisor Engagement

(scale 1-5)Expectations to Engage withAdvisor (scale l-5)Degree aspirations (scale I -5)

High school academicengagement (scale l-5)

High school social engagement(scale 1-5)

72%90%7%

n3 (M%)36 ( t4%)

t08 (42%)7.s362.928

0.688

0.768

0.5350.887

0.8940,74r

Mean

90.3327 .31

3 .55

2.41

3.s32 .15

3.894.46

Reliabili

0.835

0.836

0.689

domly assigned a series of regression analyseswere performed to account for alternative factorsassociated with engagement other than location ofadvising.

Predictors of EngagementTwo multiple regression analyses were per-

formed to examine the impact of location along withother factors associated with engagement. In allregression models, the pair-wise option wasemployed (while relatively large, the sample of257 was not large enough to use a list-wise selec-tion of cases). Prior to conducting the regressionanalysis, we examined the data for outliers andmultico linearity. Co linearity diagnostics producedtolerance levels and variance inflation factor levelswithin the acceptable limits cited in the literature.r

To examine whether variation in engagement hadmore to do with predispositions, academic ability,and demographic characteristics than with loca-tion, variables were grouped in blocks and enteredsequentially. The first block included demographiccharacteristics (i.e., gender, ethniciry and statusas first-generation college sfudent). The secondblock included measures of academic ability (highschool grade-point average [GPA] by percentileand ACT composite score), and motivation (high-est degree expected). The third block included self-

reported measures of engagement in academic andsocial activities while in high school. The fourthblock incorporates expectations toward advisingas reported during the orientation session. The fifthblock, student-advisor engagement, was used toassess the effect of the quality of engagement withadvisors on enriching educational experiences. Thesixth block in the regression model comprised thelocation where students had access to academicadvising. The order of the blocks conforms to Huand Kuh's (2002) research on the factors that mostinfluence students'likelihood to engage in univer-sity programs.

Changes in Variance ExplainedTable 2 summarizes the results of the fwo regres-

sion analyses (one for each student engagementoutcome). The R2 columns depict the proportion ofvariance explained in each engagement outcome dueto demographics, prior academic achievement, pastengagement, pre-college expectations for advis-ing, student-advfsor engagement, and location ofadvising. The A X2 columns document the changein the value of R2 accompanying the entry of theblock set. They signifo the contribution of instruc-tional practice.s to student engagement to the aug-mented differences in students' background ordemographic characteristics or their pre-college

I Cohen, Cohen, West, and Aiken (2003) suggested that variance inflation factor values of l0 or higher sig-niff multicolinearity problerns and that tolerance levels of 0.l0 or less are also problematic.

t2 IL4CADA Journal Volume 28 (1) Spring 2008

experiences and predispositions.As can be seen in Table 2, the regression mod-

els explained 14.4 and 28.9% of student-advisorengagement and enriching educational experiences,respectively. Most of the variance observed in stu,dent-advisor engagement resulted from pre-col-lege factors. This explained variance resulted fromthe expectations students had prior to their collegiateexperiences about engaging with academic advis-ing(4.2%). While students'level of engagement inhigh school also mattere4 its contribution wasrather small at approximately 3.0% of the varianceobserved. The location where advising took placedid not contribute in a significant manner.

In contrast to student-advisor engagement, bothpre-collegiate and collegiate factors significantlycontributed to having enriching educational expe-riences. Of the pre-college factors, the block ofvariables regarding predispositions to engage dom-inated in explaining this engagement factor,accounting for 6.6% of the variance observed, whileprevious acadernic achievement accounted for 43%of variance, and students'expectations for advisingaccounted for 2.8% of variance. The engagementwith advisors, however, accounted for 9.1% of thevariance observe{ or a little more than one third ofthe variance that was explained. Location factors,as a group, did not contribute in explaining enrich-ing educational experiences.

Predictors of EngagementResults of the two regression models, one each

for student-advisor engagement and enriching edu-cational experiences, are reported in Tables 3 and4.In both models, the independent variables as agroup were significantly associated with student-advisor engagement (F[ 1 ,257]: 3. 1 56; p < .01 )and enriching educational experiences (F[ 12,257]

Table 2 Contribution of factors to student engagement

Advising Spaces

: 6.98; p < .01). Each engagement factor appearsto be differentially affected by different pre-collegeand collegiate experiences.

S ruden t - adv i s or en gage m enl. Regress ion resultsconfirm the initial ANOVA test: Location whereadvising takes place did not matter to studentengagement with advisors; however, with a mod-erate size effect(B = .236),,pre-college expectationsfor advising are predictors of advisee-advisor inter-actions. For every one unit of standardized increaseon pre-college expectations, students were approx-imately 9 percentile units above the overall stu-dent-advisor engagement mean.

Enriching educational experiences. Even aftercontrolling for confounding pre-collegiate factorsand demographic characteristics, an impact wasfound on advising within a living-learning com-munity setting. Students participating in learningc ommun itie s reported h i gher enric hin g educationalexperiences than those students securing their advis-ing at conventional residential halls or in the centraladvising office. In addition to living-learning com-munities, enriching educational experiences aresi gnifi cantly associated with students' soc ial engage-ment while in high school and engagement with col-lege advisors. The effect of positive socialengagement experiences and of engaging with one'sacademic advisor was of moderate size (B:.326).For every standard deviation unit increase seen in thisvariable, enriching educational experiences increasesby almost one third standard deviation unit over orl3 percentile units above the mean.

In summary, partial support was found for thehypothetical connection between location of advis-ing and student engagement for our sample ofundecided first-year college students. Location ofacademic advising did not have a significant impacton student-advisor engagement; however, inte-

Student-AdvisorEngagement

Enriching EducationalExperiences

Blocks of Factors R2 A R 2 R J A R 2

1. Demographics2. Previous academic achievement3. Past engagernent while in high school4. Students' expectations for advising5. Student-advisor engagement6. Location

.038

.0s5

.085

.127

. t44

.017

.030*

.042**

.017

.042

.085

. l 5 l

. t 79

.289

.198

.043*

.066**

.028**

.091* *

. 019Final model RtFinal model R2 adjustedF-test, df

. t44

.0983 .156 * *, l l

.289

.2486 .98 * * , 12

,Yote. *p < .05; **p . .01.

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Arms et al.

t Table 3 ssion model for student-advisor en

VariableUnstandardized

BetasStandard

ErrorStandardized

BetasFemaleWhiteFirst-generation in collegeHigh school percentileACT compositeDegree aspirationHigh school academic engagementHigh school social engagementExpectations for advisingCentral advising office vs. othersLiving-learning community vs. others

.064-.308.t23.003

- .017-.022. l2g*

-.089.339**

-.t92.040

.t20

.178

.t97

.007

.020

.059

.060

.070

. l0 l

. t l 0

.156

.037-.t22.042.030

-.066-.026.149

-.085.236

-.r24.018

Note. *p < .05; **p . .01.

Table 4 Regression model for enriching educational experiences

VariableUnstandardized

BetasStandard

ErrorStandardized

Betas

FemaleWhiteFirst-generation in collegeHigh school percentileACT compositeDegree aspirationHigh school academic engagementHigh school social engagementExpectations for advisingStudent-advi sor engagementCenhal advising office vs. othersLiving-learning community vs. others

. r79

. 158

.t27-.006- .019.066.086.142**.148.2gl**. l 5 l.295**

.098

.r47

. l 6 l

.006

.016

.048

.050

.057

.085

.057

.091

.128

.t t7

.070

.049-.063-,080

.085

.n2

. 153

.115

.326

.109

. l 14Itlote. *p < .05; **p . .01.

grating academic advising in living-learning com-munities had a significant effect on engagementwith enriching learning experiences among thisgroup of first-year undecided students. This asso-ciation was significant even when other predictorsof engagement were held in control.

Discussion

Determinants of EngagementIn our study, we questioned whether location of

academic advising delivery has an impact on thequality of students' interactions with advisors.Grounded in the literature (e.9., Love & Tinto,1995: Schein, 1995; Zhao & Kuh, 2004), thehypothesis we posed stated that students housed inliving-learning communities and residential hallswith advising offices would be more prone todevelop personal relationships with academic advi-

t 4

sors than are those students securing their advisingin central offices. Our results indicate that locationdoes not matter. Learning communities, residentialhalls, and central offices offer equal opportunitiesfor students to engage with academic advisors in ameaningful manner.

However, we found that location of advisingdoes make a difference in exposing students toenriching educational experiences. Consistent withthe literature, students in the learning communitiesin our study reported greater gains in learning andpersonal development as a result of their experienceswith the university. Therefore, our findings validatethe claim that learning communities, as a result ofthe social relationships they foster with academicadvisors, peers, and faculty members, can be toolsupon which universities can rely for increasing stu-dent engagement and persistence (Kuh, 2A0f^: Pike,

NACADA Journal Volume 28 (1) Spring 2008

2000; Pike et al.,1997; Stassen, 2003;Tinto, 1997;Zhao & Kuh, 2004).

While seeking to answer questions regardingthe impact of advising location, we reaffirmed thecentrality of the academic advisor in student devel-opment and learning. In appraising the student-advisor relationship, our scale highlighted suchdomains as helping define personal and careergoals, discussing personal matters, expressing inter-est in students'academic and personal success aswell as the importance to the student of getting toknow his or her advisor and accessibility to theassigned advisor. It is evident that the depth ofthese relationships between advisors and studentsalong those domains empowered the student tobecome engaged in educational practices leading toenriching educational experiences.

Our results also call attention to the role of highschool counseling as a precursor to engagement withcollege academic advising. As our analyses show,students who were engaged with high school coun-selors were also more likely to be engaged with col-lege advisors. This finding is consistent withMcDonough's (1997) research showing that highschool counseling eases the transition from highschool to college. However, our results uncoveranother facet of the importance of high schoolcounseling-shaping a student's plans, predispo-sitions, and expectations toward college advising.ln other words, the high school counselor socializesthe student into ttre culture of academic advising thatwould later on prove critical for engaging collegeadvisors and acting upon their advice in using edu-cational services. This finding calls for collaboratingefforts between colleges and high schools on behalfof creating seamless advising programs.

Strengths and LimitationsSeveral limitations in this study are important to

consider when interpreting our results. First, thelength of time that lapsed between surveys may bea shortcoming: One academic year may be inade-quate to determine the real impact of academicadvising on the academic and personal plans ofstudents. However, our study captures the most crit-ical year in the academic life of the college student.Past researchers (e.g., Metzner, 1989) have shownthat the freshman year plays a pivotal role in defin-ing a student's career goals and even individual stu-dent's decisions to persist in college (see Barefoot,2000; Pascarella & Terenzini, 2005; Tinto, 19971'Upcraft & Gardner, 1989).

A second limitation rests on the possibility thatengagement with academic advisors may be based

Advising Spaces

more on the personality, commitment, and knowl-edge of the advisor than the location where theadvising takes place. Kelley and Lynch (1991, p. 3l)proposed that "the advisor who is a warrn, caringindividual will fare better in the evaluative pro-cess than one who is more distant, even thoughboth may have equal knowledge about the systemand conduct equally effective meetings." All indi-vidual advisor's openness to informal interactions,accessible personaliry and commitment to attend-ing community events may have a more significantimpact on student engagement than the locationwhere advising takes place. While it is an impor-tant consideration, this limitation is controlled tosome extent in this study. The central advisingoffice is staffed by 12 academic advisors and eachresidence hall or living-learning community officeis staffed by 2 to 4 professional advisors. Studentsmay have interacted with ditrerent advison through-out the year, even though they received advising inthe same location.

Thir{ our study is targeted to undecided frst-yearstudents; thus, generalizations to other groups are dis-couraged. The sample was selected for several rea-sons beyond its availability to the advising office: Notonly does the literature indicate that first-year studentsconstitute the majority of undecided students(Gordon, 1995), it has also been argued that address-ing their advising relationships in their first year ofcollege is important for students to return for theirsecond year (Boyer, 1987; Upc.aft & Gardner, 1989).

Despite these limitations, this study addressessome of the gaps in the literature regarding the areaof location of student services. Many research uni-versities now staffsatellite offices for student services(e.9., nwiting centers, academic advising, andpersonalcounseling) on college campuses based on theassumption that providing services at times andplaces convenient to students will encourage them totake advantage of available resources. While thenotion rests on a sound theoretical basis, empiricalresearch to back the assertion does not exist.

Implications for Further ResearchStrong student-advisor relationships are pivotal

in enhancing students' first-year educational expe-rience. This study showed that the location of aca-demic advising has an impact on students'participation in educational activities and that loca-tion ofadvising has only an indirect link to student-advisor engagement. While the living-learningcommunities in this study do not have an impact onthe quality of student-advisor engagement, theydid enhance students' participation in enriching

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Arms et al.

r educational experiences. Based on this conclusion,future researchers should consider the variablesthat impact student engagement.

Because the majority of students did not havesignificant contact (more than two meetings withtheir academic advisor during the academic yearwhen this research was conducted), research onthe impact of location of academic advising shouldutilize another measure for efflectiveness. Althoughthis study supports the conclusion that location ofadvising does not have a significant impact on stu-dent-advisor engagement, the data show that themajority of students had limited contact with aca-demic advisors. While student engagement is anaccurate measure of sfudent learning, an alternatemeasure that could more directly assess students,relationships and experiences with academic advis-ing might be a better measure for the differentialimpact of location on advising.

The impact that academic advising may have onstudent engagement may not be strong enough toovercome the nature ofthe undecided student pop-ulation to be less engaged than their decided coun-terparts (Hu & Kuh, 2002). The link betweenacademic advising and student engagement does notshow statistically significant differences, particu-larly over the course of only one school year. In afollow-up study those students that research (Hu &Kuh, 2002) shows are more likely to be engagedshould be included: declared students, juniors andseniors, and those enrolled at private or small insti-tutions. Because residential advising offices at thefocus institution are offered in living spaces pre-dominantly housing first-year students, we could notaccess the upper level students who are relativelymore likely to be engaged. In addition, the only staffat residential advising offices come from the aca-demic advising office for undecided students.Therefore, the nature of the sample population mayhave a negative impact on the results.

Imp I i c ations for Prac tic eBecause this research shows that engagement in

academic and social activities in high school andstudents' expectations for college academic advis-ing are both statistically significant for students'engagement with college advisors and for engag-ing in college educational programs, college stake-holders need to work more closely with high schoolcounselors and high school students. Both highschool counselors and college advisors workingwith high school students can develop expecta-tions for advising among students. Both groupsmust communicate the value of academic advising

t 6

and the potential benefits advising afflords to stu-dents. This is especially pertinent as students andtheir families show increasing concern about col-lege costs and express the need for a direct path tograduation.

Even as predispositions to engage and demo_graphic differences are controlled this researchshows evidence that living-learning communities areeffective in students' engagement with educationalexperiences. Academic advisors can play a largerrole in encouraging incoming college students toparticipate in a learning community if they inter-act with students while they are still in high school.Academic advisors should work more closely withhigh school guidance counselors to provide infor-mation about learning communities, advising, andother academic programs.

Conclusion

Based on the belief that convenient serviceswill have a greater impact on students than thoseoflered in a central location, a number of universi-ties now offer advising services in students'livingspaces. This study shows that advising provided inliving-learning communities has a significant impacton students' participation in educational programs.It also shows those students'expectations for advis-ing, level of engagement in high school, and pre-vious academic achievement play a significant rolein their likeliness to engage with academic advisorsand with educational activities. College academicadvisors must recognize that students develop atti-tudes and inclinations about advising before theyenter the universiry and they should work withhigh school students and counselors to ensure thatstudents urderstand the significant positive impacta relationship with their academic advisor willafford them.

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Authors'Notes

Janet H e is s Arms (i anet. heis s arms @msugf, edu) isDirector ofAcademic Initiatives at Montana StateUniversiffiollege ofTechnologt She holds a PhDin Educational Leadership and Policy Analysis

from the University of llisconsin-Madison. Shehas worked in academic and career advising atthrce difetent institutions wer the past I0 years andis now irwolved with darcloping a 2-year college atMontana State University in Bozeman.

Alberto F Cabrera is a prcfessor of EducationalPolicy and Leadership at the University ofMaryland-College Park. Dn Cabrera holds a PhDin Educational Administration and an MS inIndustrial Relations from the University ofWisconsin-Madison. He specializes in researchmethodologies, college choice, college students,classroom experiences, minorities in higher edu-cation, and economics of education.

Aaron M. Brower is a professor and Vice Prcvost

for Teaching and Learning at the University ofWisconsin-Madison. Dn Brower holds a PhD inSocial Work and Psycholog,,fom the University ofMichigan. His cunent research emphasizes dffir-ences amongAmerican subcultural groups in theireducational attainment and life-course decisionmaking and the evaluation of educational innova-tions, particularly integrative living and learningcommunities.

I8 NACADA Journal Volume 28 (1) Spring 2008