International
Journal of
Educational
Technology

If we build it, will they come? The effects of experience and attitude on traditional-aged students' views of distance education

Tracy Irani, University of Florida

Abstract

Although distance education is most often focused on the adult learner,increasingly large numbers of traditional aged (18-22) college studentsat U.S. institutions of higher education are enrolling in courses at adistance. Unlike adult learners, traditional aged students generally lackany direct experience of distance education and/or mediated course environments.They may approach the experience with different attitudes and perceptionsand, lacking some of the same motivations as adult learners, they may bemore susceptible to the influences of peers.

The purpose of this study was to examine the effect of direct experienceon traditional aged students' attitudes, perceptions and intentions towarddistance education. The study used a repeated measures design, set up tomeasure before and after effects of direct experience and peer influenceson a sample of undergraduates at a large U.S. land grant university.

Results indicated that subjects with direct experience of distance educationhad stronger, more certain attitudes toward taking a distance educationcourse, and that this certainty increased with experience. Also, for thosesubjects who did not have direct experience, peer influences that werepositive in nature increased the likelihood of their intent to take a distanceeducation course. These findings provide support for the argument that,for traditional aged students, intent to take a distance education coursemay be directly related to direct experience of distance education as wellas the attitude and perceptions of the peer groups to which they belong.

The traditional college classroom, until recently one of the few placeswhere face-to-face communication between instructor and student was seenas the desired norm, has been undergoing rapid transformation due to theimpact of distance education based communication technologies. In manycolleges and universities in the United States, not only adult learners,but also traditional aged on-campus students (typically aged 18-22) nowhave the option of taking classes off-site, via sophisticated computerand videoconferencing networks (Universities Targeting, 1998).

Key factors influencing institutional efforts to offer distance educationcourses to traditional aged students include economics and space considerations.For example, the California State University System, seeking to accommodatea projected 30-40 percent increase in enrollment over the next decade,is looking at offering its online courses to on-campus undergraduates inan attempt to utilize resources more efficiently (Carnavale, 2000). Anda recent report form the University of Illinois (2000) concludes that dueto the baby boomlet, the number of traditional aged students enrolled indistance education courses at U.S. institutions (a reported 7 million asof 1997), will continue to grow by approximately 20 percent in the nextdecade.

Purpose and Objectives

One of the ways in which we can examine the phenomenon of traditionalaged college students following older adult learners into adoption of distanceeducation is through Roger's diffusion of innovations framework. Rogers(1995; Rogers & Shoemaker, 1971) defined adoption behavior as the relationshipbetween the time at which an individual chooses to adopt a technologicalinnovation and the time at which other members of his/her social systemchoose to do so. From this perspective, adult learners and former patronsof "correspondence school courses" looking to balance educational, professionaland personal priorities can be viewed as having been in the forefront ofthe distance education adoption cycle. If this educational innovation isto be successful long term, however, adult learners must be followed bymore mainstream, traditional aged students.

Many of these later adopters are currently being offered the choiceof taking distance education courses by a growing number of institutionsof higher learning. However, since they lack experience of distance education,as well as the personal and professional incentives of the early adopters,the motivations for these students to engage in this particular adoptionbehavior may be dramatically different from those of the traditional adultlearner. The "if we build it, they will come" model of institutional developmentof distance education programs may hold little currency for this new andgrowing student market. Institutions of higher learning might find it unexpectedlydifficult to fill seats in the virtual classrooms of tomorrow without abetter understanding of how to motivate desired behavioral outcomes amongthis group.

In this context, the effect of experience on the attitudes, perceptionsand behavior of traditional age students as they relate to adopting technology-baseddistance education may be an under-examined issue. While studies have beenconducted that compare distance education to traditional "live" classroomexperience (Souder, 1993; Egan, et al., 1991), and look at the effect ofvarious components of teaching and learning styles (Schlosser & Anderson,1994; Wilkes & Burnham, 1991; Mason & Kaye, 1989), very few dealdirectly with the effect of experience and/or the traditional-aged studentpopulation.

It may be the case that traditional-aged students, most of whom lackany direct experiences of distance education, may tend to be relativelyuncertain about their attitude, and may subsequently turn to external influences,such as the views of peers and other influential individuals, when attemptingto form decisions about taking a distance education course. In such a circumstance,the resulting impact on attitudes and subsequent behavior could eitherbe very positive, or very negative, depending on prevailing points of viewwithin the relevant circles of reference. If so, this would seem to suggestthat not only the direction, but the certainty with which students holdtheir attitudes could be critical determinant factors in the success orfailure of a distance education program aiming at including traditionalaged students.

This study therefore was designed in an attempt to answer the followingquestions:

Conceptual and Theoretical Framework

The Theory of Planned Behavior

One of the reasons that the attitude-behavior relationship is so centralto the study of attitude is that behavior can be used as an indicator ofattitude. This has led researchers to develop behavioral measures fromwhich they can infer attitudes. A seminal work in attempting to understandand predict behavior and behavioral intentions which has been used extensivelyin educational research is the Theory of Planned Behavior (Ajzen, 1991).The Theory of Planned Behavior, or TOPB, is an extension of the Theoryof Reasoned Action, or TORA (Fishbein & Ajzen, 1975). The basic propositionof both models is that in order to predict a behavior B (suchas enrolling in a distance course), one must try to measure an individual'sintent to behave, or BI (such as intent to take a distancecourse), itself a function of attitudes toward the target behavior andthe influence of peers, relatives and others whose opinion is deemed important,i.e., what the researchers termed "subjective norms". In both the TORAmodel and the later theory of planned behavior (TOPB), attitudes are afunction of beliefs about and assessments of perceived consequences ofacting in a certain way, such as beliefs about the advantages or disadvantagesof technology delivered instruction. In the context of both models, subjectivenorms refers to an individual's interpretation of what important referents(peers, family, advisors) think about the desirability of a behavior, combinedwith the individual's desire and motivation to comply with what influentialothers may think or believe should be done.

In an attempt to answer critics of the TORA, who argued that most behaviorsare neither volitional (as in the initial model formulation) nor involutional,Ajzen added an additional variable to the TOPB called perceived behavioralcontrol, which measures perceptions of individual control over the targetbehavior. The resulting predictive equation can be written as follows:

B» BI=w₁AB+w₂SN+w₃PBC

where AB is attitude towards the behavior, SNis subjective norms, and PBC is the degree of perceived behavioralcontrol a subject feels over the behavior.

In the specific context of distance education, where studies have showna high correlation between attitude toward technology and student familiarity(Barron, 1987; Smith & McNelis, 1993), as well as a correlation betweenfamiliarity with technology and reduction in anxiety (Jones, 1992; Riddle,1990), one might expect the predictive value of efficacy toward behaviorssupporting distance education to be somewhat moderated by the strengthand certainty of one’s attitudes.

Along these same lines, subjective norms might also interact with attitude.Intent to take a distance education course may be a behavior perceivedto be, to a great extent, under one's own control and not subject to significantinfluence by peers, advisors, relatives and other referents. However, forthose students who have not yet adopted the technology represented by thetarget behavior, it may be the case that their weaker, less certain attitudescould be more strongly impacted by the opinions of relevant normative influences.

One of the central problems related to traditional-aged students’ adoptionof distance education might be that students are being asked to engagein an adoption behavior for which they have little contextual experiencewith which to guide themselves. In most colleges and universities, onlysmall percentages of students have actually participated in a distanceeducation course; and of those who have, there are no doubt some who holdnegative attitudes related to lack of social interaction and unfamiliaritywith the (often not very stable) technology being used.

In this context, direct experience of technology-based distance educationshould serve to strengthen and make students more certain of their attitudesand thus less susceptible to the influences of others. Yet, in a domainwhere weakly held attitudes based on limited experience are the norm, behaviorand intention to behave might be influenced by a variety of factors thatcould make predicting outcomes very difficult.

Fazio contended that variables such as direct experience strengthenedthe attitude-behavior relationship because they are more accessible, i.e.,more easily called up from the subject’s memory upon contact with the attitudeobject. Fazio held that the more accessible an attitude, the stronger andmore certain it would tend to be, and the stronger and more consistentthe relationship between attitude and subsequent behavior. In later research,Fazio has investigated the influence of attitude accessibility upon attention,categorization, judgement and behavior (Fazio, 1995; Roskos-Ewoldson &Fazio, 1992).

Eagly & Chaiken (1995), however, have argued that manipulationsof attitude accessibility might operate at least partially on the basisof the attitude's strength, certainty, or degree of extremity. Weak attitude-objectassociations, for example, may in fact be moderated by attitude strengthin addition to their accessibility. Looked at in this way, indirect experiencemay predict behavior less well than direct experience because subjectswith indirect experience hold attitudes that are weaker and less certain.When they receive new information about the attitude object, such as thatprovided by relevant normative referents, their attitude is therefore morelikely to be subject to change.

Research Design

The research design used in the study was a 2 x 2x 2 mixed model repeatedmeasures design consisting of the independent variables of experience (twolevels), time (two levels, before and after treatment), and subjectivenorms feedback (three levels). For hypotheses one and two, the model wasanalyzed with attitude certainty as the within subjects factor; for hypothesisthree, behavioral intent to take a distance education course was used asthe dependent variable.

Procedure

Subjects were drawn from a sample population of college students. Totest the effects of experience and the norms feedback stimulus over time,subjects were randomly assigned to one of two treatment conditions relatedto experience of technology-based distance education as follows:

The quasi-experiment involved two measurements of subjects' attitudes andintentions, taken before and after exposure to the direct and indirecttreatments. To conduct the study, all subjects first received a color-codedquestionnaire booklet consisting of attitude and behavior scaling measures,as well as the randomly assigned subjective norms feedback stimulus, whichwas placed as a separate page within the questionnaire packet.

After the T1 administration, subjects were randomly assigned to eitherthe experimental (direct experience) or control (indirect experience) conditions.Subjects in the control condition were dismissed and asked to return toclass for the next scheduled session as usual. Subjects in the experimentalcondition were given access to the online materials as well as the videotapecontaining the instructor’s lectures. As part of their instructions, subjectswere told they should not attend class during the experiment. After thetesting period, all subjects were re-grouped and again administered thequestionnaire instrument.

To insure validity and reliability, the experimental treatments andthe questionnaire instrument were pre-tested on an equivalent student sample.In addition, a series of manipulation checks were utilized to insure thatthe observed effects were the result of the experimental manipulations.Finally, after the questionnaire had been administered for the second time,all subjects were extensively de-briefed, and the instructor then administereda quiz to both groups to determine if any differences existed in performance.No significant differences were observed.

Definition of Scales

Scale items for the questionnaire were adapted from the TOPB framework.Attitudinal and behavioral scale items drawn from the TOPB model (attitudeand its antecedents; subjective norms and its antecedents; perceived behavioralcontrol and its antecedents; behavioral intent and behavior) consistedof seven-point semantic differential scales anchored by bipolar adjectives(good/bad; favorable/unfavorable; pro/con; appealing/unappealing; like/dislike;willing/unwilling; likely/unlikely; take/will not take).

Attitude certainty was constructed as a three-item index, comprisedof two items asking respondents how certain and confident they were intheir evaluation of their attitude toward distance education, as well asa third item asking respondents how certain they were of their evaluationtoward taking a technology based distance education course.

General demographics based on a final n of 72 subjects were obtainedfrom the sample for gender, year in school and computer ownership. Responsesindicated that 68% of the subjects were male, and 32 % were female. Eighteenpercent were freshmen, 28% were sophomores, 36% were juniors, 21% wereseniors and 1% were graduate students. Of this number, the vast majority-- over 83% -- owned a computer.

In order to conduct the analyses, indices were constructed for eachof the testing variables in the study. Each of the indices was constructedby combining the relevant index items from the questionnaire for both T1and T2 response sets. Principle component factor analysis was used on eachset of index items to find the items which loaded together as one factor,then reliability analyses were run for each resulting index using Cronbach’salpha statistic. Standardized item alphas for the indices used in the studywere .98 for attitude, .91 for attitude certainty, .69 for subjective norms,and .55 for behavioral intent. Perceived behavioralcontrol was analyzed as a single item variable.

Hypothesis Tests

To analyze the hypotheses, a repeated measures ANOVA was conducted utilizingexperience (two levels) subjective norms feedback exposure (three levels)and stimulus timing (two levels) as between subjects factors and attitudecertainty as the within subjects factor. Although the anticipated threeway interaction was not significant at p < .05, it was significant atp < .10, F (1, 58) = 2.77, p < .07.

To analyze the three-way interaction, the simple two-way interactionsbetween experience and each feedback stimulus condition were analyzed,then the simple interactions of the feedback stimulus on the direct andthen indirect experience groups was analyzed. Results of this analysissupported hypothesis one. A main effect for the direct and indirect experiencegroups, F (1, 58) = 3.83, p < .05, was found, indicating that the degreeof attitude certainty toward taking a distance education course was higherfor those subjects with direct experience, as compared to those subjectswithout experience. Table 1 shows the means for attitude certainty forthe direct and indirect experience groups.

Table 1.
Attitude Certainty Means for Direct and Indirect Experience Groups at T1 and T2.
 

Subjective Norms Feedback          Positive             Negative          Control                                                       M      n           M       n           M       n

Direct Experience                            T1                      5.63  (17)          4.71  (15)        5.09  (12)                             T2                       6.27  (17)           5.40  (15)        5.45  (12)

Indirect Experience                            T1                      5.15  (9)            5.61  (9)          4.70 (10)                            T2                      5.22  (9)            5.40  (7)          5.20 (10)

Hypothesis two predicted that for subjects in the direct experience group,attitude certainty would increase over time of exposure. This hypothesiswas supported. Analysis of the simple two-way interactions within experiencerevealed a simple simple main effect for the direct experience group, F(1, 41) = 10.22, p < .00, indicating that for this group, attitude certaintysignificantly increased from T1 to T2, but this was not the case for theindirect experience group, F (1, 23) = .24, p < .6.

Hypothesis three predicted a simple interaction effect between subjects in the indirect experience group and subjective norms feedback, such that exposure to the positive feedback information would increase their likelihood of engaging in the target adoption behavior, while exposure to the negative feedback would decrease their likelihood. This hypothesis was partially supported. ANOVA results revealed a simple interaction effect for subjects in the indirect experience condition, F (1, 22) = 2.98, which was significant at p < .1. Table 2 displays the behavioral intent means table grouped by feedback and time (T1 and T2) for subjects in the indirect experience group.

Table 2.
Means Table for the Effect of Experience, Subjective Norms Feedback and Time on Behavioral Intent*.
 

Comparison of means indicated that, for subjects in the indirect experiencegroup who were exposed to the positive stimulus, behavioral intent significantlyincreased from T1 to T2. In addition, further comparison indicated thatthe T2 positive feedback mean was significantly higher than the negativeand neutral feedback means.

Implications of the Study

The marginally significant three-way interaction and the main effectfor attitude certainty suggest that the experience manipulation did havea measurable effect in terms of serving to increase the strength and certaintyof respondents’ attitudes. However, one of the most interestingimplications of this study involves the subjects’ low levels of behavioralintent. Based on the results of this study, behavioral intent toward takinga technology based distance education course seemed to be somewhat lowin the sample population of traditional aged college students, and thedirect experience manipulation did not change this greatly. It did appear,however, that the subjective norms feedback affected behavioral intentfor subjects in the indirect treatment condition to some extent. Althoughbehavioral intent was lower at T2 for both the direct and indirect experiencegroups, the behavioral intent of subjects in the indirect experience conditionwho were exposed to the positive feedback stimulus increased over time.This seems to provide some support for the idea that subjects with weaklyheld attitudes are susceptible to subjective norms influences and messagesthat contain them.

Conclusions

Based on these results, it seems apparent that modern traditional agedstudents expect a fair amount of choice and input when it comes to beingasked to support institutional initiatives that affect their role as consumersof higher education. However, although the notion of consumer choice seemsperfectly consistent with the marketing philosophy of most of this country'ssuccessful consumer marketing organizations, it is not at all certain thatthis lesson is being heeded by institutions of higher learning.

The "if we build it, they will come" mentality of many institutionaladministrators and developers may not necessarily be enough to counterbalancesome of the issues that distance education faces with traditional agedstudents, such as perceptions of social distance and inconsistently performingtechnology, frustration due to students' learning curves in mastering coursetechnology, development costs, and lack of viability of multi-course programsneeded to achieve critical mass. Certainly, all signs point to dramaticchanges in technology in the near future that could dramatically improvethis situation and, by extension, the comfort level and attitudes of studentsand faculty alike. However, if institutions wish to prepare to take advantageof these changes, they may need to revise their marketing philosophy withrespect to looking at their students as discriminating consumers of highereducation.

Taking a page from consumer marketing, where efforts to roll outa new product usually aim at targeting potential audiences withpositive images and a great emphasis on benefits to be gained from usage, institutionsof higher learning may need to take greater care to insure that students'experiences of distance education are positive, and that quality controlis maintained, if their efforts are to be successful. Earlier in the adoptioncycle, when most U.S. based distance education programs were small, single-departmentbased efforts aimed at strongly motivated adult learner/continuing educationstudents, there was little need to think in terms of a student consumermarketplace which could influence the success or failure of a program.Now that some institutions of higher learning are turning their attentionto their traditional student base, however, it may be time to realize thatcolleges and universities need to play by the same rules as other consumermarketing organizations if they are ultimately to be successful.

     Ajzen, I. (1991). The theory of planned behavior.Organizational Behavior and Human Decision Processes, 50, 179-211.

     Barron, D.D. (1987). Faculty and student perceptionsof distance education using television. Journal of Education For LibraryAnd Information Science, 27, 257-271.

     Bray, H., (1999). Virtual campus. The BostonGlobe Magazine, Apr 11, 8-16.

     Carnavale, D. (2000). Crowded Cal State U.campuses are asked to add distance education programs. Chronicle ofHigher Education, Mar 31, A30.

     Guernsey, L. (1998). Colleges debate the wisdomof having on-campus students enroll in on-line classes. The Chronicleof Higher Education, Mar 27, A29.

     Eagly, A. H., & Chaiken, S., (1995). Attitudestrength, attitude structure and resistance to change. In R.E. Petty &J.A. Krosnick (Eds.) Attitude strength: Antecedents and consequences(pp. 413-432). Mahwah, NJ: Lawrence Erlebaum.

     Egan, M.W., Sebastian, J., & Welch, M.,(1991, March). Effective Television Teaching: Perceptions of Those whoCount Most…Distance Learners. (Proceedings of the Rural Education Symposium).Nashville, TN: Rural education Symposium. (ERIC Document Reproduction ServiceNo. ED 342 579).

     Fazio, R.H. (1995). Attitudes as object-evaluationassociations: determinants, sequences and correlates of attitude accessibility.In R.E. Petty & J.A. Krosnick (Eds.) Attitude strength: Antecedentsand consequences (pp. 247-282). Mahwah, NJ: Lawrence Erlebaum.

     Fazio, R.H., (1986). How do attitudes guidebehavior, in R.M. Sorrentino, & E.T. Higgins, Eds., Handbook ofMotivation and Cognition, (pp. 224-243). New York: NY, Guilford Press.

     Fazio, R.H., & Zanna, M.P., (1978a). Attitudinalqualities relating to the strength of the attitude-behavior relationship.Journal of Experimental Social Psychology, 14, 398-408.

     Fazio, R.H., & Zanna, M.P., (1978b). Onthe predictive validity of attitudes: the roles of direct experience andconfidence. Journal of Personality, 46, 228-243.

     Fazio, R.H., & Zanna, M.P., (1981). Directexperience and attitude-behavior consistency. In L. Berkowitz (Ed.) AdvancesIn Experimental Social Psychology, 16, 161-202, San Diego, CA: AcademicPress.

     Fishbein, M., & Ajzen, I., (1975). Belief,attitude, intention and behavior: An introduction to theory and research.Reading, MA: Addison-Wesley.

     Jones, T., (1992). IITS students’ evaluationquestionnaire for the fall semester of 1991. A summary report. (EricDocument Reproduction Service No. Ed 345 716).

     Mason, R., & Kaye, A., (1989). Mindweave:Communication, Computers and Distance Education. Oxford, England: PergamonPress.

     Reagan, D.T., & Fazio, R.H., (1977). Onthe consistency between attitudes and behavior: Look to the method of attitudeformation. Journal of Experimental Social Psychology, 13, 28-45.

     Riddle, J.F., (1990). Measuring effectivechange: Students in a distance education class. (Eric Document ReproductionService No. Ed 325 514)

     Rogers, E.M. (1995). Diffusion of Innovations(4^th ed.). New York: The Free Press.

     Rogers, E.M. & Shoemakers, F.F. (1971).Communication of Innovations. New York: The Free Press.

     Roskos-Ewoldsen, D.R. & Fazio, R.H. (1992).On the orienting value of attitudes: attitude accessibility as a determinantof an object's attraction of visual attention. Journal of Personalityand Social Psychology, 63, 198-211.

     Schlosser, C.A., & Anderson, M.L., (1994).Distance Education: A Review of the Literature. Ames: Iowa DistanceEducation Alliance, Iowa State University. (ERIC Document ReproductionService No. ED 382 159).

     Smith, D.L., & McNelis, M.J., (1993). Distanceeducation: Graduate student attitudes and academic performance. (EricDocument Reproduction Service No. Ed 360 948).

     Souder, W.E., (1993). The effectiveness oftraditional vs. Satellite delivery in three management of technology master’sdegree programs. The American Journal of Distance Education, 7(1),37-53.

     University of Illinois. (2000). Teaching atan Internet distance: the pedagogy of online teaching and learning. [Online].The Report of a 1998-1999 University of Illinois Faculty Seminar. Availableat: http://www.vpaa.uillinois.edu/tid/report/tid_report.html

     Universities Targeting Distance Coursesto Traditional Students, (1998). Chronicle of Higher Education, (3)Mar 15, 24-25.

     Wilkes. C.W., & Burnham, B.R., (1991).Adult learner motivations and electronics distance education The American Journal of Distance Education, 5(1), 43-50.