A R TIC L E S The Relationship Between Students' Computer Competency and Per­ ception of Enjoyment and Difficulty Level in Web-based Distance Learning By Yunfei Du, Wayne State University Abstract This paper discussed the concept of computer competency and investigated the relationship between students' computer competency and their perception of enjoyment and d ifficulty level of web-based dis­ tance-learning courses. Participants were 237 entering graduate students in l ibrary and information sci­ ence from a mid-southwestern state university in the United States from year 2001 to 2003. Computer competency was estimated by students' self-report of their prior knowledge of information technology skil ls in a survey called Computer Skill and Use Assessment. Statistical significance was found between the correlation of computer competency and students' perception of enjoyment level (p=.01 1) and d iffi­ culty level (p=.001 ). Introduction Distance education has become an integral component of services from libraries, i ncluding academic l ibraries that serve college of educa­ tion . In recent years, l ibrary and information sci­ ence fields have been exploring innovative ways of teaching and learning in d igital era . For ex­ ample, since the 1 990s, d istance learning in l i­ brary and information science education has developed quickly in the United States. Distance education via the World Wide Web, often com­ bined with on-site meeting requirements, has been a trend in l ibrary and information science fields (Association for Library and I nformation Science Education, 1 999, 7). The 2003 Associa­ tion for Library and Information Science Educa­ tion Statistical Report shows forty-six out of fifty­ six ALA-accredited l ibrary schools reported 1 , 1 55 courses offered as distance education, in comparison to 1 ,008 courses in 2001 , 489 courses in 2000, and 408 courses in 1 999 (As­ sociation for Library and Information Science Education , 2003). Whi le LIS practitioners and researchers are of­ fering instruction in different formats to provide Education Libraries Volume 27, No. 2 Winter 2004 students with more flexibility, they real ize the potential challenge of learn ing from a distance. For example, the instructor needs sufficient in­ structional and technical support in a same­ time/different-place learning environment (Besser, 1 996, 81 9) . Many of the library educa­ tors choose d ifferent-time/different-place models to deliver courses over the Internet to reduce cost and eliminate techn ical challenges by two­ way video-conferencing (l ighting, sound, and wiring of classrooms, et al) . I n asynchronous models, students can control the pace of instruction but students' individual intellectual and technical background will impact learning effectiveness. An onsite environment makes it possible for instructors to adjust teach­ ing strategies according to student feedback via eye-contact. Learners can think, watch , observe, and participate while communicating with the instructor and classmates. In web-based learn­ ing environments, students without adequate technological background may spend more time struggling with technology than working on course content. Computer competency, or in­ formation technology l iteracy, may influence students' satisfaction with their performance 5 within an individual course and with their general experience of web-based learning in general . Thus, chances of dropping out may increase for those students d issatisfied with their distance learn ing experience (Sherry, Fu lford, & Zhang, 1 998, 4) . Identifying factors impacting d istance learning is more valuable to education libraries because their students wil l be next generation of educa­ tors, who would explore pedagogical innovations at the rest of their life. The current study looked particularly at students at a library and informa­ tion science program. Many of them were school l ibrarians. This study explored how students' computer competency can impact their percep­ tion of enjoyment and d ifficulty level in web- based d istance learning. · Distance learning was defined in this study as the application of telecommunications and elec­ tronic devices that enable learners to receive instructions originating from some distant loca­ tion (Keegan, 1 988; Holmberg, 1 995, 51 ) . In­ struction may be synchronous or asynchronous. Distance education may employ correspon­ dence study, or audio, video, or computer tech­ nologies. Previous Studies There are abundant research studies measuring student satisfaction . Students' attitudes, espe­ cially their satisfaction with online courses, are important indicators of student success. Studies show a di rect relation between d issatisfaction of distance learning and student retention. Librari­ ans and educators have been exploring factors that enhance students' satisfaction in web-based distance learning: learning styles, technology support, and learn ing community, et a l . (Simp­ son & Du, 2004; Frey, Alman, Barron, & Steffens, 2004 ) . Previous studies reveal a pos­ sibility of effects of learn ing styles and individual abi l ity in web-based distance learning (Du, 2004). One of the abil ity factors is students' prior knowl­ edge and skil ls of computers. There is abundant research on the effects of specific knowledge and ski l ls, such as prior knowledge of computers, in distance learning. Prior knowledge is defined as the knowledge, skil ls, or abil ity that students bring to the learning process (Jonassen & Grabowski, 1 993). Prior knowledge is one of the strongest and most consistent predictors of learning. Tobias states that prior knowledge can Education Libraries Volume 27, No. 2 Winter 2004 knowledge can be tested via a pretest devel­ oped specifically for a content domain. Tests can be multiple choices, free recal l , true/false, or matching among others . Some tests include a scale for learner to indicate thei r degree of con­ fidence in their answer (Tobias , 1 982). Research suggests an inverse relationship between level of prior knowledge and instructional support. As the level of prior knowledge rises, the need for instructional support decreases; conversely, as the level of prior knowledge decreases, the need for instructional support rises (Tobias 1 976, 1 981 ; Jonassen & Grabowski , 1 993). Theoretical backgrounds related to the impor­ tance of prior knowledge are transfer of learn ing, schema theory, information-processing theory, and theory of structural knowledge. Transfer of learning theory explains how prior skil ls and knowledge transfer directly to new learning. Schema theory explains the acquisition of new information through a learner's active, construc­ tive integration of new information into existing networks of knowledge. I nformation-processing theory focuses on the roles of perception a n attention in learning and memory (Jonassen & Grabowski , 1 993, 421 ). Structural knowledge describes and facilitates the appl ication of prior knowledge to novel situations. It is the knowl­ edge of how ideas within a domain are interrf' lated . In other words, structural knowledge dt scribes how prior knowledge is interconnected (Diekhoff, 1 983). Structural knowledge is als • defined as conceptual knowledge, the conceiJ­ tual storage of meaningfu l d imensions in a given domain of knowledge. In the l ibrary and information science world, J more relevant concept is probably "computer competency", or "information technology l itel­ acy". Simonson, Maurer, Montag-Toradi , & Whitaker (1 987) define computer l iteracy as (1 ) an understanding of computer characteristics, capabilities, and applications; and (2) an abil ity to implement this knowledge in the skil lful , pro­ ductive use of computer applications. Specifi­ cally, Osika & Sharp (2002) identified technical competencies for distance learning students as seven ski l l g roups: ( 1 ) computer operations and utilities, (2) file management, (3) word process­ ing, (4) Internet, (5) PowerPoint presentations, (6) spreadsheet, and (7) databases. On the other hand, more recently Talja (2005, p. 21 ) introduced a social constructivist viewpoint of "computer l iteracy" and suggests a more dialec­ tical social constructs. I n th is study, computer 6 competency is defined as an individual's knowl­ edge and abil ity with computers. This knowledge enables an individual to use computers, soft­ ware appl ications, databases, and other tech­ nologies to achieve a wide variety of academic, work-related , and personal goals, including a successfu l learning experience in web-based courses. "Computer competency" can be an in­ d icator of "computer literacy". In this study we use them interchangeably. A related concept to "computer competency" is " information literacy". Information literacy is a concept wel l d iscussed in l ibrary l iterature, for example, by Snavely and Cooper ( 1 997), Carbo (1 997), Behrens ( 1 994 ), Kuhlthau (1 987), McClure ( 1 994 ) , Bawden (2001 ) , and Eisenberg , Lowe, & Spitzer (2004). Information literacy is a set of abi l ities requiring individuals to "recogn ize when information is needed and have the abil ity to locate , evaluate, and use effectively the needed information" (American Library Associa­ tion, 1 989). I t is believed that the two concepts are related but information literacy has broader impl ications for the ind ividual , the educational system, and society. A few studies explored the effects of computer competency to web-based distance learnjng . Jiang ( 1 999) found students' previous computer competency was not a statistical significant fac­ tor in predicting students' perceived learning, but it has significant correlation with student-student communication (p=.0 1 ), learning styles (p=.03) and time on learning (p=.03). His sample was 287 web-based students from State Un iversity of New York Learning Network. Yu, Kim, and Roh (2001 ) found students' perceived need of web use, technical training they had received, per­ ceived usefulness of the web, and their com­ puter and web competencies had positive d i rect effects on their web use in distance learning. Method This study surveyed the effects of students' computer competency to their perception of en­ joyment level and difficulty level . Two research questions were explored : 1 . How can a student's perception of enjoyment level with the course be affected by different in­ d ividual levels of computer competency? 2. How can a student's perception of d ifficulty level with the course be affected by d ifferent in­ dividual levels of computer competency? Accordingly, two nul l hypotheses are: Education Libraries Volume 27, No. 2 Winter 2004 H0(1 ) : There is no d ifference in Enjoyment Level when the subjects differ with regard to computer competency. H0(2) : There is no difference in D ifficulty Level when the subjects differ with regard to computer competency. In this study, computer competency was meas­ ured by Computer Ski l l and Use Assessment, which was developed by the researcher. The survey was adapted from the Information Tech­ nology Knowledge and Skil ls Diagnostic Tool (http://www . unt.edu/slis/apppacket/ITKS/ITKSas sess.htm), by the School of Library and Informa­ tion Sciences (SLIS) at the U niversity of North Texas. It is a technology skil l self-assessment package for entering graduate students to evaluate their computer competency. ITKS is a web-based self-assessment with 1 97 questions. It has seven sections: Basic Computer Knowl­ edge and Skil ls, Word Processing, Spreadsheet, Database, Presentation, I nternet, and web De­ velopment. Enjoyment level and d ifficult level were assessed by the Student Satisfaction Sur­ vey. The Student Satisfaction Survey consisted of 5 items with a semantic-differential scale of 1 to 7. The researcher chose the items for the in­ strument from studies by Rumpradit ( 1 999), and Osborn (2000). Both surveys were validated in previous studies (Du, 2004; Simpson & Du, 2004 ) . Participants were entering graduate stu­ dents in l ibrary and information science from a mid-southwestern state university i n the States. Findings I n total , 301 subjects participated in the Com­ puter Ski l l and Use Assessment. Among them 237 students completed the Student Satisfaction Survey. Participants in this study were graduate students enrol led in web-based distance learn­ ing courses at a mid-southwestern state univer­ sity in the Un ited States from year 2001 to 2003. The subjects were 1 00% online students; they came to campus for software training because they had never taken a WebCT course. The subjects were essential ly homogenous in age, gender, and knowledge leve l . Fourteen of them were male, the rest of them were females. Aver­ age age of the students was between 35 and 40 . Over 50% of them were school l ibrarians or schoolteachers working toward their master's or school l ibrary certification . They a l l enrolled as regular master's students. Eighty percent of al l enrolled students participated in the study. The courses they took were taught 1 00% online. 7 Table 1 : Descriptive Statistics of Computer Competency Items Item Name Mean SD Skewness Kurtosis Q1_FILE 6 . 1 4 1 .04 - 1 .29 1 .34 Q2_WORD 6.62 .70 - 1 .97 4 . 1 1 Q3_SPREAD 5.23 1 .45 - .54 - .38 Q4_CUT 6 .54 1 .05 -3.37 1 3.72 Q5_PPT 5. 1 4 1 .66 - .78 - . 1 3 Q6_EMAIL 6.67 .72 -3.36 1 7.52 Q7_1NTER 6.22 1 .03 - 1 .99 6 .37 Q8_LIST 4 .51 1 .81 -.45 - .64 Q9_ACCESS 2.05 1 .42 1 .26 .58 Q1 0_HTML 2.88 1 .82 .6 1 -.75 CC* 5.2 .80 - .66 1 ' 1 5 Note. CC (Computer Competency) stands for average of all ten items. 1 = least al ike, 7 = most alike Computer Competency Table 1 i l lustrates the descriptive statistics for the question items. Each item has a semantic­ differential scale, ranging from 1 to 7. One indi­ cates the lowest proficiency and 7 stands for the highest proficiency. The researcher expected the mean scores of the items to be between 4 and 5. From Table 1 , the author found students in this sample rated themselves fairly high at basic computer skil ls, such as Q1 (Open/Run files and Programs, mean score = 6 . 1 4 ) , Q2 (MS Word, mean score =6.62), Q3 (Spreadsheet, mean score = 5.23), Q4 (Cut and Paste, mean score= 6 .54), and Q6 (Using Email , mean score = 6.67). Students were not famil iar with com­ puter languages, such as Q9 (Access Database Language, mean score = 2 .25), and Q 1 0 (HTML, mean score = 2 .88). Several reasons may have contributed to the distribution of the current data. Firstly, students may have a tendency to rate themselves higher if they are famil iar with some skills. That might yield negatively skewed scores in question items 1 to 6. Secondly, this ALA accredited l ibrary program expects that entering students have adequate skills in basic computer operation and can complete basic academic works, such as compil ing homework using a word-processor. The students are supposed to pass the ITKS self-test when they apply for this program. There is a possibil ity that students who failed to pass the ITKS self-test but claimed to pass it were sti l l al lowed to enroll in the program. Thus, the scores of computer competency tend to be h igher than expected . Table 2 : Satisfaction Measurement Variables Name Enjoyment Level Performance Satisfaction Difficulty Level Grade Expectation Retention Tendency Satisfaction Level* Mean SD 5.48 1 . . 39 6.00 1 .2 1 4.90 1 .40 6.83 .42 6 .23 1 .48 5.89 .79 Skewness -.88 -1 .55 - .71 -2. 1 8 -2 .05 -1 ' 14 Kurtosis .21 2.7 .29 4.04 3.41 .90 Note. * Satisfaction Level is the average of al l five items (N = 237) . Education Libraries Volume 27, N o . 2 Winter 2004 8 Student Satisfaction As mentioned before, the researcher expected the mean scores of each items to be d istributed around 4 to 5 with a bell-shape curve. Table 2 ind icates a leptokurtic and negative skewed d is­ tribution of both Grade Expectation (04) and Retention Tendency (05), with Kurtosis more than 1 and Skewness less than - 1 . The data shows that most of the students expected an "A", rather than a bel l shape distribution , possi­ bly around "B", as suggested by statistical theo­ ries. This distribution l ikely occurred because graduate students are expected to get at least a "B" or better. Graduate students in the sample also had a strong tendency toward retention , meaning that these students had a strong moti­ vation to stay in this ALA accredited l ibrary pro­ gram. This University has one of only three ALA accredited programs in the State of Texas. The Satisfaction Level score, or the average of mean scores of all five items, reflected a normal d istri­ bution of the sample. Correlation Since the dependent variable, computer compe­ tency, yields continuous scores, mu ltiple regres­ sion analysis was applied to test the sign ificance of correlation between computer competency and student satisfaction level . The author con­ ducted five individual regression analyses on each question in the student satisfaction survey and found statistical significance in two Student Satisfaction items: Enjoyment Level (answers to question 1 of Student Satisfaction Survey as the dependent variable) and Difficulty Level (an­ swers to question 3 of Student Satisfaction Sur­ vey as the dependent variable). The results are l isted in the next two tables. The next table indicated that there was a statis­ tical ly significant correlation between computer competency and students' Enjoyment Level . The correlation coefficient, or � weight, is . 1 64 (p = 0.0 1 1 ). That means that computer competency as the independent variable may predict stu­ dents' Enjoyment Level on web-based courses, with a correlation coefficient of . 1 6. Table 3: ANOVA for Enjoyment Level Source Sum of Squares Regression Residual Total 1 2 .64 457. 1 5 469.79 df 1 239 240 MS 12.64 1 .9 1 3 F 6 .61 p .01 1 . 1 64 Note: Dependent Variable: Enjoyment Level . I ndependent Variable: Computer From the table below, we can conclude that there was a statistically significant correlation between computer competency and students' Difficulty Level (see Table 4 ). The � value is .205 (p = 0.001 ) . That ind icates that computer competency as the independent variable may predict students' impression of difficulty level of web-based courses, with a correlation coefficient of .205. Table 4: ANOVA Report for Difficulty Level Source Sum of Squares Regression 20.27 Residual 461 .99 Total 482 .27 df 1 239 240 MS 20.27 1 . 93 F p 0.49 .001 � .205 Note: Predictors: Dependent Variable: Difficu lty Level. Independent Variable: Computer competency There were no significant correlations between computer competency and Performance Satis­ faction (r = .052, p = .421 ), Grade Expectation (r = .034, p = .599), and Retention Tendency (r = Education Libraries Volume 27, No. 2 Winter 2004 .080; p = .21 7) . There was no significant corre­ lation between computer competency and the average of all five items (r = . 1 1 8; p = 0.069 > 0.05). Even though the correlation is not statisti- 9 cally significant, the p-value is fairly close to .05. The correlation coefficient (13) impl ies there might be a significant result if the sample size were larger. The researcher believes there might be some practical significance in enhancing stu­ dents' computer competency in web-based dis­ tance learning. However, we need more studies to investigate that assertion. Conclusion and Implications Different student computer competency levels impact students' perception of Enjoyment Level and Difficulty Level. Students feel web-based courses are easier if they have enough com­ puter background. It is notable that a sign ificant relationship between learning style and enjoy­ ment level was found in previous studies (Simp­ son & Du, 2004 ). The results of this study sug­ gest additional factors that impact student en­ joyment level in distance learning. The findings of this study are valuable to educa­ tion l ibraries. U nderstanding the effects of com­ puter competency may help faculty to del iver learning materials more effectively onl ine. Stu­ dents in education or school l ibrary programs with higher level of computer competency are l ikely to be more satisfied with d istance learning. Such students will be more creative with peda­ gogical innovation, such as utilizing Internet re­ sources, using databases, and creating digital media in teach ing and learning. Those en­ hanced ski l ls may help them to achieve a wide � variety of academic, work-related, and personal goals. To better serve distant students and enhance student retention , we suggest technology sup­ port in d istance education programs. Our resu lts are consistent with findings from previous stud­ ies (Yu et a l . , 200 1 ) suggesting that technology support should be provided to learners in order to faci l itate the participation of web-based learn­ ing. Students without adequate technology back­ ground should be g iven tutorials or training ses­ sions to help them adjust to the onl ine environ­ ment. Distance learning programs in l ibraries should provide onsite training sessions to en­ hance computer competency for students with­ out enough prior knowledge of computers. In addition, distance programs might consider de­ ploying budget in supporting students and an­ swer students' technical q · uestions in real-time Education Libraries Volume 27, No. 2 Winter 2004 via telephone, instant messaging, video confer­ ence, et al (Lowe & Mal inski, 2000). The researcher found no statistically significant relationship between computer competency and pooled score of student satisfaction from al l five question items. He sampled students from dif­ ferent majors and from d ifferent online classes. Different course types may yield a significant level of "within group variance" and thus yielded non-significant results. The researcher bel ieves with a larger sample size sign ificant results may be found. Computer competency may not the only factor predicting students' success in web­ based distance learning. Further studies are needed to explore the interaction of computer competency and different course delivering plat­ forms, such as GUI-based (such as WebCT) vs. text-based (Blackboard) systems. References American Library Association ( 1 989) . Presiden­ tial Committee on Information Literacy. Final Report. Chicago: ALA. Retrieved February 3, 2005, from: http://www.ala.org/ala/acrl/ acrlpubs/whitepapers/presidentia l .htm Association for Library and Information Science Education. ( 1 999). Educating Library and In­ formation Science Professionals for a New Century: The KALIPER Report. Oak Ridge, TN: ALISE. Retrieved February 2, 2005, from: http://www.alise.org/publ ications/ kali­ per.pdf Association for Library and Information Science Education (2003). 2003 Statistical Repo " rt. Oak Ridge, TN: ALISE. Retrieved February 2, 2005, from: http://i ls.unc.edu/ALISE/2003/ Curric/Curriculum0 1 .htm Bawden, D. (2001 ). Information and Digital Lit­ eracies: A Review of Concepts. Journal of Documentation, 57(2), 2 1 8-259. Behrens, S.J. ( 1 994) . A Conceptual Analysis and H istorical Overview of I nformation Liter­ acy. College and Research Libraries, 55(4), 309-322. Besser, H . ( 1 996). Issues and Chal lenges for the Distance Independent Environment. Journal of the American Society for Informa­ tion Science, 47(11 ) , 81 7-820. Carbo, T. ( 1 997). Mediacy: Knowledge and Ski l ls to Navigate the I nformation Super Highway. Proceedings of the lnfoethics Con­ ference, Monte Carlo, 1 0....:.1 2 March 1 997. Paris: Unesco. 1 0 Diekhoff, G . M . ( 1 983) . Relationship Judgments in the Evaluation of Structural Understand­ ing. Journal of Educational Psychology, 75, 227-233. Du, Y. (2004). Exploring the Difference between "Concrete" and "Abstract": Learning Styles in L IS D istance Education . 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Keegan, D. ( 1 988) . On Defining Distance Edu­ cation. In D. Seward , D. Keegan, B. Holm­ berg (Eds .), Distance Education: Interna­ tional Perspectives. (pp. 63-65). New York: RouUedge. · Kuhlthau , C. C. ( 1 987). Information Skills for an Information Society. Syracuse, NY: ERIC Clearinghouse. (ED 297740). McClure, C. R. ( 1 994). Network Literacy: A Role for Libraries. Information Technology and Libraries, 13, 1 1 5-1 25. Osborn, V. (2000). Identifying At-risk Students: An Assessment Instrument for Distributed courses in H igher Education . Unpubl ished doctoral d issertation, University of North Texas, Denton, Texas. Osika, E. R. , & Sharp, D. P. (2002). Minimum Technical Competencies for Distance Learn­ ing Students. Journal of Research on Tech­ nology in Education, 34(3), 3 1 8-325. Education Libraries Volume 27, No. 2 Winter 2004 Rumpradit, C. ( 1 999). An Evaluation of the Ef­ fect of User Interface Elements and User Learning Styles on User Performance, Con­ fidence, and Satisfaction on the World Wide Web. Unpubl ished doctoral dissertation, George Washington University, Washington, DC. Sherry, A. C . , Fulford, C. P . , & Zhang, S. ( 1 998). Assessing Distance Learners' Satisfaction with Instruction: A Quantitative and a Quali­ tative Method . American Journal of Distance Education 12(3), 4-28. Simonson, M. R. Maurer, M. Montag-Torard i , M .& Whitaker M. ( 1 987), Development of a Standardized Test of Computer Literacy and Computer Competency I ndex. Journal of Educational Computing Research, 3(2), 231 - 247. Simpson, C. & Du, D. (2004). Effects of Learning Styles and Class Participation on Students' Enjoyment Level in Distributed Learn ing En­ vironments Journal of Education for Library and Information Science, 45(2), 1 23-1 36. Snavely, L. and Cooper, N ( 1 997). The I nforma­ tion Literacy Debate. Journal of Academic Librarianship, 23( 1 ) , 9-20. Talja, S. (2005). The Social and Discursive Con­ struction of Computing Skil ls. Journal of the American Society for Information Science and Technology, 56(1 ), 1 3-22 Tobias, S. ( 1 976). Achievement Treatment In­ teractions. Review of Educational Research, 46(1 ), 6 1 -74. Tobias, S. ( 1 981 ). Adapting Instruction to I ndi­ vidual Differences among Students. Educa­ tional Psychologist, 16, 1 1 1 - 1 20 . Tobias, S . ( 1 982). When Do I nstructional Meth­ ods Make a Difference? Educational Re­ searcher, 11(4), 4-9. Lowe, W, & Malinski , R. (2000). Distance Learn­ ing: Success Requires Support. Education Libraries, 24(2/3), 1 5-1 7. Yu , B., Kim, K. , & Roh , S . (2001 , June). A User Analysis for Web-based Distance Education . Paper presented at the Annual Topics on Distance Learning Conference, Hammond, Indiana (ERIC Document No. ED 455 830). Retrieved February 3, 2005, from: http://www .calumet. purdue.edu/todl/proceedi ngs/200 1 /2001 proceedings. htm 1 1 Appendix: Computer Ski ll and Use Assessment Rate your abil ity to do each of the following: (circle the appropriate number, from 1 (no knowledge/abil ity) to 7 (expert user)) No knowledge 2 3 4 5 6 7 Expert user 1 . Find, open and run files and programs 2 3 4 5 6 7 2. Create a document using a word processor 1 2 3 4 5 6 7 3. Use a spreadsheet to create a document 1 2 3 4 5 6 7 4. Cut, copy, and paste text 1 2 3 4 5 6 7 5. Create a presentation document on a computer 1 2 3 4 5 6 7 6. Send and receive e-mail 1 2 3 4 5 6 7 7. Search for information on the lnterneUWorld Wide Web 1 2 3 4 5 6 7 8. Subscribe and post messages to a " l istserv" 1 2 3 4 5 6 7 9. Program a computer using a database language (such as Access, Foxpro or Oracle, etc.) 1 2 3 4 5 6 7 1 0. Create or edit a Website (using html , Java, etc.) 2 3 4 5 6 7 1 1. Electronically send and receive files 2 3 4 5 6 7 1 2. General ly, how satisfied are you with your computer abil ity (choose one) 0 Very Dissatisfied 0 Dissatisfied 0 Dissatisfied with reservation 0 Satisfied 0 Very Satisfied 0 No Basis for JudgmenU Not Applicable Comments: Yunfei Du is an Assistant Professor, Library and Information Science Program, at Wayne State University, Detroit. E-mai l : yunfei@wayne.edu Education Libraries Volume 27, No.2 Winter 2004 12