La lecture en ligne est gratuite
Le téléchargement nécessite un accès à la bibliothèque YouScribe
Tout savoir sur nos offres
Télécharger Lire

24TH SEMCON

De
8 pages
  • exposé
24TH SEMCON SCIENTIFIC METHODS AND COMMUNICATION (SMAC) DECEMBER 21th, 2011 AALBORG UNIVERSITY FREDERIK BAJERS VEJ 7 AALBORG Ø DENMARK FINAL PROGRAMME AND ABSTRACTS
  • somatosensory reflex pathways
  • physical training for youth ice hockey players
  • 11.30 idr
  • prediction of interstitial fluid glucose
  • indirect evidence from reflex studies
  • spinal excitability after unilateral locomotor training
  • vitro pig nerve model for evaluation of stimulation selectivity
  • mathematical model
  • evidence
Voir plus Voir moins
Instructional Integration of Computers to Improve Learning: Student Perception. Jared Keengwe Muskingum College Abstract Two questions were investigated in th is study: (a) what is the frequ ency of faculty integr ation of computer techno logy into classroom instru ction ? (b) To what exten t does the fr equency of f ac-ulty integration of compu ter technology, stud ents compu ter proficien cy lev els for personal ac-tivities, and studen ts computer proficiency levels for instructional activities predict th e students perceptions of the eff ect of computer technology use to improve their learn ing? Based on the evidence from th e study, it can be suggested that students need to hav e direct instru ction to use advanced applications. These programs provide skills in advan ced co mputer techno logy applica-tions that will en able f aculty to exp ect more disciplin e-specif ic co mputer-b ased projects such as developing a web quest in a web editor. Purpose of Study There is a call for evid ence r egarding the justification of the massive investments in to technology r esources (Oppenheimer, 1997, 2002), especially as it relates to stud ent learning. Even so, it is diff icult to quan tify th e use of compu ter technology to support student learn ing (Oppenheimer, 2002 ; Roblyer & Knezek, 2003 ; Strudler, 2003) and justify the lof ty spending on new technolog ical resources. It cannot b e assumed th at once technology too ls are av ailable, f ac-ulty will n ecessarily embr ace them and integrate them into their classroom instru ction. On th e contrary, successful use of these too ls to enhan ce studen t learning goes beyond the co mmon task of just providing mor e machines in the classroom. The pressure to refor m education through technology in tegration (Becker, 2001) and the emphasis on developing infor mation literacy skills for studen ts (Rockman, 2004) implies the need for curren t understand ing on compu ter technology integr ation pr actices to support stud ent learning. Therefor e, the purpose of this study was to determin e the n ature of the relationship be-tween facu lty in tegration of co mputer techno logy in instru ction and student p ercep tion of th e ef-fect of co mputer techno logy use to improve th eir learning. Research Methods The study focused on courses offer ed in 5 co lleg es in the selected institution during th e fall semester of 2005. To h ave a more repr esentative sample of the population in the p articipating university, diff erent courses were selected across the fiv e colleges. A list of all courses offered
Volume 20, Spring 2007in Education Essays
during the summer at the par ticipating institution was stratified by collegiate level, yielding four strata: course lev els 100, 200, 300, and 400. Random sampling was then used to select two courses from each of th e 4 lev els across the five colleges. The r esearch ers focused on courses with 20 studen ts and more. Giv en that ther e were 5 colleges and 8 courses drawn from each college at each lev el, a total of 40 courses, and at least 800 par ticipants were expected to b e part of the study. Th e use of this samp ling procedure was based on the following criteria: 1.er of subjects surveyed should not be unwieldy; a man The total numb ageab le sample size would be easy to collect, analyze and interpret. 1.Equal repr e five colleges.esentation of th 2.esentation of th e 4 possible course levels.Equal repr 3.ent majors.lled in differ Easy accessibility of students enro Instrument The resear chers emp loyed a survey methodo logy to co llect and tabulate the data. Th e re-searchers reviewed several existing survey instruments and determined th at none of them f it th e specific n eed for data co llection in this study. Th erefore, the researchers dev eloped an instrument based on the an alysis of pre-ex iting surveys; items from several compu ter technology survey in-struments were mod ified to fit the presen t study. The Computer Technology Integration (CTI) Survey was designed to measure facu lty in-tegration of compu ter technology into classroom instruction and stud ents perceptions of th e ef-fect of co mputer techno logy use to improve th eir learning. Following an in itial pilot study which was conducted using a convenience sample of 20 stud ents enrolled in on e course at the p artici-pating univ ersity, th e revised v ersion of the survey was ad ministered to a differ ent course in a second pilot study. This course was iden tified by the r esearch ers and the prof essor contacted. The results of th e second pilot study were tabulated by the researchers to establish preliminary results, check the appropr iateness of standard measures, determin e poten tial areas of concern, and to iden tify questions that would r equire fur ther clarif ication. Prior to the ad ministr ation of the instrument to the par ticipating students, th e Cronbachs reliab ility coeff icient was run during the second pilot study to d eter mine the reliab ility of each scale used in th is study. The Cronbach alpha for all the sections of the p ilot study instrumen t was above th e ac-ceptab le .70 (McMillan & Schu mach er, 1997) imp lying th at over h alf of th e variability was in-ternally consisten t or reliable. After data co llection, sep arate Cronbachs reliab ility coeff icien ts were calculated for th e sample respondents (N = 837). A summary of the Cronb ach alpha scores for the pilo t and th e sample respondents is provid ed in Table 1.
Volume 20, Spring 2007 Essays in Education
Table 1:ectionsCronbach Alpha Scores for th e Survey S Section # of Items Pilot alph a Sample alpha Personal Proficien cy 10 .82 .77 Instructional Proficiency 10 .81 .84 Faculty Integration 12 .71 .78 Student Percep tions 20 .73 .73 ___________________________________________________ _____________________A coefficient of .90 ind icates a high ly reliable instrument but coefficien ts ranging from .70 to .94 are accep table for most instru ments (McMillan & Schu mach er, 1997). The f ive sec-tions of the CTI surveys repor ted a Cronb ach alpha value rang ing from .73 to .83 dur ing the p ilot testing of th e CTI to a conven ience sample of 20 students and wer e ther efore found to b e within the acceptable r ange to b e used for the actu al study. The Computer Technology Instrument (CTI) surveys were admin istered to 854 students enrolled in 40 courses at a particip ating mediu m-sized Midwest pub lic un iversity in Indian a. The researcher h and-delivered and administer ed the surveys to the studen ts and collected th em af ter completion. Of the 854 surveys receiv ed, 837 cases (98%) were complete and ther efore retained for analysis. One question was prominen t. To what exten t does the fr equency of f aculty integration of computer techno logy and studen t compu ter proficien cy lev elfor personal and instru ac-ction al tivities predict studen t perception of the eff ect of computer technology use to improve their learning ? One null hypo thesis deriv ed from th is question was tested at the .05 level of sign ifi-cance. Results Most particip ants reported to be v ery comp eten t in app lications such as th e Intern et and WWW (92.5%); moderately competent in applications such as electron ic files attach ments (73.1%) and least competent in using web author ing tools, and using Hyperstud io, HyperCard, or other multimedia authoring application (13.9% and 3.9%, respectiv ely) for personal activities. Table 2 summarizes the p ersonal co mputer skills responses for the 837 studen ts scored across the three scales. The list of co mputer applications was then rank order ed based on studen t responses. Table 2:f personal computer proficien Student self-rating o cy skills * Computer Too l/Application 1 2 3 1. Internet and WWW for personal activities. .4% 7.2% 92.5% 2. Electronic mail (e-mail application). .7% 9.1% 90.2% 3. Word processing (MS Word). .5% 11.9% 87.6% 4.86.3%.5% 11.3% e.g. Windows, Mac, etc). Operating system ( 5. Electronic f iles attachmen t for personal activities.2.7% 24.1% 73.1% 6. Computers for play ing compu ter games, Videos, etc. 6.0% 28.4% 65.6% Volume 20, Spring 2007 Essays in Education
7. Internet chat roo ms for personal activ ities.48.9% 11.9% 39.2% 8. Spreadsheet to r ecord/org anize personal activities. 9.6% 50.9% 39.5% 9. Web author ing tools to bu ild personal Web p ages. 45.6% 40.5% 13.9% 10. HyperStudio, etc for p ersonal activ ities. 72.9% 23.2% 3.9% ________________________________________________________________________ * Note: Score 1 = not at all comp eten t, 2 = somewhat co mpetent, and 3 = v ery comp eten t. Question 1 The particip ants reported that their facu lty often used course management tools (40.9%) and least used web pub lishing too ls (3.8%) for instruction. Tab le 3 summarizes the results on 837 student response scored across the four scales. The list of applications was rank ordered to indicate th e most frequ ently used computer tool. Table 3:culty Integration of Techno tions of Fa to Instructionlogy in Student Percep * Computer Too l/Application 1 2 3 4 1. Using course management tools 15.7% 19.1% 24.4% 40.9% 2. Web browsers. 14.5% 24.7% 20.2% 40.6% 3. Email for feedb ack/commun ication. 10.3% 24.9% 28.9% 36.0% 4. Computer projection device. 15.8% 33.0% 19.0% 32.3% 5. Multimed ia presen tation tools. 9.0% 31.4% 28.1% 31.5% 6. Productivity tools. 13.4% 30.5% 26.0% 30.1% 7. Teach ing in a multimed ia classroom. 38.9% 29.2% 16.7% 15.2% 8. Imaging Devices. 46.8% 27.4% 16.6% 9.2% 9. Discipline Dev ices. 57.2% 26.6% 9.1% 7.0% 10. Content specific Softwar e/ CD-ROM. 37.9% 42.2% 13.5% 6.5% 11. Desktop video confer encing / chat sessions. 71.1% 19.2% 4.9% 4.8% 12. Web publishing /author ing tools. 56.6% 31.4% 8.1% 3.8% ________________________________________________________________________ * Note: Score 1 = Never, 2 = Sometimes/Few times per Semester, 3 = Often/ 1 - 3 times per Month=3; 4 = Very often /1-3 times Per Week. Question 2 isedictor variables. Th ession analysis was conducted with all three pr A simultaneous regr 2 analysis produced a significant model with the v alue of R = 0.039. In oth er words, the percent-age of the total variance in criterion that was shared with the set of the pr edictor variables was 3.9%. This was significan t at the 0.001 level. Thus we reject th e null and conclude th at th e lin ear combination of th e three predictors in th is study have a strong pr edictive r elationship with the criter ion.  The standard ized p artial regr ession coefficien t for facu lty to tal was -.204, t= -5.663, andpedictor of the stud ents= .001. Therefore, the faculty total was a significant pr perceptions of computer use to improve th eir learning ( after controlling for th e instructional and personal total). Th e standard ized p artial regr ession coefficien t for instruction to tal was -.013,t =Volume 20, Spring 2007in Education Essays
-.237, andpto tal ction al edictor of the stu-was not a significant pr = .812. Therefore, the instru dents percep tions of compu ter use to improve their learn ing (after contro lling for the faculty to-tal and p ersonal to tal). The stand ardized partial r egression coeff icient for personal total was .084, t =1.504, andpefore, the p ersonal to tal was not a significant pr edictor of the stu-= .133. Ther dents percep tions of compu ter use to improve their learn ing (after contro lling for the faculty to-tal and instructional total). A summary of th e results of th e tests is provid ed in Table 4. Table 4:Summary of Multiple Regression Ana ictors and Criterionlyses of Pred VariableBB SE p t Faculty To tal -.190 .034 -.204 -5.663 .001 Instruction to tal -.021 .090 -.013 -.237 .812 Personal total .168 .112 .084 1.504 .133 ________________________________________________________________________ Discussions el of co igh lev iciency in using the pro-mputer prof esponses indicated a h The student r ductivity software, electronic mail, electron ic files, and th e Intern et and the Wor ld Wid e Web, for both personal and instructional activities. The stud ent responses indicated a mod erate lev el of computer prof iciency in using spreadsheets and In ternet chat rooms for both personal and in-structional activities. Th e student r esponses indicated a low level of compu ter proficien cy in us-ing web authoring tools and using HyperStudio, HyperC ard, or other multimed ia au thoring ap-plication for both p ersonal activ ities and instructional activities. The major resear ch question is: To what exten t does the fr equency of f aculty integration of computer techno logy into classroom instru ction, stud ents compu ter proficien cy lev els for personal activities, and stud ents compu ter proficien cy lev els for instructional activities, pred ict student perception of the eff ect of computer technology use to improve their learn ing? The analysis includ ed a null hypothesis th at stated th at th e students personal compu ter proficien cy, students instru ction al co mputer prof iciency, and f aculty integration of compu ter technology do no t account for a sign ificant proportion of variance as predictors of the studen t perception of the effect of compu ter technology use to improv e their learning. A multiple regres-sion analysis revealed that the thr ee pred ictor v ariab les produced a significant model with the 2 value of R = 0.039; p < .05. Th erefore, a statistically signif ican t relationship was found between the three predictor var iables and the criterion –studen t perception of the eff ect of computer tech-2 nology use to improv e their learning. Based on the values of R = 0.039 obtain ed from th e si-2 2 multan eous regression model, R = 0.034 obtain ed from th e instructional model, and R = 0.039 obtained fro m the p ersonal mod el, we can infer that instruction did not add significantly to th e 2 first model. Therefor e, the p ersonal and f aculty model were th e best fit. The R of 0.039 implies that mod el accounted for 3.9% of the shar ed variance b etween the personal and faculty models.
Volume 20, Spring 2007in Education Essays
Implications of the Study Universities across the nation are experiencing r apid technolog ical innovations, continu-ous adjustments in the learning environments, and a n ew generation of studen ts exhibiting di-verse computer proficiency skills. Yet in many instances, th e rapid advances in instructional technology exceed the level of familiarity with the techno logy for effectiv e technology use in the classroom (Allen, 2001). Therefor e, this study provid ed baseline data to identify curren t com-puter integration practices of the faculty. These data can guide institu tions to ex amin e their cur-rent technology practices and provid e grounds to mak e sound techno logy-related d ecisions th at can max imize studen t learning. Educators hav e also made var ious assumptions about the relationships between computer technology integr ation, conten t instruction, and stud ent learning. For instance, f aculty may as-sume that the “cyberkids” of this technological age ar e highly competent in gener al co mputer skills and are likely to be more prep ared to learn with technology in contrast to the stud ents of past generations. In practice, this assumption imp lies less support from faculty –students ar e lef t on their own especially in courses th at heavily u tilize techno logy. The hop e, here, is that this technology savvy g eneration of studen ts will eff ectively und erstand and use the technology and the content infor mation. Evid ence fro m this study provid es data to question such thinking. In-stead, ther e is evid ence suggesting a need for training in the use of sophisticated software pro-grams. Further, the d istinction between personal and course-r elated use illustrates the n eed for faculty to create a bridge b etween p ersonal and instructional use of techno logy for improv ed stu-dent learning. The study provided eviden ce to suggest that a relationship exists between facu lty instru c-tional integr ation of computer technology and stud ent per ception of the effect of such integration to improve their learn ing. However, this relationship is negative. This unexpected f inding cou ld be attr ibuted to several factors that includ e social, contextual and/or p ersonal inf luences. For in-stance, due to the d igital div ide among college stud ents, students might per ceiv e the v alue of computing for improved learn ing differ ently and fail to understand the ro le of technology in transforming th eir courses (Beisser, Kurth, & Reinh art, 1997). Instructional practices and contex-tual factors such as student comfort levels, beliefs, and exper iences are also likely to aff ect the way the students per ceiv e the impact of co mputer use on their learn ing. The combin ed interplay of these factors might h ave led to th is unexpected find ing. Technology h as the potential for ch anging th e way teachers teach and students learn (Thompson, Schmidt, & Davis, 2003), but research ind icates that educators are less likely to use computers th an other prof essions (Hanushek, 1998). Other reports such as the on e by Cuban (2001) indicate th at faculty use computers less frequently, and in limited ways th at do not sup-port student learning. Even in this respect, facu lty play a major role in how successful technol-ogy will be in education (Yild irim & Kiraz, 1999). Therefor e, if edu cators want to max imize stu-
Volume 20, Spring 2007 Essays in Education
dent learning, th ey should invest time, money, and technological r esources in an area that can make greatest impact on studen ts, the faculty (Fabry & Higgs, 1997). Conclusion In conclusion, th e inclusion and requ irement of a nu mber of co mputer courses in the g en-eral edu cation curricula cannot b e overemphasized. The n eed to train f aculty in compu ter techno-logical applications by providing techno logical tr aining for f aculty to en able them to work more effectively with their students and p eers across campus is, now more than ever, par amount. Fur-ther, the identification of k ey facu lty who can prov ide mod eling in the b est instructional technol-ogy integration practices that support student learn ing is reco mmend ed. These k ey facu lty will also guide oth er facu lty through the necessary instructional adjustments for a successful use of technology in the classroom (Johnson & Liu, 2000). Limitations of the Study This study was limited in scop e by consider ing those var iables includ ed in th e items on the questionn aire. Due to the n ature of the study, it was not possible to accoun t for all the v ari-ables that could interf ere with the study results. For instance, the researchers did no t consider faculty r ank or tenur e status. It is possible that such variables cou ld interfere with data outco me and consequently migh t have influen ced th e study findings. References Allen, R. (Fall 2001). Techno logy and learning : How schools map routes to techno logy's promised land.ASCD Curriculum Update, 1-3, 6-8. Becker, H.J. (2001). How are teachers using compu ters in instru ction ? Paper presen ted at the  meeting of the American Education al Research Association. Retr ieved May 25, 2005,  fromhttp://www.crito.uci.edu/tlc/FINDINGS/special3/Beisser, S., Kurth, J., Reinhart, P. (1997). The teacher as a learn er: An undergradu ate stud ents  and faculty men torship success. In D. Willis, B. Robin, J. Willis, J. Price, & S. McNeil  (Eds.), Technology and Teacher Train ing Annual, 322-326. Char lottesville, VA. Association for the Advan cement of Compu ting in Education. Cuban, L. (2001). Oversold and underused : Computers in the classroom. Cambridge, MA: Harvard University Press. Fabry, D. & Higgs, J. (1997). Barriers to the eff ective use of technology in education.Journal of  Educationa l Computing,17(4), 385–395.
Volume 20, Spring 2007in Education Essays
Hanushek, E. A. (1998). Outcomes, incentiv es, and beliefs: ref lections on analysis of the  economics of schools.Educational Evaluation and Policy Analysis, 19(4), 301-308. Johnson, D. L., & Liu, L. (2000). Fist step toward a statistically g enerated infor mation technology integr ation model. Co mputers in the School, 16(2), 3-12. Oppenheimer, T. (1997). The computer delusion. The Atlantic Monthly (July), 280(1), 45-62. Oppenheimer, T. (2002). The f lick ering mind: Th e false pro mise of technology in the classroom  and how learning can be sav ed. New York: Random House. Roblyer, M. D., & Knezek, G. A. (2003). New millennium r esearch for educational technology:  A call for a n ation al research ag enda.f Research on Technology in Educa tionJournal o ,  36(1), 60-71. Rockman, I. F. (2004). Integr ating Infor mation Literacy into the Higher Education Curriculum:  Practical Models for Tr ansformation. San Francisco, CA: Jossey-Bass. Strudler, N. (2003). Answering the call: A response to Rob lyer and Knezek.f ResearchJournal o  on Technology in Education, 36(1), 72-76. Thompson, A.D., Schmidt, D.A., Davis, N.E. (2003). Technology collaborative for simu ltan eous  renewal in teacher edu cation.Technology, Research and DevelopmentEduca tional ,  51(1), 73-89. Yildirim, S., & Kir az, E. (1999). Obstacles to integr ation of on- line commun ication too ls into  preservice teacher edu cation.er EducationJournal of Computing in Teach , 15(3), 23-28.
Volume 20, Spring 2007 Essays in Education
Un pour Un
Permettre à tous d'accéder à la lecture
Pour chaque accès à la bibliothèque, YouScribe donne un accès à une personne dans le besoin