MétaCan
Menu
Retour à la cohorte
Enregistrement W7140122849 · doi:10.1108/qrde-12-2003-0012

Research Abstracts

2003· article· en· W7140122849 sur OpenAlexaboutno aff
Eric Plotnick

Notice bibliographique

RevueQuarterly review of distance education · 2003
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueOnline and Blended Learning
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDistance educationBandwagon effectGraduation (instrument)Higher educationSubject (documents)Subject matterBest practiceInformation technology

Résumé

récupéré en direct d'OpenAlex

Faculty Attitudes Towards Distance Education: Enhancing the Support and Rewards System for Innovative Integration of Technology Within Coursework. Crawford, Caroline M.; Gannon-Cook, Ruth.Distance education is an area of rapid growth at the university level, especially within the colleges, schools, and departments of education (National Center for Education Statistics, 1999; United States Distance Learning Association, 2000). However, there are some faculty who are not jumping on the bandwagon and are taking a more conservative route towards considering the impact of distance education within their courses’ attitudes towards distance education: early innovators; hangers-on (late adopters); and negative withholders (resistors) (Robinson, 1996). Each of these groups has concern not only for their students and the appropriate learning environment for their subject matter expertise, but also a concern towards the university rewards system. This paper discusses data from a research study on faculty attitudes toward distance education. Discussion includes: the rewards system; faculty motivation and distance education; ways distance education can be delivered; authentic participation; and future trends. (Contains 9 references.) 2002. 7 pp. ED 471 121System Infrastructure Needs for Web Course Delivery: A Survey of Online Courses in Florida Community Colleges. Ricci, Glenn A.This quantitative study describes the system infrastructure needs and perceptions of the 28 Florida community colleges regarding current Web course delivery. Section 1 assesses 27 (96.4%) Florida Community College Distance Learning Consortium (FCCDLC) member representative responses to a 19-item, researcher-designed survey. The study includes assessment of interview responses from five selected FCCDLC member representatives. Section 2 reviews literature and information in order to provide a historical perspective, views of technology in education, distance learning, infrastructure reliability and scalability, and institutional readiness factors for online course delivery. Methodology was planned in question design and sequencing. The study’s 19 areas of inquiry address the following larger issues: (1) What incentives and rewards are there to motivate faculty in obtaining knowledge to develop and teach online classes? (2) What academic services and technical support are available to students enrolled in online classes? (3) What processes, procedures, instructional design, and technical support are available for faculty who develop online courses? (4) What software, equipment, and facilities are being used for the development and delivery of online courses? and (5) What levels and degrees of online courses exist in relation to the size of the institution? Findings indicate that 66.6% of Florida community colleges offer incentives or rewards, and 81.4% of colleges support faculty in development of Web courses. (Contains 79 references.) 2002. 176 pp. ED 469 892Making the Transition from Traditional to Cyberspace Classrooms. Frey, Barbara A; Donehue, Ross.This study, conducted by the Instructional Technology Department (ITD) at the Community College of Allegheny County (CCAC) in Western Pennsylvania, describes the results of a faculty needs assessment in the area of technology and distance learning. The Faculty Technology Skills Survey was administered to 490 full-time faculty members in order to determine faculty proficiency in using computer technology and the most effective strategies for developing distance learning. One hundred and one faculty members completed the survey, for a response rate of 21%. Key findings include: (1) 62% of faculty reported using a computer on a daily basis, with the highest usage in word processing software and email; (2) 49% of faculty respondents did not use the Internet in their classes, and 14% were not aware of the ways the Internet could be used; (3) 46% of the faculty identified lack of time as a major barrier to integrating more technology into their classrooms, and 33% noted that adequate technical support and lack of training were barriers as well. Based on the results, recommendations were made for preparing faculty to author and teach courses through the World Wide Web. A sample survey is appended. 2002. 13 pp. ED 470 031Enrollment and Success of Hispanic Students in Online Courses. Angiello, Roanne S.This study researched whether or not Hispanic students, in spite of the digital divide, were enrolling in and succeeding at online courses. The Census Bureau projects that Hispanics will compose 25% of the United States population by the year 2050. Although the Hispanic population in Bergen County, New Jersey is only 10%, during the fall semester at Bergen Community College (BCC) the Hispanic enrollment was 22%. In spring 2002, BCC offered 70 online courses, with approximately 1,500 students registered. The literature suggests that Internet use among Hispanics is approaching, and perhaps even surpassing, that of Whites. This paper discusses whether or not the narrowing of that digital divide will translate into better performance in academics and jobs that require computer and Internet competencies. The author found that in fall 2001, 15% of those enrolled in online courses at BCC were Hispanic, while they made up 22% of the total student population. Although students of all races do not succeed at the same rate in online courses as they do in traditional courses, the success rate for Hispanics was 47%, compared with 62% for Whites. Whites are 16% more successful than Hispanics in online courses, whereas they are only 4% more successful in traditional courses. (Contains 25 references.) 2002. 17 pp. ED 469 358A Comparison of Instructor Time and Effort between Traditional and Online Classrooms. Altalib, Hasan; Dunfee, Patricia J.; Hedden, Briana L.; Lennox, Todd J.; Marenco, Rodolfo.This research study compares the time and effort involved in preparing and managing traditional versus online classrooms. A survey was used to collect data from professors with experience in teaching both formats. Previous studies showed that instructors spent more time and effort managing online versus traditional classrooms. Time and effort for this study were divided into seven hypotheses: instructor preparation for online courses will require more time and effort than instructor preparation for traditional courses; student-instructor interaction for online courses will require more time and effort than student-instructor interaction for traditional courses; course material maintenance for online courses will require more time and effort than course material maintenance for traditional courses; instructional time for online courses will require more time and effort than instructional time for traditional courses; assessment for online courses will require more time and effort than assessment for traditional courses; more time and effort will be spent on technical issues for online courses than for traditional courses; and graduate assistant aid for online courses will require more time and effort than graduate assistant aid for traditional courses. Results indicated no statistical significance of any of the hypotheses. (Contains 10 references.) 2002. 21 pp. ED 469 953The Lost Generation in E-Learning: Deep and Surface Approaches to Online Learning. Cuneo, Carl J.; Harnish, Delsworth.This study examined the independent effects of six approaches to learning in online computer conferencing: (1) deep learning: (2) comprehension learning; (3) relating ideas; (4) surface learning; (5) syllabus boundness; and (6) achievement motivation. Deep learning, comprehension learning, and relating ideas were combined into a more general index called meaning orientation or deep approach to learning. Syllabus boundness and surface learning were combined into a reproducing orientation index, a surface approach to learning. Online surveys were conducted in three school years at a Canadian university using the FirstClass proprietary software, which had been customized into an online conferencing system. The questionnaire was completed by 114, 280, and 679 students in the 3 years. Factor indexes were created of approaches to learning, active use of online conferencing, the subjective valuation of its personal importance to students, and embarrassment or anxieties over posting messages to online course conferences. Seven hypotheses were developed, the main one being that a deep approach to learning would result in greater use and personal importance of registered than unregistered course conferences. Only partial support was found for the hypotheses. The deep approach to learning resulted in a heightened active use of almost all aspects of online conferencing, increased reading and sending of messages, a greater subjective valuation by learners of the importance of participation in conferencing and nonacademic social debates, and a reduction in anxiety about postings. About 15% to 25% of the samples formed a lost generation in the e-learning world. These students scored high on a surface approach to learning and low on a deep approach. Implications for educators who want to reach these students are discussed. (Contains 32 tables and 45 references.) 2002. 37 pp. ED 466 646Documents listed may be read at any library holding an ERIC microfiche collection. Copies may also be ordered (identified by ED number) from the ERIC Document Reproduction Service (EDRS). For prices and ordering information, call EDRS toll free at 1-800-443-ERIC or visit the EDRS Web site at www.edrs.comReaders wishing to submit papers for inclusion in the ERIC database should submit them to the ERIC Clearinghouse on Information & Technology, Syracuse University, Suite 160, 621 Skytop Road, Syracuse, New York 13244-5290. www.ericit.org

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,963
Score d'incertitude au seuil0,431

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,042
Tête enseignante GPT0,436
Écart entre enseignants0,395 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2003
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueQuarterly review of distance educationMême sujetOnline and Blended LearningTravaux en français237 207