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The POSITIVES Scale: Development and Validation of a Measure of How Well the Information and Communication Technology Needs of Students with Disabilities Are Being Met.

2010· article· en· W237406099 sur OpenAlexaboutno aff
Catherine S. Fichten, Jennison V. Asuncion, Mai Nhu Nguyen, Jillian Budd, Rhonda Amsel

Notice bibliographique

RevueThe Journal of Postsecondary Education and Disability · 2010
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDisability Education and Employment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychologyScale (ratio)Measure (data warehouse)Higher educationTest validityApplied psychologyPsychometricsDevelopmental psychologyComputer science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Data on perceptions of 1354 Canadian college and university students with disabilities about how well their information and communication technology (ICT) needs are being met on and off campus were collected. These formed the basis for the POSITIVES Scale (Postsecondary Information Technology Initiative Scale). The measure contains 26 items which use a 6-point Likert scale (1 = strongly disagree, 6 = strongly agree) to indicate level of agreement with each of the positively worded items. The Scale has three factor analysis derived subscales (ICTs at School Meet Student’s Needs, ICTs at Home Meet Student’s Needs, e-Learning ICTs Meet Student’s Needs) and a total score. Reliability and validity are excellent for both English and French versions. Versions that could be completed online, on paper (printable PDF), and within a Microsoft Word document were found to be equivalent. Both the measure and the norms are provided. Skill using information and communication technologies (ICTs) has become mandatory in postsecondary education and the workplace (Stodden, Conway, & Chang, 2003). For example, literature shows that computer use on the job is linked to higher salaries for employees both with and without disabilities (Canadian Council on Social Development, 2004; Kruse, Krueger, & Drastal, 1996). This makes it important that empirical data about the degree to which ICT related needs of learners with disabilities are being met is made available to decision makers involved in ICTs in postsecondary education. Having a means of gathering of such data will help to achieve this. The use of ICTs, including e-learning, both on campus and in distance education, is ubiquitous (Campus Computing Project, 2008). By now, it is self-evident that for students to succeed in postsecondary education they need to have good access to computer technologies both on and off campus (Green, 2005). As the numbers of students with disabilities in postsecondary education continue to rise both in Canada (Fichten, Jorgensen, Havel, & Barile, 2006; Tremblay & Le May, 2005) and the US (National Council on Disability, 2003), where a recent large scale study showed that in 2003-2004, 11% of undergraduates had a disability (Snyder & Dillow, 2007), so does the need to assure that the growing array of available ICTs on campus is accessible (Konur, 2007; Waddell, 2007). General Use ICTs, E-learning, and Adaptive Computer Technologies Students need to use a variety of general use software such as Microsoft Word for writing papers and e-mail programs as well as software related to their specialties (e.g., for statistical analyses, for virtual science experiments, for language tutorials). To succeed in college or universities, learners must also adapt to the extensive use of e-learning used by faculty (Abrami et al., 2006; Weller, Pegler, & Mason, 2005). This includes PowerPoint presentations in class, web-based discusJournal of Postsecondary Education and Disability, Vol. 23, No. 2; 2010 138 sions to further in-class dialogue, and the full range of ICTs that professors use when teaching their courses entirely in the classroom, entirely online, or a combination of both. Students are expected to download materials from course web sites, to access course management systems (CMS) such as WebCT and Blackboard, and to give presentations using PowerPoint. In addition to general use and e-learning ICTs, many students with disabilities also need to acquire and learn to use adaptive software as well as software which allows them to use ICTs effectively. ICTs have many benefits for students with disabilities. These include: the availability of online course notes and course materials; the ability to work at one’s own pace and to work and learn from home; the ease of communicating with peers and professors; the availability of information anywhere and at any time; autonomy and feeling more independent, confident and less stressed; the ability to keep up with the rest of the class; and to use materials in alternate formats (Fichten et al., 2009). Nevertheless, a variety of barriers can interfere with the effective use of ICTs. These include: poor accessibility of needed hardware and software necessary in labs and student work areas; inadequate administrative support and funding for the purchase of adaptive technologies and for disability services staffing and training; lack of awareness and knowledge about adaptive technologies among students with disabilities; unreliable and incompatible hardware and software; the cost of adaptive technologies and their upgrades; difficulties acquiring alternate format course materials; technical problems connecting to websites and course management systems; difficulties encountered using online discussions and activities; poor faculty awareness and support for students who use adaptive tehcnologies; inaccessibility of adapted audio and video clips; ergonomic issues; poor accessibility of course content, PowerPoint and data projection during in-class lectures; inaccessibility of course notes and materials; inadequate technical support for adaptive technologies; lack of technology/ software required for home access needs; poor use of e-learning by professors and their lack of knowledge of how to make e-learning accessible; and time limits on online exams/assignments (Michaels, Prezant, Morabito, & Jackson, 2002; Fichten, Jorgensen, Havel, & Barile, 2005; Fichten et al., 2009). Evaluation of How Well Students’ ICT Related Needs are Being Met An important aspect of increased use of ICTs on campus includes ongoing evaluation of how well these technologies meet the needs of students, faculty and other members of the institution’s constituencies (Educause, n.d.). Evaluation should be carried out for a variety of reasons. These include ensuring a return on investment, measuring penetration and acceptance, and pinpointing areas for improvement (Bullock & Ory, 2000). A neglected topic in such evaluations has been consideration of how well students with different disabilities perceived their ICT related needs being met. It was recently noted by Burton and Nieuwenhuijsen (2008) that, “The instruments currently used to measure issues and concerns about computer-related technologies among the disabled community clearly are inadequate” (p. 105). They recommended that survey items specifically applicable to computer related ICTs for individuals with disabilities be developed. This is especially true for postsecondary students with disabilities, where ICT use is ubiquitous. Recent investigations surveyed junior/community college and university adaptive computer technologists in seven countries, including the USA and Canada (Asuncion, Draffan, Guinan, & Thompson, 2009; Thompson, Draffan, & Patel, 2009). These investigations inquired about adaptive ICT use at postsecondary institutions. While these reports are based on extensive investigations of policies and practices, they did not evaluate the views and experiences of the students themselves. To obtain the student view, the present investigation explored the types of ICTs students indicated using on and off campus. Recently, we developed a scale concerning adaptive ICTs for campus disability service providers (Fossey et al., 2005) as well as a companion measure to evaluate the availability of adaptive ICTs from the students’ vantage point (Fichten, Nguyen, Barile, & Asuncion, 2007). Because of the variety of ICTs used by students with different disabilities, it is important to evaluate not only adaptive technologies, but all types of ICTs, including e-learning, general use products, and those needed for the student’s program of study. Therefore, in the present investigation we developed the POSITIVES Scale, a brief measure to evaluate how well the ICT related needs of postsecondary students with various disabilities are being met in a variety of contexts both on and off campus. Fichten, Asuncion, Nguyen, Budd, & Amsel; The POSITIVES Scale 139

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)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,366
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,003
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 Opus0,011
Tête enseignante GPT0,296
Écart entre enseignants0,285 · 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.

Devis d'étudeObservationnel
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

Citations15
Publié2010
Routes d'admission1
Résumé présentoui

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