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Record W237406099

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 on OpenAlexaboutno aff
Catherine S. Fichten, Jennison V. Asuncion, Mai Nhu Nguyen, Jillian Budd, Rhonda Amsel

Bibliographic record

VenueThe Journal of Postsecondary Education and Disability · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyScale (ratio)Measure (data warehouse)Higher educationTest validityApplied psychologyPsychometricsDevelopmental psychologyComputer science
DOInot available

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.296
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2010
Admission routes1
Has abstractyes

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