Your search found 841 results.

Descriptors:
Human Factors Engineering; Man Machine Systems; Accuracy; Training; Intervention; Behavior Change; Behavior Modification; Attention Control; Self Management; Occupational Safety and Health; Effect Size; Replication (Evaluation); Naturalistic Observation; College Students

Abstract:
The primary purpose of this study was to replicate and extend a study by Gravina, Austin, Schroedter, and Loewy (2008). A similar self-monitoring procedure, with the addition of self-monitoring accuracy training, was implemented to increase the percentage of observations in which participants worked in neutral postures. The accuracy training required the three participants to practice self-monitoring with the experimenter at least 20 times and to meet the criteria of 90% accuracy for the last 10 monitors. Feedback was delivered after each monitor. Two postures for each of the three participants were targeted by the intervention, and all six improved with an average effect size of 4.7 (range: 3.4 to 8.2) compared to Gravina et al., in which effect sizes averaged 1.9 (range: -0.1 to 3.2). In addition, participant self-monitoring was more accurate overall (77%) when compared to Gravina et al. (44%). (Contains 2 tables and 1 figure.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Electronic Publishing; Data; Information Utilization; Information Management; Information Processing; Database Management Systems; Human Factors Engineering; Technology Planning; Information Theory; Influence of Technology

Abstract:
Colleges and universities are swimming in an ever-widening sea of data. Human beings and machines together generate about 2.5 "quintillion" (10[superscript 18]) bytes every day, according to IBM's latest estimate. The sources of all that data are dizzyingly diverse: e-mail, blogs, click streams, security cameras, weather sensors, social networks, academic research, and student portfolios, to name just a few. And it's all coming at warp speed: Google alone reportedly processes 24 petabytes (that's a "quadrillion"--10[superscript 15]--bytes) every day. The industry buzz phrase for this phenomenon is "Big Data," which loosely refers to data sets too large and/or diverse for conventional tools to manage and mine efficiently. For colleges and universities, Big Data presents a challenge that will only get...well..."bigger". But approached with the right tools and strategies, Big Data also offers an incredibly rich resource for improving retention rates, fine-tuning curricula, and supporting students, faculty, and administration in myriad ways. This article, the first installment of a two-part series, explains Big Data and its potential for improving student learning and success. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Misconceptions; Technology Planning; Best Practices; Technology Uses in Education; Delivery Systems; Gateway Systems; Information Systems; Human Factors Engineering; Performance Technology; Higher Education; Barriers; Performance Factors

Abstract:
Half of servers in higher ed are virtualized. But that number's not high enough for Link Alander, interim vice chancellor and CIO at the Lone Star College System (Texas). He aspires to see 100 percent of the system's infrastructure requirements delivered as IT services from its own virtualized data centers or other cloud-based operators. Back in 2008, Lone Star suffered from unreliable services with constant outages. Most of the hardware components in the data center were at end-of-life, and the student ERP system couldn't keep up during registration--a highly visible bruise to IT's reputation. Since then, Lone Star's IT has undergone a methodical transformation in its organization, infrastructure, and business operations, all in pursuit of hyper-virtualization. As a result, it can now boast "five nines" service levels, a standard set in 2009 and achieved regularly by late 2010. This level of performance allows for only six minutes of unplanned downtime for any given service each year. Getting to that point of IT maturity and high availability hasn't been without challenges. Typically, though, it's misplaced concerns about virtualization that get in the way. In this article, Alander and Cory Bradfield, Lone Star's infrastructure architect, address 10 common myths that can stop campuses from getting 100 percent out of their virtualization efforts. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Computer Networks; Educational Technology; Telecommunications; Technology Planning; Change Strategies; Organizational Change; Performance Factors; Human Factors Engineering; Performance Technology; Information Technology

Abstract:
Colleges and universities have got a big problem: how to bake a wireless network as good as Mom's. The problem is that enterprise wireless networks "tend to be a little more finicky" than the home ones. While the home devices are plug-and-play, enterprise networks force IT departments to manage client issues such as drivers and settings. It's a problem, furthermore, that vendors have not fully solved--no matter what some may say. So, with students showing up with expectations of ubiquitous wireless, schools are scrambling to implement a variety of strategies to give them what they want. This article looks at the six biggest challenges to providing a reliable wireless network on campus--and how schools are addressing them: (1) access point placement; (2) identity management; (3) network security; (4) rogue networks; (5) privacy protection; and (6) lost connections. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Performance Technology; Human Factors Engineering; Improvement Programs; Program Improvement; Total Quality Management; Models; Guidelines; Program Descriptions; Formative Evaluation

Abstract:
This article is part of the dialogue on human performance technology (HPT) models that began in the October 2011 issue of "Performance Improvement." In 1996, the author shared a new HPT model with "Performance & Instruction" readers. Since then, he has refined the model and added empirical research. In the spirit of continuous improvement, the author engineered the model to improve areas that were weak, ambiguous, or outdated. The current version is a "wiser" model that makes sense to business clients. (Contains 1 figure and 2 tables.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Graduate Students; Competition; Program Effectiveness; Performance Technology; Instructional Systems; Case Studies; Case Method (Teaching Technique); Problem Based Learning; Human Factors Engineering; Program Descriptions; Performance Factors; Formative Evaluation

Abstract:
In early 2011, the International Society for Performance Improvement (ISPI) invited three universities--University of North Carolina, Charlotte; Purdue University; and Wayne State University--to participate in the third annual University Human Performance Technology (HPT) Case Study Competition. Each university put together a team of three or four graduate students enrolled in HPT or instructional systems technology (IT) programs to apply their theoretical knowledge to a real-world scenario. This article examines the challenge from the perspective of the 2011 winning team from Wayne State University in Detroit, Michigan. (Contains 2 tables.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Educational Research; Innovation; Context Effect; Human Factors Engineering; Educational Technology; Technology Integration; Information Technology; Teacher Education; Researchers

Abstract:
While design research can be useful for designing effective technology integrations within complex social settings, it currently fails to provide concrete methodological guidelines for gathering and organizing information about the research context, or for determining how such analyses ought to guide the iterative design and innovation process. A case is described, in which the author explores one way that researchers might go about systematizing the analysis of contextual influences within a design research study. It borrows a method from engineering called "Cognitive Work Analysis" (CWA) (Vicente 1999), to methodically study the impact of political, organizational, team, psychological, and physical factors within an initial teacher education setting. The study illustrates how a modified CWA was helpful in making contextual information more explicit and organized. Important information in the form of human factors "constraints" were identified through the CWA, providing valuable details about context that might otherwise be overlooked during design research cycles or within the reporting process. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Artificial Intelligence; Computers; Computer Software; Human Factors Engineering; Computer Uses in Education

Abstract:
The computer engineers who refer to the education of computers do not have a definite idea of education and do not bother to justify the fuzzy ones to which they allude. Hence, they logically cannot specify the features a computer must have in order to be educable. This paper puts forth a non-standard, but not arbitrary, concept of education that determines such traits. The proposed concept is derived from the idea of education embedded in modern standard-English discourse. Because the standard concept entails that an educable entity must be capable of consciousness and voluntary action, it cannot apply to computers. If, therefore, one is to have an idea of educable computers, one must drop the feature of consciousness and omit or modify that of voluntariness. The advanced concept leaves out consciousness, alters the ordinary notion of voluntariness, but keeps in tact the other criteria of the standard idea. Thereby, it provides continuity between those who talk about education in modern ordinary English and those who talk about it in the world of artificial intelligence. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Classroom Environment; Furniture; Space Utilization; Computers; Human Posture; Grants; Human Factors Engineering

Abstract:
Students spend hours a day in classrooms, so it is critical to their learning to have places to sit that are healthful and comfortable. Schools and universities should outfit their classrooms and other learning spaces with furniture that enables students to carry out their school work comfortably and does not detract from their ability to focus and learn.

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website

Descriptors:
Library Services; Databases; Academic Libraries; Electronic Libraries; Use Studies; Data Collection; Data Processing; Statistical Data; Usability; Library Automation; Library Development; Library Research; Performance Technology; Human Factors Engineering; Research Problems

Abstract:
Any librarian who has managed electronic resources has experienced the--for want of words--"joy" of gathering and analyzing usage statistics. Such statistics are important for evaluating the effectiveness of resources and for making important budgeting decisions. Unfortunately, the data are usually tedious to collect, inconsistently organized, of dubious accuracy, and anything but a joy to work with. Once the internet became the ubiquitous way to access content, it did not take long for the library community to create standards to ease the process of collecting usage data. In 2002, librarians formed Project COUNTER (Counting Online Usage of Networked Electronic Resources). A year later, COUNTER issued Release 1 of its Code of Practice, which outlined standards for publishers and vendors to report usage statistics. In 2007, the information science standards body NISO (National Information Standards Organization) created the Standardized Usage Statistics Harvesting Initiative protocol, known casually as SUSHI, which provides an automated way to download COUNTER reports via the web. While COUNTER and SUSHI have helped libraries come a long way toward improving the adoption and availability of usage statistics for library market vendors, the author soon came to learn that there is still a good amount of work libraries must do to get the data they need to make critical collection development and database budgeting decisions. The author learned his lesson the hard way, by first turning to SUSHI with the hope it would fulfill his library's need for data about database usage. But at the end of the day and after all his work building a SUSHI client, he still ended up having to visit dozens of vendor websites to manually collect, collate, format, and analyze all the data himself, using the classic desktop applications Access and Excel. After becoming thoroughly disillusioned with SUSHI, the author wanted to know if he was alone or if other libraries were bogged down in the same statistical quagmire. The rest of this article is about the results of a survey he conducted among his peers to not only satisfy his own curiosity but with the hope that such a study would reveal insights that the vendor community and COUNTER/NISO could use to improve the standards and protocols for collecting and reporting usage statistics. (Contains 3 online resources.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

Related Items: Show Related Items

Full-Text Availability Options:

More Info:
Help | Tutorial Help Finding Full Text |  More Info:
Help Find in a Library |  Publisher's website