Thesis (Ed. D.)--Illinois State University, 1982. / Title from title page screen, viewed April 13, 2005. Dissertation Committee: Lanny Morreau (chair), John Brickell, Alan Repp, Raymond Dembinski, Robert Rittenhouse. Includes bibliographical references (leaves 81-85) and abstract. Also available in print.

Thesis--Wisconsin. / Vita. Includes bibliographical references (leaves 150-156).

Thesis (M.S.)--University of Wisconsin--Madison, 1966. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 42-45).

Thesis (Ed.M.)--Boston University

No description available.

When designing assessment measures to capture psychological symptoms it is essential to ensure the individual completing the measure understands what is being asked of them. In the most basic sense, readability relates to how easy it is to understand something when you read it. Understanding readability can inform clinicians and researchers about selecting appropriate measures for their clients and participants. One commonly used formula to determine a given text's readability is the Flesch Kincaid Grade Level (FKG). Newer approaches of measuring readability utilize technological programs, such as Coh-Metrix and Question Understanding Aid (QUAID), that analyze text characteristics to determine the impact on comprehension. The current project investigated the readability of seven measures of Internet Gaming Disorder (IGD). Assessments of IGD have been largely adapted from validated measures of other constructs (i.e., gambling disorder, internet addiction) or created based directly on the proposed criteria of IGD. Prior to the current study, researchers had not yet critically examined the readability of measures of IGD. Assessment of readability is of critical importance given IGD is most likely to impact adolescents, a population that has lower levels of literacy because critical reading skills are developing throughout adolescence. It was hypothesized that measures of IGD may be difficult to read for adolescents. Items within seven measures of IGD were examined utilizing FKG, Coh-Metrix, and QUAID formulas for calculating readability and potential problematic question characteristics. Results found that the mean FKG ranged from 5.40 to 12.28 and indicated six of the seven measures contained at least one item written above an 8th-grade reading level. Coh-Metrix analysis found all measures contained at least one and up to eight items that were written at a below average level of syntactic simplicity (z =

As time passes, more and more code is written, and as companies owna large quantity of code, the importance of readable and easy-to-understand codeincreases. It is well known that a developer's time is spent primarily on reading and understanding source code. This study aims to answer the questions: do students and professionals in software development agree on the current state-of-the-art of source code readability, what inhibits developers from writing code with high readability, and what activities they perceive to help them write code with high readability. In this context, readability is defined as the ease with which a person can read, understand, and comprehend source code, and the effort it takes to understand the logic, relations, cohesion, and ideas behind the code. A survey was distributed by email and social media to people working in the field. The respondents were asked to answer questions about readability and three self-evaluating questions about how they work with code. Responses were analyzed and compared to the state-of-the-art in readability of source code.  A literature study was performed to determine the current state-of-the-art of source code readability.The survey result showed considerable support for the findings of the literature review. The survey results also revealed that the most significant inhibitors for writing code with high readability are connected to lack of time, and the most recognized method of increasing readability is code reviews.

According to the U.S. Department of Health and Human Services (HHS), patients and providers do not understand the Health Information Privacy and Accountability Act (HIPAA). Non-compliance with HIPAA is primarily due to confusion, along with insufficient understanding. HSS has taken measures to simplify the language they use to communicate HIPAA, however, they have not taken steps that consider if one's culture, religious and social perspectives, institutional training, credentials, and comprehension of legal terminology affects medical providers and non-clinical administrative personnel's abilities to understand HIPAA. This research uses bibliometrics to examine the literature from January 2010 – September 2020 that addresses HIPAA's use of legal terminology, literacy level, and institutional training, along with religious and social perspectives, and credentials of medical providers and non-clinical administrative personnel. A total of 107 articles were examined, 42 were assigned article influence scores with values that were less than 1.00, which is a below-average influence score for the article. There were 29 articles with values equal to or above 1.00, which translates to an equal or above-average influence score. The remaining 36 articles did not have article influence scores and were assigned values as not available. Results of the review of the literature indicate that legal terminology, literacy level, training, credentialing and religious and social perspective had no or little effect in understanding HIPAA.

Research for automatic readability prediction of text has increased in the last decade and has shown that various machine learning methods can effectively address this problem. Many researchers have applied machine learning to readability prediction for English, while Modern Standard Arabic (MSA) has received little attention. Here I describe a system which leverages machine learning to automatically predict the readability of MSA. I gathered a corpus comprising 179 documents that were annotated with the Interagency Language Roundtable (ILR) levels. Then, I extracted lexical and discourse features from each document. Finally, I applied the Tilburg Memory-Based Learning (TiMBL) machine learning system to read these features and predict the ILR level of each document using 10-fold cross validation for both 3-level and 5-level classification tasks and an 80/20 division for a 5-level classification task. I measured performance using the F-score. For 3-level and 5-level classifications my system achieved F-scores of 0.719 and 0.519 respectively. I discuss the implication of these results and the possibility of future development.

No description available.