TEACHERS’ ATTITUDES TOWARD INCLUSION OF CHILDREN WITH DISABILITIES IN RURAL EL SALVADOR

The attitude of school teachers toward inclusion of children with disabilities is an important factor in the successful implementation of a national inclusion program. With the universal pressure to provide education for all and international recognition of the importance of meeting the needs of diverse populations, inclusive education has become important to governments around the world. El Salvador’s Ministry of Education seeks to establish inclusion as an integral part of their struggle to meet the needs of children across the country, but this is a difficult process, especially for a country with limited resources which still struggles to meet international expectations of educational access and quality. Teacher attitude is an important factor in the success of inclusion programs and can be investigated in relation to various factors which may affect teachers’ classroom practice. While these factors have been investigated in multiple countries, there is a need for more knowledge of the present situation in developing countries and especially in schools across the rural areas of El Salvador to meet the needs of the diverse learners in that country. My research was a mixed methods case study of the rural schools of one municipality, using a published survey and interviews with teachers to investigate their attitudes regarding inclusion. This research was the first investigation of teachers’ attitudes toward inclusion in rural El Salvador and explored the needs and challenges which exist in creating inclusive schools across this country. The findings of this study revealed the following important themes. Some children with disabilities are not in school and those with mild disabilities are not always getting needed services. Teachers agreed with the philosophy of inclusion, but believed that some children with disabilities would receive a better education in special schools. They were not concerned about classroom management. Teachers desired more training on disability and inclusion. They believed that a lack of resources, including materials and personnel, was a major barrier to inclusion. Teachers’ attitudes were consistent regardless of family and professional experience with disability or amount of inclusion training. They were concerned about the role of family support for children with disabilities.

ENHANCEMENT OF THERMAL INACTIVATION OF CRONOBACTER SAKAZAKII WITH INCLUSION OF PARABENS

Parabens are a family of p-hydroxybenzoic acid esters, which have antimicrobial activity over a broad pH range (4-8). This study was designed to evaluate the enhanced thermal inactivation of Cronobacter sakazakii by the inclusion of “parabens” and to ultimately develop mathematical models to describe this effect. A heat-resistant strain, Cronobacter sakazakii 607, was heated at three mild heating temperatures in combination with treatments with five parabens in various concentrations. Results showed the presence of parabens significantly enhanced thermal inactivation in a concentration-dependent manner, and the effect increased with increasing alkyl chain length. The concentration of parabens, alkyl side chain length, and heating temperature acted synergistically, causing bacterial inactivation even at low temperatures that were not effective in killing C. sakazakii. The survival data were used to develop primary and secondary mathematical models that accurately describe how this synergistic activity can be applied in the food industry.

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

ASSESSING QUALITY IN HIGH-UNCERTAINTY MARKETS: ONLINE REVIEWS OF CREDENCE SERVICES

In economics of information theory, credence products are those whose quality is difficult or impossible for consumers to assess, even after they have consumed the product (Darby & Karni, 1973). This dissertation is focused on the content, consumer perception, and power of online reviews for credence services. Economics of information theory has long assumed, without empirical confirmation, that consumers will discount the credibility of claims about credence quality attributes. The same theories predict that because credence services are by definition obscure to the consumer, reviews of credence services are incapable of signaling quality. Our research aims to question these assumptions. In the first essay we examine how the content and structure of online reviews of credence services systematically differ from the content and structure of reviews of experience services and how consumers judge these differences. We have found that online reviews of credence services have either less important or less credible content than reviews of experience services and that consumers do discount the credibility of credence claims. However, while consumers rationally discount the credibility of simple credence claims in a review, more complex argument structure and the inclusion of evidence attenuate this effect. In the second essay we ask, “Can online reviews predict the worst doctors?” We examine the power of online reviews to detect low quality, as measured by state medical board sanctions. We find that online reviews are somewhat predictive of a doctor’s suitability to practice medicine; however, not all the data are useful. Numerical or star ratings provide the strongest quality signal; user-submitted text provides some signal but is subsumed almost completely by ratings. Of the ratings variables in our dataset, we find that punctuality, rather than knowledge, is the strongest predictor of medical board sanctions. These results challenge the definition of credence products, which is a long-standing construct in economics of information theory. Our results also have implications for online review users, review platforms, and for the use of predictive modeling in the context of information systems research.