Comparison of repeated and two non-repeated readings conditions on reading abilities of students with emotional and/or behavioral disabilities

Students with emotional and/or behavioral disorders (EBD)present considerable academic challenges along with emotional and/or behavioral problems. In terms of reading, these students typically perform one-to-two years below grade level (Kauffman, 2001). Given the strong correlation between reading failure and school failure and overall success (Scott & Shearer-Lingo, 2002), finding effective approaches to reading instruction is imperative for these students (Staubitz, Cartledge, Yurick, & Lo, 2005). This study used an alternating treatments design to comparethe effects of three conditions on the reading fluency, errors, and comprehension of four, sixth-grade students with EBD who were struggling readers. Specifically, the following were compared: (a) Repeated readings in which participants repeatedly read a passage of about 100-150 words, three times, (b) Non-repeated readings in which participants sequentially read an original passage of about 100-150 words once, and (c) Equivalent non-repeated readings in which participants sequentially read a passage of about 300-450 words, equivalent to the number of words in the repeated readings condition. Also examined were the effects of the three repeated readings practice trials per sessions on reading fluency and errors. The reading passage difficulty and length established prior to commencing were used for all participants throughout the standard phase. During the enhanced phase, the reading levels were increased 6 months for all participants, and for two (the advanced readers), the length of the reading passages was increased by 50%, allowing for comparisons under more rigorous conditions. The results indicate that overall repeated readings had the best outcome across the standard and enhanced phases for increasing readers’ fluency, reducing their errors per minute, and supporting fluency answers to literal comprehension questions correctly as compared to non-repeated and equivalent non-repeated conditions. When comparing nonrepeated and equivalent non-repeated readings,there were mixed results. Under the enhanced phases, the positive effects of repeated readings were more demonstrative. Additional research is needed to compare the effects of repeated and equivalent non-repeated readings across other populations of students with disabilities or varying learning styles. This research should include collecting repeated readings practice trial data for fluency and errors to further analyze the immediate effects of repeatedly reading a passage.

An investigation of computer anxiety among vocational -technical teachers in Dade County, Florida

The purpose of this research was to investigate the relationship of computer anxiety to selected demographic variables: learning styles, age, gender, ethnicity, teaching/professional areas, educational level, and school types among vocational-technical educators.^ The subjects (n = 202) were randomly selected vocational-technical educators from Dade County Public School System, Florida, stratified across teaching/professional areas. All subjects received the same survey package in the spring of 1996. Subjects self-reported their learning style and level of computer anxiety by completing Kolb's Learning Style Inventory (LSI) and Oetting's Computer Anxiety Scale (COMPAS, Short Form). Subjects' general demographic information and their experience with computers were collected through a self-reported Participant Inventory Form.^ The distribution of scores suggested that some educators (25%) experienced some overall computer anxiety. There were significant correlations between computer related experience as indicated by self-ranked computer competence and computer based training and computer anxiety. One-way analyses of variance (ANOVA) indicated no significant differences between computer anxiety and/or computer related experiences, and learning style, age, and ethnicity. There were significant differences between educational level, teaching area, school type, and computer anxiety and/or computer related experiences. T-tests indicated significant differences between gender and computer related experiences. However, there was no difference between gender and computer anxiety.^ Analyses of covariance (ANCOVA) were performed for each independent variable on computer anxiety, with computer related experiences (self-ranked computer competence and computer based training) as the respective covariates. There were significant main effects for the educational level and school type on computer anxiety. All other variables were insignificant on computer anxiety. ANCOVA also revealed an effect for learning style varied notably on computer anxiety. All analyses were conducted at the.05 level of significance. ^

The effect of multimedia instruction on achievement of college history students

The purpose of this study was to investigate the effect of multimedia instruction on achievement of college students in AMH 2010 from exploration and discovery to1865. A non-equivalent control group design was used. The dependent variable was achievement. The independent variables were learning styles method of instruction, and visual clarifiers (notes). The study was conducted using two history sections from Palm Beach Community College, in Boca Raton, Florida, between August and December, 1998. Data were obtained by means of placement scores, posttests, the Productivity Environmental Preference Survey (PEPS), and a researcher-developed student survey. Statistical analysis of the data was done using SPSS statistical software. Demographic variables were compared using Chi square. T tests were run on the posttests to determine the equality of variances. The posttest scores of the groups were compared using the analysis of covariance (ANCOVA) at the .05 level of significance. The first hypothesis there is a significant difference in students' learning of U.S. History when students receive multimedia instruction was supported, F = (1, 52)= 688, p < .0005, and F = (1, 53) = 8.52, p < .005for Tests 2 and 3, respectively. The second hypothesis there is a significant difference on the effectiveness of multimedia instruction based on students' various learning preferences was not supported. The last hypotheses there is a significant difference on students' learning of U.S. History when students whose first language is other than English and students who need remediation receive visual clarifiers were not supported. Analysis of covariance (ANCOVA) indicated no difference between the groups on Test 1, Test 2, or Test 3: F (1, 4 5)= .01, p < .940, F (l, 52) = .77, p < .385, and F (1,53) =.17, p > .678, respectively, for language. Analysis of covariance (ANCOVA) indicated no significant difference on Test 1, Test 2, or Test 3, between the groups on the variable remediation: F (1, 45) = .31, p < .580, F (1, 52) = 1.44, p < .236, and F (1, 53) = .21, p < .645, respectively.

Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.

Strategic Human Resources Management Performance Metrics for Unit-Level Managers: An Exploratory Study of U.S Casual Restaurants

Many restaurant organizations have committed a substantial amount of effort to studying the relationship between a firm’s performance and its effort to develop an effective human resources management reward-and-retention system. These studies have produced various metrics for determining the efficacy of restaurant management and human resources management systems. This paper explores the best metrics to use when calculating the overall unit performance of casual restaurant managers. These metrics were identified through an exploratory qualitative case study method that included interviews with executives and a Delphi study. Experts proposed several diverse metrics for measuring management value and performance. These factors seem to represent all stakeholders’interest.

Systems Engineering Perspective of the Cruise Industry

The maturation of the cruise industry has led to increased competition which demands more efficient operations. Systems engineering, a discipline that studies complex organizations of material, people, and information, is traditionally only applied in the manufacturing sector; however, it can make significant contributions to service industries such as the cruise industry. The author describes this type of engineering, explores how it can be applied to the cruise industry, and presents two case studies demonstrating applications to the cruise industry luggage delivery process and the information technology help desk process. The results show that this approach can make the processes more productive and enhance profitability for the cruise lines.

Yield Management: Where We've Been, Where We Are, Where We're Going

The author describes yield management and the technology used to implement yield management in hotels, issues in usefulness, and legal issues concerning the use of yield management. A look into the future is provided, along with a critique of what further research may be needed in order to raise the level of usefulness of yield management systems in the hotel industry to that found in the airlines.

Temporal Mining for Distributed Systems

Many systems and applications are continuously producing events. These events are used to record the status of the system and trace the behaviors of the systems. By examining these events, system administrators can check the potential problems of these systems. If the temporal dynamics of the systems are further investigated, the underlying patterns can be discovered. The uncovered knowledge can be leveraged to predict the future system behaviors or to mitigate the potential risks of the systems. Moreover, the system administrators can utilize the temporal patterns to set up event management rules to make the system more intelligent. With the popularity of data mining techniques in recent years, these events grad- ually become more and more useful. Despite the recent advances of the data mining techniques, the application to system event mining is still in a rudimentary stage. Most of works are still focusing on episodes mining or frequent pattern discovering. These methods are unable to provide a brief yet comprehensible summary to reveal the valuable information from the high level perspective. Moreover, these methods provide little actionable knowledge to help the system administrators to better man- age the systems. To better make use of the recorded events, more practical techniques are required. From the perspective of data mining, three correlated directions are considered to be helpful for system management: (1) Provide concise yet comprehensive summaries about the running status of the systems; (2) Make the systems more intelligence and autonomous; (3) Effectively detect the abnormal behaviors of the systems. Due to the richness of the event logs, all these directions can be solved in the data-driven manner. And in this way, the robustness of the systems can be enhanced and the goal of autonomous management can be approached. This dissertation mainly focuses on the foregoing directions that leverage tem- poral mining techniques to facilitate system management. More specifically, three concrete topics will be discussed, including event, resource demand prediction, and streaming anomaly detection. Besides the theoretic contributions, the experimental evaluation will also be presented to demonstrate the effectiveness and efficacy of the corresponding solutions.

Hybrid Power System Intelligent Operation and Protection Involving Plug-in Electric Vehicles

Two key solutions to reduce the greenhouse gas emissions and increase the overall energy efficiency are to maximize the utilization of renewable energy resources (RERs) to generate energy for load consumption and to shift to low or zero emission plug-in electric vehicles (PEVs) for transportation. The present U.S. aging and overburdened power grid infrastructure is under a tremendous pressure to handle the issues involved in penetration of RERS and PEVs. The future power grid should be designed with for the effective utilization of distributed RERs and distributed generations to intelligently respond to varying customer demand including PEVs with high level of security, stability and reliability. This dissertation develops and verifies such a hybrid AC-DC power system. The system will operate in a distributed manner incorporating multiple components in both AC and DC styles and work in both grid-connected and islanding modes. The verification was performed on a laboratory-based hybrid AC-DC power system testbed as hardware/software platform. In this system, RERs emulators together with their maximum power point tracking technology and power electronics converters were designed to test different energy harvesting algorithms. The Energy storage devices including lithium-ion batteries and ultra-capacitors were used to optimize the performance of the hybrid power system. A lithium-ion battery smart energy management system with thermal and state of charge self-balancing was proposed to protect the energy storage system. A grid connected DC PEVs parking garage emulator, with five lithium-ion batteries was also designed with the smart charging functions that can emulate the future vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-house (V2H) services. This includes grid voltage and frequency regulations, spinning reserves, micro grid islanding detection and energy resource support. The results show successful integration of the developed techniques for control and energy management of future hybrid AC-DC power systems with high penetration of RERs and PEVs.