Comparing novice designer performance using a rule -based approach versus a pattern -based approach in conceptual data modeling tasks

Conceptual database design is an unusually difficult and error-prone task for novice designers. This study examined how two training approaches---rule-based and pattern-based---might improve performance on database design tasks. A rule-based approach prescribes a sequence of rules for modeling conceptual constructs, and the action to be taken at various stages while developing a conceptual model. A pattern-based approach presents data modeling structures that occur frequently in practice, and prescribes guidelines on how to recognize and use these structures. This study describes the conceptual framework, experimental design, and results of a laboratory experiment that employed novice designers to compare the effectiveness of the two training approaches (between-subjects) at three levels of task complexity (within subjects). Results indicate an interaction effect between treatment and task complexity. The rule-based approach was significantly better in the low-complexity and the high-complexity cases; there was no statistical difference in the medium-complexity case. Designer performance fell significantly as complexity increased. Overall, though the rule-based approach was not significantly superior to the pattern-based approach in all instances, it out-performed the pattern-based approach at two out of three complexity levels. The primary contributions of the study are (1) the operationalization of the complexity construct to a degree not addressed in previous studies; (2) the development of a pattern-based instructional approach to database design; and (3) the finding that the effectiveness of a particular training approach may depend on the complexity of the task.

Differences in learning style preference among students of diverse ethnicities, gender, academic placement level, and academic achievement

This investigation studied the differences in learning styles among ethnically diverse secondary science students from a multicultural urban high school. It examined whether there were learning style differences among samples based on ethnicity, gender, academic grouping, and academic achievement. The learning style elements were based on scores of the Dunn, Dunn, and Price Learning Style Inventory (LSI) (1997). The sample (n = 476) consisted of students enrolled in Life Science courses. The analyses of data were made by one way analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). ^ Significant differences were found among students for three of the four groups tested. The largest numbers of differences in learning style element preference were in academic grouping, with eight significant differences showing small or medium effect sizes. There were four significant differences between genders and one significant difference among ethnic groups. Effect size was small. The data analyses showed that individual differences have a much bigger effect than group differences on learning style, and that proportions in learning style element categories reveal more information than means of groups. ^ This study implied the need to increase awareness of differences in learning styles among students and help educators to understand them. Other predictors of learning styles might account for a large amount of the unexplained variation. Overall, this study reinforces the body of existing literature. ^

Nursing faculty intention to use service learning as pedagogy in higher education

The purpose of this study was to identify the factors that motivate nursing faculty to use service learning. The study was based on the theory of planned behavior (TPB), which implies that the target behavior of intention to use service learning in higher education is influenced by the predictor variables of behavior beliefs (attitude), normative beliefs (peer influence), and control beliefs (confidence and resources). External variables were also considered (years of teaching experience, tenure status, and the type of curriculum). ^ Group interviews and a pilot test were conducted to create the instrument for the study, and Cronbach alpha were calculated for survey item reliability. The participants were full time undergraduate nursing faculty members ( n = 160) in the Southeastern United States who taught in universities with accredited nurse education programs. Demographic data as well as scores on scaled survey responses were used to evaluate the intention of nursing faculty to use service learning in their classes. ^ Pearson product moment correlation coefficient and path analysis were applied to the data. The correlation findings indicated that there were statistically significant relationships between behavior beliefs, normative beliefs, and control beliefs and nursing faculty intention to use service learning. The path analysis also indicated that behavior beliefs and normative beliefs were significant, while control beliefs were not a strong influence on intention to use service learning. Normative beliefs showed the strongest direct influence. The use of a community based curriculum also had a positive influence on intention, and faculty with tenure status were more likely to have positive behavior beliefs (attitude) towards service learning. Finally, as teaching experience increased, positive attitudes towards the intention to use service learning decreased. Seventy-nine percent of the variation in the intention to use service learning was explained by the theory of planned behavior, the type of curriculum, teaching experience, and tenure status. These results will assist nursing administration and faculty to design strategies to facilitate the implementation of service learning pedagogy, as well as a community based curriculum which will help meet the 21st century goals set forth from the American Association of Colleges of Nursing. ^

Neural network based off-line handwritten text recognition system

This dissertation introduces a new system for handwritten text recognition based on an improved neural network design. Most of the existing neural networks treat mean square error function as the standard error function. The system as proposed in this dissertation utilizes the mean quartic error function, where the third and fourth derivatives are non-zero. Consequently, many improvements on the training methods were achieved. The training results are carefully assessed before and after the update. To evaluate the performance of a training system, there are three essential factors to be considered, and they are from high to low importance priority: (1) error rate on testing set, (2) processing time needed to recognize a segmented character and (3) the total training time and subsequently the total testing time. It is observed that bounded training methods accelerate the training process, while semi-third order training methods, next-minimal training methods, and preprocessing operations reduce the error rate on the testing set. Empirical observations suggest that two combinations of training methods are needed for different case character recognition. Since character segmentation is required for word and sentence recognition, this dissertation provides also an effective rule-based segmentation method, which is different from the conventional adaptive segmentation methods. Dictionary-based correction is utilized to correct mistakes resulting from the recognition and segmentation phases. The integration of the segmentation methods with the handwritten character recognition algorithm yielded an accuracy of 92% for lower case characters and 97% for upper case characters. In the testing phase, the database consists of 20,000 handwritten characters, with 10,000 for each case. The testing phase on the recognition 10,000 handwritten characters required 8.5 seconds in processing time.

Nursing faculty intention to use service learning as pedagogy in higher education

The purpose of this study was to identify the factors that motivate nursing faculty to use service learning. The study was based on the theory of planned behavior (TPB), which implies that the target behavior of intention to use service learning in higher education is influenced by the predictor variables of behavior beliefs (attitude), normative beliefs (peer influence), and control beliefs (confidence and resources). External variables were also considered (years of teaching experience, tenure status, and the type of curriculum). Group interviews and a pilot test were conducted to create the instrument for the study, and Cronbach alpha were calculated for survey item reliability. The participants were full time undergraduate nursing faculty members (n=-160) in the Southeastern United States who taught in universities with accredited nurse education programs. Demographic data as well as scores on scaled survey responses were used to evaluate the intention of nursing faculty to use service learning in their classes. Pearson product moment correlation coefficient and path analysis were applied to the data. The correlation findings indicated that there were statistically significant relationships between behavior beliefs, normative beliefs, and control beliefs and nursing faculty intention to use service learning. The path analysis also indicated that behavior beliefs and normative beliefs were significant, while control beliefs were not a strong influence on intention to use service learning. Normative beliefs showed the strongest direct influence. The use of a community based curriculum also had a positive influence on intention, and faculty with tenure status were more likely to have positive behavior beliefs (attitude) towards service learning. Finally, as teaching experience increased, positive attitudes towards the intention to use service learning decreased. Seventy-nine percent of the variation in the intention to use service learning was explained by the theory of planned behavior, the type of curriculum, teaching experience, and tenure status. These results will assist nursing administration and faculty to design strategies to facilitate the implementation of service learning pedagogy, as well as a community based curriculum which will help meet the 21st century goals set forth from the American Association of Colleges of Nursing.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

Inquiring Minds Want to Know: Inquiry-Based Learning in the General Education and Inclusion Science Classroom

This study sought to apply the concepts of inquiry-based learning by increasing the number of laboratory experiments conducted in two science classes, and to identify the challenges of this instruction for students with special needs. Results showed that the grades achieved through lab write-ups greatly improved grades overall.

Project-Based Learning, Achievement, and Engagement in Second Graders

Second graders have fertile minds that are constrained by dull curriculums. Teachers fail to foster their interests, students are unengaged and as a result, their achievement suffers. This research will implement Project-Based learning (PBL) with the intention of increasing engagement, which is predicted to also increase achievement.