A Model for Assessing the Quality of Learning in Distance Education

The factors contributing to the quality of learning in distance education were investigated. These factors were used to develop an empirically based model for the assessment of the quality of learning in distance education. The affective, cognitive, and conative domains of learning and the ethical domain of distance education were defined and used as the theoretical foundation for a search of distance education studies and evaluations that identified over 150 variables that potentially affected quality of learning. A Delphi technique was used to validate the variables and construct a survey instrument. Surveys were distributed to 3 groups totaling 523 students attending 4 classes during the Fall, 1995 semester at ODU. The three groups were Teletechnet students (class via satellite), studio students (class at the satellite transmission site), and students attending identical on-campus classes taught by the same instructor. Tests of significance for the demographic data, and ANOVA, MANOVA, and factor analyses for the scale data were conducted for the 248 surveys returned. There were 3 significant demographic differences between Teletechnet and on-campus students: age (p\u3c.01), student status (p $\u3c$.001), and work status (p \u3c.01). There were no significant differences between the on-campus and studio classes and these classes were combined for the factor analysis. The factor analysis reduced the 150 variables to 7 factors for Teletechnet students and 8 factors for on-campus students that accounted for over 50% of variance in the analysis. These factors were given descriptive names and their contribution to variance was used to construct the quality of learning assessment model for distance education. These factors showed Teletechnet students more concerned with the lower levels of the cognitive domain of learning and on-campus students more concerned with the higher levels of the cognitive domain. The analysis showed that neither technology nor affective domain related variables contributed significantly to the factors defining the quality of learning for these students. It was recommended that evaluations for Teletechnet and on-campus students contain questions to reflect the factors that are most important to each group and that courses be designed to facilitate student interchange at distant sites

Learning Schemes for Power System Protection

In this paper, learning algorithms are leveraged to advance power system protection. Advancements in power system protection have come in different forms such as the development of new control strategies and the introduction of a new system architecture such as a microgrid. In this paper, we propose two learning schemes to make accurate predictions and optimal decisions related to power system protection and microgrid control. First, we present a neural network approach to learn a classifier that can predict stable reconnection timings for an islanded sub-network. Second, we present a learning-based control scheme for power system protection based on the policy rollout. In the proposed scheme, we incorporate online simulation using the commercial PSS/e simulator. Optimal decisions are obtained in real time to prevent cascading failures as well as maximize the load served. We validate our methods with the dynamics simulator and test cases RTS-96 and Poland

An Examination of the Relationship Between High School Seniors and High School Seniors Participating in a Multi-Year, Looping, School-Based Mentoring Program

Researchers have indicated that secondary school students were more likely to be truant than primary school students which lead to students dropping out of school and becoming less productive citizens as adults. Researchers have also shown that participation in a school-based mentoring program can positively influence the students and help them make better life decisions. The purpose of this study was to determine to what extent a multi-year, looping, school-based mentoring program has on the attendance data, behavioral data, achievement data, and graduation rate of high school seniors at a rural high school in west Georgia. The researcher conducted a mixed methods study to analyze the relationship between a high school with a mentoring program and a high school without a mentoring program through attendance data, behavior data, test score data, and graduation percentage. For the quantitative portion, attendance data, behavior data, test score data, and graduation percentage were obtained and analyzed using descriptive statistics and t-tests. The overall findings were in favor of the school without the mentoring program or not statistically significant. For the qualitative portion, six individual teacher interviews were conducted at the school with the mentoring program to obtain data on their perception of the impact the mentoring program had on high school seniors. The overall findings were positive teacher perceptions of their impact on student attendance, behavior, test scores, and graduation. The mentoring program did not statistically impact student attendance, behavior, test scores, and graduation; however, the relationships and impact that the teachers had on the students could impact the students well into their futures

A comparison of salivary testosterone measurement using immunoassays and tandem mass spectrometry

Enzyme immunoassays (EIAs) are widely used to measure salivary testosterone. However, little is known about how accurately different EIAs assess testosterone, partially because estimates across various EIAs differ considerably. We compared testosterone concentrations across EIAs of three commonly used manufacturers (DRG International, Salimetrics, and IBL International) to liquid chromatography tandem mass spectrometry (LC–MS/MS). Relative to EIAs from Salimetrics and IBL International, EIAs supplied by DRG International provided the closest approximation to LC–MS/MS testosterone concentrations, followed closely by EIAs from Salimetrics, and then IBL. Additionally, EIAs tended to inflate estimates of lower testosterone concentrations in women. Examining our results and comparing them to existing data revealed that testosterone EIAs had decreased linear correspondence with LC–MS/MS in comparison to cortisol EIAs. Overall, this paper provides researchers with information to better measure testosterone in their research and more accurately compare testosterone measurements across different methods

Enhancing Resolution and Scalability of Power Grid Reliability Algorithms

This Thesis aims to determine whether we can improve the accuracy, resolution, and speed of calculations for common power system problems using simple computational models that scale well to machine learning and high performance computing solutions. The second chapter of this Thesis implements more precise aging and degradation models for power grid equipment for a more precise Contingency Analysis. Contingency Analysis, a widely used reliability metric in the grid, determines whether a single (or multiple) transmission or generator outage will place the grid in an unstable state. After the aging models are defined, aging related failure rates of the models are integrated into the grid by constraining power limits. Contingency Analysis experiments are then performed, determining whether increasing the age of the grid will increase the number of contingencies on the grid. This effectively defines Contingency Analysis simulations that are more robust with respect to deterioration of equipment, traditionally ignored in this type of simulation. The third chapter applies Critical Slowing Down, a statistical method that can be used as a feature engineering technique, the process of extracting key properties of data, to Phasor Measurement Units (PMU). In this case, Critical Slowing Down has been shown to be indicative of critical transitions in power systems data. Critical Slowing Down analysis is applied to the data to enhance the training time, accuracy, and time localization of Artificial Neural Network classification of events on the grid, by allowing easier localization of critical transitions. The fourth chapter compares GPU architectures and CPU architectures for the implementation of widely used machine learning algorithms for PMU data. GPU architecture leverages mass parallelization, which is useful for many complex algorithms. However, CPU architecture is much faster for serial processes, which many of the algorithms are also based upon. Many of these algorithms contain some mix of serialization and parallelization, making it generally unclear on whether CPU architecture or GPU architecture is more applicable for the algorithm at hand. This chapter compares the computational performance of GPU architectures and CPU architectures of commonly used machine learning algorithms for PMU data. The dataset sizes are varied for each of the algorithms determining whether CPU architectures or GPU architectures scale better for large datasets

Testosterone fluctuations in response to a democratic election predict partisan attitudes toward the elected leader

Intergroup competitions such as democratic elections can intensify intergroup polarization and conflict. Partisan attitudes toward the elected leader can also shift from before to after an election, but the biology underlying these attitudinal shifts remains largely unknown. An important factor could be the hormone testosterone, which is theorized to fluctuate during competition and to influence status seeking. In a naturalistic study of 113 registered voters, we measured changes in testosterone levels and attitudes toward the winner of the 2012 US Presidential Election. We found that supporters of the losing candidate (Mitt Romney) showed acute increases in testosterone levels compared to supporters of the winner (Barack Obama) on the evening of Election Day. Supporters of the losing candidate also demonstrated flatter diurnal testosterone slopes on Election Day that persisted up to two days after the election. Furthermore, greater increases in acute testosterone levels and flatter diurnal slopes among supporters of the losing candidate were associated with less positive evaluations of the winning candidate. These testosterone-moderated attitudinal shifts observed in the days after the election showed a directionally similar pattern with a weaker effect size six months later. Finally, we confirmed that the main results were robust to alternative data analytic choices using multiverse specification curve analysis. The findings from this paper suggest that hormonal responses to large-scale intergroup competitions may shape how we perceive our elected leaders, shedding light on the biology of intergroup relations

Who is the Teacher and Who is the Student? The Dual Service- and Engaged-Learning Pedagogical Model of Anatomy Academy

Anatomy Academy is a simultaneous service-learning experience for preprofessional school undergraduate students and preclinical professional students acting as classroom paraprofessional teachers (Mentors), and engaged-learning experience for fourth to sixth grade elementary school children (Students). Using didactic and kinesthetic active learning teaching strategies in small-group classroom environments, Mentors taught anatomy, physiology, and nutrition concepts to Students. In this study of the program's early years (2012-2014), overall objectives of improving Mentors' pedagogical confidence; and Students' science interest, science knowledge, and exercise self-efficacy were assessed. Mentors showed (89% response of 595 surveyed) improvement in content delivery (P < .001), student engagement (P < .001), classroom management (P < .001), and professionalism (P = .0001). Postprogram Mentor reflections were categorized into 7 major themes that demonstrated personal growth through the service-learning opportunity: (1) realization of an ability to make a difference in the world now; (2) acknowledgment of the importance of listening in teaching; (3) recognition that lives can and will change with "a little love"; (4) insight into the effectiveness of guiding Students through material rather than lecturing; (5) awareness of the value of respect in the learning environment; (6) cognizance of the power of individualized attention to motivate Students; and (7) reflection of one's own personal growth through the open influence of Students. Students showed (88% response of 1259 surveyed) improvement in science knowledge (P = .014) and exercise self-efficacy (P = .038), but not science interest (P = .371). Thus, while Students are learning more science and becoming more aware of their health, we need to be more overt in our presence as scientists in the educational arena

Health Aspects of the Pre-Departure Phase of Migration

In the second article in a six-part PLoS Medicine series on Migration & Health, Brian Gushulak and Douglas MacPherson discuss the pre-departure phase of migration and the specific health risks and policy needs associated with this phase