Creative Aging in America's Libraries: Year Two Evaluation Report

Creative Aging in America's Libraries is a three-year national leadership initiative to improve, expand and sustain Creative Aging programming in public libraries. It was designed and is led by Lifetime Arts, Inc., in partnership with Westchester Library System. Major support is provided by the Institute for Museum and Library Services. Twenty library systems across the country (listed in Appendix A) are participating in this initiative. The initiative began in the spring of 2014 with a preparation phase, equipping participating libraries to plan and conduct instructional arts programs for older adults according to the model developed by Lifetime Arts. Activities included a series of webinars; a pair of three-day regional training institutes (one in the east, one in the west) to which each library system sent a team of up to five librarians and administrators; launch of a Wikispace where participants can raise questions, exchange information and engage in peer learning and mutual support; access to Lifetime Arts' online resources; and customized assistance from Lifetime Arts staff. The second phase, implementation, began in late 2014 and runs through mid-2016. During this phase, each system is conducting between two and five Creative Aging programs, with support from Lifetime Arts. Goals are to demonstrate and evaluate the model including testing any local variations, build capacity, and begin building sustainability for this kind of programming. Altogether, 67 Creative Aging programs are planned across the 20 systems (plus additional programs in several systems conducted with funding from other sources). A final phase, from July through December 2016, will analyze final results and lessons from all sites, and do further exploration and planning to sustain and expand libraries' Creative Aging activities into the future

Student loyalty assessment with online master\u27s programs

Relationship marketing is attracting, maintaining, and, in multi-service organizations, enhancing customer relationships. Educational programs and services, like those of businesses, depend highly on the repeated purchases of their loyal customers. The purpose of this descriptive research is to investigate the relationships between factors that may lead to student loyalty in online graduate educational programs. Specifically, the study seeks to examine the relationships between service quality, technology, trust, commitment and satisfaction, reputation and ultimately loyalty. A new model is presented, which includes the results of testing these variables. The results of this study concluded that satisfaction with the program has the highest degree of association with student loyalty. Although service quality was found to be one of the key correlates to student loyalty to the program in face-to-face educational settings, it was found to bean insignificant element in assessing student loyalty to the program in online courses. Surprisingly, the construct of technology was found to be an important factor, especially, the role of using synchronous online tools. The findings also support relationship marketing theory (Morgan & Hunt, 1994) and the roles of commitment and trust. However, this research found the role of commitment more important than that of trust. This study recognized reputation of the university as a vital mediator for building a mutually beneficial relationship between students and universities. The research includes implications to help service providers (educational institutions) improve their marketing strategies to ensure that online students (customers) remain with their desired online programs

What Leader should Do in Keeping Staff? Simplification of Human Resources Management Theory

This study aims to develop a model of human resource retention to address the issue of workforce retention that has emerged as the most critical Human Resource Management challenge. It is based on a critical analysis of human resource trends which shows that a shortage of skilled employees will occur. Therefore, an appropriate strategy is needed to win the war or labor market competition. The researcher uses the Research & Development approach by adapting Kalk's spiral development model to the stages: definition, design, demonstration, development, and testing. Researchers involve human resource management experts and refer to previous concepts, theories and relevant previous studies for developing models. The Google Form Online Mailed Instrument was also used to see the responses of the respondents, totaling 128 people. The results of the study formulated the "R&R" retention model by integrating procurement programs and retention programs. By using this model, employee loyalty will always be maintained and the organization will always be in the best performance so that maximum productivity can be achieved. While the response of the participants placed the level of achievement in the R & R model with a very decent decision.This study aims to develop a model of human resource retention to address the issue of workforce retention that has emerged as the most critical Human Resource Management challenge. It is based on a critical analysis of human resource trends which shows that a shortage of skilled employees will occur. Therefore, an appropriate strategy is needed to win the war or labor market competition. The researcher uses the Research & Development approach by adapting Kalk's spiral development model to the stages: definition, design, demonstration, development, and testing. Researchers involve human resource management experts and refer to previous concepts, theories and relevant previous studies for developing models. The Google Form Online Mailed Instrument was also used to see the responses of the respondents, totaling 128 people. The results of the study formulated the "R&R" retention model by integrating procurement programs and retention programs. By using this model, employee loyalty will always be maintained and the organization will always be in the best performance so that maximum productivity can be achieved. While the response of the participants placed the level of achievement in the R & R model with a very decent decision

PyMieDAP: a Python--Fortran tool to compute fluxes and polarization signals of (exo)planets

PyMieDAP (the Python Mie Doubling-Adding Programme) is a Python--based tool for computing the total, linearly, and circularly polarized fluxes of incident unpolarized sun- or starlight that is reflected by, respectively, Solar System planets or moons, or exoplanets at a range of wavelengths. The radiative transfer computations are based on an adding--doubling Fortran algorithm and fully include polarization for all orders of scattering. The model (exo)planets are described by a model atmosphere composed of a stack of homogeneous layers containing gas and/or aerosol and/or cloud particles bounded below by an isotropically, depolarizing surface (that is optionally black). The reflected light can be computed spatially--resolved and/or disk--integrated. Spatially--resolved signals are mostly representative for observations of Solar System planets (or moons), while disk--integrated signals are mostly representative for exoplanet observations. PyMieDAP is modular and flexible, and allows users to adapt and optimize the code according to their needs. PyMieDAP keeps options open for connections with external programs and for future additions and extensions. In this paper, we describe the radiative transfer algorithm that PyMieDAP is based on and the code's principal functionalities. And we provide benchmark results of PyMieDAP that can be used for testing its installation and for comparison with other codes. PyMieDAP is available online under the GNU GPL license at http://gitlab.com/loic.cg.rossi/pymiedapComment: 15 pages, 7 figures, 4 tables. Accepted for publication in Astronomy and Astrophysic

Develop a formative assessment protocol to examine the practice of Popping dance in students online learning

The present article presents an innovative and versatile approach to formative assessment that allows educators to assess students’ aptitude for online learning and their mastery of Popping dance. Popping dance, a form of breakdance, is increasingly being taught through distance learning programs. Despite the growing interest in Popping dance culture across different regions worldwide, few studies have focused on developing a comprehensive formative assessment model that combines Popping dance abilities and online learning. Therefore, we have developed a novel formative assessment framework for Popping dance students, which utilizes various online formats and offers a diversified assessment model. The article outlines five distinct assessment methods with practical examples that can enable students in heavily impacted areas to improve their Popping dance technique at home. These assessment methods cover software operation, theory testing, and technical testing, all of which are suitable for online use. While this model is still in its early stages, it provides a fundamental methodological framework for assessing the Popping dance proficiency of students

Alumni Giving From Online Learners: A Logistic Regression Study

Alumni giving is increasingly important to the vitality of institutions of higher education. With governmental financial support of public institutions steadily declining, understanding philanthropic giving is more important than ever. Advancement offices that serve alumni populations benefit from analyzing predictors of the behavioral phenomenon of charitable giving. It is also known that enrollment in distance education programs is on the rise in the United States. For university administrators and advancement professionals to be proactive in cultivating relationships with alumni that attended through an online modality, more research was needed to understand what motivates philanthropic giving from this alumni constituency. Using the theory of planned behavior as a theoretical framework, this quantitative study employed logistic regression to explore if independent variables proven to predict philanthropic giving from alumni that attended through a residential education experience also predicted philanthropic giving from alumni who completed 80% or more of their learning experience through the online modality. The population of participants was alumni who have graduated from a midsized master’s degree-granting public university in the Midwest with a degree from an online program. The institution in this study was at the forefront of implementing distance education programs, with beta testing starting in 1995 and full programs launched in 1998. The logistic regression model tested was statistically significant. Of the 11 predictor variables, nine were statistically significant. Overall, the strongest predictor of alumni giving in the model was having also earned a degree through a residential experience at the institution. Other strong predictors were alumni event attendance, marital status of married and divorced when compared to single, and having graduated from the Kinesiology academic program when compared to the Aviation academic program. The findings of this study can help inform decision making and strategy creation of higher education leaders in securing financial support from this emerging audience of prospective donors while also helping inform student engagement and alumni engagement strategies regarding the subset of students that attend university through distance education. Ultimately, the results add to the body of research about philanthropic giving from alumni that attended universities through the online modality

Factors Motivating Individuals to Consider Genetic Testing for Type 2 Diabetes Risk Prediction

The purpose of this study was to identify attitudes and perceptions of willingness to participate in genetic testing for type 2 diabetes (T2D) risk prediction in the general population. Adults (n = 598) were surveyed on attitudes about utilizing genetic testing to predict future risk of T2D. Participants were recruited from public libraries (53%), online registry (37%) and a safety net hospital emergency department (10%). Respondents were 37 ± 11 years old, primarily White (54%), female (69%), college educated (46%), with an annual income ≥$25,000 (56%). Half of participants were interested in genetic testing for T2D (52%) and 81% agreed/strongly agreed genetic testing should be available to the public. Only 57% of individuals knew T2D is preventable. A multivariate model to predict interest in genetic testing was adjusted for age, gender, recruitment location and BMI; significant predictors were motivation (high perceived personal risk of T2D [OR = 4.38 (1.76, 10.9)]; family history [OR = 2.56 (1.46, 4.48)]; desire to know risk prior to disease onset [OR = 3.25 (1.94, 5.42)]; and knowing T2D is preventable [OR = 2.11 (1.24, 3.60)], intention (if the cost is free [OR = 10.2 (4.27, 24.6)]; and learning T2D is preventable [OR = 5.18 (1.95, 13.7)]) and trust of genetic testing results [OR = 0.03 (0.003, 0.30)]. Individuals are interested in genetic testing for T2D risk which offers unique information that is personalized. Financial accessibility, validity of the test and availability of diabetes prevention programs were identified as predictors of interest in T2D testing

Development of a Reflective Learning Program in Online Tutorials for the Biology Education Department

This study aims to produce a reflective learning program that can be applied to the online tutorial in the Biology Education Department which can improve the reflective thinking skills of Biology Education students. The study was conducted in three years with using the research and development (R & D) which adapts the 4 D model from Thiagarajan et al. The research steps carried out preliminary studies, program design, program development -which includes program trials and program revisions-, and program implementation. What will be discussed in this paper is limited to how the program development process goes from the start until the right prototype is obtained to help students hone their reflective thinking skills. Reflective learning programs in online tutorials require several stages to arrive at the most appropriate prototype that is able to increase student participation in reflection which has the potential to increase reflective thinking skills. After going through several stages of testing and revising the placement of the most appropriate reflection activities, reflection activities are given every two sessions, namely in the second, fourth and sixth session

Using Machine Learning to Identify Patterns in Learner-Submitted Code for the Purpose of Assessment

Programming has become an important skill in today’s world and is taught widely both in traditional and online settings. Instructors need to grade increasing amounts of student work. Unit testing can contribute to the automation of the grading process but it cannot assess the structure or partial correctness of code, which is needed for finely differentiated grading. This paper builds on previous research that investigated machine learning models for determining the correctness of programs from token-based features of source code and found that some such models can be successful in classifying source code with respect to whether it passes unit tests. This paper makes two further contributions. First, these results are scrutinized under conditions of varying similarity between code instances used for model training and testing, for a better understanding of how well the models generalize. It was found that the models do not generalize outside of groups of code instances performing very similar tasks (corresponding to similar coding assignments). Second, selected binary classification models are used as a base for multi-class prediction with two different methods. Both of these exhibit prediction success well above the random baseline, with potential to contribute to automated assessment with multi-valued measures of quality (grading schemes), in contrast to the binary pass/fail measure associated with unit testing

Comparing the effectiveness of testing methods in improving programs: the effect of variations in program quality

We compare the efficacy of different testing methods for improving the reliability of software. Specifically, we use modelling to compare “operational” testing, in which test cases are chosen according to their probability of occurring in actual use of the software, against “debug” testing methods, in which the testers look for test cases which they consider likely to cause failure, or that satisfy some coverage criterion. We base our comparisons on the reliability reached by the program at the end of testing. Differently from previous studies, we consider the probability distribution of the achieved reliability, and thus the probability of satisfying specific requirements, rather than just the average reliability achieved. We take account of two sources of variation. The variation between the actual test histories that are possible for a given program and a given test method: and the fact that different programs start testing with different faults and initial reliability levels. By necessity, we use very simplified models of reality. Yet, we can show some interesting conclusions with important practical consequences. In general, there are stronger arguments in favor of operational testing than previous studies have show