THE USE OF PRIVATE LANDS IN THE U.S. FOR OUTDOOR RECREATION: RESULTS OF A NATIONWIDE SURVEY

Land Economics/Use, Resource /Energy Economics and Policy,

OUTDOOR RECREATION TRENDS AND MARKET OPPORTUNITIES IN THE UNITED STATES

In 1994 and 1995, the National Survey of Recreation and Environment (NSRE) was accomplished by interviewing approximately 17,000 Americans over age 15 in random-digit-dialing telephone samplings. The primary purpose was to learn about the outdoor recreation activities of people over age 15 in the United States. They were asked about their participation in 62 specific recreation activities.Resource /Energy Economics and Policy,

Electrically insulating high pressure seals for internally heated pressure vessels

The design of an electrically insulating, high pressure seal using neoprene, nylon, Teflon, and alumina washers is presented. The seals are of use with internally heated pressure vessel systems

Electronic band structure and carrier effective mass in calcium aluminates

First-principles electronic band structure investigations of five compounds of the CaO-Al2O3 family, 3CaO.Al2O3, 12CaO.7Al2O3, CaO.Al2O3, CaO.2Al2O3 and CaO.6Al2O3, as well as CaO and alpha-, theta- and kappa-Al2O3 are performed. We find that the conduction band in the complex oxides is formed from the oxygen antibonding p-states and, although the band gap in Al2O3 is almost twice larger than in CaO, the s-states of both cations. Such a hybrid nature of the conduction band leads to isotropic electron effective masses which are nearly the same for all compounds investigated. This insensitivity of the effective mass to variations in the composition and structure suggests that upon a proper degenerate doping, both amorphous and crystalline phases of the materials will possess mobile extra electrons

Associations between Library Usage and Undergraduate Student GPA, 2016-2019

This paper was presented at the Library Assessment Conference on March 17, 2021.We present the results of a study of the association between online resource use of licensed content provided by the library and short- and long-term student performance. We capture library usage using EZproxy logs, or more precisely whether an individual has at least one EZproxy session in an academic term. We measure student performance using the grade point average (GPA), specifically semester (short-term) and cumulative (long-term) GPA. Relying on models of information behavior, we generate a theoretical framework that suggests that student performance is a function of factors that apply to all students, such as race and gender (the “fixed” effects). But student performance is also impacted by factors such as academic background (e.g., schools, colleges, etc.) that cluster student behaviors and outcomes, and unobserved, time invariant factors at the student-level such as grit and motivation (the “random effects”). We therefore run panel linear mixed effects regression models of the association between library usage and student performance. The results show that library usage, as measured by access to library-licensed content, is significantly associated with both semester and cumulative GPA. The magnitude of the effect is larger for semester GPA, but also varies depending on if a student resides on- or off-campus. The library usage effect on semester GPA is larger for off-campus students compared to their on-campus peers. The reverse is true for the library usage effect on cumulative GPA as it is larger for on-campus students. This study shows how connecting identifiable library data to other institutional can yield shed important insights into how library usage shapes student outcomes.Institute of Museum and Library Studies (IMLS) - LG-96-18-0040-18http://deepblue.lib.umich.edu/bitstream/2027.42/166998/1/Associations between library usage and undergraduate student GPA, 2016-2019 03242021.pdfDescription of Associations between library usage and undergraduate student GPA, 2016-2019 03242021.pdf : Main articleSEL

The mediating effect of task presentation on collaboration and children's acquisition of scientific reasoning

There has been considerable research concerning peer interaction and the acquisition of children's scientific reasoning. This study investigated differences in collaborative activity between pairs of children working around a computer with pairs of children working with physical apparatus and related any differences to the development of children's scientific reasoning. Children aged between 9 and 10 years old (48 boys and 48 girls) were placed into either same ability or mixed ability pairs according to their individual, pre-test performance on a scientific reasoning task. These pairs then worked on either a computer version or a physical version of Inhelder and Piaget's (1958) chemical combination task. Type of presentation was found to mediate the nature and type of collaborative activity. The mixed-ability pairs working around the computer talked proportionally more about the task and management of the task; had proportionally more transactive discussions and used the record more productively than children working with the physical apparatus. Type of presentation was also found to mediated children's learning. Children in same ability pairs who worked with the physical apparatus improved significantly more than same ability pairs who worked around the computer. These findings were partially predicted from a socio-cultural theory and show the importance of tools for mediating collaborative activity and collaborative learning

An Algorithmic Approach to Missing Data Problem in Modeling Human Aspects in Software Development

Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. This, in turn, leads to an increase in defect density. The performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially "missing data" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ Expectation-Maximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the "missing data" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem

Epistemic and social scripts in computer-supported collaborative learning

Collaborative learning in computer-supported learning environments typically means that learners work on tasks together, discussing their individual perspectives via text-based media or videoconferencing, and consequently acquire knowledge. Collaborative learning, however, is often sub-optimal with respect to how learners work on the concepts that are supposed to be learned and how learners interact with each other. One possibility to improve collaborative learning environments is to conceptualize epistemic scripts, which specify how learners work on a given task, and social scripts, which structure how learners interact with each other. In this contribution, two studies will be reported that investigated the effects of epistemic and social scripts in a text-based computer-supported learning environment and in a videoconferencing learning environment in order to foster the individual acquisition of knowledge. In each study the factors ‘epistemic script’ and ‘social script’ have been independently varied in a 2×2-factorial design. 182 university students of Educational Science participated in these two studies. Results of both studies show that social scripts can be substantially beneficial with respect to the individual acquisition of knowledge, whereas epistemic scripts apparently do not to lead to the expected effects

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in