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Blended learning options vary and universities are exploring an assortment of instructional combinations, some involving video lectures as a replacement for face-to-face (f2f) lectures. This methodological study investigates the impact of the provision of lecture choice (online or f2f) on overall student achievement and course engagement. This research uses a within-group design to obtain baseline data on a single set of physics students (n = 168), and investigates the impact of providing a lecture viewing choice (online, f2f) mid-semester on student achievement (tests, homework, and standardized conceptual evaluation scores), and course engagement (student lecture viewing, homework submissions, bonus project submissions, and note taking behaviors). The study reveals that the type of lecture does not serve to significantly impact overall student achievement or engagement. However, although recorded and f2f lectures demonstrate an overall educationally equivalent impact, students who elect a high level of recorded lecture use were significantly lower performing and less engaged before the option to watch recorded lectures was introduced and largely continued to be so after the option was introduced, but there was evidence of a reduction in achievement and engagement differences after the option is introduced. Therefore, results of this study suggest weaker performing students self-select higher levels of recorded lecture use, and the use of these video lectures may assist this specific group of students in closing the gap between themselves and students who were initially higher performing and more engaged.

Deep-learning neural networks continue to improve in effectiveness and capability. Specifically, Convolutional Neural Networks (CNNs) have proven to offer highly accurate results in differentiating graphical images. Using satellite imagery, CNNs have been highly successful in the classification of land use. This successful classification, however, depends on the model structure, data components, and classification methods utilized. While alternative approaches exist, the complexity and sophistication of such classification methods impose increased computational costs. Effective manipulation of CNN components is shown to offer computational savings and accurate classification of land use in satellite images. [ABSTRACT FROM AUTHOR]

During the past century, scientists across the globe have attempted to reintroduce species to areas where they previously thrived, but are now extinct. These reintroduction efforts are attempts to reverse the human carelessness of the past and redress the ecological balance. From a project and process point of view, these undertakings species translocation involve considerable complexity. In light of this complexity, there has been minimal effort to access, evaluate, or predict the specific factors that may contribute to the success or failure of such ventures. In the current study, machine learning was used to classify the outcome of large carnivore translocations, predict the probability of success, and determine specific essential factors. Using the Random Forest method, a model was developed to classify success or failure of these translocations with a high degree of accuracy. In addition, specific contributing, and controllable factors that increase the probability of a successful reintroduction of species were identified. [ABSTRACT FROM AUTHOR]

Introduces a technique using linguistic references in the communication of a physics class to characterize class integration. Measurement of a traditional physics class shows only marginal integration; however, measurement of a traditional physics class shows that integration can be dramatically improved. (Author/CCM)

Extends the modeling system presented in Part I. This model is applied to two widely diverse educational processes: "Student Actions" and "Do Homework Problem." Its computational syntax imposes completeness criteria on the modeling and its uniformity. (AIM)

Presents a measurement of the time and resources committed to traditional student actions such as reading and working homework. The perception of the educational value of each action for both students and faculty is captured. Basic educational efficiencies are computed for a traditional mechanics course and a nontraditional hands-on Electricity and Magnetism course. (Author/DKM)

In North America, wolf populations have been relentlessly hunted and persecuted since Europeans landed in the new world. In recent years, in an effort to restore the balance of flora and fauna in ecosystems, wolves have been reintroduced in some areas. In other areas, wolf populations are still hunted, based upon the premise of "managing them." Prior studies have suggested that physiological indicators, specifically elevated hormone levels, are symptomatic of higher stress levels in individual wolf subjects in heavily-hunted populations. This stress has far-reaching implications for reproduction, social structure and pack dynamics. The current study supports prior studies that used statistics to show elevated stress levels in hunted wolf populations and classification of individual wolf subjects as belonging to hunting-based stressed populations, based on physiological data, and by using machine learning. [ABSTRACT FROM AUTHOR]

The preservation of Wolf populations in North America has been controversial for hundreds of years. The preservation of ecosystems or the reintroduction of wolf populations in areas to redress the ecological balance has taken place in recent decades. In other areas, wolves are hunted in an effort to manage them. Previous studies have identified physiological characteristics as an indicator of higher stress levels in individual wolf subjects in heavily hunted populations. This stress impacts reproduction, social structure and pack dynamics. The current study supports a prior study that used statistics to show elevated stress levels in hunted wolf populations. Using machine learning (k-nearest neighbor), we were able to classify individual wolf subjects as belonging to hunting-based stressed populations based on physiological data with high accuracy. [ABSTRACT FROM AUTHOR]

Background: The ability to identify horses at risk for catastrophic injuries continues to be a pressing issue for the racing industry, especially given recent events in North America. Objectives: Since most catastrophic injuries occur in areas of existing pathology and this pathology is likely to elicit an inflammatory response, it was hypothesised that analysis of messenger RNA (mRNA) expression would detect significant changes in select genes in horses at risk for a catastrophic injury. Study design: Prospective cohort study. Methods: Five racing jurisdictions across the United States participated in this study. A total of 686 Tempus® RNA Blood Tube samples were collected for mRNA analysis from 107 catastrophically injured horses, as well as from noninjured horses sampled either prerace (n = 374) or postrace (n = 205). A subset of horses (n = 37) were sampled both prerace and postrace for analysis of expression changes during the postrace period. Results: Of 21 genes analysed via RT‐qPCR, the expression of 12 genes (ALOX5AP, CD14, IL‐10, IL‐1β, IL‐6, IL‐8, MMP1, PTGS2, TLR4, TNFα, TNFSF13B and VEGFA) changed significantly within 45 minutes after a race and were excluded. Of the remaining nine genes (BMP‐2, IGF‐1, IL1RN, MMP2, MMP9, Osteoprotegrin, RANKL, SAA1 and TGFβ), three genes (IGF‐1, IL1RN and MMP2) were found to be significantly different between catastrophically injured and noninjured horses using multiple logistic regression modelling. Receiver operating characteristic analysis of models, which included mRNA expression, demonstrated sensitivities from 76%‐82% (95% CI: 67%‐93%) and specificities from 84%‐88% (95% CI: 71%‐94%) at the Youden Index. Main limitations: Samples were collected as soon as possible postinjury (within 30 minutes). Conclusions: Analysis of mRNA expression of specific genes in the future may be considered as an economical, accessible and noninvasive means by which horses at risk for catastrophic injury can be identified. [ABSTRACT FROM AUTHOR]

The term Data Science has been used frequently in the media and in advertisements in recent years. Yet, few can offer a clear and concise definition of the term. This lack of a definition complicates the communication and understanding among entities that focus on the collaboration, use, and application of Data Science. Further complicating the landscape is the fact that advances in technology have altered the perception of Data Science, and consequently, the ways in which it is defined and applied. The goals of this paper are to derive definitions that capture the current collective academic and industry interpretations and perceptions of Data Science (and its components), map these views to emerging areas in Data Science, and determine how well this mapping translates to an applied curriculum for academia. Specific questions addressed include: 1) are we adequately defining Data Science within the confines of how it is currently applied, and how we aspire to apply it?, and 2) are we adequately preparing students to make contributions to, and lead in, this field in terms of the application and advancement of Data Science, based on the current programs being offered? Finally, this study explores what a Master of Science in Data Science should look like, based on the perceived requirements from industry, and discusses the constraints on creating a comprehensive, distinctive, and high-quality Master’s-level program in Data Science. [ABSTRACT FROM AUTHOR]

The term Artificial Intelligence (AI) has been used frequently in the media and in advertisements in recent years. Yet, few can offer a clear and concise definition of the term "AI." This lack of a definition complicates the communication and understanding among entities that focus on the collaboration, use, and application of AI. Further complicating the landscape is the fact that advances in technology have altered the perception of AI, and consequently, the ways in which they are defined. The goal of this paper is to derive a definition that capture the current collective scientific, rather than the societal or media-based definition of the discipline (and its subcategories), map this definition to emerging areas in AI such as AIoT, and determine how well this mapping translates to an applied curriculum at the highest ranked schools. Specifically, are we adequately defining AI within the confines of how it is currently applied, and how we aspire to apply it? Are college degree programs preparing students to make contributions to this field, in terms of the application and advancement of AI?. [ABSTRACT FROM AUTHOR]

Traditional forecasting methods have been used for several years and, and therefore, have become pervasive in a variety of industries and fields of study. With the recent COVID-19 pandemic, as with any area of uncertainty, it has been difficult to predict the incidence rates for future periods. The importance of forecasting cannot be overstated, when planning for the level of medical and first responder resources necessary to adequately treat afflicted patients. This study compares the accuracies of some common, traditional forecasting methods to the actual incidence rates, in order to determine which methodology contributes the most useful level of incidence predictability. [ABSTRACT FROM AUTHOR]

Data Analytics, Data Mining, and Machine-Learning have become increasingly prevalent in higher-education, in both on-ground and online curricula. However, few studies have thoroughly and objectively studied the effectiveness of specific Data Mining software (both commercially-licensed and open-source), on learning outcomes. The current study used the Independent Samples T-Test to objectively compare two popular data mining software tools. Specifically, undergraduate, and graduate Information Systems students enrolled in an Introduction to Data Mining course were tested on the use of an open-source data mining tool (i.e., R Programming Language), and on a commercially-licensed data mining tool (i.e., XLMiner from Frontline Solvers). The current study assessed the impact of both tools on undergraduate and graduate student learning outcomes. The findings of this study will be of interest to higher-education faculty who are using, or who may consider using, data mining and machine-learning software in their undergraduate or graduate Information Systems Curriculum. [ABSTRACT FROM AUTHOR]

Education Technology (EdTech) has become increasingly prevalent in higher-education, in both on-ground and online curricula. However, few studies have thoroughly and objectively studied the effectiveness of specific EdTech components, such as Screencast Technology, on learning outcomes. The current study used objective pre-test and post-test results to assess the learning effectiveness of Screencast Technology on students. Specifically, undergraduate, and graduate Information Systems students enrolled in a Data Mining course were tested before, and after watching online Screencast video tutorials. The findings of the study will be of interest to higher-education faculty who are using, or who may consider using, Screencast Technology in their undergraduate or graduate Information Systems curriculum. [ABSTRACT FROM AUTHOR]

The determination of the 2-keto-3-deoxyoctonic acid (KDO) content of a lipopolysaccharide obtained from Escherichia coli ATCC 12408 using the thiobarbituric acid and semicarbazide methods for its estimation points to the presence of a KDO trisaccharide unit in the structure. On partial hydrolysis of the lipopolysaccharide by the method of Painter, using polystyrene sulphonic acid, up to 60% of the KDO was lost. A core oligosaccharide, with a molecular weight in the region of 3500 to 5000 and containing heptose, glucose, galactose, KDO and phosphorus in the molar ratio of 2:1.44:0.6:0.3:1.3, was isolated on fractionation of the dialysate on Sephadex G-15. Sodium borohydride reduction of the oligosaccharide results in the loss of both reducing power and KDO, indicating a linear-type structure with KDO only at the reducing end. Removal of phosphate from the oligosaccharide had no significant effect on its molecular weight. Periodate oxidation, followed by borohydride reduction, and methylation studies showed the presence of certain structural features similar to those already found for the core region of lipopolysaccharides from other gram-negative bacteria. [ABSTRACT FROM AUTHOR]