Background and Context: While ample research has examined undergraduate students' participation in Computer Science I (CS1), far less attention has been paid to Computer Science II (CS2) outcomes. Inspired by self-efficacy, Object-Oriented Programming (OOP) and CS2, we expand our understanding of the traditional computer programming curriculum sequence in CS curricula guidelines (CS1, CS2, and data structures and algoritluns). Objectives: This research aims to design, develop, and provide preliminary validity and reliability evidence of a measure of self-efficacy in OOP concepts for undergraduate students who completed a CS2 course. We present a conceptual framework at the intersection of CS2, Object-Oriented Programming (OOP), and the notion of self efficacy. We also analyze the impact of self efficacy on the overall course performance. Method: Following systematic procedures inspired by classical test theory and our conceptual framework, we created a 27-item instrtunent to measure self-efficacy in OOP among undergraduate CS students who already took CS2. A total of n = 292 undergraduate students enrolled in a data structures and algorithms course at two public universities in the same state in the southeastern United States (U.S.) completed the survey at the start of a traditional 16-week academic semester. We ran multiple linear regression models to predict undergraduate students' final grades in a data structures and algoritluns course as predictive validity evidence. Findings: Using Exploratory Factor Analysis, we examined the underlying structure of the instrument, which resulted in four distinct and internally consistent factors: 1) Class Design and Data Manipulation, 2) Flow of Control, 3) Class Hierarchy and Inheritance, and 4) Class Behavior and Method. The four-factor model accounted for approximately 64% of the variability in these data, and there were little to no cross-loadings in the pattern matrix. Implications: We discuss the findings in light of our study's limitations and delimitations and provide actionable implications for CS educational researchers and educators. The SES-OOP shows promising preliminary evidence of validity and internal consistency reliability for low-stakes purposes, though additional validation such as test -retest reliability and confirmatory analyses is needed.

There are inconsistencies in conclusions drawn from the studies that address the same research question in the biomedical literature. This paper presents preliminary work on the approaches taken to build an inconsistency detection and explanation model starting with the development of a gold-standard contradiction sentences corpus. First, we utilize SemRep, a third-party tool that can automatically segment any biomedical sentence into the form of a subject, predicate, and object. A pair of sentences with the same subject/object but different predicates is identified as contradictory sentences. These sentences are then manually curated by domain experts to filter out noise. In the future, we plan to generate a large manually curated gold-standard contradiction sentence dataset and use that for developing an automated tool for detecting and extracting contradictions in biomedical and health text.

Individuals with trauma experience negative mental health impacts and are at risk of poor cardiovascular outcomes. Unmanaged, these conditions may worsen, compromising healing and wellbeing. Yoga, particularly trauma-informed, may improve outcomes. The current pilot study explores the impact of a novel trauma-informed yoga and mindfulness curriculum on wellbeing in two parts. The first examined mental health (stress, mood) outcomes in four trauma-impacted populations: adults who were incarcerated (INC), individuals in recovery from substance use disorders (SU), veterans (VA), and vulnerable youth (YTH) assessing both the impact of individual class participation and impact of attending at least four curriculum sessions. For the subgroup of incarcerated individuals, impact by theme was examined. After curriculum sessions, stress was reduced, and mood improved. Across multiple sessions both the largest decreases in stress and greatest increase in mood occurred after participant in the first session. Further, a specific exploration of curriculum class impact by theme for participants who were incarcerated indicated no difference in impact by theme. The second part of this study explored cardiovascular outcomes for the population of those in recovery from substance use. Reductions in systolic blood pressure occurred immediately after the first curriculum session, and diastolic blood pressure reduced over three consecutive sessions.

In this research, we analyzed voter registration and elections data released by the Florida Division of Elections to investigate the profile of Florida voter participation. The utilized data was associated with federal general elections from 2014 to 2020. Data preparation issues were resolved during the data merging, including exact duplicates, multiple associated vote types, and misclassified vote types. The merged data consisted of voter ID, registration county code, zip code, sex, ethnicity, age, vote type for each general election year, voting indicator for each general election year, and county code. Boosted Tree model (with a misclassification rate of 0.22) identified zip code, age, and voter status are key factors that influence voter participation. Based on voter eligibility and total vote counts in each general election held between 2014 and 2020, voters were classified into the following profile categories: always-voted (participated in all elections), increasing-in-voting (participated in recent elections but not in the past elections), intermittent-in-voting (participated in some elections but not all), decreasing-in-voting (participated past elections but not recently), never-voted (didn’t participate in the elections), and not-eligible (registered but under 18 years of age). Voter profile counts information was merged with Census demographic information at the zip code level. To find insights into the voter profiles, we created Tableau dashboards to view voter profiles, voting methods, and the effect of census variables on voter turnout at the zip code level. We hope this dashboard helps organizations like the League of Women Voters of Florida target their voter participation and engagement activities at the zip code level.

The University of North Florida (UNF) offered to leverage resources generated as a result of its CAE-EDU designation to assist Edward Waters University (EWU), a local Minority-Service Institution (MSI), with establishing cyberse-curity educational opportunities for their STEM faculty and undergraduate students. This project, funded by NSA as part of their Cybersecurity Education Diversity Initiative (CEDI) program, designs the framework and cultivates the infrastructure by which EWU gains access to UNF instructional resources (e.g., courses, facilities), faculty expertise (i.e., curriculum, consulting) and student activities (e.g., clubs, advising) in cyber defense education. Project plans included (i) outreach activities with support from faculty of the Florida State College of Jacksonville (FSCJ), a local community college, also CAE-designated, as well as (ii) proof-of-concept assessments with support from faculty and students of Edward Waters University (EWU), an MSI with no existing cybersecurity programs. Some of the activities performed under this project and their outcomes are reported in this paper.

In this work, we consider distance-based clustering of partial lexicographic preference trees (PLP-trees), intuitive and compact graphical representations of user preferences over multi-valued attributes. To compute distances between PLP-trees, we propose a polynomial time algorithm that computes Kendall's tau distance directly from the trees and show its efficacy compared to the brute-force algorithm. To this end, we implement several clustering methods (i.e., spectral clustering, affinity propagation, and agglomerative nesting) augmented by our distance algorithm, experiment with clustering of up to 10,000 PLP-trees, and show the effectiveness of the clustering methods and visualizations of their results.

Vegetation monitoring is one of the major cornerstones of environmental protection today, giving scientists a look into changing ecosystems. One important task in vegetation monitoring is to estimate the coverage of vegetation in an area of marsh. This task often calls for extensive human labor carefully examining pixels in photos of marsh sites, a very time-consuming process. In this paper, aiming to automate this process, we propose a novel framework for such automation using deep neural networks. Then, we focus on the utmost component to build convolutional neural networks (CNNs) to identify the presence or absence of vegetation. To this end, we collect a new dataset with the help of Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) to be used to train and test the effectiveness of our selected CNN models, including LeNet-5 and two variants of AlexNet. Our experiments show that the AlexNet variants achieves higher accuracy scores on the test set than LeNet-5, with 92.41\% for a AlexNet variant ondistinguishing between vegetation and the lack thereof. These promising results suggest us to confidently move forward with not only expanding our dataset, but also developing models to determine multiple species in addition to the presence of live vegetation.

The purpose of this research was to examine college students' conceptions of learning computer science and approaches to learning computer science and to examine the relationships among these two important constructs and possible moderating factors. Student data (N = 193) were collected using the conceptions of learning computer science and the approaches to learning computer science surveys at one public research institution in the southeastern United States. Data were analyzed with descriptive statistics, Confirmatory Factor Analysis models, internal consistency reliability, Pearson correlations, stepwise multiple regression models, and Multivariate Analysis of Variance models. The results suggest that college students most favorably employ a deep strategy approach for learning computer science in which prior knowledge is activated and meaningful learning strategies are used. College students appear to be more extrinsically motivated to learn computer science than intrinsically. Higher level learning conceptions are associated with a deep strategy approach to learning (e.g., Seeing in a new way) whereas low-level conceptions are associated with a surface strategy (e.g., Memorizing) approach to learning. Male college students have slightly higher conceptions of programming than their female counterparts. The findings are discussed and both limitations and delimitations of the study are enumerated.

Background and context: Researchers have been looking into the complexity of computer science (CS) education and tried to apply rigorous and relevant educational research methods to understand and facilitate the learning experience of students. Objective: The purpose of this study was to explore college students' conceptions of learning CS to shed light on student learning activities, feelings, contexts, and beliefs about their learning. Method: Draw-a-picture technique was used as an emerging research technique in CS education. Using a modified coding checklist, we analyzed the drawings into 9 categories with 50 sub-categories. Findings: College students most frequently expressed computer programming as the learning activity by illustrating syntax and semantics of programming languages in the drawings. Problem-solving constructs like decomposition and abstraction were also recorded along with other tools like diagramming techniques. Gender and prior computer science experience were analyzed as moderators.

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder resulting from skeletal muscle weakness and fatigue. An early common symptom is fatigable weakness of the extrinsic ocular muscles; if symptoms remain confined to the ocular muscles after a few years, this is classified as ocular myasthenia gravis (OMG). Diagnosis of MG when there are mild, isolated ocular symptoms can be difficult, and currently available diagnostic techniques are insensitive, non-specific or technically cumbersome. In addition, there are no accurate biomarkers to follow severity of ocular dysfunction in MG over time. Single-fiber electromyography (SFEMG) and repetitive nerve stimulation (RNS) offers a way of detecting and measuring ocular muscle dysfunction in MG, however, challenges of these methods include a poor signal to noise ratio in quantifying eye muscle weakness especially in mild cases. This paper presents one of the attempts to use the electric potentials from the eyes or electrooculography (EOG) signals but obtained from three different forms of sleep testing to differentiate MG patients from age- and gender-matched controls. We analyzed 8 MG patients and 8 control patients and demonstrated a difference in the average eye movements detected between the groups. A classification accuracy as high as 68.8% was achieved using a linear discriminant analysis based classifier.

Attracting and retaining customers in an online environment is crucial to remain a successful retail business. Although purchase intentions have been recognized as a major factor affected by the website quality, few studies have examined how initial purchase intention affects the continued purchase intention. We want to further investigate the relationship between website quality (system, information, and service) and purchase intention categories with perceived risk as the moderator. We developed a questionnaire and three different websites for a fictional office furniture retail business to aid our investigation. The questionnaire was distributed to university students in a large U.S metropolitan city. We received 256 valid responses. Our empirical results confirmed that information and service quality of the e-tail website positively impact initial purchase intentions, and consequently continued purchase intention. Of the three website quality variables measured, the perceived risk seems to have an adverse effect only on the relationship between system quality and purchase intentions. Based on our findings, e-tailing websites should consider focusing more on website quality factors such as responsiveness, utility, reliability, availability, and the content of the website as well as the service provided such as customization, users feedback and rating, and good tracking of user complaints.

Ventricular arrhythmias (VA) are life-threatening pathophysiological conditions that seriously impact the normal functioning of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two well known types of VA. VF is the lethal of the VAs and could be characterized by its organizational progression over time. The success of cardiac resuscitation strongly depends on the type of VA, its evolution over time and response to therapy. Due to the time critical nature of VF, computationally efficient quantification of VAs and swift feedback are essential. This work attempted to arrive at computationally efficient and data-driven techniques based on Empirical Mode Decomposition for classifying and tracking VAs over time. The approaches are divided into two aims: (1) ‘in-hospital’ scenarios for characterizing the dynamics of VA episodes to assist clinicians in planning long-term therapy options, and (2) ‘out-of-hospital’ scenarios for providing near real-time feedback to detect/track the progression of VAs over time to assist medical personnel select/modify therapy options. Using an ECG database of 61 60-s VA segments obtained for classifying VT vs. VF and sub-classifying VF into organized VF (OVF) and disorganized VF (DVF), maximum classification accuracies of 96.7% (AUC = 0.993) and 87.2% (AUC = 0.968) were obtained for classifying VT vs. VF and OVF vs. DVF during ‘in-hospital’ analysis. Additionally, two near real-time approaches were presented for ‘out-of-hospital’ analysis where average accuracies of 71% and 73% were achieved for VT/VF and OVF/DVF classification, as well as demonstrating strong potential for monitoring VA progressions over time.

Background and Context: The use of block-based programming environments is purported to be a good way to gently introduce novice computer programmers to computer programming. A small, but growing body of research examines the differences between block-based and text-based programming environments. Objective: Thus, the purpose of this study was to examine the overall effect of block-based versus text-based programming environments on both cognitive and affective student learning outcomes. Method: Five academic databases were searched to identify literature meeting our inclusion criteria and resulted in 13 publications with 52 effect size comparisons on both cognitive and affective outcomes. Findings: We found small effect size (g = 0.245; p = .137; with a 95% confidence interval of -0.078 to 0.567) in favor of block-based programming environments on cognitive outcomes, and a trivial effect size (g = 0.195, p = .429; with a 95% confidence interval of -0.289 to 0.678) on affective outcomes. Both effect size calculations were statistically insignificant using random effects models. The effect sizes were examined for moderating effects by education level, learning environment, and study duration. Some evidence of publication bias was detected in these data.

Ventricular arrhythmias (VA) can lead to lethal conditions depending on their characteristics and temporal progression. Hence, it is essential to detect the type of VA and track its transitions over time to provide feedback in choosing appropriate therapy options. In this work, Empirical Mode Decomposition was used to extract intrinsic mode functions (IMFs) and construct the Hilbert energy spectrum (HS) from the 60-s long ECG segments during VAs. From the HS, instantaneous mean frequency and squared instantaneous bandwidth were extracted to track the progression of VAs. In addition, the energy ratio variance was computed from the IMFs. Using the extracted features, quantification of the performance was evaluated by a two-stage binary classification with a linear discriminant analysis based classifier and leave-one-out cross validation. A classification accuracy of 84% was achieved in classifying VT from VF, and 75% was achieved in classifying the correctly classified VF from the first stage into organized and disorganized VF.

Several experiments on the effects of pair programming versus solo programming in the context of education have been reported in the research literature. We present a meta-analysis of these studies that accounted for 18 manuscripts with 28 independent effect sizes in the domains of programming assignments, exams, passing rates, and affective measures. In total, our sample accounts for N = 3,308 students either using pair programming as a treatment variable or using traditional solo programming in the context of a computing course. Our findings suggest positive results in favor of pair programming in three of four domains with exception to affective measures. We provide a comprehensive review of our results and discuss our findings.

In the past decade, research efforts dedicated to studying the process of collaborative web search have been on the rise. Yet, limited number of studies have examined the impact of collaborative information search process on novice's query behaviors. Studying and analyzing factors that influence web search behaviors, specifically users' patterns of queries when using collaborative search systems can help with making query suggestions for group users. Improvements in user query behaviors and system query suggestions help in reducing search time and increasing query success rates for novices. In this paper, we present an empirical study plan designed to investigate the influence of collaboration between experts and novices as well as use of a collaborative web search tool on novice's query behavior. In this research-in-progress study, we intend to use SearchTeam as our collaborative search tool. The results of this study are expected to provide information that could help collaborative web search tool designers to find ways to improve the query suggestions feature for group users. Additionally, this study will test the hypothesis that - having domain experts working with non-experts using collaborative search systems would immensely increase the query success rates for non-expert users, and help them learn querying strategies over the course of time. If the above hypothesis is proven, then use of collaborative web search tools during training of interns would be highly recommended.