Middle Tennessee State University

About: Middle Tennessee State University is a education organization based out in Murfreesboro, Tennessee, United States. It is known for research contribution in the topics: Population & Higher education. The organization has 2527 authors who have published 4843 publications receiving 95486 citations. The organization is also known as: MTSU.

The Subsystems Approach to Genome Annotation and its Use in the Project to Annotate 1000 Genomes

Abstract: The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.

How Learning in an Inverted Classroom Influences Cooperation, Innovation and Task Orientation.

Abstract: Recent technological developments have given rise to blended learning classrooms. An inverted (or flipped) classroom is a specific type of blended learning design that uses technology to move lectures outside the classroom and uses learning activities to move practice with concepts inside the classroom. This article compares the learning environments of an inverted introductory statistics class with a traditional introductory statistics class at the same university. This mixed-methods research study used the College and University Classroom Environment Inventory (CUCEI), field notes, interviews and focus groups to investigate the learning environments of these two classrooms. Students in the inverted classroom were less satisfied with how the classroom structure oriented them to the learning tasks in the course, but they became more open to cooperative learning and innovative teaching methods. These findings are discussed in terms of how they contribute to the stability and connectedness of classroom learning communities.

Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites

Abstract: Social Networking Sites (SNSs) such as Facebook are one of the latest examples of communications technologies that have been widely-adopted by students and, consequently, have the potential to become a valuable resource to support their educational communications and collaborations with faculty. However, faculty members have a track record of prohibiting classroom uses of technologies that are frequently used by students. To determine how likely higher education faculty are to use Facebook for either personal or educational purposes, higher education faculty (n = 62) and students (n = 120) at a mid-sized southern university were surveyed on their use of Facebook and email technologies. A comparison of faculty and student responses indicate that students are much more likely than faculty to use Facebook and are significantly more open to the possibility of using Facebook and similar technologies to support classroom work. Faculty members are more likely to use more “traditional” technologies such as email.

Prioritizing test cases for regression testing

Abstract: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites.

PLS-SEM or CB-SEM: updated guidelines on which method to use

Abstract: Numerous statistical methods are available for social researchers. Therefore, knowing the appropriate technique can be a challenge. For example, when considering structural equation modelling (SEM), selecting between covariance-based (CB-SEM) and variance-based partial least squares (PLS-SEM) can be challenging. This paper applies the same theoretical measurement and structural models and dataset to conduct a direct comparison. The findings reveal that when using CB-SEM, many indicators are removed to achieve acceptable goodness-of-fit, when compared to PLS-SEM. Also, composite reliability and convergent validity were typically higher using PLS-SEM, but other metrics such as discriminant validity and beta coefficients are comparable. Finally, when comparing variance explained in the dependent variable indicators, PLS-SEM was substantially better than CB-SEM. Updated guidelines assist researchers in determining whether CB-SEM or PLS-SEM is the most appropriate method to use.