Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Improved Lion Optimization based Enhanced Computation Analysis and Prediction Strategy for Dropout and Placement Performance Using Big Data

Background: Predicting the undergraduate’s placement performance is vital as it impacts the credibility of educational institutions. Hence, it is significant to predict their performance based on placement in the early days of degree program. Objectives: The study intends to predict the undergraduate’s placement performance through the introduced ANN-R (Artificial Neural Network based Regression) as it is able to handle fault tolerance. For efficient prediction, relevant feature selection is needed that is performed by the proposed ILO (Improved Lion Optimization) algorithm as it has the ability to find nearest probable optimal solution. Methodology: Initially, the parameters and population are initialised. Subsequently, first best-agent is stated in accordance with fitness function. Subsequently, position of present search agent is updated. This iteration continues until all the features are selected and optimized result is attained. Here best score is computed using the proposed ILO for feature selection. Finally, the dropout analysis and placement performance of students is predicted using the introduced ANN-R through a train and test split. Results/Conclusion: Performance of the proposed system is analysed in accordance with loss metrics. Additionally, internal comparison is performed to find the extent to which the actual and predicted values correlate with one another during prediction using the existing and proposed system. The outcomes revealed that the proposed system has the ability to predict the student’s placement performance along with domain of interest with minimum errors than the traditional system. This makes the proposed system to be highly suitable for predicting student’s performance

Essential competencies of exceptional professional software engineers

Department Head: Rodney R. Oldehoeft.1991 Fall.Includes bibliographical references (pages 141-144).This dissertation presents a differential study of exceptional and non-exceptional professional software engineers in the work environment. The first phase of the study reports an in-depth review of 20 engineers. The study reports biographical data, Myers-Briggs Type Indicator test results, and Critical Incident Interview data for 10 exceptional and 10 non-exceptional subjects. Phase 1 concludes with a description of 38 essential competencies of software engineers. Phase 2 of this study surveys 129 engineers. Phase 2 reports biographical data for the sample and concludes that the only simple demographic predictor of performance is years of experience in software. This variable is able to correctly classify 63% of the cases studied. Phase 2 also has the participants complete a Q-Sort of the 38 competencies identified in Phase 1. Nine of these competencies are differentially related to engineer performance. A10 variable Canonical Discriminant Function is derived which is capable of correctly classifying 81% of the cases studied. This function consists of three biographical variables and seven competencies. The competencies related to Personal Attributes and Interpersonal Skills are identified as the most significant factors contributing to performance differences

Who Stays and Who Leaves? Predicting College Student Persistence Using Comprehensive Retention Models

The purpose of this study was to use a comprehensive framework to examine academic, psychosocial, noncognitive, and other background factors that are related to retention at a large, public four-year institution in the southeastern United States. Specifically, the study examined what factors are most important in predicting first-to-second year retention both before the student enrolls at the university and after completion of their first semester of coursework. Data were drawn from institutional records, a survey instrument designed to measure psychosocial constructs, the ACT student record, and the National Center for Education Statistics. The sample for the study consisted of 12,342 students. Hierarchical generalized linear models and ensemble tree-based methods were utilized to identify important predictors of retention, ascertain the nature of the significant relationships, and to build models for predicting retention outcomes. An initial model was built for prediction before students enrolled followed by a second model with first semester performance variables added. Predictive validity was assessed by splitting the sample into a training and test set. Findings from the study showed that nontraditional factors were significant predictors of retention along with traditional predictors such as high school GPA. The results showed that the influence of financial factors and high school characteristics were among the most significant predictors of retention. Moreover, the results showed that multiple psychosocial factors are influential variables in retention outcomes. This study demonstrated that considering a variety of factors when forecasting postsecondary retention outcomes is vital for more accurate predictions. The models in this study showed that pre-college predictive models have the potential to be nearly as effective as models incorporating college performance and activity. The results of this study have important implications for higher education policymakers, college administrators, and high schools. Several of the relationships revealed have significant policy implications related to budget concerns, university programming, and college preparatory initiatives at the high school level. The study also provides a useful model for identifying students at risk of not being retained that could be adapted for implementation at other institutions and points the importance of a holistic understanding of the total student

Exploring Academic Opportunities for Military-Connected Students: A Systems Theory Approach

Military-connected children experience frequent disruptions to their daily lives as a result of military lifestyle demands like frequent relocations and service-related separations from their service member parent. These disruptions impact all areas of their lives including their homes and schools. While the body of research concerning military-connected children’s experiences at school has grown over recent decades, little is known about specific individual and contextual factors that may serve as assets or constraints. Knowing more about specific factors that influence school experiences for military-connected youth is a critical step in promoting and scaling home- and school-based interventions. This three-paper dissertation begins by situating extant literature into Bronfenbrenner’s bioecological systems theory framework to provide a model for conceptualizing the numerous factors directly and indirectly influencing the school experiences of military-connected students. Next, it explores patterns in individual survey responses from military-connected parents to identify relationships between demographic variables, military lifestyle demands, and parent-school satisfaction. Finally, the dissertation uses a positive youth development framework to examine the school experiences of military-connected teens through focus group discussions with teens, their parents, and their teachers. Taken together, these pieces help to provide a cohesive framework and updated foundation for understanding the school experiences of military-connected children. The results of this dissertation highlight the strengths of military-connected students and families and the immense opportunities all stakeholders have to support them and address their evolving needs. The findings for all three papers provide necessary insight for understanding the military-connected student’s experience and intentionally leveraging new and existing resources to meet their needs

Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

Software Engineering Laboratory Series: Collected Software Engineering Papers

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of application software. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that includes this document

KNOWING THE NATURAL WORLD: THE CONSTRUCTION OF KNOWLEDGE ABOUT EVOLUTION IN AND OUT OF THE CLASSROOM

Evolution is a central underlying concept to a significant number of discourses in civilized society, but the complexity of understanding basic tenets of this important theory is just now coming to light. Knowledge about evolution is constructed from both formal and free-choice opportunities, like television. Nature programs are commonly considered educational by definition, but research indicates the narratives often promote creationist ideas about this important process in biology. I explored how nature programs influenced knowledge construction about evolutionary theory using a combination of qualitative and quantitative approaches. Because misconceptions about evolution are common, I examined how students` conceptual ecologies changed in response to information presented in an example of a particularly poor nature film narrative. Students` held a diversity of misconceptions, proximate conceptions, and evolutionary conceptions simultaneously, and many of their responses were direct reflections of the nature program. As a result, I incorporated the same nature program into an experiment designed to examine the effects of narrative and imagery on evolution understanding. After completing an extensive pre-assessment that addressed attitudes and beliefs about science knowledge, students viewed one of four versions of the nature program that varied in the quality of science and imagery presented. The effect of watching different versions was only vaguely apparent in students with a moderate understanding of evolution. The relationship was much more complex among students with a poor understanding of evolution but suggested a negative effect that was more influenced by public discourses about this controversial subject than conceptual understanding. The relationships warranted examining learning from the perspective of the consumers of these programs. I surveyed audience beliefs about the educational value of nature programs and found that an overwhelming majority believed the programs were educational and designed to teach about nature. The results were particularly alarming because beliefs about the educational value may strongly impact learning outcomes. An informal survey of nature programs aired during a sweeps month indicated that poor presentation of science, and specifically evolutionary theory, was indeed the norm. Indeed, nature programs may be contributing to the deconstruction of knowledge about evolution both in and out of the classroom

Achieving broad access to satellite control research with zero robotics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This thesis was scanned as part of an electronic thesis pilot project.Cataloged from PDF version of thesis.Includes bibliographical references (p. 307-313).Since operations began in 2006, the SPHERES facility, including three satellites aboard the International Space Station (ISS), has demonstrated many future satellite technologies in a true microgravity environment and established a model for developing successful ISS payloads. In 2009, the Zero Robotics program began with the goal of leveraging the resources of SPHERES as a tool for Science, Technology, Engineering, and Math education through a unique student robotics competition. Since the first iteration with two teams, the program has grown over four years into an international tournament involving more than two thousand student competitors and has given hundreds of students the experience of running experiments on the ISS. Zero Robotics tournaments involve an annually updated challenge motivated by a space theme and designed to match the hardware constraints of the SPHERES facility. The tournament proceeds in several phases of increasing difficulty, including a multi-week collaboration period where geographically separated teams work together through the provided tools to write software for SPHERES. Students initially compete in a virtual, online simulation environment, then transition to hardware for the final live championship round aboard the ISS. Along the way, the online platform ensures compatibility with the satellite hardware and provides feedback in the form of 3D simulation animations. During each competition phase, a continuous scoring system allows competitors to incrementally explore new strategies while striving for a seat in the championship. This thesis will present the design of the Zero Robotics competition and supporting online environment and tools that enable users from around the world to successfully write computer programs for satellites. The central contribution is a framework for building virtual platforms that serve as surrogates for limited availability hardware facilities. The framework includes the elaboration of the core principles behind the design of Zero Robotics along with examples and lessons from the implementation of the competition. The virtual platform concept is further extended with a web-based architecture for writing, compiling, simulating, and analyzing programs for a dynamic robot. A standalone and key enabling component of the architecture is a pattern for building fast, high fidelity, web-based simulations. For control of the robots, an easy to use programming interface for controlling 6 degree-of-freedom (6DOF) satellites is presented, along with a lightweight supervisory control law to prevent collisions between satellites without user action. This work also contributes a new form of student robotics competition, including the unique features of model-based online simulation, programming, 6DOF dynamics, a multi-week team collaboration phase, and the chance to test satellites aboard the ISS. Scoring during the competition is made possible by possible by a game-agnostic scoring algorithm, which has been demonstrated during a tournament season and improved for responsiveness. Lastly, future directions are suggested for improving the tournament including a detailed initial exploration of creating open-ended Monte Carlo analysis tools.by Jacob G. Katz.Ph.D