The Potential for Student Performance Prediction in Small Cohorts with Minimal Available Attributes

The measurement of student performance during their progress through university study provides academic leadership with critical information on each student’s likelihood of success. Academics have traditionally used their interactions with individual students through class activities and interim assessments to identify those “at risk” of failure/withdrawal. However, modern university environments, offering easy on-line availability of course material, may see reduced lecture/tutorial attendance, making such identification more challenging. Modern data mining and machine learning techniques provide increasingly accurate predictions of student examination assessment marks, although these approaches have focussed upon large student populations and wide ranges of data attributes per student. However, many university modules comprise relatively small student cohorts, with institutional protocols limiting the student attributes available for analysis. It appears that very little research attention has been devoted to this area of analysis and prediction. We describe an experiment conducted on a final-year university module student cohort of 23, where individual student data are limited to lecture/tutorial attendance, virtual learning environment accesses and intermediate assessments. We found potential for predicting individual student interim and final assessment marks in small student cohorts with very limited attributes and that these predictions could be useful to support module leaders in identifying students potentially “at risk.”.Peer reviewe

Enhancing Employee Communication Behaviors for Sensemaking and Sensegiving in Crisis Situations: Strategic Management Approach for Effective Internal Crisis Communication

Purpose The purpose of this paper is to explore the organizational effectiveness of internal crisis communication within the strategic management approach, whether it enhanced voluntary and positive employee communication behaviors (ECBs) for sensemaking and sensegiving. By doing so, this study provides meaningful insight into: new crisis communication theory development that takes a strategic management approach, emphasizing employees’ valuable assets from an organization, and effective crisis communication practice that reduces misalignment with employees and that enhances voluntary and positive ECBs for the organization during a crisis. Design/methodology/approach This study conducted a nationwide survey in the USA among full-time employees (n=544). After dimensionality check through confirmatory factor analysis, this study tested hypothesis and research question by conducting ordinary least squares multiple regression analyses using STATA 13. Findings This study found that strategic internal communication factors, including two-way symmetrical communication and transparent communication, were positive and strong antecedents of ECBs for sensemaking and sensegiving in crisis situations, when controlling for other effects. The post hoc analysis confirmed theses positive and strong associations across different industry areas. Originality/value This study suggests that voluntary and valuable ECBs can be enhanced by listening and responding to employee concerns and interests; encouraging employee participation in crisis communication; and organizational accountability through words, actions and decisions during the crisis. As a theoretical implication, the results of this study indicate the need for crisis communication theories that emphasize employees as valuable assets to an organization

21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success