A case study of the Ellison model's use of mentoring as an approach toward inclusive community building

The Ellison Executive Mentoring Inclusive Community Building (ICB) Model is a paradigm for initiating and implementing projects utilizing executives and professionals from a variety of fields and industries, university students, and pre-college students. The model emphasizes adherence to ethical values and promotes inclusiveness in community development. It is a hierarchical model in which actors in each succeeding level of operation serve as mentors to the next. Through a three-step process—content, process, and product—participants must be trained with this mentoring and apprenticeship paradigm in conflict resolution, and they receive sensitivity and diversity training through an interactive and dramatic exposition. ^ The content phase introduces participants to the model's philosophy, ethics, values and methods of operation. The process used to teach and reinforce its precepts is the mentoring and apprenticeship activities and projects in which the participants engage and whose end product demonstrates their knowledge and understanding of the model's concepts. This study sought to ascertain from the participants' perspectives whether the model's mentoring approach is an effective means of fostering inclusiveness, based upon their own experiences in using it. The research utilized a qualitative approach and included data from field observations, individual and group interviews, and written accounts of participants' attitudes. ^ Participants complete ICB projects utilizing The Ellison Model as a method of development and implementation. They generally perceive that the model is a viable tool for dealing with diversity issues whether at work, at school, or at home. The projects are also instructional in that whether participants are mentored or serve as apprentices, they gain useful skills and knowledge about their careers. Since the model is relatively new, there is ample room for research in a variety of areas including organizational studies to determine its effectiveness in combating problems related to various kinds of discrimination. ^

The efficacy of the Ellison model as a retention initiative for first semester freshmen

The freshman year is the most critical year of matriculation for students in higher education. One in four freshman students drops out of higher education after the first year. In fact, the first two to six weeks of college represent a very critical transition period when students make the decision to persist or depart from the institution. Many students leave because they are unable to make a connection with the institution. Retention is often profoundly affected by student involvement in the academic environment, satisfaction with the campus climate and the institution's response to diversity. Therefore, the purpose of this study was to examine and evaluate an effective institutional response that promotes freshman retention and academic success. The tenets (diversity training, conflict management, and community building) of a mentoring model were applied to the freshman experience seminar class (experimental group) as a pedagogical method of instruction to determine its efficacy as a retention initiative when compared with the traditional freshman experience seminar class (comparison group). ^ The quantitative study employed a quasi-experimental research design based on Astin's (1993) I-E-O model. The model examined the relationships between the characteristics students bring with them to college, called inputs, their experiences in the environment during college, and the outcomes students achieved during matriculation. Fifty-two students enrolled in the freshman seminar class participated in the study. ^ Demographic data and input variables between groups were analyzed using chi-square, t-tests and multivariate analyses. Overall, students in the experimental group had significantly higher satisfaction (campus climate) scores than the comparison group. An analysis of the students' willingness to interact with others from diverse groups indicated a significant difference between groups, with the experimental group scoring higher than the comparison group. Students in the experimental group were significantly more involved in campus activities than students in the comparison group. No significant differences were found between groups relative to the mean grade point average and re-enrollment for fall semester 2001. ^ While the mentoring model did not directly affect re-enrollment of students, the model did promote student satisfaction with the institution, an appreciation for diversity of contact and it encouraged involvement in the campus community. These are all essential outcomes of a quality retention program. ^

An empirical study of Kirkpatrick's evaluation model in the hospitality industry

This study examined Kirkpatrick’s training evaluation model (Kirkpatrick & Kirkpatrick, 2006) by assessing a sales training program conducted at an organization in the hospitality industry. The study assessed the employees’ training outcomes of knowledge and skills, job performance, and the impact of the training upon the organization. By assessing these training outcomes and their relationships, the study demonstrated whether Kirkpatrick’s theories are supported and the lower evaluation levels can be used to predict organizational impact. The population for this study was a group of reservations sales agents from a leading luxury hotel chain’s reservations center. During the study period from January 2005 to May 2007, there were 335 reservations sales agents employed in this Global Reservations Center (GRC). The number of reservations sales agents who had completed a sales training program/intervention during this period and had data available for at least two months pre and post training composed the sample for this study. The number of agents was 69 ( N = 69). Four hypotheses were tested through paired-samples t tests, correlation, and hierarchical regression analytic procedures. Results from the analyses supported the hypotheses in this study. The significant improvement in the call score supported hypothesis one that the reservations sales agents who completed the training improved their knowledge of content and required skills in handling calls (Level 2). Hypothesis two was accepted in part as there was significant improvement in call conversion, but there was no significant improvement of time usage. The significant improvement in the sales per call supported hypothesis three that the reservations agents who completed the training contributed to increased organizational impact (Level 4), i.e., made significantly more sales. Last, findings supported hypothesis four that Level 2 and Level 3 variables can be used for predicting Level 4 organizational impact. The findings supported the theory of Kirkpatrick’s evaluation model that in order to expect organizational results, a positive change in behavior (job performance) and learning must occur. The examinations of Levels 2 and 3 helped to partially explain and predict Level 4 results.

Leadership Styles, Behaviors, and Outcomes of Athletic Training Education Program Directors

Context: With the increase in athletic training education programs, the demand for a highly qualified faculty member to serve as program director (PD) and fill the multiple roles and responsibilities of the position has increased. A successful PD must possess leadership skills crucial for the evolving academic environment. Research suggests that educational leaders must provide both transactional and transformational leadership if athletic trainers are to secure a legitimate place as healthcare providers. Objective: To describe the leadership styles and behaviors of athletic training education PDs and to describe the associations between leadership style, behavior, outcome, and experience. Design: We will utilize a survey design to identify the leadership styles, behaviors, outcomes, and experiences of athletic training education PDs. Setting: On-line questionnaire. Participants: The population of this study will be limited to the academic PDs of the 360 accredited entry-level athletic training education programs in the United States. Intervention: The investigation will utilize the Multifactor Leadership Questionnaire (MLQ). The MLQ is a validated tool composed of 45 items that identify and measure key leadership and effectiveness behaviors shown to be strongly linked with both individual and organizational success. In addition to the leader, it is recommended that all persons working above, below, and directly at the same organizational level as the leader rate the leader. Raters evaluate how frequently, or to what degree, they have observed the leader engage in 32 specific behaviors. Main Outcome Measure(s): Statistical analysis will be utilized to describe the associations between leadership styles, behaviors, outcomes, and experiences.

The impact of cross -training on team processes and performance

Recently, researchers have begun to investigate the benefits of cross-training teams. It has been hypothesized that cross-training should help improve team processes and team performance (Cannon-Bowers, Salas, Blickensderfer, & Bowers, 1998; Travillian, Volpe, Cannon-Bowers, & Salas, 1993). The current study extends previous research by examining different methods of cross-training (positional clarification and positional modeling) and the impact they have on team process and performance in both more complex and less complex environments. One hundred and thirty-five psychology undergraduates were placed in 45 three-person teams. Participants were randomly assigned to roles within teams. Teams were asked to “fly” a series of missions on a PC-based helicopter flight simulation. ^ Results suggest that cross-training improves team mental model accuracy and similarity. Accuracy of team mental models was found to be a predictor of coordination quality, but similarity of team mental models was not. Neither similarity nor accuracy of team mental models was found to be a predictor of backup behavior (quality and quantity). As expected, both team coordination (quality) and backup behaviors (quantity and quality) were significant predictors of overall team performance. Contrary to expectations, there was no interaction between cross-training and environmental complexity. Results from this study further cross-training research by establishing positional clarification and positional modeling as training strategies for improving team performance. ^

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as ƒ-test is performed during each node's split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

A Case Study of the Ellison Model's Use of Mentoring as an Approach Toward Inclusive Community Building

The Ellison Executive Mentoring Inclusive Community Building (ICB) Model is a paradigm for initiating and implementing projects utilizing executives and professionals from a variety of fields and industries, university students, and pre-college students. The model emphasizes adherence to ethical values and promotes inclusiveness in community development. It is a hierarchical model in which actors in each succeeding level of operation serve as mentors to the next. Through a three-step process--content, process, and product--participants must be trained with this mentoring and apprenticeship paradigm in conflict resolution, and they receive sensitivitiy and diversity training, through an interactive and dramatic exposition. The content phase introduces participants to the model's philosophy, ethics, values and methods of operation. The process used to teach and reinforce its precepts is the mentoring and apprenticeship activities and projects in which the participants engage and whose end product demontrates their knowledge and understanding of the model's concepts. This study sought to ascertain from the participants' perspectives whether the model's mentoring approach is an effective means of fostering inclusiveness, based upon their own experiences in using it. The research utilized a qualitative approach and included data from field observations, individual and group interviews, and written accounts of participants' attitudes. Participants complete ICB projects utilizing the Ellison Model as a method of development and implementation. They generally perceive that the model is a viable tool for dealing with diversity issues whether at work, at school, or at home. The projects are also instructional in that whether participants are mentored or seve as apprentices, they gain useful skills and knowledge about their careers. Since the model is relatively new, there is ample room for research in a variety of areas including organizational studies to dertmine its effectiveness in combating problems related to various kinds of discrimination.

Personality and Performance: Assessing the Mediating Role of Mental Model Formation in the Personality-Performance Relationship

Personality has long been linked to performance. Evolutions in this relationship have brought forward new questions regarding the true nature of how personality impacts performance. Both direct and indirect relationships have been proven significant. This study further investigated potential indirect relationships by including a mediating variable, mental model formation, in the personality-performance relationship. Undergraduate students were assessed in a 6-week period, Time 1 - Time 2 experiment. Conceptualizations of personality included measures of the Big 5 model and Self-efficacy, with performance measured by content quiz and overall course scores. Findings showed that the Big 5 personality traits, extraversion and agreeableness, positively and significantly impacted commonality with the instructor’s mental model. However, commonality with the instructor’s mental model did not impact performance. In comparison, commonality with an expert mental model positively and significantly impacted performance for both the content quiz and overall course score. Furthermore, similarity with an expert mental model positively and significantly impacted overall course performance. Hypothesized full mediation of mental model formation for the personality-performance relationship was not supported due to a lack of direct effect relationships required for mediation. However, a revised conceptualization of results emerged. Findings from the current study point to the novel and unique role mental models play in the personality-performance relationship. While personality traits do impact mental model formation, accuracy in the mental models formed is critical to performance.

GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.