Labor turnover : its causes, consequences, and control strategy in five selected restaurants in the Miami area

The purpose of this study is to identify and analyze the basic causes of food service employee turnover in five selected restaurants in the Miami area. The withdrawal behavior in this study is treated in terms of controllable turnover, for the purpose of management, learning more about what action to take to solve this problem which has eaten into the fabric of the hospitality industry. The aim is to find out from the food service employees and management view of work for the purpose of identifying the variables which cause an employee to voluntarily leave a job. The objective is therefore, to analyze and describe the problem of labor turnover in these selected restaurants. Such description must precede efforts to arrive at solutions to the problem if these efforts are ever to be more than haphazard and superficial. Sigmund Freud once stated: "The true beginning of scientific activity consists in describing phenomena and only then in proceeding to group, classify and correlate them."1 The nature of the study is basically descriptive survey. Data is collected by the use of management questionnaire, food service employee questionnaire and finally employees job description index. The survey consisted of a series of well defined questions with open and closed endings dealing with employee with employee turnover. As Robert Ferber and P. J. Verdoom state in their book titled Research Method in Economics of Business: "Structured questionnaires, by supplying question formulations in very specific terms as well as the different possible answers are easier for the sample members to answer and also serve to reduce the danger of interviewer bias."2 The answers to the prepared questionnaire by sample members were then recorded. The results of the questionnaire responses were then compiled for presentation and analysis. 1 Julian Simon, Basic Research Methods in Social Science. Random House, New York, 1969, p.53. 2 Robert J. Ferber and P.J. Verdoon, Research Methods in Economics and Business, The McMillan Company, 1962, p. 20 9 .

Independent Projects Environmental Studies 2001: Environmental Auditing Strategy for Sir Wilfred Grenfell College

Overconsumption has caused environmental degradation, while creating a dependence on convenience commodities. It is the disposal of solid waste which will prove problematic in the future with growing world populations requiring resources and the use of the land. Universities, as institutions of higher learning, have an opportunity to reduce their environmental impact through its daily operations. Adopting an environmental management system and creating an environmental policy is the means by which these institutions become sustainable campuses. Stewardship policies are developed for universities, such as Sir Wilfred Grenfell College by analyzing current consumptive practices of the students, Faculty, and staff at the institution, often by way of an environmental audit

Engineering the future! Innovation in learning & teaching:an empirical approach

Learning and teaching approaches to engineering are generally perceived to be difficult and academically challenging. Such challenges are reflected in high levels of student attrition and failure. In addressing this issue, a unique approach to engineering education has been developed by one of the paper authors. This approach, which is suitable for undergraduate and postgraduate levels, brings together pedagogic and engineering epistemologies in an empirically grounded framework. It is underpinned by three distinctive concepts: Relationships, Variety & Alignment. Based upon research, the R + V + A approach to engineering education provides a learning and teaching strategy which in enhancing the student experience increases retention and positively impacts student success. In discussing the emergent findings of a study into the pedagogical value of the approach the paper makes a significant contribution to academic theory and practice in this area.

Communities of practice as professional and organizational development strategy in local public health organizations in Québec, Canada : an evaluation model

Communities of practice (CoPs) are among the professional development strategies most widely used in such fields as management and education. Though the approach has elicited keen interest, knowledge pertaining to its conceptual underpinnings is still limited, thus hindering proper assessment of CoPs' effects and the processes generating the latter. To address this shortcoming, this paper presents a conceptual model that was developed to evaluate an initiative based on a CoP strategy: Health Promotion Laboratories are a professional development intervention that was implemented in local public health organizations in Montreal (Quebec, Canada). The model is based on latest theories on work-group effectiveness and organizational learning and can be usefully adopted by evaluators who are increasingly called upon to illuminate decision-making about CoPs. Ultimately, validation of this conceptual model will help advance knowledge and practice pertaining to CoPs as well as professional and organizational development strategies in public health.

The Effects on Students' Self-Efficacy Beliefs Regarding Their Comprehension of American Literature When Aesthetic Reading and Reader Response Strategy are Implemented

High-stakes testing and accountability have infiltrated the education system in the United States; the top priority for all teachers must be student progress on standardized tests. This has resulted in the predominance of reading for test-taking, (efferent reading), in the English, language arts, and reading classrooms. Authentic uses of print activities, like aesthetic reading, that encourage students to engage individually with a text, have been pushed aside. During a 3-week time period, regular level, English 3/American literature students in a Title I magnet high school, participated in this quasi-experimental study (N = 62). It measured the effects of an intervention of reading American literature texts aesthetically and writing aesthetically-evoked reader responses on students’ self-efficacy beliefs regarding their comprehension of American literature. One trained teacher and the researcher participated in the study: student participants were pre- and post- tested using the Confidence in Reading American Literature Survey which examined their self-efficacy beliefs regarding their comprehension of American literature. Several statistical analyses were performed. The results of the linear regression analyses partially supported a positive relationship between aesthetically-evoked reader responses and students’ self-efficacy beliefs regarding their comprehension of American literature. Additionally, the results of the 2 (sex) x 2 (treatment) ANCOVAs conducted to test group differences in self-efficacy beliefs regarding the comprehension of American literature between treatment and control groups indicated a main effect for treatment (but not sex; nor was there a significant sex x treatment interaction), suggesting the treatment was partially effective in increasing students’ self-efficacy beliefs. Seven of the twelve ANCOVAs indicated a statistically significant increase in the treatment group’s adjusted group mean self-efficacy belief scores as a result of being exposed to the intervention. In six of these seven analyses, increases in self-efficacy beliefs occurred in tasks that required three or more higher-order levels of thinking/learning. The results are discussed in terms of theoretical, empirical and practical significance. Future research is recommended to extend the intervention beyond the narrow confines of a Title I magnet school to settings where the intervention could be tested longitudinally, e. g., honors and gifted students, elementary and middle schools.

Individual differences and personality correlates of navigational performance in the virtual route learning task

Research on the mechanisms and processes underlying navigation has traditionally been limited by the practical problems of setting up and controlling navigation in a real-world setting. Thanks to advances in technology, a growing number of researchers are making use of computer-based virtual environments to draw inferences about real-world navigation. However, little research has been done on factors affecting human–computer interactions in navigation tasks. In this study female students completed a virtual route learning task and filled out a battery of questionnaires, which determined levels of computer experience, wayfinding anxiety, neuroticism, extraversion, psychoticism and immersive tendencies as well as their preference for a route or survey strategy. Scores on personality traits and individual differences were then correlated with the time taken to complete the navigation task, the length of path travelled,the velocity of the virtual walk and the number of errors. Navigation performance was significantly influenced by wayfinding anxiety, psychoticism, involvement and overall immersive tendencies and was improved in those participants who adopted a survey strategy. In other words, navigation in virtual environments is effected not only by navigational strategy, but also an individual’s personality, and other factors such as their level of experience with computers. An understanding of these differences is crucial before performance in virtual environments can be generalised to real-world navigational performance.

Territorial administrative reorganization as a strategy for optimizing the structure of local government

Through the tough economic and social situation and the lack of values that has been experienced in recent years, local governments especially in Greece, Portugal, Italy and Spain have been forced to strongly consider the structure and size of their public sector. Despite some initiative to reduce the number of municipalities and provinces, little substantive progress has been made in improving the management of the local public Administration during this crisis. In this study a territorial administrative reorganization is proposed as a strategy to optimize the structure of local government, analyzing the Spanish situation in general, and an autonomous community in particular.

Completion strategy or emphasis manipulation? Task support for teaching information problem solving

While most students seem to solve information problems effortlessly, research shows that the cognitive skills for effective information problem solving are often underdeveloped. Students manage to find information and formulate solutions, but the quality of their process and product is questionable. It is therefore important to develop instruction for fostering these skills. In this research, a 2-h online intervention was presented to first-year university students with the goal to improve their information problem solving skills while investigating effects of different types of built-in task support. A training design containing completion tasks was compared to a design using emphasis manipulation. A third variant of the training combined both approaches. In two experiments, these conditions were compared to a control condition receiving conventional tasks without built-in task support. Results of both experiments show that students' information problem solving skills are underdeveloped, which underlines the necessity for formal training. While the intervention improved students’ skills, no differences were found between conditions. The authors hypothesize that the effective presentation of supportive information in the form of a modeling example at the start of the training caused a strong learning effect, which masked effects of task support. Limitations and directions for future research are presented.

A satisfação e a intenção de continuidade de uso em servços de e-learning:validação empírica de um modelo aplicado no serviço público

The present study aims to investigate the constructs of Technological Readiness Index (TRI) and the Expectancy Disconfirmation Theory (EDT) as determinants of satisfaction and continuance intention use in e-learning services. Is proposed a theoretical model that seeks to measure the phenomenon suited to the needs of public organizations that offer distance learning course with the use of virtual platforms for employees. The research was conducted from a quantitative analytical approach, via online survey in a sample of 343 employees of 2 public organizations in RN who have had e-learning experience. The strategy of data analysis used multivariate analysis techniques, including structural equation modeling (SEM), operationalized by AMOS© software. The results showed that quality, quality disconfirmation, value and value disconfirmation positively impact on satisfaction, as well as disconfirmation usability, innovativeness and optimism. Likewise, satisfaction proved to be decisive for the purpose of continuance intention use. In addition, technological readiness and performance are strongly related. Based on the structural model found by the study, public organizations can implement e-learning services for employees focusing on improving learning and improving skills practiced in the organizational environment

Student attitudes towards cooperative learning in education

Abstract: Active or participatory learning by the student within a classroom environment has been fairly recently recognized as an effective, efficient, and superior instructional technique yet few teachers in higher education have adopted this pedagogical strategy. This is especially true in Science where teachers primarily lecture to passively seated students while using static visual aids or multimedia projections. Teachers generally teach as they were taught and lecture formats have been the norm. Although student-learning theories as well as student learning styles, abilities, and understanding strategies have changed, traditional teaching techniques have not evolved past the “chalk and talk” instructional strategy. This research looked into student’s perceptions of cooperative learning or team-based active learning in order to gain insight and some understanding as to how students felt about this learning technique. Student’s attitudes were then compared to student grades to detennine whether cooperative learning impeded or ameliorated academic performance. The results revealed significant differences measured in all the survey questions pertaining to perception or attitudes. As a result of the cooperative learning activities, respondents indicated more agreement to the survey questions pertaining to the benefits of cooperative learning. The experimental group exposed to cooperative learning thus experienced more positive attitudes and perceptions than the groups exposed only to a lecture-based teaching and learning format. Each of the hypotheses tested demonstrated that students had more positive attitudes towards cooperative learning strategies. Recommendations as to future work were presented in order to gain a greater understanding into both student and teacher attitudes towards the cooperative learning model.||Résumé: Lapprentissage actif ou préparatoire par létudiant au sein d’une classe a été reconnu assez récemment comme une technique d’enseignement plus efficace. Cependant, peu d’enseignants ont adopté cette stratégie pedagogique pour l'éducation post-secondaire. Ceci est particulièrement le cas dans le domaine des sciences où les enseignants font surtout usage de cours magistraux avec des étudiants passifs tout en utilisant des aides visuelles statiques ou des projections multimédias. Les professeurs enseignent generalement comme on leur a eux-même enseigné et les cours magistraux ont été la norme par le passé. Les techniques traditionnelles d'enseignernent n'ont pas évolué au-delà de la craie et du tableau noir et ce même si les théories sur l’apprentissage par les étudiants ont changé, tout comme les styles, les habiletés et les stratégies de compréhension d’apprentissage des étudiants. Cette recherche se penche sur les perceptions des étudiants au sujet de l'apprentissage coopératif ou de l'apprentissage actif par équipe de telle sorte qu'on puisse avoir un aperçu et une certaine compréhension de comment les étudiants se sentent par rapport à ces techniques d'apprentissage. Les attitudes des étudiants ont par la suite été comparées aux notes de ceux-ci pour déterminer si l'apprentissage coopératif avait nui ou au contraire amélioré leurs performances académiques. Les résultats obtenus dans l'étude d'ensemble révèlent des différences significatives dans toutes les questions ayant trait à la perception et aux attitudes.

Cultural and linguistic context and lexical inferencing in English language learning: A case study

In an increasingly multilingual world, English language has kept a marked predominance as a global language. In many countries, English is the primary choice for foreign language learning. There is a long history of research in English language learning. The same applies for research in reading. A main interest since the 1970s has been the reading strategy defined as inferencing or guessing the meaning of unknown words from context. Inferencing has ben widely researched, however, the results and conclusions seem to be mixed. While some agree that inferencing is a useful strategy, others doubt its usefulness. Nevertheless, most of the research seem to agree that the cultural background affects comprehension and inferencing. While most of these studies have been done with texts and contexts created by the researches, little has been done using natural prose. The present study will attempt to further clarify the process of inferencing and the effects of the text’s cultural context and the linguistic background of the reader using a text that has not been created by the researcher. The participants of the study are 40 international students from Turku, Finland. Their linguistic background was obtained through a questionnaire and proved to be diverse. Think aloud protocols were performed to investigate their inferencing process and find connections between their inferences, comments, the text, and their linguistic background. The results show that: some inferences were made based on the participants’ world knowledge, experience, other languages, and English language knowledge; other inferences and comments were made based on the text, its use of language and vocabulary, and few cues provided by the author. The results from the present study and previous research seem to show that: 1) linguistic background is a source of information for inferencing but is not a major source; 2) the cultural context of the text affected the inferences made by the participants according to their closeness or distance from it.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

EXAMINING THE IMPLEMENTATION CHALLENGES OF PROJECT-BASED LEARNING: A CASE STUDY

Title of Thesis: Thesis directed by: ABSTRACT EXAMINING THE IMPLEMENTATION CHALLENGES OF PROJECT-BASED LEARNING: A CASE STUDY Stefan Frederick Brooks, Master of Education, 2016 Professor and Chair Francine Hultgren Teaching and Learning, Policy and Leadership Department Project-based learning (PjBL) is a common instructional strategy to consider for educators, scholars, and advocates who focus on education reform. Previous research on PjBL has focused on its effectiveness, but a limited amount of research exists on the implementation challenges. This exploratory case study examines an attempted project- based learning implementation in one chemistry classroom at a private school that fully supports PjBL for most subjects with limited use in mathematics. During the course of the study, the teacher used a modified version of PjBL. Specifically, he implemented some of the elements of PjBL, such as a driving theme and a public presentation of projects, with the support of traditional instructional methods due to the context of the classroom. The findings of this study emphasize the teacher’s experience with implementing some of the PjBL components and how the inherent implementation challenges affected his practice.

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

“English Language Learning Strategies used by the Foreign Language Department Seminar Students Year 2006 with Either Above Eight Grade or Below it Obtained in Their Studies in the Basic Skills Area.

Nowadays language communication plays an important role in the world. For the technological explosion in the 20th century, the electronic mass media collapsed space and time barriers in human communication, enabling people to interact and live on a global scale. In this sense, the earth has been turned into a village by the electronic mass media. It not only changes the distance between countries, societies, but also shortens it between people. It means that the technological advancement makes the earth become a village. Since the distance between people is shortened, language communication becomes more important than before. To enhance language abilities, people can apply many different types of language learning strategies according to the learning styles that they have in order to learn the target language. In the Foreign Language Department of University of El Salvador Seminar students year 2006 apply different language learning strategies which make some of them get a grade either above eight or below it. To understand learning strategies, people can go back to basic term, strategy. This word comes from the ancient Greek term strategia meaning generalship or the art of war. A different, but related, word is tactics, which are tools to achieve the success of strategies. The two expressions share some basic implied characteristics: planning, competition, conscious manipulation, and movement toward a goal.