Search results

Teacher pay in Arkansas public schools varies widely from district to district across the state. This pay discrepancy is driven by both the funds available to a district and by how these funds are allocated. A standard per student budget is given to districts across the state, but this budget can be supplemented by additional property taxes collected on property within a district. This leaves districts with more highly valued property at an advantage. Districts are free to allocate their budget for teacher pay as they see fit, with constraints on number of students per teacher and minimum teacher salary.

Using public data available through the Arkansas Department of Education, this research investigates what variables affect student performance in Arkansas public schools using feature selection and predictive modelling and determine the cost-effectiveness associated with changing possible decision variables in terms of improving student performance.

It was found that the most cost-effective ways for districts to increase student performance are to (1) increase average teacher salary and (2) increase average years of teacher experience. This result is validated by education research, as both of these methods have been identified in literature as being effective ways to increase teacher quality and increase student performance. Furthermore, districts should consider increasing student–teacher ratio and applying the resulting savings toward teacher salaries.

This methodology gives a fresh perspective on the most cost-effective use of resources in publicly funded schools.

All educators will inevitably face unwanted student behavior that they need to address. A ubiquitous response to unwanted behavior is exclusionary discipline practices, including time-out, office discipline referrals, and suspensions. However, extensive research has demonstrated that these practices are associated with negative outcomes, including increased likelihood of further unwanted behavior, decreased achievement, and racial/ethnic discipline disparities. In this chapter, we provide a preventative alternative to exclusionary practices, school-wide positive behavioral interventions and supports (SWPBIS). SWPBIS is an evidence-based framework for implementing systems to reduce unwanted behavior and increase prosocial behavior, decreasing the need for exclusionary practices.

Fashion advertising as a contemporary ideological form has the power to deliver semiotic messages, which frames individuals' perception. Promoting perfection in consumer culture has resulted in the creation of unrealistic self-image and negative effects that led to psychological illnesses and pathological behaviours. The purpose of this paper is to investigate the ideology behind the contemporary fashion advertising that embraces imperfection and is linked to consumer subjective well-being.

Nine fashion ads were selected based on the WGSN consumer report Embracing Imperfection. A sample of images was analysed applying semiotic analysis combined with the criteria of content analysis.

The results supported the notion that some contemporary fashion communication brands are challenging the conventional idea of perfection. Five themes were identified as the characteristics that bridged the visual surface of advertising with its hidden ideologies of imperfection. By supporting “anonymity”, fashion brand communication is against excessive self-focus and helps reducing anxiety due to being imperfect. By promoting “rawness”, it encourages authenticity and uniqueness. “Banality” rejects materialism and promotes the beauty of the boring day. “Ugliness” advocates for removal of the single standards and celebrate individual differences, and “spontaneity” is interchangeable for humanity, freedom, openness and acceptance of self.

This study is among the few attempts to conduct semiotic analysis of fashion advertising images aiming to identify the visual components and ideologies that could potentially be linked to subjective well-being in fashion communication.

The case is based on a real $25 million project at a major U.S.-based computer manufacturer. For confidentiality reasons the company has been disguised as A&D High Tech. The Web-based online ordering system project is required by sales and marketing for the fall holiday season. If the project misses this window, the firm will lose substantial market share to competitors. Part (B) takes place three months into the original project plan. The project manager has just been fired and the management challenge is to find out what is wrong with the project and recommend fixes. In addition, the scope of the project has changed: the VP of marketing has an additional promotional bundle requirement. A&D High Tech (A) examines how to create and analyze a project plan in Microsoft Project. In order to make the case manageable for students we reduced the size of the project, and corresponding number of resources, to approximately $1 million, but retained all of the features of the original project. Part (B) gives actual work done on each task three months into the project. Students must answer the management questions: Can the project be fixed and completed in time for the holiday season? Can the additional requirements be incorporated, and if so, what is the best approach? In order to answer these questions, earned value data can be extracted from Microsoft Project and analyzed. These data provide important insights into the root cause of problems with the project. The next step is to reduce the scope of the project and reassign resources. However, one must be aware that indiscriminately adding people can slow a project down, not speed it up. Finally, the additional promotional bundle requirement from the VP of marketing provides an important outsourcing management discussion. The case can also be taught using other project management software tools, such as Primavera.

The case teaches students how to analyze a project in trouble using Microsoft Project (or other project management software tool). More important, the case teaches prospective executives how to manage a project in trouble by first accurately diagnosing the problems, then reducing scope where necessary, and finally replanning the project with reallocated resources. In addition, students will learn the tradeoffs of outsourcing to highly specialized professionals vs. average contractors.

The case is based on a real $25 million project at a major U.S.-based computer manufacturer. For confidentiality reasons the company has been disguised as A&D High Tech. The Web-based online ordering system project is required by sales and marketing for the fall holiday season. If the project misses this window, the firm will lose substantial market share to competitors. The A&D High Tech case examines how to create and analyze a project plan in Microsoft Project. Specifically, data is given to build the project plan step-by-step and then analyze the plan using the Microsoft project management tool. In order to make the case manageable for students we reduced the size of the project, and corresponding number of resources, to approximately $1 million, but retained all of the features of the original project. The project plan that students construct from the data given in the case is fraught with risks, and students must apply risk management techniques to diagnose the plan. Ultimately, students must answer the management question: Will the project be completed for the holiday shopping season? This case is the first in a series; the second is the case entitled “A&D High Tech (B): Managing Scope Change.” The case can also be taught using other project management software tools, such as Primavera.

The case teaches students how to build a project plan in Microsoft Project (or other project management software tools). More important, the case teaches prospective executives how to analyze a project plan and identify risks of the plan, and define strategies to mitigate these risks. Students learn that in the planning stage of any project the risks are highest, but this is the best opportunity for proactive management intervention.

Mobile fitness apps (MFAs) are increasingly popular for people to promote physical activity (PA) and further enhance health status via behavioral change techniques (BCTs), but the phenomenon of users abandoning MFAs is still common. For improving users' PA and decreasing dropout rates of MFAs, this study intends to gain insights into the effects of major BCTs-based incentive factors on users' PA under MFAs context and the gender differences in their effects.

Based on self-determination theory, three major incentive factors were chosen from the perspective of self-peer-platform incentives, i.e. self-monitoring (SM), social support (SS) and platform rewards (PR). A dataset of 4,530 users from a popular mobile fitness app was collected and was analyzed using fixed effects models.

The results show that all three types of incentive factors are positively associated with users' PA. The estimated effect sizes can be ordered as: SM > PR > SS. Moreover, social support has a stronger positive impact on PA of females than males, whereas platform rewards have a weaker positive effect on PA of females than males. In addition, the results also indicate there are no significant gender differences in the effect of self-monitoring.

There is insufficient research on systematically examining the effects of different types of incentive factors of MFAs on users' PA in one study. This study extends the current understanding of incentive factors by simultaneously examining different incentive factors and the role of gender. The findings can also provide insightful guidance for the design of MFAs.

This paper suggests a novel methodology for building robust information processing systems based on wavelets and artificial neural networks (ANN) to be applied either in decision‐making tasks based on image information or in signal prediction and modeling tasks. The efficiency of such systems is increased when they simultaneously use input information in its original and wavelet transformed form, invoking ANN technology to fuse the two different types of input. A quality control decision‐making system as well as a signal prediction system have been developed to illustrate the validity of our approach. The first one offers a solution to the problem of defect recognition for quality control systems. The second application improves the quality of time series prediction and signal modeling in the domain of NMR. The accuracy obtained shows that the proposed methodology deserves the attention of designers of effective information processing systems.