Optimizing energy efficiency and minimizing environmental impact in buildings depends critically on managing cooling requirements. The application of Support Vector Regression Model to the prediction of cooling load is explored in this study. It enhances these models with two cutting-edge optimization methods: Crystal Structure Algorithm and Reptile Search Algorithm. SVR, a machine learning technique renowned for its flexibility and interpretability, is used in this study to capture complex relationships between various building parameters and cooling load. The dependent variable, CL, is analyzed about various factors, including relative compactness, wall area, roof area, orientation, surface area, overall height, glazing area, and glazing area distribution. SVR proves to be adept at understanding non-linear relationships, making it suitable for these kinds of applications. The modelling process gains intelligence from the combination of RSA and CSA optimizers. Building management systems stand to benefit greatly from this advancement, which will allow for more precise control over cooling systems and efficient use of energy. Moreover, the hybrid SVCS model, with its minimal RMSE value of 0.747 and remarkable R2 value of 0.994, consistently yields reliable results for CL prediction. This study advances the field of energy-efficient building management by demonstrating how machine learning methods and clever optimization algorithms can be used to predict cooling loads accurately.

Predicting time series, especially those originating from chaotic and nonlinear dynamic systems, is a critical research area with broad applications across various fields. Neural networks and fuzzy systems have emerged as leading methods for forecasting chaotic time series. This study introduces an improved adaptive neural-fuzzy inference system (ANFIS) specifically tailored for forecasting chaotic time series. Unlike traditional ANFIS models, which are primarily designed for static problems, this enhanced version incorporates self-feedback relationships from previous outputs to capture the time dependencies inherent in dynamic systems. Additionally, a hybrid approach combining the Imperialist Competitive Optimization Algorithm (ICA) and Least Squares Estimation (LSE) is employed to train the neural-fuzzy system and update its parameters. This method circumvents challenges associated with training gradient-based algorithms. The proposed technique is applied to predict and model multiple nonlinear and chaotic time series from real-world scenarios. Comparative analyses with recent works demonstrate the superior performance of the proposed method, particularly in terms of the prediction total error criterion for time series modeling and forecasting. These results highlight the effectiveness of incorporating self-feedback relationships and utilizing the CCA-LSE hybrid approach in enhancing the predictive capabilities of adaptive neural-fuzzy inference systems for chaotic time series.

This chapter presents a case study analysis of a graduate program that moved from initial design to effective implementation of student-centered online instruction. The authors describe their experiences designing, implementing, and evaluating an online M.Ed. degree program in a college of education—the first fully online degree program at a large, Midwestern, regional institution of higher learning. The design and approval process took almost four years, including both internal and external approvals. Initially implemented in 2011, the authors gathered three years of follow-up data about the program and evaluated its success using a variety of factors, including course- and program-level data. Program design, development, implementation, and evaluation are all addressed in this case study.

With continued growth in online courses and programs" in higher education a pressing need exists to evaluate their perceived quality and effectiveness. Evaluation criteria - course evaluations, student surveys and retention data - from previous online program evaluations were used in this study. An illuminative evaluation using descriptive and scientific analysis was undertaken for a graduate degree program in educational technology. Course and program-level data were analyzed to compare quality for two programs - an existing hybrid and new online. Analysis of student enrollments, course evaluations, survey results, retention, and time to completion reveal similar experiences reported from students in both programs. Results suggest that a majority of students were satisfied with their graduate experience and view those experiences as worthwhile. This illuminative evaluation provides evidence that online graduate programs are comparable and can satisfy stakeholders' expectations while maintaining high levels of quality. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.

This article reports on a survey of an exemplary group of teachers who use (or plan to use) technology in their classrooms. The 93 (out of 118) teachers included in this study were selected to receive a state technology grant through a competition that asked them to offer project ideas for technology integration. Each of these teachers has, at the very least, proposed an innovative project that exemplifies effective and appropriate uses of technology in education. In this article, we attempt to paint a comprehensive portrait of this group in terms of their knowledge, skills, attitudes, behaviors, and beliefs. We hope this article will serve two purposes: (1) to provide some beginning baseline information to help conceptualize what teachers should know to take advantage of modern technologies; and (2) to help develop professional development programs in technology that are more connected to practice and reality.

This article explores the implementation of various K-12 one-to-one computing initiatives to determine if patterns exist. These initiatives are funded in times of limited resources and constitute a serious investment in technology for the schools and districts adopting them. The goals of this study were to understand how and why one-to-one computing initiatives are being implemented, how these initiatives are funded and supported, and expectations or assumptions of stakeholders that are driving adoption of this type of technology. The results suggest that these school districts, and those like them, will face many challenges-some financial, some technical, and some procedural-as they work to integrate technology into instruction and assessment. Common themes or challenges identified from this work, and linked with previous research, include leadership and vision, funding, teacher professional development, and project evaluation.

As instructors adopt web‐based learning environments, they must consider how students’ evaluations of learning reflect the overall quality of instruction. Traditional course evaluations are used for faculty retention, tenure and promotion decisions, but also provide instructors with valuable information on the quality of their instruction. This study looks at response rates and compares instructional quality, using student course evaluations along with additional data from online and face‐to‐face graduate education courses, to evaluate the effectiveness of instruction. A statistical analysis of students’ course evaluations showed no significant difference in instructional quality based on the format used. Together with comparisons of student work, these results provide additional evidence in support of the finding of no significant difference between formats in the area of instructional quality.

As more and more higher education programs are offered using Web-based learning environments, instructors need to better understand the implications of using BlackBoard or similar tools to maximize student participation and facilitate learning through rich, thoughtful discussions. This study explores student participation in electronic discourse (e-discourse) in several graduate-level courses in an educational technology program, using discourse analysis methods to help understand online students’ participation patterns. The instructor used discursive moves in the threaded discussions in these classes to stimulate student participation and improve the overall quality of participation with the hopes of increasing learning. The results are promising with regard to the potential of Web-based environments to challenge students and promote conceptual learning through discussion.