Hasil untuk "Education"

N. Dabbagh, A. Kitsantas

P. Grossman, Christa Compton, D. Igra et al.

Background/Context This study investigates how people are prepared for professional practice in the clergy, teaching, and clinical psychology. The work is located within research on professional education, and research on the teaching and learning of practice. Purpose/Objective/Research Question/Focus of Study The purpose of the study is to develop a framework to describe and analyze the teaching of practice in professional education programs, specifically preparation for relational practices. Setting The research took place in eight professional education programs located in seminaries, schools of professional psychology, and universities across the country. Population/Participants/Subjects Our research participants include faculty members, students, and administrators at each of these eight programs. Research Design This research is a comparative case study of professional education across three different professions—the clergy, clinical psychology, and teaching. Our data include qualitative case studies of eight preparation programs: two teacher education programs, three seminaries, and three clinical psychology programs. Data Collection and Analysis For each institution, we conducted site visits that included interviews with administrators, faculty, and staff; observations of multiple classes and field-work; and focus groups with students who were either at the midpoint or at the end of their programs. Conclusions/Recommendations We have identified three key concepts for understanding the pedagogies of practice in professional education: representations, decomposition, and approximations of practice. Representations of practice comprise the different ways that practice is represented in professional education and what these various representations make visible to novices. Decomposition of practice involves breaking down practice into its constituent parts for the purposes of teaching and learning. Approximations of practice refer to opportunities to engage in practices that are more or less proximal to the practices of a profession. In this article, we define and provide examples of the representation, decomposition, and approximation of practice from our study of professional education in the clergy, clinical psychology, and teaching. We conclude that, in the program we studied, prospective teachers have fewer opportunities to engage in approximations that focus on contingent, interactive practice than do novices in the other two professions we studied.

G. Hein

V. Souitaris, Stefania Zerbinati, A. Al-Laham

Ralph T. Putnam, H. Borko

D. D Brown, Gordon A. Ewy, Loryn Feinberg et al.

M. Eraut

T. Bodenheimer, K. Lorig, H. Holman et al.

R. Shavelson, J. Hubner, G. Stanton

Historically, education goals have tended to fluctuate from emphasis solely on cognitive outcomes to major concern with social and affective ones. The emphasis on achievement and the "cult of efficiency" (Callahan, 1962) early in this century was followed by a shift in the 1930's to the comprehensive high school with its social and affective concerns (cf. the Eight Year Study, Aikin, 1942). Then Sputnik initiated a rapid and dramatic reemphasis on cognitive outcomes (Bruner, 1960) from which the current trend seems to be moving in its emphasis on "humanistic" aspects of education. The sharp increase in the number of studies on self-concept is one reflection of the reemphasis on noncognitive outcomes of education. (For references to current educational studies, see reviews by Coller, 1971; Purkey, 1970; Yamamoto, 1972; Zirkel, 1971.) Another symptom of this shift has taken the form of increased concern with enhancing the child's self-concept, espe-

N. Cross

D. Gruenewald

J. Knight

James R. Rest

Yvonne Lai

In the past two decades, teaching and outreach have come to hold an expected place in more missions of mathematics departments and organizations. Still, there is more to do as a mathematical sciences community for mathematics education and mathematics educators to be fully included. This essay, published in the AWM Newsletter, demonstrates the prevalence of mathematics education in mathematics departments, and then discusses the variously supportive and unsupportive climates that mathematics educators experience in mathematics departments. The essay describes fractious, fragile, and fertile environments in mathematics departments.

E. Becker, J. Wünsche, J. M. Veith et al.

Providing effective, personalized support is critical for helping students overcome conceptual difficulties in physics. However, established scaffolding methods, such as structured tiered support, are often too resource-intensive for widespread implementation. Therefore, this study, investigates whether an easily adaptable, custom-configured AI chatbot can offer comparable affective benefits and cognitive relief. We conducted a quasi-experimental field study with 273 ninth-grade students in Germany. Classes were randomly assigned to solve a buoyancy problem using one of three conditions: an AI chatbot, a tiered support system, or traditional textbook-style explanations. We measured intrinsic and extraneous cognitive load and affective outcomes (enjoyment, hope, hopelessness, self-efficacy, situational interest) via research-validated questionnaires. Results revealed that both interactive support systems -- the custom-configured AI chatbot and tiered hints -- were significantly more effective than the textual support in reducing students' intrinsic and extraneous cognitive load. Furthermore, the AI chatbot yielded the most comprehensive affective benefits, demonstrating significant improvements across all measured affective dimensions, when compared to the textual support. While the chatbot consistently trended more positively than the tiered hints on affective measures, these differences were not statistically significant. These findings suggest that while structured guidance is key to managing cognitive load, the interactive and social nature of AI chatbots holds unique potential for simultaneously fostering positive affective experiences, marking a promising direction for developing effective and holistic learning support tools in physics education.

Emma Harvey, Allison Koenecke, Rene F. Kizilcec

Education technologies (edtech) are increasingly incorporating new features built on large language models (LLMs), with the goals of enriching the processes of teaching and learning and ultimately improving learning outcomes. However, the potential downstream impacts of LLM-based edtech remain understudied. Prior attempts to map the risks of LLMs have not been tailored to education specifically, even though it is a unique domain in many respects: from its population (students are often children, who can be especially impacted by technology) to its goals (providing the correct answer may be less important for learners than understanding how to arrive at an answer) to its implications for higher-order skills that generalize across contexts (e.g., critical thinking and collaboration). We conducted semi-structured interviews with six edtech providers representing leaders in the K-12 space, as well as a diverse group of 23 educators with varying levels of experience with LLM-based edtech. Through a thematic analysis, we explored how each group is anticipating, observing, and accounting for potential harms from LLMs in education. We find that, while edtech providers focus primarily on mitigating technical harms, i.e., those that can be measured based solely on LLM outputs themselves, educators are more concerned about harms that result from the broader impacts of LLMs, i.e., those that require observation of interactions between students, educators, school systems, and edtech to measure. Overall, we (1) develop an education-specific overview of potential harms from LLMs, (2) highlight gaps between conceptions of harm by edtech providers and those by educators, and (3) make recommendations to facilitate the centering of educators in the design and development of edtech tools.

Mohey Eldeen H. H. Ali, Ahmed F. Tayel, Hossam M. Ezzat et al.

Energy plays a crucial role in national development, influencing critical sectors such as industry, agriculture, healthcare, and education. Accurate energy consumption prediction is essential for efficient energy management, helping prevent imbalances between supply and demand and potential energy shortages. This study aims to forecast the total primary energy supply (TPES), using Egypt as a case study for the first time in literature and utilizing several models (ordinary differential equations (ODEs), regression, and ANN models). Although ordinary differential equations (ODEs) offer flexibility and convenience, their application in energy forecasting remains limited. One of the main objectives of this research is to evaluate the effectiveness of ODEs in predicting energy consumption. Various ODE and regression models are employed to identify the most suitable model amongst each category for forecasting energy demand. Additionally, an artificial neural network (ANN) is developed, trained, validated, and tested for the same forecasting task. The study compares the performance of the selected ODE model (Mendelsohn), with the selected regression model (Polynomial), and an ANN model predicting Egypt’s TPES until 2035. By assessing multiple forecasting methods, this work improves the accuracy and reliability of energy consumption predictions, which is crucial for sustainable energy planning and policy development.

Aashish Ghimire, James Prather, John Edwards

The rapid advancement of artificial intelligence (AI) and the expanding integration of large language models (LLMs) have ignited a debate about their application in education. This study delves into university instructors' experiences and attitudes toward AI language models, filling a gap in the literature by analyzing educators' perspectives on AI's role in the classroom and its potential impacts on teaching and learning. The objective of this research is to investigate the level of awareness, overall sentiment towardsadoption, and the factors influencing these attitudes for LLMs and generative AI-based tools in higher education. Data was collected through a survey using a Likert scale, which was complemented by follow-up interviews to gain a more nuanced understanding of the instructors' viewpoints. The collected data was processed using statistical and thematic analysis techniques. Our findings reveal that educators are increasingly aware of and generally positive towards these tools. We find no correlation between teaching style and attitude toward generative AI. Finally, while CS educators show far more confidence in their technical understanding of generative AI tools and more positivity towards them than educators in other fields, they show no more confidence in their ability to detect AI-generated work.

C. Romero, S. Ventura

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

Joshua Ebere Chukwuere

Higher education scholars are interested in an artificial intelligence (AI) technology called ChatGPT, which was developed by OpenAI. Whether ChatGPT can improve learning is still a topic of debate among experts. This concise overview of the literature examines the application of ChatGPT in higher education to comprehend and produce high-level instruction. By examining the essential literature, this study seeks to provide a thorough assessment of the advantages and disadvantages of utilizing ChatGPT in higher education settings. But it's crucial to consider both the positive and negative elements. For this rapid review, the researcher searched Google Scholar, Scopus, and others between January 2023 and July 2023 for prior research from various publications. These studies were examined. The study found that employing ChatGPT in higher education is beneficial for a number of reasons. It can provide individualized instruction, and prompt feedback, facilitate access to learning, and promote student interaction. These benefits could improve the learning environment and make it more fun for academics and students. The cons of ChatGPT are equally present. These problems include the inability to comprehend emotions, the lack of social interaction chances, technological limitations, and the dangers of depending too much on ChatGPT for higher education. Higher education should combine ChatGPT with other teaching techniques to provide students and lecturers with a comprehensive education. However, it is crucial to consider the positives, negatives, and moral issues before adopting ChatGPT in the classroom.