Hasil untuk "Education"

Phillip D. Long, George Siemens

L. Atkins, S. Wallace

Ido Roll, Ruth Wylie

M. Seligman, Randal M. Ernst, J. Gillham et al.

Gail A. Herndon

James M. Clark, A. Paivio

C. Bereiter

Alan B. Krueger

A. Greiner, E. Knebel

M. Boler

L. Mclean, J. Guilford, B. Fruchter

A. Stoler

M. Winkleby, D. Jatulis, E. Frank et al.

R. Castro

Mayleen Dorcas B. Castro, Gilbert M. Tumibay

V. Ratten, P. Usmanij

Abstract Entrepreneurship education has blossomed as an area of research due to its practical significance and role in expediting the economic wellbeing of the global economy. Despite its popularity there is still some way to go before we fully understand the nature and ability of entrepreneurship education to transform society. The goal of this article is to highlight the current trends in entrepreneurship education by providing some paths for future research that take an anthropcosmic view of education. This will help more researchers embrace the distinctive nature of entrepreneurship by tying it to new emerging employment trends such as the gig economy and the digital transformation of the workplace. Suggestions for how entrepreneurship education needs to further progress are given as a way of shaping the future development of the field.

Daniel S. Schiff

S. Z. Salas-Pilco, Yuqin Yang

Over the last decade, there has been great research interest in the application of artificial intelligence (AI) in various fields, such as medicine, finance, and law. Recently, there has been a research focus on the application of AI in education, where it has great potential. Therefore, a systematic review of the literature on AI in education is therefore necessary. This article considers its usage and applications in Latin American higher education institutions. After identifying the studies dedicated to educational innovations brought about by the application of AI techniques, this review examines AI applications in three educational processes: learning, teaching, and administration. Each study is analyzed for the AI techniques used, such as machine learning, deep learning, and natural language processing, the AI tools and algorithms that are applied, and the main education topic. The results reveal that the main AI applications in education are: predictive modelling, intelligent analytics, assistive technology, automatic content analysis, and image analytics. It is further demonstrated that AI applications help to address important education issues (e.g., detecting students at risk of dropping out) and thereby contribute to ensuring quality education. Finally, the article presents the lessons learned from the review concerning the application of AI technologies in higher education in the Latin American context.

S. Z. Salas-Pilco, Kejiang Xiao, Xinyun Hu

In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced into the field of education, where their use has great potential to enhance the teaching and learning processes. Researchers have focused on applying these technologies to teacher education, as they see the value of technology for educating. Therefore, a systematic review of the literature on AI and LA in teacher education is necessary to understand their impact in the field. Our methodology follows the PRISMA guidelines, and 30 studies related to teacher education were identified. This review analyzes and discusses the several ways in which AI and LA are being integrated in teacher education based on the studies’ goals, participants, data sources, and the tools used to enhance teaching and learning activities. The findings indicate that (a) there is a focus on studying the behaviors, perceptions, and digital competence of pre- and in-service teachers regarding the use of AI and LA in their teaching practices; (b) the main data sources are behavioral data, discourse data, and statistical data; (c) machine learning algorithms are employed in most of the studies; and (d) the ethical clearance is mentioned by few studies. The implications will be valuable for teachers and educational authorities, informing their decisions regarding the effective use of AI and LA technologies to support teacher education.

Kathy Smith, N. Maynard, A. Berry et al.

Developing teacher knowledge, skills, and confidence in Science, Technology, Engineering, and Mathematics (STEM) education is critical to supporting a culture of innovation and productivity across the population. Such capacity building is also necessary for the development of STEM literacies involving the ability to identify, apply, and integrate concepts from STEM domains toward understanding complex problems, and innovating to solve them. However, a lack of visible models of STEM integration has been highlighted by teachers as a challenge to successfully implementing integrated STEM education in schools. Problem Based Learning (PBL) has been well-established in higher education contexts as an approach to learning in the STEM disciplines and may present an effective way to integrate knowledge and skills across STEM disciplines in school-based STEM education and support the development of students as capable, self-directed learners. However, if PBL is to effectively contribute to STEM education in schools and build teacher capacity to teach STEM, then this approach needs to be better understood. This paper aims to generate a set of principles for supporting a PBL model of STEM education in schools based on insights from the literature and expert focus groups of PBL professionals. Four principles of PBL emerged from the data analysis: (a) flexible knowledge, skills, and capabilities; (b) active and strategic metacognitive reasoning; (c) collaboration based on intrinsic motivation; and (d) problems embedded in real and rich contexts. The study outcomes provide evidence-informed support for teachers who may be considering the value of adopting a PBL approach in school-based STEM education.