Optimizing the Educational Process: Integrating Digital Educational Technologies with Evidence-Based Teaching Methods
Abstract
This article examines the issues of effective integration of digital technologies into the educational process. The author analyzes key approaches to teaching that should be considered when designing digital tools, such as cognitive load theory, constructivism, active learning theory, and metacognitive regulation models. The article highlights key principles that ensure effective information assimilation using information and communication technologies (ICT). The professional competencies required for educators to work in a digital environment are also discussed, including skills in integrating technologies into the instructional process, creating digital content, and organizing learning and assessment using ICT. The article describes mechanisms through which digital tools can contribute to personalized learning and enhance student motivation. The author provides recommendations for striking a balance between automation and maintaining the active role of the teacher, as well as cautioning against overestimating the role of technology. As a result, the article presents a theoretical synthesis of the main approaches to optimal integration of digital technologies in order to enhance the effectiveness of the educational process.
General Information
Keywords: education digitization, integration of Information and Communication Technologies (ICT), principles of teaching, professional competencies of educators, personalization of education, activation of the learning process. balance between digital and traditional methodologies, learning effectiveness
Publication rubric: Psychological and Pedagogical Aspects of Teaching in a Digital Educational Environment and Issues of Digital Didactics
Article type: theses
For citation: Amelina Y.M. Optimizing the Educational Process: Integrating Digital Educational Technologies with Evidence-Based Teaching Methods. Digital Humanities and Technology in Education (DHTE 2023),, pp. 616–632.
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