Educational technologies are not homogeneous. This chapter proposes a framework to categorize various technologies in the K-12 educational setting into groups of operational technologies and pedagogical technologies by whether they directly participate in the process of teaching and learning. Furthermore, pedagogical technologies are split into tool-based and program-based technologies based on whether they are teacher-driven tools or algorithm-driven learning programs. Efficient adoption of tool-based technologies requires a redefinition of learning goals to embrace student-centered education. Program-based technologies need more research to be fully understood and improved, and current ones are under-researched and fail to engage and motivate students to learn.
Part of the book: Pedagogy in Basic and Higher Education
Although educational research acknowledges that social perception processes are relevant for understanding but also evaluating situations, the topic of impression management (IM) has achieved only little attention so far. Individuals have discussed rather as passively exposed to the mechanism of social interaction and perception processes. This contribution changes perspectives and addresses the question of conscious impression management within classes. The chapter asks whether students use self-presentation tactics in order to deliberately navigate the impression their teachers should have of them. By means of an empirical study, country- and gender-specific differences with regard to impression management were found. Likewise, students with a high educational aspiration and good school grades scored higher or at least differently on impression management than students with a low educational aspiration level and low school grades. And students with a high educational aspiration but low grades try to overcome this discrepancy by means of personally adapting to the teachers’ expectations. Even though the influence mechanism of impression management on school success cannot conclusively be answered, this paper opens new perspectives on the scientific discourse of social inequality as well as teaching quality and discusses implications for teacher education.
Part of the book: Pedagogy in Basic and Higher Education
This conceptual chapter discusses how requirements for teacher educator professionalism may be impacted by the integration of Artificial Intelligence (AI) in teacher education. With the aim to continuously facilitate high-quality teacher education, teacher education institutions must evolve in alignment with the rapidly changing landscape of AI and the respective shifting educational needs. Amidst this evolution, we argue that profound AI Literacy and AI-related ethical knowledge constitute two additional and inextricably intertwined knowledge facets of teacher educator professionalism essential for an ethical and effective integration of AI into teaching practices – and thus crucial for high quality teacher education. The paper explores avenues through which these facets of teacher professional competence and quality education can be fostered on the micro, meso and macro levels of institutional education. By consolidating the specific requirements in a framework for teacher educator professionalism in the age of AI, we highlight the necessity for continuous adaptation of teacher education institutions, ongoing multidisciplinary collaboration, and the provision of periodic professional development of educators. Finally, the chapter presents a concrete practical example and future research directions in AI and education with the aim to contribute to the advancement of quality education in the AI era.
Part of the book: Artificial Intelligence and Education