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Eurasian Society of Educational Research
Eurasian Society of Educational Research
Christiaan Huygensstraat 44, Zipcode:7533XB, Enschede, THE NETHERLANDS
Eurasian Society of Educational Research
Headquarters
Christiaan Huygensstraat 44, Zipcode:7533XB, Enschede, THE NETHERLANDS

'lesson planning' Search Results

Teachers Underutilize Their Learning Styles in Developing Thought-Provoking Questions: A Case Study

critical thinking learning styles thought-provoking questions

Agustiani Putri , Abdur Rahman As’ari , Purwanto , Sharifah Osman , Selly Anastassia Amellia Kharis


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Teachers' learning styles are a crucial part of the learning process as they determine how teachers' brains capture and integrate information linked with the senses. Kurnia, identified as an auditory teacher, was expected to capture written information in a provided numeracy problem. Nevertheless, she prefers to capture visual information, like tables or figures, and utilize them to develop thought-provoking questions. Thus, this study intends to investigate her reasons and the factors affecting Kurnia's decision to utilize visual information as a reference in developing questions. This research adopts a qualitative design covering a case study. Kurnia was selected from 32 teachers from 28 schools; roughly 43% were from public schools, and 57% from private schools. Kurnia placed more emphasis on pictorial information before proposing questions, which was caused by situational factors: the subject matter, the grade level, the student's engagement in the class, the teacher's experience, the teaching experience, and the diversity of students' learning styles. This article recommends that teachers recognize their learning styles to know their strengths and weaknesses in teaching mathematics, and that they convey understandable information utilizing effective instructional methods that represent each learning style of students in the classroom.

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10.12973/eu-jer.13.2.479
Pages: 479-495
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Understanding graphs in the dynamics of market (DM) is a challenge to learners; its teaching demands a specific kind of teacher’s knowledge. This study aims to examine the topic-specific pedagogical content knowledge (TSPCK) of experienced economics teachers in teaching graphs in DM to enhance learners’ understanding of the topic. It reports using a qualitative approach underpinned by the TSPCK framework for teaching specific topics developed by Mavhunga. Data were collected through classroom observations and analyzed thematically using a case study of two economics teachers. The study revealed that adopting a step-by-step approach and the use of worked graphical examples promote an understanding of graphs in DM. It also established that active learning is preferable to the predominant chalk-and-talk (lecture) method of teaching graphs in DM. The study proposed a Dynamics of Market Graphical Framework (DMG-Framework) to enable teachers, particularly pre-service teachers in lesson delivery, to enhance learners’ understanding of graphs in DM. The result of this study will broaden the international view in the teaching of graphs in DM.

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10.12973/eu-jer.13.3.1247
Pages: 1247-1262
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How Is the Insight Overview of Artificial Intelligence Research in High School?

artificial intelligence bibliometric high school insight overview

Widayanti , Haryanto , Edi Istiyono , Antomi Saregar , Khusnatul Amaliah


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The world is looking forward to advancements in artificial intelligence (AI) technology, with significant research underway regarding the application of AI in education. This study analyzed publications on the potential of AI in secondary schools, focusing on its bibliometric aspects. Data from the Scopus database revealed 1,764 publications from 2019 to 2024. The analysis showed a steady annual growth in publications in this area. China and the USA were the leaders in the number of publications. Xiaoyue Wang was the most prolific researcher, having authored 71 AI-related articles. Yueying Li, Xiaoxu Chen, Yanzhu Zhang, and Yi Liu contributed to the field with 56, 55, 53, and 51 articles, respectively. The themes that emerged from 2019 to 2022 are related to media, application, study, institutions, artificial, digital, learning, factors, development, technologies, medical, automated, perception, support, and sustainability. From 2023 to 2024, the topics discussed in AI are related to students, education, perception, algorithms, digital, prediction, networks, challenges, writing, teachers, AI-powered, curriculum, century, integration, technology, and framework. The difference in research in 2019-2022 and 2023-2024 is focusing the theme's focus from the general to the specific. The co-occurrence analysis revealed that prominent keywords appeared in 3 clusters. Cluster 1 is the most popular in recent times. It deals with the application, assessment, and management of AI. Cluster 2 relates to AI relationships and models, while Cluster 3 relates to AI data sources.

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10.12973/eu-jer.13.4.1917
Pages: 1917-1930
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