Malaysian Online Journal of Educational Technology

Adaptation of the Teaching Engineering Self-Efficacy Scale to Turkish and Analysis of Science Teachers' Teaching Engineering Self-Efficacy Beliefs across Different Variables

Derya Erdemir-Yılmaz — 2024-10-09

The objective of this study was to adapt the Teaching Engineering Self-Efficacy Scale (TESS) to Turkish through comprehensive validity and reliability assessments and to analyze science teachers' (specializing in science, physics, chemistry, biology) Teaching Engineering Self-Efficacy (TES) beliefs concerning various variables. The study employed the TESS, originally developed by Yoon, Evans, and Strobel (2014), alongside a personal information form, and followed a descriptive survey model as its research methodology. The adapted TESS's validity and reliability were evaluated using data from 476 science teachers in schools under the Ministry of National Education (MNE) in X province, and the main study involved 221 science teachers from Y province. The assessment of teachers' TES beliefs in relation to different factors was executed using independent samples t-tests, One-Way ANOVA, and the Scheffe test. It revealed that science teachers' TES beliefs significantly varied based on factors such as their involvement in providing engineering education, the integration of engineering education into their teaching, and their specific teaching subjects, but not gender. The study contributes a valid and reliable scale to the academic literature.

Acknowledgements

We extend our heartfelt gratitude to the faculty members of X University for their invaluable assistance during the adaptation of the scale. Our thanks go to Dr. Research Assistant Eray KARA and Dr. Research Assistant Melike GÜNBEY from the School of Foreign Languages, Assoc. Prof. Dr. Müzeyyen ALTUNBAY and Assoc. Prof. Dr. Mehmet ALVER from the Faculty of Education, Dr. Researcher Gizem UYUMAZ from the Department of Educational Sciences, Assoc. Prof. Dr. Fethiye KARSLI-BAYDERE from the Department of Science Education, and Dr. Research Assistant Zeynep ÖZTÜRK-DUMAN from the Faculty of Arts and Sciences, Department of Western Languages and Literatures at Artvin Çoruh University. We also express our appreciation to teachers Güngör Arman GENÇ, Hakan ER, and Yavuz DEMİRCİ for their contributions.

A Systematic Review of Flipped Learning in EFL Education

Kevser Hava — 2024-10-09

Research on flipped learning in English as a Foreign Language (EFL) education has garnered substantial scholarly attention in recent years. This systematic review aims to elucidate the potential of flipped learning within the framework of foreign language education. A total of thirty articles from Social Science Citation Index (SSCI) journals, published between 2018 and 2022, were included in this review based on predetermined inclusion and exclusion criteria. The analysis focused on examining the research contexts, methodologies and foci, learning management systems, learning theories, and learning activities. The findings indicate that the majority of studies predominantly involved higher education students, with a significant number employing a quasi-experimental research design. Furthermore, the flipped learning process was largely informed by constructivist learning theory. The results suggest that the primary advantage of flipped learning lies in its capacity to enhance students' writing, speaking, and overall academic performance. Additionally, the review underscores that the integration of emerging technologies, such as augmented reality, automatic recognition, and artificial intelligence chatbots, into learning activities serves as an effective strategy for improving students' language skills and engagement.

Predicting Students' Academic Performances Using Machine Learning Algorithms in Educational Data Mining

Şenay Kocakoyun-Aydoğan — 2024-10-09

In every culture and era, education is considered the most fundamental reality and rule that societies prioritize and deem essential. Throughout the process spanning thousands of years, from the emergence of writing to the present day, education has undergone various forms and formats of change. Education has been a continuous guide for shaping, influencing, sustaining societies, and maintaining its dynamics throughout these historical processes. The continuous evolution and growth of education systems and formats worldwide, with changes affecting the quality of education, have the potential to influence nations and societies in every field, ultimately leading to the emergence of an informed society, achievable only through quality education. In this study, the aim is to determine the factors affecting students' academic performance and predict students' end-of-term academic grades using machine learning algorithms within the scope of Earned Value Management (EVM). Such studies have great potential to increase efficiency in education, improve student achievement and improve education policies. With the use of machine learning algorithms, these goals can be achieved more quickly and efficiently. Five different machine learning algorithms, namely RF, KA, KNN, SVM, and NB, have been employed in the study. Binary and multiclass classification methods were used in prediction processes, and among these methods, the Random Forest (RF) algorithm achieved the highest success prediction rates of 0.97 and 0.93, respectively, in both classification methods.