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The COVID-19 epidemic has precipitated a rapid and widespread adoption of online education, leading to its normalization in contemporary society. Online education is evident across several educational levels. However, assessing the efficacy and effectiveness of these training programs can only be achieved by implementing a suitable evaluation methodology. One of the primary challenges associated with online education is the difficulty in assessing its quality and effectiveness. One of the contributing factors to this issue is the instructor's lack of technological skills and knowledge relevant to online teaching. This research aims to develop a scale (Online Evaluation Awareness-OnEvA) to determine the instructors' awareness of online evaluation. 165 participants' data from 63 universities was used for exploratory factor analysis. The items of the scale are designed to measure awareness in both pedagogical and technological dimensions. The items in the pedagogical dimension include the essential competencies that instructors should possess, and alternative evaluation methods offered online. In the technological dimension, the items are created to determine instructors' awareness of the use of technological platforms and tools. For the next step, a confirmatory factor analysis was performed to establish the scale's construct validity with 161 instructors. Additionally, the scale's internal consistency was determined to be high, with a Cronbach's alpha of 0.964. It is affirmed that this awareness scale developed within the scope of this research will contribute to the development of future models or frameworks related to the dimensions of evaluation knowledge in online learning.

Artificial intelligence is a popular technology that affects many fields today. Its impact is noticeable daily, especially in education, design, and other essential areas. This technology, which stands out with the convenience it creates for users and its help in organizing the workflow, will play a critical role in our lives in the future. This study is aimed to touch upon the innovations provided by artificial intelligence in the field of education and some of its basic features. Some software, especially ChatGPT, is included based on the potential of language models and their essential usage areas. In addition, in the study, it is mentioned how artificial intelligence can be evaluated as a material in text and visual form, the essential functions of artificial intelligence technologies, which are rapidly changing today, are explained in this article, and stimulating points about the current situation are mentioned for those who will work in the field.

This study aims to examine the lesson plan development processes and related experiences of prospective teachers, and determine the potential effects their lesson plan development efforts on their perceived TPACK. The research was conducted as part of a 14-week Mathematics Teaching course. During the course, prospective teachers underwent a six-week training program focused on designing technology-supported mathematics lessons, followed by an eight-week period dedicated to developing lesson plans for different topics and objectives within the mathematics curriculum. Data for this case study was collected from sample of 44 prospective teachers through participant diaries, observation notes, interviews, and pre- and post-lesson administration of the Turkish version of Schmidt et al.'s (2009) TPACK scale. Qualitative data was thematically analyzed to complement quantitative analysis using a t-test. The findings indicate that engaging in lesson plan preparation activities during mathematics teaching courses positively influenced prospective teachers' perceived TPACK levels. Specifically, an increase in TPACK levels was observed after attending the course. Furthermore, several factors have been identified that enable prospective teachers' perception of TPACK to be positively affected. These findings suggest the need for technology-supported course design within the mathematics curriculum of teacher training programs. Providing elective courses that offer prospective teachers additional opportunities to develop lesson plans is recommended.

Although the Rasch model is used to measure latent traits like attitude or ability where there are multiple latent structures within the dataset it is best to use a technique called the Mixture Rasch Model (MRM) which is a combination of a Rasch model and a latent class analysis (LCA). This study used data from a survey for teachers, teacher candidates, and teacher education program faculty with a sample of 296 candidates, 648 graduates, and 501 program faculty. Survey items based on these competencies were administered to in one Western state to ascertain how well the program candidates attended prepared them for the teaching profession. The 40 items common to surveys of the three groups were submitted to mixture Rasch analysis to determine whether distinct patterns of item response were discernible. Analyses yielded two classes which brings the construct validity of the survey into question. Results showed that the Mixture Rasch Model is and can be useful to determine subgroups for survey researchers. This research presents a demonstration of usefulness of the Mixture Rasch Model for the analysis of survey data.

The learning styles of students vary both among individuals and across different disciplines they choose to study. In higher education, academic discipline appears to be a key factor influencing students' distinctive learning styles. This study, which aims to determine the dominant learning styles and models of talented pre-service teachers, was designed with the survey model. The study group research consisted of 1019 volunteer pre-service teachers who continued their education in Physical Education and Sports, Music, and Arts & Craft Teaching departments. In the study, the VARK Learning Style Inventory was used as a data collection tool; students were asked to answer questions in a digital environment through Google Forms. According to the research results, kinesthetic style is the dominant learning preference for preservice teachers in the visual arts and sports sciences, and both kinesthetic and aural styles are the dominant learning preferences for pre-service teachers in music education. Although talented pre-service teachers tend to learn predominantly kinesthetic and aural styles when evaluated, 61.2% of pre-service teachers prefer to learn with a Multiple Learning Model, which covers the entire instructional diversity, rather than sticking to just one learning style.

We analyzed the links between access to higher education, exams, and equity in education in Türkiye. Next, we made numerous recommendations and referred to a phenomenon: "The Magnus Effect" to delve into the problems of the relevant processes. After conducting a review of the literature, we came up with four core sources that produce equity issues: tracking, stratification, socioeconomic status, and the qualifying elimination system. One of the ironies was that access to higher education institutions is enabled via high-stakes national exams, which seem to legitimize the process while leading to countless more inequities.

This study explored pre-service teachers' technological pedagogical content knowledge levels and their blended teaching readiness. In this context, the study employed the cross-sectional survey design, and the variables were analyzed descriptively and correlationally. The study was carried out with the participation of 477 pre-service teachers. Data were collected using the Blended Teaching Readiness Instrument and the Technological Pedagogical Content Knowledge-Deep (TPACK deep) Scale. The analyses put forth that pre-service teachers technological pedagogical content knowledge levels were high in the dimensions of design, exertion, and proficiency, whereas their levels were moderate in the dimension of ethics. In addition, pre-service teachers' blended teaching readiness levels were found to be high in the dimensions of dispositions, online integration, data practices, personalizing instruction, and online interaction. Furthermore, a significant and positive high-level correlation was revealed between pre-service teachers' technological pedagogical content knowledge levels and their blended teaching readiness. Also, pre-service teachers' technological pedagogical content knowledge levels could explain 66% of the variance in their blended teaching readiness.

Purpose: This study aimed to examine the relationship between leadership self-efficacy and the decision to be a school principal and the mediating effects of motivation-to-lead and worries-about-leadership in this relationship. Design/methodology/approach: A total of 375 teachers working in public high schools in Ankara, Türkiye participated in this cross-sectional predictive study. Descriptive statistics, structural equation modeling and bootstrapping were used in the analysis of the data. Findings: Leadership self-efficacy was found to increase both motivation-to-lead and worries-about-leadership. Motivation-to-lead positively influenced the decision to be a school principal, whereas worries-about-leadership did so negatively. Furthermore, leadership self-efficacy had an indirect positive effect on the decision to be a school principal through motivation-to-lead and worries-about-leadership. Practical implications: Based on the findings, it is recommended to offer teachers training programs on school administration and leadership. Furthermore, we suggested providing teachers with administrative internship and mentorship opportunities to help them gain field experience and benefit from the experiences of incumbent school principals in school leadership. Originality/value: This study confirmed that theoretical assumptions about self-efficacy are also relevant in the decision-making process regarding the school principalship: leadership self-efficacy can be the source of both motivation and worry. This study also implied that teachers make their choices about the school principalship by considering the potential advantages and disadvantages of the position.

Mind mapping refers to the use of a specific graphic organizer to support learning. This paper describes the effect of mind mapping on pre-schoolers' geometric learning. Using a pre- and posttest control group quasi-experimental model, researchers found that the use of mind maps resulted in a statistically significant difference in geometry learning for pre-schoolers (mean age = 65.0 months). These results are discussed in terms of their ramifications for pre-school geometry education, as well as for the use of mind maps with pre-school children.

This study explores the influence of feedback mode on learners' behavioral and affective engagement through a mixed method research design. Fourteen English as a foreign language (EFL) learners at a Turkish university performed four writing tasks (i.e., two essays and two reports) throughout an authentic seven-week distance foreign language (L2) writing course as part of their assessment. All learners were provided with e-written feedback on two of their writing performances (i.e., one essay and one report) and screencast (video) feedback on the other two. Their writing performances were analyzed in terms of behavioral engagement as measured by responsiveness to feedback; and their responses to a questionnaire of nine open-ended questions were analyzed qualitatively and inductively in terms of affective engagement. Results indicated that learners' behavioral engagement did not seem to differ depending on the feedback mode. Furthermore, while learners' affective engagement was higher in screencast feedback condition, several factors were identified in favour of screencast feedback (i.e., perceived cognitive and motivational benefits) and in favour of e-written feedback (i.e., perceived practical benefits).