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Linguistic landscape studies (LLS) involve analyzing public signs in specific areas (e.g., streets, entire cities) to reveal the socio-cultural and sociolinguistic structures present. Turkey has been influenced by various cultures, particularly following the refugee influx starting in 2011 and recent internationalization efforts. Such social phenomenon's linguistic repercussions on multiculturalism, linguistic diversity, and vitality is an underexplored area. Therefore, this study explores the linguistic landscape of Turkey's capital, Ankara, to understand the extent of multilingualism in public spaces. Over a year, 1,291 signs were photographed across central areas of Ankara. Researchers were trained to ensure data collection, sorting, and analysis consistency. The study employed descriptive statistics with a quantitative approach to analyze language choice and prominence. Findings revealed that the linguistic landscape of Ankara was mainly Turkish monolingual and English-Turkish bilingual. The study's implications are mainly for language teacher educators,so they embed linguistically aware language teacher education models, and for language education policymakers, they incorporate authentic, multimodal, and multilingual LL to English as a foreign language education.

Higher education professionals often are tasked with providing evidence to stakeholders that programs, services, and practices implemented on their campuses contribute to student success. Furthermore, in the absence of a solid base of evidence related to effective practices, higher education researchers and practitioners are left questioning what works--and what does not--on college campuses. The goal of this essay is to advance a conversation about evidence within the higher education literature through an illustration of a nonrandom research study designed to evaluate the impact of a national interfaith organization on institutions' efforts to promote interfaith learning and development. We briefly provide an overview of the relationship between evidence and evaluation efforts, then move to illustrating one project as a vehicle for understanding how evidence emerging from a nonrandom evaluation design can be done effectively in a national study related to one dimension of student success, and finally, offer support for the essay's central thesis--"that evaluation designs can present reliable evidence of impact in the absence of randomization."

In this research, multistage adaptive tests (MST) were compared according to sample size, panel pattern and module length for top-down and bottom-up test assembly methods. Within the scope of the research, data from PISA 2015 were used and simulation studies were conducted according to the parameters estimated from these data. Analysis results for each condition were compared in terms of mean RMSE and bias. According to the results obtained from the MST simulation based on the top-down test assembly method, mean RMSE values reduced when the module length increased and when the panel pattern changed from 1-2 to 1-2-2 and 1-2-3 for MST applied to small and large samples. Within the scope of the research, data from PISA 2015 were used and simulation studies were conducted using the parameters estimated from these data. Analysis results for each condition were compared in terms of mean RMSE and bias.

This research aimed to determine the trends related to blended learning studies conducted in science education through descriptive content analysis. This study was performed using the document review method. For this purpose, 120 studies on blended learning in science education were determined between 2005 and 2022 in the Web of Science (WoS) database. The studies were examined by considering themes such as keywords, country, purpose, method, sample, online system, and result. The research findings showed that more studies occurred between 2020 and 2022. The findings showed that studies mostly use quantitative research methods and experimental designs. Most of the studies were conducted with undergraduate-level sample groups. Findings revealed that the blended learning environment is designed more to teach physics subjects, and parametric tests were generally used for data analysis. The more preferred course management systems in the studies were the online systems designed by the schools. A considerable number of studies revealed that blended learning environments designed for science education have a positive effect on variables such as achievement, skill, and motivation.

Access to authentic research is limited at the 7-12 science education level. At the same time, many local restoration projects would benefit from, but don't have access to a long-term system of monitoring. This project seeks to unite those two needs by developing a protocol for 7-12 classrooms to be able to participate in authentic research through long-term monitoring of a local restoration project. The protocol developed in this project was used by Jenison High School students at Grand Ravines Park. Grand Ravines Park is a recently acquired Ottawa County park with a history of anthropogenic disturbances. Shortly after the acquisition of the final section of the park, Ottawa County Parks and Recreation (OCPR) staff seeded the portions of the north side of the park with native grasses and forbs, and the south side of the park was not supplementally seeded. This study aimed to develop protocols for the student collection and analysis of vegetation and invertebrate biodiversity data in both the north and south sides of the park. Various metrics of plant and invertebrate biodiversity were compared between the seeded and unseeded areas. Students from Jenison High School were involved in the data collection and analysis, increasing their exposure to scientific research and field sampling and developing their scientific literacy. A long-term database was developed, with the goals of facilitating park management decisions and driving future student research questions.

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift, covariate-shift. Succinctly, these events drove changes in the stationarity of the target variable and the predictors. The result is a student body with unknown performance qualities entirely distinct from previous cohorts. This study explains and illustrates: (1) how to test for academic model drift in academe; and (2) sets forth two methods used to repair vitiated student-body performance properties. Formally, it frames the data-drift outcome as a One Class problem which allows the deployment of two well-known One-Class algorithms: Support Vector Machines and Isolated Random Forests. The study shows their use in reconstructing a representative sample of the student-body

This study analyzed studies done in Turkey in the context of curriculum evaluation (CE) by asking, "How is it made? The study was carried out in two stages. In the first stage, the document analysis method used 215 theses written between 1991 and 2020 on CE were analyzed according to the "thesis review form." In the second stage, depth analysis was made through semi-structured interviews with the authors (students) and the field experts (supervisors of the authors) of the theses to make the results of the first stage more understandable. Interviews were conducted with 32 participants. A maximum sampling method was used to determine the participants. The data analysis calculated percentage and frequency values for the data obtained in the first stage. In the second stage, descriptive analysis and content analysis were carried out with the MAXQDA 2020 qualitative data analysis program. The majority of theses did not employ a CE model as a consequence of the research, and the CIPP model was the most popular CE model. Many of the theses were not justified in using the CE model. Model usage increased as time passed to the present day. Many theses used quantitative models but did not explicitly state the sampling technique. Teachers were mainly used in this research as a source for data gathering, and participant numbers ranged from 10 to 50. Additionally, most studies used questionnaires and interviews as the primary data-gathering tools. All of these findings suggest that CE studies have several flaws.

Language sample analysis (LSA) is valuable clinical tool and an important component of comprehensive language assessment. However, LSA is underutilized in real-world practice. SLPs have identified time constraints, insufficient training, and lack of confidence in LSA skills as barriers to regular LSA implementation. Communication science and disorders (CSD) programs have opportunities to provide LSA instruction to address these barriers and prepare students to reliably, feasibly, and confidently use LSA in clinical practice. This pilot study examined CSD students' perspectives on LSA instruction using the Language ENvironment Analysis System (LENA). Undergraduate students (n = 38) completed a series of two electronic surveys prior to and following LSA instruction using LENA. Changes in students' self-ratings of knowledge and skills in collecting and analyzing language samples, attitudes towards studying LSA and child language development, and critical thinking skills were assessed. Significantly higher student ratings were found for all items measuring language sampling knowledge and skills following digital LSA instruction compared to baseline. In addition, student ratings of enthusiasm for and confidence in studying LSA and child language development were also significantly higher. Students' self-ratings of critical thinking skills did not increase significantly following instruction in digital LSA. Implications for LSA teaching and learning are discussed.

The rapid development in the world of technology and communication has contributed directly to the teaching and learning process in schools. Therefore, a paradigm shift towards learning methods in the education system needs to be implemented to meet the educational aims of the 21st century. This is due to the current methods of delivery in teaching and learning are still conventional. Therefore, this study aims to develop Trig-GO apps for mathematics in secondary schools by focusing on the topic of trigonometry. The development process of this application is guided by the model of Dick, Carey and Carey. This paper describes the design and development of Trig-GO apps prototype based on Design and Development Research (DDR) methods and approaches.The findings of the study indicated that a prototype of the Trig-GO application has been developed. The results of the development of the application prototype were evaluated through qualitative methods. The data was obtained from five experienced teachers and six students via observations, interviews and document analysis. Therefore, the results of the analysis showed that the Trig-GO application prototype developed need improvement. Thus, this also will give impact to the quality and production of the application prototype. It is expected to assist students in solving trigonometry questions at the highest level. The development of this prototype application is intended to assist teachers in providing teaching aids that can be used in the teaching of trigonometry through a digital approach.

This study is designed to reveal the research trends of graduate theses published in the field of computer programming in K-12 between 2018 and 2022. Document analysis was used for data collection in this study. The data was divided into 9 categories, and the results demonstrated that the scholars in the Departments of Computer Educational and Instructional Technologies (CEIT) at Atatürk University and Ondokuz Mayis University of Türkiye mainly published master's theses in this field in 2019. Another result was that though CEIT is also the main department for doctoral theses, no difference exists in terms of years or universities. "Teaching, learning, and training programming" and "computational thinking" are the most frequently used keywords in graduate programs, respectively. While quantitative and mixed method (parallel, convergent), convenience sampling, and psychometric scales are preferred in master's theses, mixed method (explanatory, mixed), purposive sampling, interviews/focus group interviews, and psychometric scales are preferred in doctoral theses, respectively. Observation and semi-structured interview forms are used as data collection tools in the theses. Although quantitative methods are mainly utilized in data analysis, parametric and non-parametric statistical tests are also used, and content analysis is resorted to as part of qualitative methods. Whilst validity and reliability studies have been carried out measurement tools are employed in master's theses, scales have been developed in doctoral theses. The present study also offers some suggestions for future research on teaching computer programming.

In the context of Artificial Intelligence, Generative Adversarial Nets (GANs) allow the creation and reproduction of artificial data from real datasets. The aims of this work are to seek to verify the equivalence of synthetic data with real data and to verify the possibilities of GAN in educational research. The research methodology begins with the creation of a survey that collects data related to the self-perceptions of university teachers regarding their digital competence and technological-pedagogical knowledge of the content (TPACK model). Once the original dataset is generated, twenty-nine different synthetic samples are created (with an increasing N) using the COPULA-GAN procedure. Finally, a two-stage cluster analysis is applied to verify the interchangeability of the synthetic samples with the original, in addition to extracting descriptive data of the distribution characteristics, thereby checking the similarity of the qualitative results. In the results, qualitatively very similar cluster structures have been obtained in the 150 tests carried out, with a clear tendency to identify three types of teaching profiles, based on their level of technical-pedagogical knowledge of the content. It is concluded that the use of synthetic samples is an interesting way of improving data quality, both for security and anonymization and for increasing sample sizes.

The quality of mixing methods has been widely debated in the field of applied linguistics (AL) and the integration of data from both quantitative and qualitative research paradigms has always been open to controversy. The present study was aimed at recognizing the status quo of MMR in AL, investigating the nature of various sections of MMR studies, and specifying the way the pattern of employing MMR has changed over the past few years. From a total of 1,314 articles in seven peer-reviewed accredited AL journals, 220 articles were finally identified to be mixed in method which were subsequently scrutinized based on already established frameworks regarding their research questions, research designs, sampling designs, and inference quality to explore the status quo of mixed methods research (MMR) in AL. We went through two phases of screening to identify the articles which met the principles of MMR and analyzed the selected articles qualitatively based on a coding scheme. The findings revealed (a) only a few research questions were hybrid, (b) both concurrent research designs and concurrent sampling designs were employed more frequently than their sequential counterparts, and (c) only a few articles made their inferences mixed in a principled manner. The findings of several extracts and an open-ended questionnaire showed a growing interest in MMR and the challenges and problems of conducting MMR, respectively. The results might imply that the new paradigms of research favor mixing methods and that the researchers employ it more due to its strengths.

To date, there has been mounting research evidence on the impact of written corrective feedback. In response to this, reviews have been conducted to condense either findings or methodologies through the combined analysis of EFL and ESL studies. Although syntheses shed light on the topic, no one provides exclusive insights into EFL realities. Therefore, this systematic literature review was proposed to synthesize methodologies, findings, and research suggestions of EFL articles analyzing direct and indirect feedback, two widely debated and employed strategies in classrooms, with either a focused or unfocused scope. Through a seven-step protocol suggested by Petticrew and Roberts (2006), 21 EFL primary studies published between 2012 and 2022 were selected from the bibliometric dataset of Crosthwaite et al. (2022), which contains articles retrieved from the Scopus database. The papers were read through to answer five research questions on methodologies, findings, and study suggestions. The results show that most EFL studies were quasi-experimental adopting a quantitative approach with a pretest-posttest-delayed-posttest or pretest-posttest design, and the strategies significantly impinged on grammar and non-grammar aspects. Suggestions are given to further study the impact of feedback under different conditions such as targets, proficiency levels, and strategies and scope with a comparative nature.

When constructed-response items are administered for a second time, it is necessary to evaluate whether the current Time B administration's raters have drifted from the scoring of the original administration at Time A. To study this, Time A papers are sampled and rescored by Time B scorers. Commonly the scores are compared using the proportion of exact agreement across times and/or t-statistics comparing Time A means to Time B means. It is common to treat these rescores with procedures that assume a multinomial sampling model, which is incorrect. The correct, product-multinomial model reflects the stratification of Time A scores. Using direct computation, the research report demonstrates that both proportion of exact agreement and the t-statistic can deviate substantially from expected behavior, providing misleading results. Reweighting the rescore table gives each statistic the correct expected value but does not guarantee that the usual sampling distributions hold. It is also noted that the results apply to a wider class of situations in which a set of papers is scored by one group of raters or scoring engine and then a sample is selected to be evaluated by a different group of raters or scoring engine.

The aim of this research is to examine the postgraduate studies conducted in the field of mathematics education with individuals diagnosed as gifted and talented students in Türkiye. For this purpose, master's and doctoral studies in the database of the National Thesis Center of the Council of Higher Education [CHE] of Türkiye were examined. The first graduate thesis carried out in the field of mathematics education with gifted and talented students was carried out in 2009. In this context, the research includes 55 master's and doctoral studies from 2009 to 2020. In this study, in which the qualitative research approach was preferred, thematic content analysis (meta-synthesis) one of the content analysis types was used. The theses included in the scope of the study were examined under different headings such as year, type, subject, approach, research design, sample, data collection tool, and data analysis techniques. This research is important in terms of giving a general idea about the studies carried out in the field of gifted students and mathematics education, providing information pertaining to the relevant literature for the studies to be carried out, helping expand the field with studies with more different content by determining the subjects and scopes that have not been researched in the field, and giving inspiration related to different research ideas that will contribute to the literature.

While many instructors are aware of the "Literary Digest" 1936 poll as an example of biased sampling methods, this article details potential further explorations for the "Digest's" 1924-1936 quadrennial U.S. presidential election polls. Potential activities range from lessons in data acquisition, cleaning, and validation, to basic data literacy and visualization skills, to exploring one or more methods of adjustment to account for bias based on information collected at that time. Students can also compare how those methods would have performed. One option could be to give introductory students a first look at the idea of "sampling adjustment" and how this principle can be used to account for difficulties in modern polling, but the context is rich in other opportunities that can be discussed at various times in the course or in more advanced sampling courses.

This paper extends existing work on teacher weighting in student-centered surveys by looking into aspects of practical implementation of deriving and using weights for teacher-centered analysis in the Trends in International Mathematics and Science Study (TIMSS) and the Progress in International Reading Literacy Study (PIRLS). The formal conditions to compute teacher-centered weights are detailed, including mathematical equations. We provide a proposal on how to define the targeted populations as well as how to collect data that is needed to derive teacher-centered weights, yet currently unavailable. We also tackle the issue of teacher nonresponse by proposing a respective adjustment factor, as well as mentioning the challenge of multiple selection probabilities when teachers teach in multiple schools. The core part of the paper focuses on studying the level of accuracy that can be expected when estimating teacher population characteristics. We use TIMSS 2019 data and simulate likely scenarios regarding the variance in weights. The results show that (i) the different weighting scenarios lead to relatively similar estimates; however, the differences between the scenarios are sufficient to justify the recommendation to use correctly derived teacher weights; (ii) differences between estimated standard errors based on complex sampling and corresponding estimates based on simple random sampling are sufficiently consistent to support use of a procedure to estimate standard errors that accounts for both sample weights and the complex sampling design; (iii) sample sizes and variance in weights significantly limit estimate precision, so that total population estimates with sufficient precision are available in the majority of countries but subpopulation features are generally not sufficiently precise. To provide a critical evaluation of our results, we recommend implementation of the proposed method in one or more countries. This recommended study will permit examination of logistical considerations in implementation of required changes in data acquisition and will provide data to replicate the analysis with teacher-centered weights.

Interactive computer simulations are commonly used as pedagogical tools to support students' statistical reasoning. This paper examines whether and how these simulations enable their intended effects. We begin by contrasting two theoretical frameworks--"dual processes" and "grounded cognition"--in the context of people's conceptions about statistical sampling, setting the stage for the potential benefits of simulations in learning such conceptions. Then, we continue with reviewing the educational literature on statistical sampling simulations. Our review tentatively suggests benefits of the simulations for building "statistical habits of mind." However, challenges seem to persist when more specific concepts and skills are investigated. With and without simulations, students have difficulty forming an aggregate view of data, interpreting sampling distributions, showing a process-based understanding of the law of large numbers, making statistical inferences, and context-independent reasoning. We propose that grounded cognition offers a framework for understanding these findings, highlighting the bidirectional relationship between perception and conception, perceptual design features, and guided perceptual routines for supporting students' meaning making from simulations. Finally, we propose testable instructional strategies for using simulations in statistics education.

Undergraduate institutions serve as stepping stones to students' careers. Teaching and learning in science classrooms require quality and integrated teaching-research learning experiences that prepare students to advance their careers. Using publicly available data sets and open-access data analyses software can be impactful for engaging students in meaningful research experiences while keeping institutional expenses associated with implementing and supporting research opportunities low. In this article, we share two examples of student-led data research that utilized open data and software to analyze microbial sequence data. As part of this process, we also recognized the need for discipline-specific databases that can hold a record of publicly available data. Such a setup will support undergraduate educators and students in accessing data relevant to their requirements, thereby narrowing the gap between data availability and accessibility. Finally, both examples illustrate the benefits of using open resources in supporting student learning, with the possibility of using such resources to develop course-based undergraduate research experiences as part of course curricula.

Physics education researchers are interested in using the tools of machine learning and natural language processing to make quantitative claims from natural language and text data, such as open-ended responses to survey questions. The aspiration is that this form of machine coding may be more efficient and consistent than human coding, allowing much larger and broader datasets to be analyzed than is practical with human coders. Existing work that uses these tools, however, does not investigate norms that allow for trustworthy quantitative claims without full reliance on cross-checking with human coding, which defeats the purpose of using these automated tools. Here we propose a four-part method for making such claims with supervised natural language processing: evaluating a trained model, calculating statistical uncertainty, calculating systematic uncertainty from the trained algorithm, and calculating systematic uncertainty from novel data sources. We provide evidence for this method using data from two distinct short response survey questions with two distinct coding schemes. We also provide a real-world example of using these practices to machine code a dataset unseen by human coders. We offer recommendations to guide physics education researchers who may use machine-coding methods in the future.

The main aim of this research is to display research trends in studies on augmented reality (AR) in teaching English as a foreign language by using bibliometric mapping and content analysis. For this purpose, 64 studies in total published up to 2019 were accessed for bibliometric analysis. In addition, 49 articles published between 2007 and 2019 were reached for content analysis. The bibliometric mapping results indicated that related studies mostly focused on the effectiveness of mobile and ubiquitous learning. In addition, early childhood education, mobile learning, and gamification have become the focus of recent research. While Thorne, Azuma and Squire are the mostly cited authors in this field, Computers & Education, Educational Technology & Society, British Journal of Educational Technology and Computers in Human Behavior are among the journals cited most. Content analysis results revealed that academic achievement and motivation were the most commonly examined variables in the studies. These results revealed that most of the studies were grounded on quantitative research designs, especially experimental designs. Questionnaires and achievement tests were the most used data collection tools. While purposive sampling was the most preferred sampling method, there was an increase in the adoption of random sampling in 2011. Finally, descriptive statistics, T-tests, ANOVA-ANCOVA, and correlation analysis were mostly performed quantitative analysis methods, and content analysis was mostly used in qualitative analysis.

Partnerships between educational researchers and school-based administrators and educators is imperative to successfully identifying evidence-based practices to improve pedagogy, curriculum, and student outcomes. In this paper, we recognize how being approached by educational researchers can be overwhelming for administrators and educators. We offer a perspective that may help school-based personnel understand the motivation behind research requests and how administrators can frame their response to these requests using a critical lens to ensure these partnerships are beneficial to their school community.

Large-scale assessment survey (LSAS) data are collected via complex sampling designs with special features (e.g., clustering and unequal probability of selection). Multilevel models have been utilized to account for clustering effects whereas the probability weighting approach (PWA) has been used to deal with design informativeness derived from the unequal probability selection. However, the difficulty of applying PWA in multilevel models (MLM) has been generally underestimated and practical guidance is scarce. This study utilizes an empirical as well as a Monte Carlo simulation investigation to examine the performance of the multilevel pseudo maximum likelihood (MPML) estimation based on information derived from the Early Childhood Longitudinal Study Kindergarten cohort of 2010-2011 (ECLS-K:2011). Variance components and fixed effects estimators across four estimation methods including three MPML estimators (i.e., weighted without scaling, weighted size-scaled and weighted effective-scaled) and the unweighted estimator are provided. Practical guidance about the use of sampling weights in MLM analyses of LSAS data is also offered.

We discuss analysis of 5-grade Likert type data in the two-sample case. Analysis using two-sample "t" tests, nonparametric Wilcoxon tests, and ordinal regression methods, are compared using simulated data based on an ordinal regression paradigm. One thousand pairs of samples of size "n"=10 and "n"=30 were generated, with three different degrees of skewness. For all sample sizes and degrees of skewness, the ordinal probit model has highest power. This is not surprising since the data was generated with this model in mind. Slightly more surprising is that the "t" test has higher power than the Wilcoxon test in all studied situations, even for skewed data. For "n"=30, the differences between the methods are small.

Researchers strive to create learning environments where they can apply technology and different teaching methods together. Flipped learning has been a popular approach in recent years because it offers opportunities for both online and offline learning. The present study aims to conduct a thematic analysis of articles on the use of the flipped learning model in mathematics courses. Meta-thematic analysis was adopted as the research method. In this context, for the study, 69 articles were selected from among those published between 2015 and June 2020. As a result, it was found that these studies on flipped learning in mathematics education were conducted primarily with undergraduate students, whereas insufficient numbers of studies have been conducted with primary school, middle school, and high school students and pre-service teachers. Researchers have often preferred the subject of analysis while implementing the model. Studies on the flipped learning model in mathematics have mainly aimed to examine academic performance and students' perceptions regarding the model. Although it can be argued that the flipped learning model positively affects the mathematical performance of students, studies have also reported that it had no effect on performance compared to traditional teaching methods. In addition, the present study provides comprehensive data on the positive and negative aspects of the use of the flipped learning model in mathematics courses. However, comprehensive, extensive, and long-term studies are needed to provide more clear results on the implementation of flipped learning in mathematics courses.

Several branches of the potential outcome causal inference literature have discussed the merits of blocking versus complete randomization. Some have concluded it can never hurt the precision of estimates, and some have concluded it can hurt. In this article, we reconcile these apparently conflicting views, give a more thorough discussion of what guarantees no harm, and discuss how other aspects of a blocked design can cost, all in terms of estimator precision. We discuss how the different findings are due to different sampling models and assumptions of how the blocks were formed. We also connect these ideas to common misconceptions; for instance, we show that analyzing a blocked experiment as if it were completely randomized, a seemingly conservative method, can actually backfire in some cases. Overall, we find that blocking can have a price but that this price is usually small and the potential for gain can be large. It is hard to go too far wrong with blocking.

Previous findings on the preconditions of teachers' collaboration are inconsistent. This might be related to the research methods used to assess the teachers' collaborative practice. Retrospective assessments by self-report on a relatively general level prevail. The validity of these self-reports is limited, however. In contrast, time-sampling methods have the potential to investigate collaborative practice specifically and longitudinally as a day-to-day process over time validly. But to date, no research on collaborative activities in schools based on time-sampling methods is available. In this study, we extended the current state of research by analysing the variability and preconditions of teachers' collaboration at four secondary schools over three weeks based on time-sampling data collected by a newly developed online practice log. Recorded were collaborative activities outside of teaching with a focus on administrative and organisational tasks and on school subject-specific tasks. The results revealed that teachers' collaborative activities varied significantly between weekdays, showing a linear decrease from Monday to Friday, regardless of the content of collaboration. Collaboration that focused on administrative-organisational tasks seemed to be quite stable over the weeks and was hardly influenced by teachers' individual characteristics. Instead, collaborative activities that focused on school subject-specific tasks varied significantly between weeks; moreover, they were influenced by teachers' leadership role and gender. The results indicate that rather stable routinised patterns of day-to-day collaboration over the weeks decrease the influence of teachers' individual characteristics. Hence, by collecting data that is closer to content-specific day-to-day collaborative activities, time-sampling methods can be seen as a driver for new insights.

The objectives of this research on Cognitive Technology for Academic Counselling in the New Normal were (1) to design an architecture of cognitive technology for academic counselling in the new normal, (2) to develop a system of cognitive technology for academic counselling in the new normal, (3) to assess the academic performance of students using cognitive technology for academic counselling in the new normal, and (4) to assess the satisfaction of students using cognitive technology for academic counselling in the new normal. The sample group in this study were 30 students at a secondary school level 1 from Ongkharak Demonstration School, Srinakharinwirot University, in the 2021 academic year. The students were enrolled on a course in Design and Technology and selected by multistage randomization, i.e. 1) group randomization and, 2) simple random sampling. The research tools were, (1) a system of cognitive technology for academic counselling in the new normal, (2) a performance assessment form, (3) a test and worksheets on design and technology, and (4) a satisfaction assessment form. The research results found: (1) learning outcomes after using the system were significantly better than before at the .01 level, and (2) the overall satisfaction of students using cognitive technology for academic counselling in the new normal was at the highest level (Mean = 4.50, S.D. = 0.64).

Science education focuses on the methods of thinking about and using process skills rather than memorizing scientific facts. 5E educational model aims to learn by discovering scientific knowledge and engaging students in learning environments. The aim of this study was to examine the articles in the field of education related to the 5E educational model and science process skills (SPS) according to some criteria. The search in databases was carried out to cover the articles conducted in the last twelve years (2010-2021). Over the last 12 years, 522 articles on the 5E educational model and science process skills have been examined. In this descriptive content analysis study, randomized sampling came to the fore as the preferred sampling method, lower-secondary school students as the sample type, 11-50 as the sample size, and studies in which the effectiveness of a method was tested as the research type, lower-secondary school science as the research discipline, quasi-experimental as a research design, achievement tests as data collection tools, and frequency/percentage/charts were frequently used in data analysis.

This study aims to investigate the trustworthiness of empirical research published in distance education (DE) journals, an area that has yet to be systematically explored. The review covers 238 empirical studies which were based on primary data and published in 2021 in eight DE journals listed in Social Science Citation Index (SSCI) or Emerging Sources Citation Index (ESCI). Findings suggest that they are fairly good in terms of clarity in context of study, purpose of study, and duration if a study involves an intervention or is otherwise length-sensitive, albeit the existence of small room for improvement. In contrast, a large proportion of the studies reviewed are less rigorous to different but considerable and even alarming degrees in terms of research approach and design, sampling strategies, source of data, researcher bias, ethical concerns, and limitations. Put specifically, less than one quarter of the sample studies adopted the qualitative approach while over 90% of the quantitative studies followed the survey and correlational designs, resulting in a staggering disproportion from the perspective of diversity in research approach and design. Over 60% of the sample studies did not spell out their sampling strategies and only about 20% of the specified sampling strategies were probabilistic in nature, limiting the generalizability of the findings. Over 90% employed questionnaires/scales/rubrics and/or interview protocols to collect data but over 70% of these two types of instruments were neither reviewed nor piloted before put to use with less than 50% available in full content, hence likely to undermine the value of the findings. Researcher bias, ethical concerns, and limitations were addressed in 10%, 50%, and 70% of the studies respectively. Implications for future research are also discussed in the light of these findings.

Qualitative inquiry is an effective way to explore the perceptions of participants and unlock their experiences. In social science research (e.g., applied linguistics, sociology, sociolinguistics, psychology, psycholinguistics), there is a strong tendency among qualitative researchers to collect their required data through interviewing. Interviews are useful tools to glean the related data from the participants, and different types of interviews are commonly used to delve into the participants' perceptions. In this study, we address three types of interviews (structured, semi-structured, and unstructured), and compare them in detail. In addition, we focus on effective ways to form questions, offer a brief review of qualitative sampling, and provide guidelines for conducting an interview in qualitative studies. Finally, two examples of interviews with different structures are put forth to explore the process and to compare them. Having done this, we will juxtapose both the process and content to explore different types of questioning during the process of an interview. This study suggests the potential differences among different types of interviews. It also suggests how to select the best type of interview to collect the required data based on the nature of the study and research questions.

The purpose of this study was to examine the studies related to augmented reality applications in sports science and to discuss the results based on this. Articles selected for analysis were found through searches journals in the Web of Science database from 1975 to 2018 were examined and searched using the keywords ("physical education" or "physical activity" or "sport$") and ("augmented reality"). As a result of the search, 44 articles were reached, and 11 articles were excluded from the research because they were not related to augmented reality applications or sports science. The publication classification form consists of the titles "Years, Authors Number, Country, Journals, Age, Sample Size, Variables, Sample Method, Research Method, Data Collection Method and Data Analysis Method." There are a limited number of studies where sports and augmented reality technology are used and interacted together. With the spread of the use of augmented reality and similar technologies in the field of sports sciences, it is thought that such fields as health and education will be positively affected.

The present study aimed to remediate pre-service teachers' misconceptions about sampling distributions and to develop their conceptual understanding through the use of conceptual change texts (CCTs). The participants consisted of 84 pre-service teachers. To determine the pre-service teachers' conceptual understanding of sampling distributions, an achievement test was utilized. Five conceptual change texts were prepared. In this study, the number of correct responses of pre-service teachers increased from pretest to post-test and delayed test. This increase was statistically significant in favor of the post-test and delayed test. The results demonstrated that, due to the knowledge gained from the CCTs, the pre-service teachers improved their conceptual understanding about sampling distributions. Moreover, this study represents an important effort to integrate CCTs in mathematics and statistics education.

Despite the sensitivity of fit indices to various model and data characteristics in structural equation modeling, these fit indices are used in a rigid binary fashion as a mere rule of thumb threshold value in a search for model adequacy. Here, we address the behavior and interpretation of the popular Comparative Fit Index (CFI) by stressing that its metric for model assessment is the amount of misspecification in a baseline model and by further decomposition into its fundamental components: sample size, number of variables and the degree of multivariate dependence in the data. Simulation results show how these components influence the performance of CFI and its rule of thumb in practice. We discuss the usefulness of additional qualifications when applying the CFI rule of thumb and potential adjustments to its threshold value as a function of data characteristics. In conclusion, we at a minimum recommend a dual reporting strategy to provide the necessary context and base for meaningful interpretation and even more optimal, a move to using CFI as a real incremental fit index intended to evaluate the relative effect size of cumulative theoretically motivated model restrictions in terms of % reduction in misspecification as measured by the baseline model.

This paper focuses on the considerations in determining the number of participants for qualitative research because of the lack of clear guidelines in this area. The study has employed a semi-systematic literature review that is embedded with the researcher's experience. The study has concluded that the purpose of the research, methodological choices, theoretical framework and analytical strategy, data saturation, researcher's knowledge and experience, and institutional and supervisor's requirements need to be considered while choosing the participants in qualitative research. In addition, the focus has been to explore in-depth information from small number of participants. Generally, participants in qualitative research can be added or removed during the research process rather than the prior determination. This paper suggests that the researchers are autonomous to select the participants in qualitative research and they can choose from a single to twenty samples that can be varied upon the depth of the information required and the nature of the inquiry. While conducting the narrative inquiry, one to twenty or more participants can be selected with justification.

Two families of analysis methods can be used for differential item functioning (DIF) analysis. One family is DIF analysis based on observed scores, such as the Mantel-Haenszel (MH) and the standardized proportion-correct metric for DIF procedures; the other is analysis based on latent ability, in which the statistic is a measure of departure from measurement invariance (DMI) for two studied groups. Previous research has shown, that DIF and DMI do not necessarily agree with each other. In practice, many operational testing programs use the MH DIF procedure to flag potential DIF items. Recently, weighted DIF statistics has been proposed, where weighted sum scores are used as the matching variable and the weights are the item discrimination parameters. It has been shown theoretically and analytically that, given the item parameters, weighted DIF statistics can close the gap between DIF and DMI. The current study investigates the robustness of using weighted DIF statistics empirically through simulations when item parameters have to be estimated from data.

In recent years, harmonic regression models have been applied to implement quality control for educational assessment data consisting of multiple administrations and displaying seasonality. As with other types of regression models, it is imperative that model adequacy checking and model fit be appropriately conducted. However, there has been no literature on how to perform a comprehensive model adequacy evaluation when applying harmonic regression models to sequential data with seasonality in the educational assessment field. This paper is intended to fill this gap with an illustration of real data from an English language assessment. Two types of cross-validation, leave-one-out and out-of-sample, were designed to measure prediction errors and check model validation. Three types of R-squared (R[superscript 2], R[superscript 2][subscript adj], and R[superscript 2][subscript pred]) and various residual diagnostics were applied to check model adequacy and model fitting.

Equating the scores from different forms of a test requires collecting data that link the forms. Problems arise when the test forms to be linked are given to groups that are not equivalent and the forms share no common items by which to measure or adjust for this group nonequivalence. We compared three approaches to adjusting for group nonequivalence in a situation where not only is randomization questionable, but the number of common items is small. Group adjustment through either subgroup weighting, a weak anchor, or a mix of both was evaluated in terms of linking accuracy using a resampling approach. We used data from a single test form to create two research forms for which the equating relationship was known. The results showed that both subgroup weighting and weak anchor approaches produced nearly equivalent linking results when group equivalence was not met. Direct (random groups) linking methods produced the least accurate result due to nontrivial bias. Use of subgroup weighting and linking using the anchor test only marginally improved linking accuracy compared to using the weak anchor alone when the degree of group nonequivalence was small.