Search

This study explored and compared metaphorical perceptions of "Innovative Education" among 100 in-service teachers from Turkey and South Africa during the 2020-2021 academic year. A phenomenological research design was adopted for this qualitative investigation. Teachers accessed an online platform to complete the open-ended prompt: "Innovative Education is like a ……because……". The data was categorized through content analysis into four themes: vision, value, novelty, and process. Rogers' Diffusion of Innovation theory provided a framework for interpreting the findings. The results revealed that teachers in both countries perceived innovation as a relative advantage, likely due to the significant changes experienced during the pandemic. However, South African teachers' metaphors also indicated complexity, potentially reflecting a lack of adequate training and support during the implementation of innovative practices. Conversely, Turkish teachers' metaphors suggested a more welcoming attitude, possibly due to sufficient support structures. Based on these findings, the study recommends that promoting teacher confidence during educational innovation should involve strong advocacy alongside comprehensive training and support programs.

Culture shapes pupils' mathematical learning, their performances and life trajectories of participation (César, 2013a, 2013b). It also contributes to the senses they attribute to mathematical learning (Bakhtin, 1929/1981). Using collaborative work and interempowerment mechanisms facilitates knowledge appropriation (César, 2009). This is particularly important for pupils participating in minority cultures, socially undervalued and whose L1 is not the instruction language. Bi-univocal culture mediation (César, 2017b) is important regarding empowerment. We used an instrument to evaluate pupils' abilities and competencies (IACC), conceived by the "Interaction and Knowlwdge" (IK) team (Machado, 2014), and other mathematical tasks. The goal we address is to trace the differences between their approaches to problems, mathematical reasoning and solving strategies used by pupils whose L1 is ideographic (Creole, Cape Verde) or phonetic (Portuguese). We developed an intrinsic case study (Stake, 1995). The main participants are the pupils from almost 600 classes (all over Portugal and Cape Verde) who participated in the IK. The analysis of some examples illustrates that L1 shapes pupils' approaches to problems, mathematical reasoning and solving strategies. This evidence plays an important role in their access to school achievement and in teachers' understanding about how they can promote pupils' mathematical learning.

AI-generated paraphrasing tools, especially QuillBot and Paraphrasing Tool, play a crucial role in preventing plagiarism in academic writing. However, their effectiveness and proficiency have been questioned, particularly regarding the adequacy of their strategies. This qualitative study analyzed the paraphrasing strategies and proficiency levels of QuillBot and Paraphrase Tool. Using a purposive sampling technique, all 30 abstracts from one issue of the "Journal of Second Language Writing" were paraphrased using the two paraphrasing tools in their standard modes, and the results were analyzed using the frameworks of Keck (2014) and Nabhan et al. (2021). The results of the study indicated that both tools primarily used synonym substitution, with QuillBot favoring word-level changes and Paraphrase Tool emphasizing sentence restructuring. QuillBot tended to show minimal revision, followed by moderate revision, while Paraphrase Tool exhibited more moderate revision, followed by minimal and substantial revision. Paraphrase Tool exhibited broader paraphrasing capability than QuillBot, but both tools show some paraphrasing limitations. Overall, while these tools may enhance some writing, writers should thoroughly review the core concepts of the original texts and grammatical structures in specific contexts. For novice writers, paraphrasing practice in classrooms should be conducted under teachers' guidance. AIgenerated tools should be secondary.

Language learning strategies may have a key role in foreign language learning because they may foster learner autonomy and motivation. Keeping the learner-centred nature of language learning strategies in mind, this study set out to understand the impact of overt listening and reading strategy instruction on learners' listening and reading achievement. The study employed an explanatory mixed-method research design. Research instruments were reading and listening achievement tests and semi-structured interviews. The treatment involved overt listening and reading strategy instruction that lasted for four weeks. The findings revealed that overt listening and reading strategy instruction fostered learners' reading and listening achievement. Similarly, semi-structured interviews revealed that learners were willing to transfer the strategies they learned to new learning situations and keep using these strategies in future learning situations.

Organizational culture acts as the mortar that connects the bricks. Every institution has its own unique culture which has been adopted and shared by all employees over the years. As establishment stories, ways of doing business, and communication styles change, culture also adopts itself to these in a way that is specific to that organization. Since there is no single understanding of culture covering all types of institutions, research has focused on classifying the concept of organizational culture according to types and concentrated on situational studies. As in for-profit businesses, culture is also significant in organizations that prioritize social goals. In this research, an academic organization within the realm of public institutions is discussed. The purpose of the article is to reveal the functioning of the elements that make up the organizational culture of the Open Education Faculty in the institution and the types of culture that are seen as dominant in the organization. In the case study, semi-structured interviews were conducted with the managers who have taken important roles in the history of the institution, and the documents containing information about the faculty culture were analyzed. The results of the qualitative content analysis revealed that Anadolu University Open Education Faculty has a strong and established culture, and this contributes to the work done and institutional development. Considering the dominant cultural styles in the institution; it is noteworthy that it started as an effective, collaborative, dynamic and work-based culture. With the institutional growth being experienced over time, the faculty culture has evolved towards a more protective, bureaucratic, and analytical one due to the difficulties in the organizational environment and the risks in the decisions taken. However, considering the scope, structural features, and stakeholder diversity of open education at home and abroad, the faculty still maintains a dynamic, collaborative and developmental cultural style with its ability to coordinate between units, its flexibility to respond to environmental changes, and its efforts to take innovative steps.

ICAP is a framework that classifies learning processes based on students' explicit behaviors. The framework is developed for testing the hypothesis that interactive exercises are better than constructive exercises, and active exercises are better than the passive exercises for higher cognitive engagement and better learning outcomes. The ICAP Framework is intended to assist researchers, instruction designers and instructors in determining the activities appropriate for the aimed research and teaching. This study aims to evaluate articles based on or supported by the ICAP Framework from various aspects. In the study, employing the descriptive survey method, data collection was conducted through document analysis, while content analysis was utilized to analyze the data. The 71 articles reviewed within the study's scope were examined through the "data collection form" developed by the authors. In this context, the articles' general and methodological characteristics and themes and the findings related to ICAP contexts are presented. As a result of the research, no study regarding the ICAP Framework was conducted in Turkey. It was revealed that most of the studies, which have increased in number since 2017 in various countries, utilize the ICAP framework at the analytical level or create models-modules and develop toolsscales based on the ICAP Framework. Additionally, it was observed that mostly undergraduate and K12 students were studied in face-to-face education, with social sciences as the leading disciplinary field and teaching-learning approaches and design-development-evaluation as the most frequently studied topics, while in studies in which mixed and qualitative methods were the leading methods, exploratory and experimental approaches were more preferred. In line with these results, recommendations are presented to contribute to the field.

This study aims to analyze the factors influencing the typology of Turkic words to examine the specifics of the way students learn Turkic languages in higher education institutions. A hypotheticdeductive, survey, and comparative method was used for the study. Results showed that the learners have trouble constructing oral discourse and do not achieve coherence and cohesion in the field of communication they produce, as they do not integrate the parts into the whole under a structured system. Results also suggest that the systematization of Turkic words and expressions forms the trunk of the numerous Eastern Mediterranean and Asia languages, where most of them are very similar. It is concluded that categorizing words and expressions systematically by teachers through linguistic and pedagogical activities can contribute to one's learning and teaching of Turkic languages.

Languages that have complex syllable patterns also share linguistic features with each other. These features can be identified through diachronic paths developed by these syllable patterns this study aimed to show the universality of syllabemes in Kazakh and other languages, focusing on questions like evolution of syllables in the Turkic languages; whether a syllable can be called universal in Turkic languages, and whether CV-type syllable be called universal. The study used a qualitative research design to reconstruct linguistic forms in the Turkic languages. This approach is highly valuable for diachronic phonology, which studies existing models of phonological structures and retrospectively determine the proto-language model characteristic of modern languages. This method helps to restore the phonological system of a proto language, by bringing together synchronous slice of one language or different synchronous slices of several related languages. This method is comparative and typological; and focused on both ancient and modern languages including Bulgarian, Chuvsh, Yakut (ancient) and New Turkic languages like Azerbaijani, Gagauz, Uzbek, Turkmen, Kazakh and Tatar. The data revealed the dynamism of the Turkic languages, showing that they constantly changed, developed, and improved. A comparative analysis of closely related languages morpheme was also done to make an etymological reconstruction. The results suggest that highly complex syllable structure is a linguistic type distinct from but sharing some characteristics with other proposed holistic phonological types, including stress-timed and consonantal languages. The study contributes to understanding the syllable theory in diachronic development of syllable patterns and syllable structures.

The intricate and tricky case of semantic analysis, especially of the enigmatic realm of nouns, the red thread of linguistics and literary exploration. This academic research explores the rich sphere of Russian literature in-depth to determine connections between function and the embodiment of theme through proper names. This research is aiming at studying the role of proper nouns in the literary masterpieces by focusing on the ideas of character development, thematic cohesiveness, and the narrative flow. This papers deals with profound significance of proper nouns as linguistics pointers that take you beyond their literal meaning to different levels of connotative value. It takes most of its ideas from the late linguistics and literary critique. This research gets down to serious business by cherry-picking and analysing a set of Russian masterpieces. An intensive method of semantic analysis was used by the study to properly identify and categorize all the structural features of proper nouns. Also, it conducts an examination of the usage of language by these instrumentalities so as to discover their common features and themes which lead to the invention of the main narrative. The results of this study add to our comprehension of proper noun semantics in Russian literature and provide us with a stepping-stone to broader discussions on the linguistic theory and literary studies. This cognitive effort exceeds the linguistic borders, as it takes into consideration the cultural and historical value of the proper nouns being presented. The report shows why it is necessary to reconsider the theoretical design to account for the distinctive linguistic and particular cultural features of the Russian literary heritage. This research is a step toward unravelling noun semantics' mystery and adds to the already existing linguistic aesthetics and cultural semiotics debates.

Virtual reality has emerged as an influential technology in the educational landscape, offering learners and teachers immersive and interactive experiences that enhance traditional teaching methods. However, despite the increasing importance of virtual reality in education, a systematic description and classification of virtual reality use cases in education is still lacking. This limits the understanding and comparability of virtual reality use cases and highlights the need for a structured approach. This study asks the research question: How can virtual reality educational use cases, identified in the literature, be described, and classified? To classify these use cases, this study develops a state-of-the-art taxonomy. The taxonomy was developed in a combination of a conceptual-to-empirical and empirical-to-conceptual approach. The first stage to develop the taxonomy was based on a conceptual-to-empirical approach. Here, the concepts of virtual reality, use case and education serve as meta-characteristics and theoretical structure. To further detail the dimensions and characteristics, a systematic literature review was conducted by following a PRISMA-guided search and selection process. Therefore, the scientific databases Science Direct, AISel and Springer Link were used to search for studies between 2018 and 2023, obtaining a sample of 39 publications. As the conceptual-to-empirical approach did not richly describe the analysed virtual reality use cases from the studies, additional dimensions and characteristics were identified inductively. Therefore, a second iteration was conducted relating to the empirical-to-conceptual approach. This process explored the practical aspects of virtual reality scenarios and added applicable and practical characteristics to the initial theoretical foundation. The result is a comprehensive taxonomy of virtual reality use cases in education that contributes significantly to existing knowledge and provides a solid foundation for future research. The final taxonomy includes 17 dimensions and 37 characteristics. These findings can support educators to understand the nature of virtual reality use cases, enabling them to describe and implement such use cases effectively within educational settings. [Note: The publication year (2023) shown in the citation on the PDF is incorrect. The correct publication year is 2024.]

Library staff developed a project to reclassify the Curriculum Materials Collection of a specialty library on the campus of a regional comprehensive university. Materials were reclassified from a custom classification system derived from those developed by Lois Belfield Watt in 1962 and Harlan Johnson in 1973, to the Dewey Decimal Classification. While the original system had the advantage of browsability for patrons who were familiar with it, its unusual nature made training patrons and staff challenging. The transition to Dewey is expected to ease training and use, enhance resource sharing, and allow student teachers to transfer their library skills to the schools they will be teaching in.

The present study aimed to analyze the developmental trajectories of English achievement and exposure time through private education, utilizing univariate and multivariate latent growth curve analysis. This study used a subset of the Gyeonggi Education Panel Study data from 2012 to 2017. The results indicated consistent disparities in the growth of English achievement, depending on the student's proficiency levels at the starting point. In particular, the early exposure to private tutoring was significantly and closely related to the initial status and the growth trajectory of English achievement. When the sample was classified by urbanicity, the findings suggested that the gap in English achievement was likely to increase over time across the two regions, with a stronger correlation with private tutoring found in urban areas. These results underscore the need for educational intervention for students in less advantageous conditions, and they provide valuable pedagogical implications for teaching English in the Korean EFL context.

Biology education plays a vital role in nurturing the understanding of learners about the intricacy of life. Various efforts have emerged to strengthen learning biological concepts but there were still studies that showed that learners have low mastery in some aspects. To determine how well students understood various biological topics, including human physiology, Concept inventory tests (CIT) were used. The concept inventory test may be able to spot students' misconceptions and ultimately lead to improved comprehension. The crafted CIT developed with the aid of a table of specifications based on Bloom's taxonomy of cognitive domain was assessed according to its validity and reliability. In validation, content validity and item analysis were considered while reliability test was employed through Cronbach's alpha. Distractor analysis was also performed to determine possible source of misconception per item. The CIT was administered to 120 senior high school STEM students (50.8% from the private schools, 37.5% in regular public schools and 11.7% from public schools with special programs in science). The results displayed high content validity with a mean of 4.83 for content validity and an average Aiken's validity coefficient of 0.98. It also highlighted that the test is moderately difficult with the test difficulty of 0.58, as well as, discriminatory with a discriminating level of 0.46. After item classification, 63 items were retained (39 accepted, 24 for revisions) and Chronbach's alpha ([alpha]=0.74) indicated good internal consistency. The concept inventory test propounds to be a good classroom test science with minor items to be revised.

In light of the increasing frequency of misleading information in social media regarding environmental issues, this study aimed to identify misleading information spread through TikTok videos and to discuss why such content is considered misleading, drawing on relevant literature. Hashtags with large numbers of views, such as #climatechange, #sustainability, #pollution, #biodiversity, #environmentalprotection, #environmentalissues, #energysource, and #environmentalproblems, were used for data collection through web scrapper called Apify (https://apify.com/). A total of 29 misleading videos were found. Content analysis was applied to identify and classify the topics and misleading claims. The topics of misleading videos, according to the most frequent mentions, were energy sources, followed by climate change, pollution, biodiversity, and environmental degradation. Among the misleading claims, videos related to pyramids as nonpollutant power plants and conspiracy related to pollution exhibited the highest frequency. The results show various misleading claims in videos related to environmental topics. Also, emphasized the importance of science education in addressing misleading information. In addition, the importance of an interdisciplinary approach for addressing environmental issues was reinforced.

The impetus of this study is to investigate students' attitudes towards online language learning based on their previous academic experiences and year of study, including the decision to major or minor (i.e., motivation). A total of 975 students completed a survey questionnaire consisting of background information, Likert scale items, and open-ended questions. The quantitative data were analyzed using an exploratory factor analysis and one-way ANOVAs and were complemented with qualitative data based on students' responses. Findings indicate that students generally want consistent access to online learning, and that students with prior online-learning experience or with a desire to take an online course presented a statistically significant more positive perception of online language classes. There were also differences in perception of success in the online classes between those students who intended to major or minor in the language and those who did not. The results further revealed a decline in perception of success in online classes with the higher-level classifications (i.e., year of study). This study provides baseline attitudinal data to be built upon in future research and informs stakeholders of language programs in their curricular decisions.

This study provides a multidimensional analysis of the structural and semantic peculiarities of phraseological units to identify the general, the similar, and the national-specific, which are different in terms of phraseological figurativeness. Most of the magical images and symbols became the basis of the symbolism of the numerals, which became part of the phraseological units. The article focuses on problems such as the implementation of the concept "number" in Russian phraseological units with a numerical component, and it also considers the frequency of using numerical components in phraseological units of the Russian language.

In order to identify the acquisitions foreseen for middle school children in the book "Iz Pesinde" (In Pursuit of the Trail) in Mavisel Yener's "Dolunay Dedektifleri" (Full Moon Detectives) series, this study was conducted. The study sample is composed of the author's book "Iz Pesinde". Sub problems, assumptions, the importance and purpose of the research were included in the study. The work examined in this study, in which qualitative research method was used, was analyzed using descriptive analysis method. Based on this analysis, the work was read, examined, labeled, classified, and then associated with the acquisitions determined for middle school students in the Turkish Language Course (Grades 1-8) Curriculum (2019). It was concluded that Mavisel Yener's "Iz Pesinde" includes examples that match the learning outcomes in the Turkish Language Course (Grades 1-8) Curriculum. Therefore, it is believed that this work written in the field of children's literature is appropriate for the curriculum and can be used as a material in Turkish Language courses.

The purpose of this study is to examine how the categorization skills of the 6th grade students in fractions are distributed and to determine the classification errors made by the students, along with the procedural errors of the students in fractions. The descriptive survey model, one of the quantitative research methods, was used in the study. In this study, 292 6th grade students from four middle schools in two districts of a province in Turkey participated. Developed by the researcher, the fraction operation skill test and classification skills identification test were used. The results showed that students had difficulty in distinguishing examples and characteristics of fractions and fraction types, and even though they partially succeeded in operations, they made procedural errors. The researchers recommend combining procedural knowledge with conceptual knowledge and explaining the basic characteristics of the concept by comparing examples with non-examples.

Despite undeniable advances in research on language learning strategies in the last several decades, empirical investigations of actions and thoughts that learners engage in to better understand and use grammar structures in different contexts, or grammar learning strategies (GLS), remain scarce. Moreover, there is a paucity of studies examining the effects of pedagogical interventions in this area, both with respect to the use of different types of GLS and the mastery of grammar structures in terms of explicit and implicit (automatized) knowledge. There is also no empirical evidence concerning the mediating role of individual difference (ID) factors in this respect. The paper outlines a study currently in progress that seeks to address all of these gaps. The first part is devoted to a brief overview of key issues related to the definition and classification of GLS as well as a synthesis of available research in this area. This is followed by a description of an intervention-based research project which explores the effects of strategies-based instruction (SBI) targeting GLS by English majors in Poland, also taking into account the moderating impact of selected ID variables. In particular, the methodology of the study is presented and the envisaged contributions of the intervention are considered.

MOOCs (massive open online courses), because of their scale and accessibility, have become a major area of interest in contemporary education. However, despite their growing popularity, the question of their quality remains a central concern, partly due to the lack of consensus on the criteria establishing such quality. This study set out to fill this gap by carrying out a systematic review of the existing literature on MOOC quality and proposing a specific quality assurance framework at a micro level. The methodology employed in this research consisted of a careful analysis of MOOC success factor's using Biggs' classification scheme, conducted over a four-year period from 2018 to 2022. The results highlighted the compelling need to consider various indicators across presage, process, and product variables when designing and evaluating MOOCs. This implied paying particular attention to pedagogical quality, both from the learner's and the teacher's point of view. The quality framework thus developed is of significant importance. It offers valuable guidance to MOOC designers, learners, and researchers, providing them with an in-depth understanding of the key elements contributing to MOOC quality and facilitating their continuous improvement. In addition, this study highlighted the need to address aspects for future research, including large-scale automated evaluation of MOOCs. By focusing on pedagogical quality, MOOCs can play a vital role in providing meaningful learning experiences, maximizing learner satisfaction, and ensuring their success as innovative educational systems adapted to the changing needs of contemporary education.

The paper has three major objectives. The first objective of the paper is to synthesize and define common categories of meta-analysis. The second objective is to propose a way to comprehend these common categories of meta-analysis through learning from their respective generic conceptual frameworks. The third objective is to point out which R packages to use to conduct a meta-analysis for each category presented in this paper. In practice, research novices may not know where to begin to learn a topic of meta-analysis for their research. Furthermore, they may not know how the topic of meta-analysis progresses from basics to advanced. This makes it almost next to impossible to use meta-analysis in their research. Certainly, this is problematic. Thus, this paper intends to help research novices overcome this problem. To achieve this, the three objectives mentioned at the beginning were implemented. To propose a synthesis of common categories of meta-analysis along with their respective definitions, a literature review was used as a method to synthesize and define the common categories of meta-analysis presented in this paper. Here, synthesizing meta-analysis categories in one figure would make it much easier for research novices to see the breadth of the subject in question. To propose an easy way to understand the concepts of meta-analysis through learning from their respective generic conceptual frameworks, personal experience was used as a method to make that offering. Here, it is encouraged that research novices should go beyond the definitions of categories of meta-analysis. They can try to frame their research by using example conceptual frameworks presented in the paper as a starting point. To point out which R packages to use with what categories of meta-analysis, a literature review was used as a method to identify those appropriate R packages available. Here, it would be useful for research novices to know which R packages they may need for their meta-analysis research. In summary, the contributions of the paper here would make research novices see and understand the common categories of meta-analysis from the bottom to the top of the pyramid, also offers an easy way to understand the common categories of meta-analysis by illustrating how to develop conceptual frameworks for the common categories of meta-analysis, and finally recommending the R packages to be used with the categories of meta-analysis. Ultimately, we hope that research novices will be able to use meta-analysis successfully in their research.

Classifying, naming, and identifying insects have been complicated topics among science teachers and students. This problem is due to the highly technical collection protocols, safekeeping procedures, the unavailability of appropriate learning resources, and the closure of school laboratories and facilities due to the COVID-19 pandemic. Using the remote learning setup, this study utilized Improvised Insect Traps (IITs) to provide an authentic learning experience in teaching and learning insect taxonomy at home. The study aimed to determine the effects of using IITs on students' self-efficacy beliefs and perceived levels of engagement. In this quasi-experimental study, 42 students designed and developed their improvised insect traps. Insect collection and classification were accomplished within four weeks. A 20-item validated survey questionnaire on self-efficacy and engagement levels was administered via Google Forms. Students' feedback was gathered using virtual focus group discussions and open-ended questions. Results revealed that IITs effectively improved students' self-efficacy beliefs (Z=0.033, p-value=0.022, g=0.68), while no improvement was noted in students' perceived levels of engagement (Z=0.143, p-value=0.188, g=0.07) in teaching and learning insect taxonomy. Moreover, students' feedback and responses were classified as Affordances or Constraints. Subthemes such as motivation, satisfaction, authentic learning, safety, and parental involvement were generated in the thematic analysis. Overall, this study found that the IITs activity is relevant in teaching insect taxonomy and delivering practical learning experiences among students in a distance learning modality.

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations.

Much is unknown about those who do not finish high school and there is a need to understand who enters and completes alternative high school completion programs, such as the HiSet (High School Equivalency Test), GED (General Education Development Test), or growing number of state sponsored high school equivalency programs. The purpose for conducting the study was to describe the perceptions of adult education program coordinators about how to best categorize potential high school credential alternative completers. Using a phenomenological framework and semi-structured interviews, 12 adult education program administrators were interviewed about who enrolls in and completes their programs. A thematic analysis of these responses indicated distinct categories of individuals including opportunists (those looking for better opportunities), exceptionals (those with exceptionalities such as a disability that prevented the individual from completing a traditional high school diploma), immigrants (those validating learning in another country or language learners), and those who have been reformed in some way (e.g., those with a history of difficulty with legal situations). By identifying these initial categories of adult learners taking part in high school equivalency programs, recruitment efforts as well as the programs themselves might be better structured to correspond to learner needs.

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term implications. This has prompted research on fairness metrics meant to capture and quantify such biases. Nonetheless, current metrics primarily focus on predictive performance comparisons between groups, without considering the behavior of the models or the severity of the biases in the outcomes. To address this gap, we proposed a novel metric in a previous work (Verger et al., 2023) named "Model Absolute Density Distance" (MADD), measuring algorithmic unfairness as the difference of the probability distributions of the model's outcomes. In this paper, we extended our previous work with two major additions. Firstly, we provided theoretical and practical considerations on a hyperparameter of MADD, named "bandwidth," useful for optimal measurement of fairness with this metric. Secondly, we demonstrated how MADD can be used not only to measure unfairness but also to mitigate it through postprocessing of the model's outcomes while preserving its accuracy. We experimented with our approach on the same task of predicting student success in online courses as our previous work, and obtained successful results. To facilitate replication and future usages of MADD in different contexts, we developed an open-source Python package called maddlib (https://pypi.org/project/maddlib/). Altogether, our work contributes to advancing the research on fair student models in education.

Within the past decade, there has been a shift in how our guiding professional documents conceptualise information literacy (IL) - evolving from a skills-based conceptualisation to one emphasising ways of thinking and knowing. This has been both productive and disruptive. Our professional documentation does not provide a framework for making this shift or for scaffolding learning to develop complex and sophisticated ways of thinking and knowing. In this study, we apply Bloom's revised taxonomy for educational objectives to instructor descriptions of IL (n=51) to develop a draft taxonomy that attempts to build a bridge between these two conceptualisations. The data was drawn from a survey that was administered to instructors and instructional support staff immediately preceding their participation in a multi-day teaching professional development workshop related to IL. We believe that this model has implications for how we approach the development of learners' IL with intentionality, both in collaboration with faculty and for our own teaching practices as librarians.

We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most influential in dropout prediction. To evaluate its effectiveness, we compared the predictive performance of our hybrid model with two other survival analysis models: the piecewise exponential model and a hybrid model combining a fully-connected neural network with a piecewise exponential model. Additionally, we compared it to five other cross-sectional models: Ridge Logistic Regression, Lasso Logistic Regression, CART decision tree, Random Forest, and XGBoost decision tree. Our findings demonstrate that the hybrid model outperforms or performs comparably to the other models when predicting dropout among students at the University of Delaware in Spring 2020, Spring 2021, and Spring 2022. Moreover, by categorizing predictors into three distinct groups--academic, economic, and social-demographic--we discovered that academic predictors play a prominent role in distinguishing between dropout and retained students, while other predictors may indirectly influence predictions by impacting academic variables. Consequently, we recommend implementing an intervention program aimed at identifying at-risk students based on their academic performance and activities, with additional consideration for economic and social-demographic factors in customized intervention plans.

Predicting and improving the academic achievement of university students is a multifactorial problem. Considering the low success rates and high dropout rates, particularly in open education programs characterized by mass enrollment, academic success is an important research area with its causes and consequences. This study aimed to solve a classification problem (successful or unsuccessful), predict students' academic success, and identify those at risk. The primary objective was to predict the academic success status with 26,708 students enrolled in Istanbul University open and distance education programs between 2011 and 2017. Predictions were based demographic data and success grades in Turkish, Atatürk's Principles and History of Revolution, English, and Disaster Culture courses. The study utilized classification models from supervised learning algorithms and was conducted using the SPSS Modeler 18 program. Initially, the data was divided into 70% training and 30% test data. Then, models were constructed by using Random Forest, Tree-AS, C&RT, C5.0, CHAID, QUEST, Naive Bayes, Logistic Regression, NeuralNet, and SVM algorithms. Model performances were compared according to accuracy, sensitivity, specificity, F1 score, positive predictive value, negative predictive value, and Matthews Correlation Coefficient criteria. The C&RT model demonstrated the best performance, achieving the highest specificity value of 0.915.

This study describes a typology of the teaching styles of practicing teachers. Teaching style refers to a teacher's distinct qualities that are persistent from situation to situation regardless of the content. The Teaching Style Assessment Scale, which measures teaching style, was completed by 1,261 nursing faculty in Japan. Prior cluster analysis revealed four naturally-occurring groups based on their teaching style, with teachers practicing learner-centered and teacher-centered styles nearly equally. The dual purposes of this study were (1) to measure the differences between these groups to determine if they were unique and (2) to describe any teaching style differences among these groups. Analysis of variance and Scheffé descriptive statistics supported the conclusion that a typology exists for classifying teaching styles as they are practiced in the field by teachers. Discriminant analysis identified the distinctions among the groups. The four distinct groups clearly described in this Teaching Styles Typology of Practicing Teachers are "Distinctly Teacher-Centered," "Moderately Teacher-Centered," "Consistently Learner-Centered," and "Decidedly Learner-Centered." This typology of teaching styles can be a valuable tool for teachers to improve their professional practice. It has implications for increased professionalism by facilitating increased self-awareness and assisting in developing an educational philosophy.

Student learning outside the formal classroom is inextricably linked to informal learning environments. In many countries, most activities that employ informal learning prioritize the integration of science, technology, engineering, and mathematics (STEM) disciplines and have shown a positive impact on increasing students' interest, self-efficacy, and awareness of the STEM field. Thus, this study aims to systematically review the activities reported in the relevant studies focusing on informal STEM learning for K-12. High-index journals published under SCOPUS and Web of Science databases were utilized using a predetermined search strategy and retrieved two research team members' screened articles. Only empirical studies containing the terms "STEM education", "summer camp", and "informal learning" in the title, abstract, and keyword were included. Data were coded and organized into a matrix that was qualitatively assessed and categorized into themes. Based on the 25 studies reviewed, it was found that the integration of STEM disciplines in informal learning is notably varied. The characteristics of the STEM activities can be organized into seven themes: inquiry, focus on problems, design, cooperative learning, student-centred, hands-on, and 21st-century skills. Practice recommendations include a quality curriculum that aligns with informal STEM learning needs.

Purpose: This study explores debate seeking and conflict avoidance as dimensions of disagreement orientation, and how factors such as citizenship education and individual background may impact how young people engage in situations with conflicting political perspectives. The aim is to study whether how we facilitate citizenship education may affect young people to be more comfortable with political disagreement. Design/methodology/approach: Multilevel modelling and factor analysis is performed using a survey among a selection of Norwegian fifteen-year-olds. Findings: Citizenship education characteristics impact conflict avoidance but not debate seeking. Debate seeking is rather related to individual background factors such as political interest and attention paid to the news. Finally, the dimensions form basis of a potential typology of young people's disagreement orientation. Research limitations/implications: Further research is needed to establish causality, however the results have implications for the role of disagreement in teacher education and classrooms.

Regional Public Universities (RPUs) advance public good missions to foster postsecondary access, student-centeredness, and regional wellbeing. Despite the important contributions that RPUs make to their regions and students, scholars and the media often view the sector as amorphous and undistinguished, or they define RPUs by what they are not (e.g., elite, selective, community colleges, etc.). This sequential mixed methods research study grounded in organizational ecology theory and involving content analysis and cluster analysis proposes a typology of RPUs. Specifically, five RPU types are proposed: accessible, midsized master's-degree granting RPUs; regionally focused Historically Black Colleges and Universities; rural-serving and CUNY RPUs; large, urban regional-serving RPUs; and Puerto Rican Hispanic-serving RPUs. This study significantly advances conceptual understanding about RPUs by disrupting deficit-based understandings about the sector being shapeless and amorphous by showing that the sector contains distinct institutional types that represent unique approaches to fulfilling the RPU public good mission. This typology is also among the first to document the institutional diversity of RPUs.

In this essay, the authors explore the dilemmas facing researchers, educators, and policymakers in how to refer to multilingual students who are deemed in need of language support in school. There is a growing concern with the label "English learner," the term currently used in U.S. federal legislation, as focusing exclusively on students' English language acquisition and ignoring their multilingual resources. Alternative terms such as "emergent bilingual" and "multilingual learner" that underscore students' multilingual assets have become widespread alternatives. The authors argue that the critiques of the term "English learner" must be taken seriously. They also argue that, given that being classified as English learners in schools has material consequences for students' academic and occupational trajectories, this label cannot simply be wished away. In fact, debating over labels alone carries the danger of mislocating the primary site of struggle for equity and justice in the labels themselves, rather than in the systems that position and frame linguistically minoritized students in a deficit light in the first place. As researchers who have worked with this population for years, the authors, too, are grappling with how to refer to them. Each of them shares the dilemmas they have encountered, and they conclude by offering a multidimensional tool that might assist scholars, educators, and policymakers in reflecting on what is gained and lost by using different terms.

Concept mapping is widely used as a tool for assessing students' understanding of science. To fully realize the diagnostic potential of concept mapping, a scoring method that not only provides an objective and accurate assessment of students' drawn concept maps but also provides a detailed understanding of students' proficiency and deficiencies in knowledge is necessary. However, few of the existing scoring methods focus on the latent constructs (e.g., knowledge, skills, and cognitive processes) that guide the creation of concept maps. Instead, they focus on the completeness of the concept map by assigning a composite score, which makes it difficult to generate targeted diagnostic feedback information for advancing students' learning. To apply the diagnostic classification model to the quantitative analysis of concept maps, this study introduced the novel application of the item expansion-based diagnostic classification analysis (IE-DCA) for this purpose. The IE-DCA can not only assess students' concept mapping abilities along a continuum but also classify students according to their concept mapping attributes when constructing the concept maps. The application and benefits of this approach were illustrated using a physics concept-mapping item related to particle and rigid body. Results showed that the estimated attribute profiles via the IE-DCA provided more detailed information about students' latent constructs than the composite score. Overall, this study illustrates the feasibility and potential of applying IE-DCA to analyze concept maps. Future applications of IE-DCS in other assessments in science education are discussed.

Given decision-makers often prioritize causal research that identifies the impact of treatments on the people they serve, a key question in education research is, "Does it work?". Today, however, researchers are paying increasing attention to successive questions that are equally important from a practical standpoint--not only does it work, but for whom and under what circumstances? Invalid conclusions to any of these questions can result in the adoption of ineffective educational practices. This article discusses the enduring legacy of Shadish, Cook, and Campbell's validity typology, and its associated threats to validity, for improving the validity of inferences in education research. The validity typology provides a system for classifying and improving inferences related to four validity types, including ensuring a causal relationship between a treatment and outcome (internal validity) that is precisely estimated (statistical validity), well understood (construct validity), and generalizes to the necessary circumstances (external validity). Here, we provide an overview of these four validity types and discuss proactive approaches to addressing them. We conclude by discussing how the validity typology framework may help researchers understand and address contemporary critiques of quantitative causal research.

The adoption of large language models (LLMs) in education holds much promise. However, like many technological innovations before them, adoption and access can often be inequitable from the outset, creating more divides than they bridge. In this paper, we explore the magnitude of the country and language divide in the leading open-source and proprietary LLMs with respect to knowledge of K-12 taxonomies in a variety of countries and their performance on tagging problem content with the appropriate skill from a taxonomy, an important task for aligning open educational resources and tutoring content with state curricula. We also experiment with approaches to narrowing the performance divide by enhancing LLM skill tagging performance across four countries (the USA, Ireland, South Korea and India-Maharashtra) for more equitable outcomes. We observe considerable performance disparities not only with non-English languages but with English and non-US taxonomies. Our findings demonstrate that fine-tuning GPT-3.5 with a few labelled examples can improve its proficiency in tagging problems with relevant skills or standards, even for countries and languages that are underrepresented during training. Furthermore, the fine-tuning results show the potential viability of GPT as a multilingual skill classifier. Using both an open-source model, Llama2-13B, and a closed-source model, GPT-3.5, we also observe large disparities in tagging performance between the two and find that fine-tuning and skill information in the prompt improve both, but the closed-source model improves to a much greater extent. Our study contributes to the first empirical results on mitigating disparities across countries and languages with LLMs in an educational context.

This study presents a list of 2022 Bulgarian-Turkish cognate words that can be used to facilitate vocabulary acquisition and cross-linguistic awareness. The list was created manually through a multi-step process that involved scanning the database of an online dictionary to identify Bulgarian words that had the same or similar forms in Turkish. For each cognate word identified, its Bulgarian and Turkish forms and English meanings were recorded, and their accuracy was checked through additional sources and expert consultation. The final list of cognate words includes only those confirmed based on linguistic criteria and expert judgement. The use of cognate words in language teaching methods such as total physical response and teaching proficiency through reading and storytelling can enhance the effectiveness of these methods by helping students understand and remember new vocabulary and building their confidence in language learning. Additionally, this study highlights the potential of cognate awareness to promote peace in the world by emphasizing the commonalities among languages and cultures. By recognizing the abundance of cognates across different languages, we can reinforce the idea that we are all part of the same human family and share a common linguistic heritage. Moreover, the study demonstrates how a new and innovative multilingual education can be achieved through cognates, showing that language education does not necessarily have to be limited to bilingual instruction. The use of cognates as a tool for vocabulary acquisition and cross-linguistic awareness can open up opportunities for multilingualism and facilitate communication across linguistic and cultural boundaries. Future research could explore the use of cognate words in other language teaching contexts and investigate the potential benefits and limitations of using cognates in language learning.

This article shares research, an empirical psychological case study, about cognition in higher degree art education. It proposes cognitive curation as a concept and practice that can develop knowledge and learning autonomy in and beyond the academy. Informed by the autoethnographic stories, interviews, and artworks of three academic art educators, this article's research demonstrates how cognition and its curation can manifest and develop in the teaching, research, and practice of higher degree art education. Open coding and framework alignment (cognitive, nexus orientated and visual) helped understand, locate, and exemplify cognition and curation in the research. Informed by the data, this article acknowledges how movement, identities, and frameworks, as learning strategies, can help facilitate cognition and cognitive curation. Cognitive curation provides means to responsibly form and follow learning, it is consequently relevant to the arts, education, and life. Art education's cognitive value is often questioned, this article dialogically contributes to the defense of its cognitive integrity whilst foregrounding cognitive curation.

Categorization is the foundation of many cognitive functions. Importantly, the categories we use to structure the world are informed by the language we speak. For example, whether we perceive dark blue, light blue, and green to be shades of one, two, or three different colors depends on whether we speak Berinmo, English, or Russian, respectively. Different languages, then, differ by how granular their categories are, but the source of these differences is still poorly understood. Understanding the source of cross-linguistic differences in linguistic categorization is important because categorization influences communicative efficiency and cognitive performance. Here we use computational simulations to show that community structure and specifically community size and community interconnectivity influence the categorization systems that communities create. In particular, the simulations show that the obstacles for diffusion that large communities encounter push them to develop categorization systems that are more expressive and better understood, but only if they have sufficiently long memory to do so. The simulations also show that larger communities are better at creating useful references to rarely communicated meanings, thus further boosting communication in these cases. These findings demonstrate how taking social structure, and especially community size, into account can illuminate why languages evolved to have their current forms. They further show how social constraints, such as those encountered by large communities, can drive the creation of better and more robust systems. As categorization is a building block for many cultural products, these results also have implications for our understanding of cultural evolution more broadly.

The last decade witnessed a spectacular rise in the volume of available textual data. With this new abundance came the question of how to analyze it. In the social sciences, scholars mostly resorted to two well-established approaches, human annotation on sampled data on the one hand (either performed by the researcher, or outsourced to microworkers), and quantitative methods on the other. Each approach has its own merits -- a potentially very fine-grained analysis for the former, a very scalable one for the latter -- but the combination of these two properties has not yielded highly accurate results so far. Leveraging recent advances in sequential transfer learning, we demonstrate via an experiment that an expert can train a precise, efficient automatic classifier in a very limited amount of time. We also show that, under certain conditions, expert-trained models produce better annotations than humans themselves. We demonstrate these points using a classic research question in the sociology of journalism, the rise of a "horse race" coverage of politics. We conclude that recent advances in transfer learning help us augment ourselves when analyzing unstructured data.

Automated image analysis has received increasing attention in social scientific research, yet existing scholarship has mostly covered the application of supervised learning to classify images into predefined categories. This study focuses on the task of unsupervised image clustering, which aims to automatically discover categories from unlabelled image data. We first review the steps to perform image clustering and then focus on one key challenge in this task--finding intermediate representations of images. We present several methods of extracting intermediate image representations, including the bag-of-visual-words model, self-supervised learning, and transfer learning (in particular, feature extraction with pretrained models). We compare these methods using various visual datasets, including images related to protests in China from Weibo, images about climate change on Instagram, and profile images of the Russian Internet Research Agency on Twitter. In addition, we propose a systematic way to interpret and validate clustering solutions. Results show that transfer learning significantly outperforms the other methods. The dataset used in the pretrained model critically determines what categories the algorithms can discover.