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This paper argues that engaging students in informal statistical reasoning from early school years is essential for the development of statistical understanding. We investigated if and how children aged six-seven years old identified variation in a table of data and made predictions through the design of a teaching experiment. The classroom teaching experiment was comprised of four 45 minutes lessons addressing the understanding and interpretation of data sets. In order to describe students' informal predictive reasoning, we used the framework of "data lenses". More specifically, we analyzed the different types of answers the students produced as they engaged in predictive reasoning during an interview given before and after the teaching experiment. The participation of students in (classroom) and out-of-school (family) communities of practice was also taken into consideration. Our results demonstrate that the students benefited from their learning experience and developed data understanding.

Data literacy, which is considered among 21st century skills, is becoming increasingly important. In particular, incentives for data-driven decision-making draw attention to data literacy. Data literacy is also critical for teachers who need to use data in educational settings. Therefore, there is a need for data on whether teachers are equipped with data literacy competencies before their service. In this study, pre-service teachers' data literacy competencies were examined. The case study approach was used in the study, which was conducted as qualitative research. The study group consisted of 61 pre-service teachers studying in three different undergraduate programs. Data were collected through the "What do the data say?" activity instrument. Rubrics were used to analyze the data. The findings of the study showed that while there was no difference in terms of using data and data communication, there was a statistically significant difference between the programs in terms of data recognition, comparing data and establishing relationships between data competencies, as well as total data literacy. In addition, it was found that the majority of pre-service teachers were partially inadequate or inadequate in terms of using data, data communication and total data literacy. Nearly half of them were partially inadequate or inadequate in comparing data and establishing relationships between data. The results indicated that preservice teachers have certain deficiencies regarding data literacy.

Learning to interpret visual representations of data is an important step towards becoming an informed consumer of research. The current study assesses the effectiveness of two versions of a scaffolded online module in improving students' ability to identify main effects and interactions in 2 × 2 factorial designs. Across two experiments (N = 313), we compared exam performance between sections of a lower-division conceptual statistics course that completed the module (in addition to other course activities) to a section that did not complete the module (n = 91). The first iteration of the module (used in Experiment 1, n = 96) was completed once individually and once with peers and did not enhance students' individual performance on conceptually-related exam questions. However, performance on the module was low, indicating that students may have needed more support to benefit from this experience. In Experiment 2 (n = 126), we made three empirically-driven changes to better scaffold student learning: we added a worked example, offered a greater variety of examples, and instructed students to complete the whole activity with peers. Under these conditions, performance increased on related exam questions. We conclude that this freely available module is a promising intervention to strengthen students' ability to understand factorial designs.

To best prepare students for the real-world research environment, key skills, including experimental design, data analysis, communication of results, and critical thinking, should be key components of undergraduate science courses. Furthermore, the impact of the COVID-19 pandemic on in-person teaching has resulted in a need to develop courses that enable flexible learning. This paper details the laboratory component of a senior-level toxicology class that was developed to emphasize all these skills and allow for flexible learning. The aim of the laboratory class was for students to determine how curcumin protected against acetaminophen-induced hepatoxicity. To stimulate critical thinking, students were required to choose a maximum of four experiments from the six on offer. Before conducting an experiment, students stated a hypothesis and selected the appropriate treatment groups. Once an experiment was completed, students were given access to a complete dataset, on which they performed statistical analysis and drew conclusions. Students who were unable to attend the laboratory session in person were able to complete the required pre-lab work and access the dataset. Following each experiment, students could write a lab summary, and receive thorough feedback. The final assessment was a written manuscript of their findings as well as a chance to respond to reviewer comments. This teaching approach prioritized the critical thinking, analysis, and experimental design aspects of scientific research. Overall, this structure was well received by students and it could easily be adapted for use on other life science courses.

This study surveyed 183 college and senior high school students' graph-interpretation competence in scientific and daily contexts. Specifically, whether students' graph interpretation in scientific and daily contexts differed across educational levels was investigated. Furthermore, the questions of whether students' graph interpretation competence in scientific contexts might predict their graph interpretation in daily contexts were explored. Other factors including age and gender were also considered and examined to discern significant factor(s) predicting graph interpretation competence in daily contexts. Results indicated that there were statistically significant differences across educational levels in terms of graph interpretation competence in both scientific and daily contexts. Furthermore, the students' graph interpretation competence was related between scientific and daily contexts. The important factors predicting students' graph interpretation in daily contexts include age and graph-interpretation in scientific literacy. Implications are discussed.

Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed data set and use the number of factors suggested by most of the data copies or average the correlation matrices across all data copies, followed by applying the parallel analysis to the average correlation matrix. Both approaches for pooling the results provide a single suggested number without reflecting the uncertainty introduced by missing values. The present study proposes the use of an alternative approach, which calculates the proportion of imputed data sets that result in k (k = 1, 2, 3 . . .) factors. This approach will inform applied researchers of the degree of uncertainty due to the missingness. Results from a simulation experiment show that the proposed method can more likely suggest the correct number of factors when missingness contributes to a large amount of uncertainty.

Equating is a statistical procedure used to adjust for the difference in form difficulty such that scores on those forms can be used and interpreted comparably. In practice, however, equating methods are often implemented without considering the extent to which two forms differ in difficulty. The study aims to examine the effect of the magnitude of a form difficulty difference on equating results under random group (RG) and common-item nonequivalent group (CINEG) designs. Specifically, this study evaluates the performance of six equating methods under a set of simulation conditions including varying levels of form difference. Results revealed that, under the RG design, mean equating was proven to be the most accurate method when there is no or small form difference, whereas equipercentile is the most accurate method when the difficulty difference is medium or large. Under the CINEG design, Tucker Linear was found to be the most accurate method when the difficulty difference is medium or small, and either chained equipercentile or frequency estimation is preferred with a large difficulty level. This study would provide practitioners with research evidence-based guidance in the choice of equating methods with varying levels of form difference. As the condition of no form difficulty difference is also included, this study would inform testing companies of appropriate equating methods when two forms are similar in difficulty level.

This article examines the use of the repertory grid technique as a method to investigate learner perceptions in language education research. An important issue raised in this article concerns how far a researcher's agenda may be unintentionally imposed onto a research study which is investigating learners' perceptions, and how far the ensuing data may provide an accurate representation of the learners' viewpoints. A discussion of conventional research methods in perception research indicates the limitations of many structured research instruments such as questionnaires, surveys and interviews with regard to gaining reliable insights into learners' views. The article considers the potential of using the repertory grid technique in perception research in terms of minimizing researcher influence and obtaining data that reflects learners' perceptions. Taking a research study on the perceptions of secondary school learners of the language classroom as an example, the article demonstrates how building repertory grids can reveal insights into the learners' thought processes and give the researchers access into the different ways learners view the classroom. The article also considers some issues in terms of data analysis and the interpreting of language data. It concludes that the repertory grid technique is a useful approach in perception research which can provide valuable data that is revealing of participants' actual views.

Several studies compared three different types of questions (conceptual, algorithmic, and graphical) across various topics, however, few focused specifically on gifted students. This study addressed this gap. The aim of the study, hence, was to determine whether there were notable differences in gifted students' performance in the three types of tests. The study involved 115 gifted students aged between 17 and 18 years old. They responded to the three different tests including 10 test items in each with the same content. Significant differences were observed in their performances between the conceptual versus algorithmic in favour of the conceptual test, the conceptual versus graphical in favour of the conceptual test, and the algorithmic versus graphical in favour of the algorithmic test. Moreover, the statistical analysis results revealed that the students performed markedly poorer performance on the graphical test in comparison to both the algorithmic and conceptual tests. These results suggest the need to enhance students' graphical skills to facilitate a better understanding of physics concepts. Proper steps should be taken to improve their proficiency in interpreting and analysing graphical representations.

Nowadays, the assessment of student performance has become increasingly technology-based, a trend that can also be observed in the evaluation of scientific reasoning, with more and more of the formerly paper-based assessment tools moving into the digital space. The study aimed to examine the reliability and validity of the paper-based and computer-based forms of the Science-K Inventory, which assesses children's scientific reasoning in three aspects: experimentation, data interpretation, and understanding of the nature of science. The pilot study involved 84 fourth-grade Hungarian students, with 39 students taking the paper-based test and 45 students taking the computer-based test. Rasch measurements and reliability tests have indicated that both the paper-based and computer-based test versions are equally valid for assessing the scientific reasoning skills of fourth graders. Students achieved high test scores in both mediums, and there were no significant differences between boys' and girls' scientific reasoning in either test type. The novelty of this research was that the Science-K Inventory had not yet been tested in a computer-based format. The results demonstrate that the Science-K Inventory can be effectively utilized in digital testing to provide teachers with rapid and valuable information for fostering the development of their students' scientific reasoning.

Graphic interpretation is as critical in physics education as problem-solving. However, we know that today's classes focus more on problem-solving. This study uses a survey to determine college students' graphic interpretation skills. The study consists of two phases. The first phase includes the development and statistical analysis of the survey. The second phase includes comparing and discussing the data resulting from the application of the developed survey. The research data were analyzed using both exploratory factor analysis and confirmatory factor analysis techniques. The survey on graphic interpretation skills, including the understanding and analysis processes, consisted of 17 items based on analysis results. The survey data were collected using purposive sampling from 113 college volunteers during the fall semester of 2022-2023 at Dokuz Eylul University in Turkey. The participants consisted of 57 geoscience students and 56 mining students. The survey results showed that the kinematic interpretation skills of mining engineering students were higher than those of geoscience students. These differences between geoscience and mining engineering students in cognitive, affective, and psychomotor behaviors were discussed.

Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how students' histogram interpretations alter during an assessment. The research question is: In what way do secondary school students' histogram interpretations change after solving dotplot items? Two histogram items were solved before solving dotplot items and two after. Students were asked to estimate or compare arithmetic means. Students' gaze data, answers, and cued retrospective verbal reports were collected. We used students' gaze data on four histogram items as inputs for a machine learning algorithm (MLA; random forest). Results show that the MLA can quite accurately classify whether students' gaze data belonged to an item solved before or after the dotplot items. Moreover, the direction (e.g., almost vertical) and length of students' saccades were different on the before and after items. These changes can indicate a change in strategies. A plausible explanation is that solving dotplot items creates readiness for learning and that reflecting on the solution strategy during recall then brings new insights. This study has implications for assessments and homework. Novel in the study is its use of spatial gaze data and its use of an MLA for finding differences in gazes that are relevant for changes in students' task-specific strategies.

The increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students' online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that help to characterize what actions students take, when, and where (in which course and in which part of the course). Temporal analysis has been shown to be relevant in learning analytics (LA) research, and capturing time-on-task as a proxy to model learning behaviour, predict performance, and prevent drop-out has been the subject of several studies. In Moodle, one of the most used learning management systems, while most events are logged at their beginning, other events are recorded at their end. The duration of an event is usually calculated as the difference between two consecutive records assuming that a log records the action's starting time. Therefore, when an event is logged at its end, the difference between the starting and the ending event identifies their sum, not the duration of the first. Moreover, in the pursuit of a better user experience, increasingly more online learning platforms' functions are shifted to the client, with the unintended effect of reducing significant logs and conceivably misinterpreting student behaviour. The purpose of this study is to present Moodle's logging system to illustrate where the temporal dimension of Moodle log data can be difficult to interpret and how this knowledge can be used to improve data processing. Starting from the correct extraction of Moodle logs, we focus on factors to consider when preparing data for temporal dimensional analysis. Considering the significance of the correct interpretation of log data to the LA community, we intend to initiate a discussion on this domain understanding to prevent the loss of data-related knowledge.

This conceptual article examines how consultants use a mundane policy device, the powerpoint presentation, to manage education policy relations between international lenders and education ministries in the global South. The article theorizes presentations as socio-material assemblages that combine consultants, software, visualization conventions, presentation styles, and participant structures in ways that enable consultants to control public interpretations of program evidence. Our analysis is a "constitutive argument" (Pacewicz, 2022) about how to conceptualize presentations. We draw evidence from diverse sources including published accounts by consultants, descriptions of presentations in corporate settings, internet discussions of slide construction, online archives of slides, and data from semi-structured interviews with 12 current or former members of a education ministry in a South Asian country. We argue that consultants use mundane presentation technologies to establish closed interpretive spaces in which they can control interpretation by rendering project information in visually coded forms and structuring interaction through language ideologies and participant structures that ritualize presentation discourse. We close by highlighting implications of these arguments and noting lacunae in our account to be addressed in further research.

Close reading has long been heralded as a humanities-specific methodology with significant potential for SoTL. This essay fills a gap in SoTL literature with a full case study demonstrating what, exactly, close reading shows us about our data that social science-based quantitative and qualitative analyses may not. Close reading-based analysis of first-year writing students' pre-surveys on gender-inclusive language entails attention to the interrelated form and content of students' self-reflections. This analysis reveals nuances and complexities that, if overlooked, would result in inadvertent misrepresentation of the data. This case study responds not only to calls for humanities-specific SoTL methodologies but also to related calls for greater legitimation of diverse forms for SoTL dissemination, some of which originate in the humanities. It is therefore cast as a reflective essay based on its author's scholarly personal narrative (SPN) as a new, humanities-based SoTL researcher. Finally, this case study demonstrates the value of flexible, deliberately unscientific study designs that are responsive to emergent conditions but foreign to SoTL's dominant social science paradigm. As guides to instruction, pre-surveys are necessary complements to pre-quizzes: learning what students think they know about a concept or skill, their attitudes towards it, and their contexts of prior learning about it--not just their knowledge of it, which is all pre-quizzes can tell us--is an important precursor to effective instruction. But maximizing pre-surveys' potential to guide instruction requires flexible study designs so we can change our pedagogy, including our study's "intervention," if necessary, on the fly.

Driven by the increased availability of Learning Management System data, this study explored its value and sought understanding of student behaviour through the information contained in activity level log data. Specifically, this study examined analytics data to understand students' engagement with online videos. Learning analytics data from the Moodle[superscript TM] and Vimeo® platforms were compared. The research also examined the impact of video length on engagement, and how engagement with videos changed over the course of a semester when multiple video resources were used in a course. The comparison in platform learning analytics showed differences in metrics thus offering a caution to users relying on unidimensional metrics. While the results support the notion that log data do provide educators with an opportunity for review, the time and expertise in extracting, handling, and using the data may stifle its widespread adoption.

Purpose: Data storytelling courses position students as agents in creating stories interpreted from data about a social problem or social justice issue. The purpose of this study is to explore two research questions: What themes characterized students' iterative development of data story topics? Looking back at six years of iterative feedback, what categories of data literacy pedagogy did instructors engage for these themes? Design/methodology/approach: This project examines six years of data storytelling final projects using thematic analysis and three years of instructor feedback. Ten themes in final projects align with patterns in feedback. Reflections on pedagogical approaches to students' topic development suggest extending data literacy pedagogy categories -- formal, personal and folk (Pangrazio and Sefton-Green, 2020). Findings: Data storytelling can develop students' abilities to move from being consumers to creators of data and interpretations. The specific topic of personal data exposure or risk has presented some challenges for data literacy instruction (Bowler et al., 2017). What "personal" means in terms of data should be defined more broadly. Extending the data literacy pedagogy categories of formal, personal and folk (Pangrazio and Sefton-Green, 2020) could more effectively center social justice in data literacy instruction. Practical implications: Implications for practice include positioning students as producers of data interpretation, such as role-playing data analysis or decision-making scenarios. Social implications: Data storytelling has the potential to address current challenges in data literacy pedagogy and in teaching critical data literacy. Originality/value: Course descriptions provide a template for future data literacy pedagogy involving data storytelling, and findings suggest implications for expanding definitions and applications of personal and folk data literacies.

This paper draws on my experiences as a doctoral student undertaking a project to examine the professional adaptation of Asian immigrant teachers in Australia. Using a reflexive narrative approach, I examine how my position influenced my access to participants, the understanding of their contexts, and the nature of my relationship and interaction with the participants. The paper discusses the tensions that resulted from my insider-outsider positions and how they shaped my qualitative study's research process and outcomes. I conclude with recommendations for researchers to carefully consider the possible influence of their positionality in any research setting and its implications for informing future research.

Background: Sexual health includes physical, emotional, mental, and social wellbeing related to sexuality. Given people with mild intellectual disabilities' reliance on relatives and support staff, it is important to explore the latter's understanding of sexual health. Method: Relatives (n = 7) and support staff (n = 15) of people with mild intellectual disabilities participated in a concept mapping procedure that included brainstorming, sorting and ranking activities. An expert group interpreted the results. n n Results: Relatives and support staff identified aspects of sexual health of people with mild intellectual disabilities, including sexual preferences, sexual behaviour and support and education. While relatives prioritised relationships, support staff emphasised sexual identity. Conclusions: The study highlights the necessity of adopting a comprehensive approach to sexual health for people with mild intellectual disabilities. This approach should consider developmental perspectives, incorporating support, education and a positive attitude towards their sexual health. Implications for research and practice are discussed.

Data literacy, an important goal for social studies education, involves teaching students how to comprehend, analyze, interpret, evaluate, create, and argue with data and data visualizations such as timelines, maps, and graphs. Digital data visualizations support rapid inquiry and explorations that would be difficult on paper - such as adding data to an existing data visualization or creating multiple data visualizations of the same variable at multiple times or places for easy comparison. The process of asking questions and finding answers through data exploration is an important part of social studies education. While the authors recognize that there are many online data visualization tools that can be used in classrooms, they designed their tools, DV4L and Timeline Builder, specifically for use in social studies classrooms and based on feedback from prospective and current teachers. The authors have taken care to include features that teachers said would be useful in social studies data inquiry lessons and activities. These are still in a prototype phase, but are free to use and online.

The global COVID-19 health pandemic caused major interruptions to educational assessment systems, partially due to shifts to remote learning environments, entering the post-COVID educational world into one that is more open to heterogeneity in instructional and assessment modes for secondary students. In addition, in 2020, educational inequities were brought to the forefront of social conscience. The purpose of this study is to empirically explore how contextual (i.e., school-level) race and economic factors may relate to and explain measurement challenges that can arise during shifts to remote learning. We fit a series of multilevel models to explore school-level factors in assessment data alongside psychometric problems of differential item functioning and person fit in classroom assessment measurement models. Our results demonstrate ways in which our project's classroom assessments were impacted by shifts to remote learning, emphasizing the importance of researchers and practitioners evaluating such concerns when seeking validity evidence for interpretation of classroom assessment data.

This paper argues for three aspects of tolerance with respect to QCA research: tolerance with respect to different approaches to QCA; producing QCA research with tolerance (work that is resistant to criticism); and for QCA researchers to be clear about the tolerance of the solutions they present -- especially in terms of calibration and truth table construction. Giving examples that are based on macrocomparative research, the paper organises its structure around how this approach would apply in terms of QCA research design, calibration, analysis, and interpretation, presenting the dilemmas that can result at each stage, along with the choices they require of researchers. It concludes by advocating for greater acceptance of different approaches to QCA ('perspectivalism') but also being clear about what researchers using it should expect from one another in producing high-quality research.

Using the transactional theory (Rosenblatt, 1995, 2001) and translanguaging pedagogy (García, 2009) as guiding lenses, this study explores how 1st-grade Korean-English bilingual students engage in storytelling and meaning construction using two languages when they encounter one of the chosen wordless picturebooks. The findings indicate that although visualizing is the main strategy to create the plot from the wordless picturebook, the students incorporate other reading comprehension strategies (i.e. using prior knowledge, making connections, predicting, revising and verifying their predictions, and inferencing) to construct the meaning of the story. Since storylines are delivered through illustrations only in wordless picturebooks, the bilingual students flexibly translanguage by deploying their linguistic resources from both languages during their story creation. The findings imply that using wordless picturebooks can be an influential pedagogical instrument to help bilingual students employ a broader language repertoire in their oral narrative production so that their responses from different perspectives and experiences are valued during literary activities in the classroom.

This article presents an innovative way to engage schoolchildren in discussions on multilingualism and multilingual identity using research data they helped generate. Adopting an exploratory, participatory approach to research, our study uses digital data visualisations in interactive sessions aimed at engaging lower secondary students in identity formation and negotiation. The paper starts with a contextualisation of multilingualism and language learning in Norwegian education and the contributions of our study to relevant research in the field. Next, we discuss the epistemological and pedagogical implications of our participatory approach and its integration within the general mixed methods framework of the Ungspråk project, a three-year study that investigates different aspects of multilingualism in Norwegian lower secondary schools. The paper then focuses on the development of the main pedagogical tools used in the interactive sessions: digital visualisations based on data from an online questionnaire previously answered by participants. Particular attention is paid to the design of the visuals in promoting students' engagement with the data and autonomy in interpreting research findings. The paper concludes with a discussion of the main findings from interactive sessions in which participants engaged in reflections on multilingualism and multilingual identity via interaction with the visuals, researchers and their peers.

Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as "black boxes", and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.

The Meaningful Learning in the Laboratory Instrument (MLLI) was designed to measure students' expectations before and after their laboratory courses and experiences. Although the MLLI has been used in various studies and laboratory environments to investigate students' cognitive and affective laboratory expectations, the authors of the instrument reported a discrepancy between the intended factor structure of the MLLI and the factor structure suggested by the data collected in preliminary studies. Therefore, the aim of this study was to investigate the validity and reliability evidence related to data collected with the MLLI, especially that related to structural validity. Evidence to support structural validity would provide greater meaning for the reporting and interpretation of MLLI scores. In this study, two possible "a priori" models for the factor structure of data collected from multiple institutions with the MLLI were investigated using confirmatory factory analysis (CFA). This initial investigation found poor data-model fit for each of the two tested models. Cognitive interviews and free response items were then used to inform modifications to the two "a priori structures," and a third alternative structure, which included a negative method factor, was also investigated. Once a best fitting model was identified, further model revisions were informed by a combination of modification indices and qualitative data. Evidence of adequate-to-good data model fit was found for the final revised version of the MLLI, deemed the MLLIv2. Additionally, evidence of both internal structure validity and single administration reliability were found for each of the MLLIv2 factors. The structure of the data from these items leads to scale scores that likely represent student expectations that contribute to meaningful learning and student expectations that detract from meaningful learning. As the results of this study provide the first psychometrically supported scales for MLLI data, they have implications on the future reporting and analyses of MLLI scores.

World university rankings have had an impact on academic competition worldwide. The comparability of ranking results depends on how data is collected within each university. When data concepts are interpreted differently, data variety is introduced and ranking results cannot be used in a meaningful manner. In this case study, the effect of semantic harmonisation of the data concepts of the "Times Higher Education" World University Ranking is evaluated. Based on two independent initiatives, in Italy and in Belgium, the data and results of this ranking before and after interuniversity harmonisation of data collection are compared. This study demonstrates that, despite the data definitions provided by the ranking organisation, the data concepts 'academic staff' and 'students' are interpreted differently within each university. These differences can affect how universities are positioned relative to each other in ranking tables. A profound level of semantic harmonisation is then required to ensure semantically comparable data.

A shift from teaching to learn to learning to think is necessary to promote critical thinkers. Since teachers tend to replicate the educational models they have experienced as students, new references are needed. In order to address these concerns, we present an interdisciplinary intervention in initial teacher education and analyse its impact on teachers' critical thinking and awareness of the importance to promote it. The intervention is based on the potential of socio-scientific issues to support argumentation and provide meaningful contexts for graph interpretation. The content analysis of participants' responses before and after inquiring about climate change shows an increase in the level of argumentation and the capacity to read beyond data, providing evidence of the development of critical thinking. In addition, pre-service teachers explicitly refer to their future responsibility in preparing students to face current challenges, demonstrating awareness of the need to promote critical thinking.

In the past decade, moderated mediation analysis has been extensively and increasingly employed in social and behavioral sciences. With its widespread use, it is particularly important to ensure the moderated mediation analysis will not bring spurious results. Spurious effects have been studied in both mediation and moderation analysis, but this issue remains unexplored in moderated mediation analysis. To fill this gap, we examined the conditions under which a spurious moderated mediation effect in a dual stage moderated mediation model might occur. Specifically, with a hypothetical example and three theorems, we illustrated how the index of moderated moderated mediation may conclude a moderated mediation effect which does not actually exist. As a remedy to rule out the spurious results, we proposed two methods which are simple and easy to implement. Based on the simulation results, we offer researchers some practical guidelines to apply the methods in empirical research.

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification algorithms to create prediction models. The results show that SMOTE with Edited Nearest Neighbors is superior, and the balanced random forest classifier performs better when using SMOTE-ENN, achieving 96% accuracy, precision, and F-value. Smote also has faster execution time. For model interpretability, combining Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) provides deeper insights. LIME is suitable for single-prediction interpretation, while SHAP is better for overall model interpretation. This research offers guidelines to mitigate data imbalance and improve fairness in education through data-driven innovations like early warning systems. It also educates academics on explainability approaches to facilitate wider use of machine learning methods.

Physical activity promotion is a best buy for public health because it has the potential to help individuals feel better, sleep better, and perform daily tasks more easily, in addition to providing disease prevention benefits. There is strong evidence that individual-level theory-based behavioral interventions are effective for increasing physical activity levels in adult populations but causal inference from these interventions often is unclearly articulated. A directed acyclic graph (DAG) can be, but rarely is, used to determine if the causal effect of an individual-level theory-based physical activity-promoting intervention is identifiable (e.g. stripped of any spurious association). The primary objective of the current study was to demonstrate how a DAG can be used to determine if the total causal effect of an individual randomized physical activity-promoting intervention is identifiable. The demonstration was based on the Well-Being and Physical Activity study (ClinicalTrials.gov, identifier: NCT03194854). Annotated files from DAGitty and Mplus are provided.

The linear dependence of age, period, and birth cohort is a challenge for the analysis of social change. With either repeated cross-sectional data or conventional panel data, raw change cannot be decomposed into over-time differences that are attributable to the effects of common experiences of alternative birth cohorts, features of the periods under observation, and the cumulation of lifecourse aging. This article proposes a rolling panel model for cohort, period, and aging effects, suggested by and tuned to the treble panel data collected for the General Social Survey from 2006 through 2014. While the model does not offer a general solution for the identification of the classical age-period-cohort accounting model, it yields warranted interpretations under plausible assumptions that are reasonable for many outcomes of interest. In particular, if aging effects can be assumed to be invariant over the course of an observation interval, and if separate panel samples of the full age distribution overlap within the same observation interval, then period and aging effects can be parameterized and interpreted separately, adjusted for cohort differences that pulse through the same observation interval. The estimated cohort effects during the observation interval are then interpretable as effects during the observation interval of entangled period and cumulated aging differences from before the observation interval.

The robustness of qualitative comparative analysis (QCA) results features high on the agenda of methodologists and practitioners. This article aims at advancing this debate on several fronts. First, in line with the extant literature, we take a comprehensive view on robustness arguing that decisions on calibration, consistency, and frequency thresholds should all be tested. Second, we introduce the notion of "sensitivity range" as the range of values for any of these parameters within which the solution formula remains unchanged. Third, we argue that interpreting robustness is more intricate than simply checking if solutions remain unchanged. Beyond sensitivity ranges, researchers should assess robustness by evaluating changes in parameters of fit and the classification of cases as robust, shaky, or possible. Fourth, we enable researchers to perform more than one robustness test at a time by proposing the notions of a "test set": the overlap between conceptually plausible alternative solutions that can be generated; and of a "robust core": that part of a QCA solution that withstands the robustness checks. Fifth, we present functionalities implemented in the R package SetMethods that enable researchers to put in practice our proposals. These advancements are integrated into a comprehensive QCA Robustness Test Protocol consisting of three main tests: sensitivity ranges, fit-oriented robustness, and case-oriented robustness. We illustrate the protocol's implementation with an example on high life expectancy across the globe.

This work reports on the implementation of a self-contained data-literacy exercise designed for use in undergraduate classes to help students practice data literacy skills such as interpreting and evaluating evidence and assessing arguments based on data. The exercises use already developed data-visualizations to test and develop students' ability to evaluate arguments based on data presented visually. Moreover, the exercises are designed to teach students positive lessons from evaluating flawed examples of data usage. We show that repeated use of these exercises has the potential to help students develop tools they can use across multiple contexts when evaluating data. Student feedback and panel survey data show that students like to learn using these tools and they report increased comfort levels in working with data. The paper argues that instructional tools like these data visualization exercises are a quick and effective tool for teaching data literacy skills and that they have significant utility in non-research-methods courses.

In this teaching case, we describe an experiential learning project that allows students to perform sentiment analysis on a set of tweets (posts made on the social media platform, Twitter) by collecting and analyzing posts that include key words selected by the students. Sentiment analysis refers to the process of identifying and categorizing opinions expressed in a piece of text. The project requires students to make edits to an R script, execute the script to save a collection of tweets that contain specific keywords, then open the file and paste the results into a macro-enabled Excel file that is provided. Students then edit the dataset to cleanse the data and write a report to interpret the findings. The assignment requires only a cursory knowledge of programming and Excel. We assign the project to students taking an introductory information systems course but the project could be suitable for courses in business analytics, marketing, social media, computer science, and other subjects.

Learning to interpret data in context is an important educational outcome. To assess students' attainment of this outcome, it is necessary to examine the interplay between their contextual and statistical reasoning. We describe a research method designed to do so. The method draws upon Toulmin's (1958, 2003) model of argumentation for the first stage of qualitative data analysis and the Structure of the Observed Learning Outcome (SOLO) (Biggs & Collis, 1991) model for the second stage. Toulmin analyses help identify the justifications and expressions of uncertainty students provide in their interpretive arguments. Subsequent analyses based on the multi-modal conceptualization of SOLO help characterize the quality of student arguments relative to one another. Existing literature and an empirical example are drawn upon to explain how the Toulmin and SOLO models can be used in tandem to analyze students' interpretations of contextualized data. We also explain how pairing Toulmin and SOLO can address theoretical and practical limitations that arise when using just one of the two models on its own.

Is public engagement with science deliberative and evidence-based? The public is often perceived as underprepared to use data and susceptible to partisan and emotional manipulation. Consequently, educational efforts focus on the ability to identify reliable information. We posit that effective engagement with science goes beyond this and hinges on data literacy. We leveraged the unique circumstances of COVID-19, where diverse people inundated with pandemic-related data representations in the media needed to make consequential decisions, to examine whether people use data and what factors affect such use. In a survey of a representative Israeli adult sample, participants reported their information habits and beliefs before and during COVID-19. On being presented with graphs and datasets, they answered data literacy and COVID-19-related functional reasoning assessments (e.g., would you travel abroad?). Data literacy distinguished those who incorporated data from those who did not. Yet, participants incorporated moral, social, and economic considerations at all data literacy levels, suggesting that people may be deliberative even when they do not attend to quantified data. Moreover, participants' trust in science and data interpretation competence were key factors mediating the relationship between self-efficacy in data interpretation skills and the incorporation of data in reasoning. The findings extend beyond COVID-19 to a broader understanding of the factors influencing public engagement with quantitative representations. Rather than focusing solely on remediating data interpretation, we suggest that educative efforts work on multiple fronts and that cultivating trust in science is key to a broader, more deliberative engagement with science.

Development of purpose is an important goal of post-secondary education. This study advances the measurement of purpose by (a) enriching the construct through incorporating the facet of horizon; (b) providing a framework for Rasch/Guttman Scenario score interpretation; and (c) providing evidence of convergent, divergent, and known groups validity.

This study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI) algorithms. Results indicate that, from 1997 to 2009, AIoL research focused on the combination of intelligent tutoring systems and distance learning. In 2010-2014, AIoL research emphasized learner-oriented learning. In 2015-2019, learner-system interactions to facilitate personalized, adaptive, and collaborative learning became the main focus. "Intelligent tutoring systems" have played the most important role in AIoL, followed by "profiling and prediction," and "adaptive systems with personalization." Accordingly, the roles and research trends as well as several suggestions for future research in the field of AIoL are provided as a reference for researchers and policy makers.

Meaning-making in teaching-learning mathematical processes is a relevant issue analysed through different philosophical and educational frames. In particular, the use of digital tools in mathematics education affects the meaning-making processes. This paper discusses meaning-making from a phenomenological standpoint, in which interpretative activities are relevant. This approach requires a careful analysis of the semiotic resources' evolution, including those related to the used digital tools. The paper aims to introduce an analytical tool, the Timeline. This tool is an elaboration on previous analysis tools, like the interaction flowchart and the semiotic bundle. Such a tool allows the analysis of relationship among interactions, semiotic resources, and meaning-making. In this paper, the Timeline is used to analyze two episodes from two different learning experiments where GeoGebra and augmented reality are used. High school students from Italy and Israel participated in this study. Video recording has been used to document the entire learning experiments. The analysis provides evidence that the Timeline enables investigating the relationship between students-teacher-artifacts interactions and meaning-making. Moreover, results may give teachers ideas for using digital tools to foster students' meaning-making.

As many studies focus on improving scientific literacy skills in students, there are still many gaps, thus an appropriate learning innovations are needed to get maximum results. This study aimed to evaluate the implementation of project-based blended learning in enhancing students' scientific literacy skills at the university. The experimental study involved 40 students of class A from the Department of Biology Education as the experimental group and 40 students of class B in the same department as the control group. The research instrument used the Economic Co-operation and Development (OECD) data. The results showed a significant increase in all aspects of scientific literacy skills after the implementation of project-based blended learning at the university such as an explaining phenomena scientifically, designing and evaluating scientific inquiry, and interpreting data and evidence scientifically. This research is beneficial for university lecturers for a solution to the problem of scientific literacy skills in students especially in university. Furthermore, there needs to be similar research at the elementary, junior high, and senior high school levels.

Introducing kindergarten children to the engineering design process (EDP) is an important objective of early STEM education. Studies indicate that children often miss the crucial steps of testing and optimising during the EDP and do not persist in making solutions better. The present study explores how children's goal awareness, self-evaluation ability, domain-specific content knowledge, spatial skills and intelligence relate to their persistence, testing and optimising behaviour and to solution quality. In a standardized procedure, 41 children (4 to 7 years) in Germany worked on an engineering task in the domain of gears. The engineering process was videotaped, children's testing and optimising, solution quality, goal awareness, self-evaluation, and task persistence were rated with a coding scheme and with interviewer questions. Domain-specific content knowledge, mental rotation ability and figural reasoning were measured with standardized tests. Correlational analyses indicated that goal awareness was positively related to solution quality. However, most children required support by the interviewer to retrieve the goal specifications. Moreover, children's self-evaluation was negatively related to task persistence. Most children were satisfied with their first solution, even when it did not meet the requirements. Our findings emphasize the important role of teachers in helping children to tackle challenges with the EDP.