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The research sought to study potential efficiency in course design and execution using Constructive Alignment, and then classroom workflow grounded in eduScrum based on the Agile Project Framework of Scrum for graduate classes in managerial analytics. The research measured performance based on the Scrum concept of Velocity, defined as the rate of improvement in learning as measured by the number of Constructive Alignments' Learning Objectives achieved. The process of class design creates a list of activities for each class that lends itself to a standardized learning workflow. Scrum, as a project framework originated within software development in the early 2000s, but has now become a managerial method of choice for projects in a wide variety of industries and sectors. The final product to be delivered is broken into increments of value that can be created by the team in short work periods, also called Sprints. EduScrum mimics the same approach, using each class session to act as a sprint in which the students are assigned to self-managed teams of students and assigned a list of learning activities to achieve. The teacher/professor's role shifts to coach, moving from team to team and improving workflow, overcoming barriers, providing resources, and ensuring each class maximizes value creation. Scrum and eduScrum rely on a short reflective learning session at the end of the Sprint, called a Retrospective, in which students and professors assess how they can improve the velocity of learning. Assessments are embedded for individuals as part of Constructive Alignment, and are connected to the Teaching/Learning Activities.

Data analytics is a rapidly growing field that plays a crucial role in extracting valuable insights from large volumes of data. A data analytics practicum course provides students with hands-on experience in applying data analytics techniques and tools to real-world scenarios. This practicum is intended to serve as a bridge between the student's academic environment and the professional application of their skills in an employment and internship setting. This study examined the design of a data analytics practicum course. The main objectives included (1) the identification of topics and skills employers look for in new hires in data analytics-related internships and entry-level positions, (2) the development and implementation of a Data Analytics practicum course and (3) reflection on the first-time offering of the course and suggested improvements for the next iteration. As part of this study, industry and organization survey responses drove the design of the course and development of key student learning gains for five learning modules throughout the semester. Faculty within the departments of information technology (IT), mathematics, and statistics collaborated in the construction, development, and implementation of team-teaching instructional practices of the Data Analytics Practicum in Spring 2023. This study applies an interdisciplinary approach to data analytics practicum development and instruction.

There has been an increasing demand for healthcare analytics skills and competence by healthcare organizations. Although many universities have established programs and courses on healthcare analytics, most of these curricula have been designed for information systems (IS), information technology (IT), or analytics students. It is unclear how these curricula would fit the needs of healthcare professionals who have little IT knowledge and background yet also need analytics for their clinical or administrative job roles. This research reports on the design of an executive MBA course intended for an audience of healthcare professionals. The learning objectives, topic coverage, software tools, and assessment methods are presented along with students' perceptions of these aspects of the course. Several important lessons learned are shared and future directions are proposed, which can help other educators design similar healthcare analytics courses for professional audiences.

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

This study explores an integrated approach for sustainable urban early childhood education and development within the context of urban planning and preschool infrastructure in rapidly urbanizing areas. It focuses on a case study conducted in both private and public preschools in Addis Ababa. The study adopts a qualitative case study design, utilizing data from secondary sources such as documents related to urban planning and design policies, preschool education policies, standards for early childhood care and education, policy implementation reports, academic literature, and theories on child education. The primary objective is to identify policy, institutional, and practice gaps in the assessment of urban design, planning, preschool education infrastructure, and early childhood care and education policies. The data collection instrument employed is document review, and the data analysis technique is a discourse thematic data analysis approach.

Background/purpose: School-based management (SBM) has gained international attention for the promotion of student learning and educational development. Senegal is one of the initiatives of French speaking African countries that adopted the move towards promoting SBM at the school level, but lacks substantial evidence of its effectiveness in enhancing educational quality. In addition, there has yet to be a clear consensus on the heterogeneity of SBM on learning achievement. This study aimed to examine the influences of SBM characteristics on learning achievement and investigate the relationship between them and students' gender status in Senegal primary education. Materials/methods: The study adapted the framework on what matters most in marginalized autonomy and school accountability for better education results. It linked autonomy and accountability, requiring parents and the community to participate in and trust the school. The study applied a multilevel regression model using the cross-sectional dataset of Program d'Analyse des Systemes Educatifsde la Confemen (PASEC) 2019 learning achievements survey. Results: The study revealed that parental and community participation has a positive and statistically significant association with reading scores regardless of student gender. On the other hand, a positive correlation was found regarding information sharing with parents, and a negative correlation concerning school autonomy, but with no significant relationship to reading scores. Conclusion: Participation from parents and the community can improve student learning and mitigate learning gaps in gender status. The findings of this study are expected to provide insight into narrowing the gap in student learning, especially for neighboring countries facing similar issues to Senegal.

Background/purpose: Awareness of the mathematical skills and knowledge children possess in their early years is widely accepted. This includes various common positive aspects, not only for educators but also for researchers and policymakers. This study presents a systematic review conducted to meticulously identify empirical studies published in the Scopus-Index Journal database about the mathematical skills children aged 3 to 8 years old have mastered. Materials/methods: This review followed the PRISMA guidelines and the research database comprised of Scopus-indexed journals. The technique followed used "keywords" and Boolean operators. The screening processes included reviewing abstracts, scanning complete texts of published articles, and rejecting those not meeting preset inclusion criteria. Moreover, systematic reviews, meta-analyses, and papers not written in English were also excluded. Of the 801 studies initially identified, a total of 15 empirical studies were included in the systematic review. Results: Children master various math skills from a very young age, mainly in "numbers and operations", but face difficulties in skills related to "algebra" as well as "geometry and measurement". Additionally, several preschoolers' characteristics help to explain the acquisition of these skills, with "age" being the primary factor. Researchers use various research instruments and mainly conduct individual semi-structured interviews. Children's geometry skills and knowledge appear to have been studied to a small extent. The areas of "measurement" and "data analysis and probability" were found to be under active investigation. Conclusion: It is worth noting that not only does a noticeable research gap exist for the math domains of "measurement," "geometry," and "data analysis and probability". Factors that seem to affect young children's math skills, such as "gender", "parents' educational level", and "attendance to a preparatory preschool" need further investigation. The implications of the current study's results extend beyond academia, providing valuable insights that educators and policymakers can leverage to enhance the quality of mathematics education during the early years period.

This study employed a critical inquiry paradigm to explore the significance of equity and social justice in teaching qualitative methodologies and methods to graduate students. Graduate students of multiple minoritized identities and a faculty member conducted a two-year inquiry into the research apprenticeship experience, including the stages of student-led conceptualization, data collection, analysis, international conference presentation, and publication. The study investigated the experiences of the faculty and students as well as the responsibilities of the institution against the backdrop of historical and contemporary pandemics. Consequently, we problematized hidden curriculum and unconscious assumptions to suggest research course design sequencing improvements. The findings emphasize the impact of these experiences on qualitative research teaching and learning practices and institutional responsibility to graduate student research apprenticeships. The Graduate Research Apprenticeship Nested Design Pathway is presented as a tool that institutions worldwide can adapt and implement to serve the complex needs of their students.

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th, 7th, and 8th grades attending a school in Central Anatolian City in Turkey, participated in this study in the 2022-2023 school year. Data was collected with a. A deep learning method called deep neural network, one of the ways of machine learning, was used in data analysis. The average number of books read per year had the highest importance among the variables affecting academic performance in science courses. In addition, deep learning predicted students' final science scores with 90% accuracy. According to the results of this study, the percentage of the academic achievement prediction might be raised by reproducing the required data set for the data analysis method with deep learning. A forecast of the student's academic achievement with artificial intelligence and detecting the importance of variables' percentage might be researched for other courses in addition to the Science course.

The main objective of this article is to highlight the importance of training in digital tools at the university level to foster the development of innovative and efficient data analysis from a scientific perspective. In an increasingly digitised world, the acquisition of digital skills has become a fundamental requirement for success in various disciplines, especially in conducting academic studies and research. The implementation of Computer-Aided Qualitative Data Analysis (CAQDAS) software, such as the ATLAS.ti platform, for text analysis not only enriches the educational experience but also prepares students to excel in an ever-evolving digital environment and raise the quality of their research. We present here a practical example of textual analysis in ATLAS.ti that can serve as a reference guide for similar studies based on content analysis of interviews. Various qualitative and quantitative data analysis options and techniques are explored that allow researchers to identify patterns, trends and relationships in the texts analysed, which contributes to a deeper understanding of the topics under study by transcending traditional methods of text analysis.

This study examines the impact of early Algebra I coursework on advanced Carnegie credits among 12th graders from diverse racial/ethnic backgrounds, using data from the NCES HSTS (1990-2019). Findings indicate that early Algebra students, particularly Black and Latinx, earn more advanced credits, revealing a widening gap in advanced course attainment. The study underscores the importance of early Algebra in college readiness and STEM preparation, highlighting the need for interventions to address educational disparities. Future research should explore additional factors influencing college readiness.

Internationally representative data of middle-aged adults 45 - 65 years old [n(United States) = 2,150; n(Japan) = 2,318; n(South Korea) = 2,800] from the 2012 Program for International Assessment of Adult Competencies were analyzed to examine the roles of education and literacy in relation to the digital divide. Results from survey-weighted binary logistic regressions showed that both educational attainment and literacy were positively associated with all four measures of information and communication technology (use of the computer, email, online information and transaction) use in all three countries. The middle-aged adults in the United States benefited more from the educational attainment than those in Japan, in terms of email and online information use. The middle-aged adults with lower education and basic skills (i.e., literacy) may benefit from the educational intervention and additional information and communication technology training, and in turn, improve the digital divide in later life, regardless of differences in culture and economy.

This paper analyzes data from Native American students' attainment in a first-semester Spanish language course at an indigenous-serving institution before, during, and after the pandemic. The gap between Native American and non-Native American students increased during the first post-pandemic semester to the point that just one out of 11 Native Americans passed the course in the fall of 2021. After that, the gap between Native American and non-Native American students gradually narrowed until reaching the lowest failing grades of 23% in the spring 2023. In my teaching, Native American students benefit from a classic teaching style involving longer lecture time, monitored note-taking, consistent attendance requirements, in-person communication, and clearly communicated differentiation strategies for grading. This may align with the cycle of learning outlined by Benally (1994): Nitsáhákees (Thinking), Nahat'á (Planning), Iiná (Living) and Sihasin (Assuring).

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.

Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modelling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students' performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performances and identify the critical areas that need significant improvement.

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.

Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have uncovered issues with the metrics and procedures commonly used to analyze these transitions. As such, our goal in this work is to address these issues by outlining an alternative procedure that is based on the use of marginal models. We begin by looking at the specific mechanisms responsible for a recently discovered statistical bias with several metrics used in sequential data analysis. After giving a theoretical explanation for the issue, we show that the marginal model procedure appears to adjust for this bias. Next, a related problem is that the common practice of removing transitions to repeated states has been shown to have unintended side-effects--to account for this issue, we develop a method for extending the marginal model procedure to this specific type of analysis. Finally, in a recent study evaluating the problem of multiple comparisons and sequential data analysis, the Benjamini-Hochberg (BH) procedure, a commonly used approach to control for false discoveries, did not perform as expected. By applying a technique from the biostatistics and epidemiology literature, we show that the performance of the BH procedure, when used with the marginal model method, can be brought back to its expected level. In all of our analyses, we evaluate the proposed method by both running simulations and using actual student data. The results indicate that the marginal model procedure seemingly compensates for the problems observed with other transition metrics, thus resulting in more accurate estimates of the importance of transitions between states.

Artificial intelligence (AI) is now affecting all aspects of our social lives. Without always knowing it, we interact daily with intelligent systems. They serve us invisibly. At least that is the goal we assign to them: to make our lives better, task by task. Artificial intelligence has the potential to make biology education more engaging, personalized, and effective by providing students with interactive simulations, personalized learning experiences, and other tools that help them understand complex biological concepts. In this paper, we discuss the integration of AI into the virtual classroom, which significantly enhances student learning experiences in various ways. The study shows that an effective integration of technology into the virtual classroom requires a thoughtful approach that aligns with educational goals and the specific needs of students. In fact, interactive simulations can help make biology more engaging and memorable for students. Besides, personalized learning AI algorithms can help biology students receive a more tailored and effective learning experience, helping them to better understand the course material and develop a deeper appreciation for the natural world. In this work, we will discuss the use of AI to enhance interactive simulation-based cellular processes, with additional application in anatomy, physiology, and ecology teaching. Moreover, this paper discusses how AI could be used to analyze student data and propose personalized learning using adaptive assessments, content recommendations, and data sciences. This paper illustrates examples of AI algorithms that could be useful for teaching biology.

Identifying motivation for enrollment in MOOCs has been an important way to predict participant success rates. But themes for motivation have largely centered around themes for enrolling in any MOOC, and not ones specific to the course being studied. In this study, qualitatively coding discussion forums was combined with topic modeling to identify participants' motivation for enrolling in two successive statistics education professional development online courses. Computational text mining, such as topic modeling, is a learning analytics field that has proven effective in analyzing large volumes of text to automatically identify topics or themes. This contrasts with traditional qualitative approaches, in which researchers manually apply labels (or codes) to parts of text to identify common themes. Combining topic modeling and qualitative research may prove useful to education researchers and practitioners in better understanding and improving online learning contexts that feature asynchronous discussion. Three topic modeling approaches were used in this study, including both unsupervised and semi-supervised modeling techniques. The three topic modeling approaches were validated and compared to determine which participants were assigned motivation themes that most closely aligned to their posts made in an introductory discussion forum. A discussion of how each technique can be useful for identifying topical themes within discussion forum data is included. Though the three techniques have varying success rates in identifying motivation for enrolling in the MOOCs, they do all identify similar themes for motivation that are specific to statistics education.

Extensive research into wilderness therapy has not explored who benefits the most and who does not thrive in these programs. The present study examined demographic, clinical, and familial characteristics that distinguished adolescents who improve most in wilderness therapy programs from those who deteriorate. Using data collected by the National Association of Therapeutic Schools and Programs Practice Research Network, the study sample consisted of 5639 adolescents attending wilderness therapy programs which collected and contributed data to the Practice Research Network between 2017 and 2022. Measures included the Youth Outcome Questionnaire-Self Report and the McMaster Family Assessment Device, as well as demographic, familial, and clinical data collected by program staff. Using binary and univariate logistic regression, the study found individual and familial factors that predicted membership in the top 10% of adolescent participants in terms of mental health improvement from pre-to-post wilderness therapy and those factors which predicted membership in the bottom 10% in terms of poorer mental health from pre-to-post program. Considering the intensity, length, and financial resources associated with wilderness therapy program participation, these findings have important implications for wilderness therapy program staff professional development, communication of expectations to adolescents' parents/caregivers, and program admission decisions.

Parent rating scales are used at increasing rates across disciplines to track child development and determine diagnoses/needs. This study explored relationships among parental stress and the validity of parents' ratings of their child's behaviors. Participants include children who were referred for assessment of behavioral, social-emotional, and development difficulties at a major metropolitan hospital in the United States. Parents completed rating scales regarding their own stress (PSI-4-SF) and child behaviors (BASC-3). Present findings indicate caution is warranted when interpreting parent report data of their child's behavior in the context of significant parenting stress. Relying exclusively on parent-report information to assess young children in clinical setting appears contraindicated. Family/situational factors beyond the individual child are important to assess.

This research considers Pell grant restoration for incarcerated people for the field of higher education in prison. Using the original data, we outline the limits of Pell funding in the prison context by surfacing persistent funding challenges that the Pell grant alone cannot address and may exacerbate. By providing the necessary investments to support higher education in prison, Pell restoration could be an effective lever for advancing racial and economic justice. Using a lens of racial and socioeconomic justice, we identify gaps in prison program costs and argue that the long-term effects of Pell reinstatement will depend on whether the expansion is accompanied by investments in a range of institutional infrastructures and resources. Concluding are implications and recommendations to adequately and responsibly support the growth and infrastructure of higher education in prison programs.

Background: Data literacy (DL) is vital for teachers, as it enables them to build on data and improve teaching and learning. Therefore, developing DL among pre-service teachers is critical. Objectives: The purpose of this study is threefold: to evaluate whether a feedback visualisation of peer assessment-based teaching approach (FVPA-based teaching approach) can (1) promote pre-service teachers' DL; (2) enhance their learning motivation; and (3) improve their cognitive load. Methods: The research was conducted based on a pre-test-post-test control group quasi-experimental design. With 20 participants in the experimental group and 21 in the control group, a total of 41 pre-service teachers were included in the study. The pre-service teachers in the experimental group adopted the FVPA-based teaching approach, and those in the control group adopted the traditional peer assessment-based learning approach. Results and Conclusions: The experimental group participants outperformed the control group participants in DL, learning motivation, and cognitive load. FVPA was conducive to helping pre-service teachers critically interpret data, understand their teaching and learning issues, and improve self-reflection. The findings indicate a reciprocal relationship between learning motivation and DL; improving the learning motivation of pre-service teachers could promote their DL. Implications: This study contributes to current knowledge by providing empirical evidence on the benefits of an FVPA-based teaching approach in improving pre-service teachers' DL, motivation, and cognitive load. The study findings, limitations, and prospects for future research are discussed.

Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop an effective automated grading model to analyse the data and predict learners' learning results. Objectives: The present study aimed to propose an automated grading model with reliability detection. Methods: A total of 109,327 instances of peer assessment from a large-scale teacher online learning program were tested in the experiments. The reliability detection approach included three steps: recurrent convolutional neural networks (RCNN) was used to detect grade consistency, bidirectional encoder representations from transformers (BERT) was used to detect text originality, and long short-term memory (LSTM) was used to detect grade-text consistency. Furthermore, the automated grading was designed with the BERT-RCNN model. Results and Conclusions: The effectiveness of the automated grading model with reliability detection was shown. For reliability detection, RCNN performed best in detecting grade consistency with an accuracy rate of 0.889, BERT performed best in detecting text originality with an improvement of 4.47% compared to the benchmark model, and LSTM performed best with an accuracy rate of 0.883. Moreover, the automated grading model with reliability detection achieved good performance, with an accuracy rate of 0.89. Compared to the absence of reliability detection, it increased by 12.1%. Implications: The results strongly suggest that the automated grading model with reliability detection for large-scale peer assessment is effective, with the following implications: (1) The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results. (2) This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. (3) This solution could shift educational institutions from labour-intensive grading procedures to a more efficient educational assessment pattern, allowing for more investment in supporting instructors and learners to improve the quality of peer feedback.

Teachers form expectations that can influence their students' performance, and there are a variety of ways these expectations can be communicated. In the current study, we tested a novel method for communicating expectations via examples of student work--examples that contain basic, entry-level work and communicate low, but manageable expectations or examples that contain complex, advanced-level work and communicate high and challenging expectations. Across three semesters, 91 college students in a data management course completed a class assignment that involved exploratory coding activities. Prior to the assignment, students were randomly assigned to view basic or advanced examples of student work. Students assigned to the advanced-examples condition reported higher perceptions of task difficulty and frustration, but they also exhibited higher levels of performance in terms of the complexity of their own work. Results suggest that setting challenging expectations can create a desirable difficulty that ultimately benefits students' performance in an authentic learning environment.

As computational methods are widely used in science disciplines, integrating computational thinking (CT) into classroom materials can create authentic science learning experiences for students. In this study, we classroom-tested a CT-integrated geoscience curriculum module designed for secondary students. The module consisted of three inquiry investigations that were age-appropriately translated from the computational practices of volcanologists who study tephra hazards and risks. We developed a domain-specific, block-code-based computational modeling environment where students carried out 1 of the 3 science practices in each inquiry investigation: experimentation, data visualization and interpretation, and modeling. We examined (1) student learning outcomes using pre- and post-tests and (2) student reflections on the computationally supported science practices surveyed at the end of each inquiry investigation during the module. Results indicate that students made statistically significant gains in science content as well as in computationally supported experimentation, data visualization and interpretation, and modeling practices. Over 80% of students identified different ways in which block coding supported their practices during inquiry investigations. Based on these findings, we discuss implications for the future development of computationally supported inquiry investigations in science.

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the previous research do not adequately capture the structure of the data. By conducting a secondary analysis of aggregated datasets across nine different studies (n = 528) and using more appropriate statistical methods, this study provides a more accurate description of the development of scale errors. We implemented the zero-inflated Poisson (ZIP) regression that could directly handle the count data with a stack of zero observations and regarded developmental indices as continuous variables. The results suggested that the developmental trend of scale errors was well documented by an inverted U-shaped curve rather than a simple linear function, although nonlinearity captured different aspects of the scale errors between the laboratory and classroom data. We also found that repeated experiences with scale error tasks reduced the number of scale errors, whereas girls made more scale errors than boys. Furthermore, a model comparison approach revealed that predicate vocabulary size (e.g., adjectives or verbs), predicted developmental changes in scale errors better than noun vocabulary size, particularly in terms of the presence or absence of scale errors. The application of the ZIP model enables researchers to discern how different factors affect scale error production, thereby providing new insights into demystifying the mechanisms underlying these phenomena.

Objective: The purpose of this paper was to examine variables associated with food insecurity among community college caregivers during the COVID-19 pandemic. Methods: We used data from a multi-institutional survey of 15,051 caregivers enrolled at 130 community colleges in 42 states in fall 2020. We used a logistic regression to examine whether demographic, academic, caregiving-related, financial, or COVID-19-related variables were associated with caregivers' food insecurity. Results: Over half (52%) of community college caregivers experienced food insecurity. Transgender caregivers, first-generation caregivers, and caregivers who were divorced or single, had multiple disabilities, were previously in foster care, and had a family that experienced trouble making ends meet growing up had significantly (p < 0.05) higher probabilities of experiencing food insecurity. Community college caregivers who used childcare and those with at least one child up to 12 years old also had increased probabilities of experiencing food insecurity. Moreover, caregivers who felt childcare was not affordable and believed that they did not earn enough money to make employment worthwhile after paying for childcare expenses had higher probabilities of experiencing food insecurity, as did those who experienced housing insecurity and used Pell grants, student loans, and support from friends or family to pay for college. Losing a job, experiencing cuts to work hours or wages, employment as a frontline worker, and contracting COVID were associated with higher probabilities of food insecurity. Contributions: Community college caregivers experienced high rates of food insecurity during the pandemic and some caregivers were at greater risk of exacerbated probabilities of food insecurity. We advocate for targeted interventions, wraparound services, and increased advocacy for legislation to support student caregivers.

Recital 33 GDPR has often been interpreted as referring to 'broad consent'. This version of informed consent was intended to allow data subjects to provide their consent for certain areas of research, or parts of research projects, conditional to the research being in line with 'recognised ethical standards'. In this article, we argue that broad consent is applicable in the emerging field of Computational Social Science (CSS), which lies at the intersection of data science and social science. However, the lack of recognised ethical standards specific to CSS poses a practical barrier to the use of broad consent in this field and other fields that lack recognised ethical standards. Upon examining existing research ethics standards in social science and data science, we argue that they are insufficient for CSS. We further contend that the fragmentation of European Union (EU) law and research ethics sources makes it challenging to establish universally recognised ethical standards for scientific research. As a result, CSS researchers and other researchers in emerging fields that lack recognised ethical standards are left without sufficient guidance on the use of broad consent as provided for in the GDPR. We conclude that responsible EU bodies should provide additional guidance to facilitate the use of broad consent in CSS research.

We study the 1992 higher education expansion reform in Turkey and examine how the expansion program changed higher education attainment and labor market access, particularly for women, who are disadvantaged on both accounts. We use the 2011 Population and Housing Census and employ a difference-in-differences estimation strategy. We find that the establishment of universities in localities where universities did not exist before increases the higher education attainment of women by 12-13% and their labor force participation by 4%. In contrast, we do not find a program effect for men for either of the two outcomes. That the program did not affect high school graduation suggests that the improvement for women stems from the change in the behavior of the same pool of high school graduates due to reduced monetary and psychic costs. The absence of a program effect for men further suggests that the reduction in schooling costs was not high enough to overcome the lower benefit of having to attend a less reputable local university. As a result of the expansion policy, the gender gap in higher education attainment and labor force participation shrinks. A battery of robustness checks that include an IV estimation supports our findings.

Systematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be comprehensively evaluated against humans, and no study has tested Generative Pre-Trained Transformer (GPT)-4, the biggest LLM so far. This pre-registered study uses a "human-out-of-the-loop" approach to evaluate GPT-4's capability in title/abstract screening, full-text review and data extraction across various literature types and languages. Although GPT-4 had accuracy on par with human performance in some tasks, results were skewed by chance agreement and dataset imbalance. Adjusting for these caused performance scores to drop across all stages: for data extraction, performance was moderate, and for screening, it ranged from none in highly balanced literature datasets (~1:1) to moderate in those datasets where the ratio of inclusion to exclusion in studies was imbalanced (~1:3). When screening full-text literature using highly reliable prompts, GPT-4's performance was more robust, reaching "human-like" levels. Although our findings indicate that, currently, substantial caution should be exercised if LLMs are being used to conduct systematic reviews, they also offer preliminary evidence that, for certain review tasks delivered under specific conditions, LLMs can rival human performance.

This study was grounded in the spatial computational thinking model developed by the "3D Weather" project funded by the NSF STEM+C program. The model reflects a discipline-based perspective towards computational thinking and captures the spatial nature of computational thinking in meteorology and the reliance of computational thinking on spatial thinking for geospatial analysis. The research was conducted among nineteen teachers attending the summer workshop offered by the project in its third project year to prepare them for teaching spatial computational thinking with IDV ("Integrated Data Viewer," downloadable at https://www.unidata.ucar.edu/software/idv/) visualization of weather data. Quantitative survey data were collected measuring these teachers' meteorology content knowledge, spatial computational thinking, self-efficacy for teaching spatial computational thinking, and epistemic cognition of teaching meteorology. The data were analyzed to examine the effects of the workshop in terms of these variables and the correlations among them were also explored.

Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of misconceptions. The expectation-maximization algorithm was developed to estimate the model parameters. A simulation study was conducted to evaluate to what extent the parameters can be accurately recovered under varied conditions. A set of real data in science education was also analyzed to examine the viability of the proposed model in practice.

This work considers group discussion data, as recorded in video conferencing settings, and demonstrates the ability to use readily available computational tools to glean important information characterizing the dynamics of the group discussion. In particular, our toolbox reveals a number of important characteristics of a discussion, including who is speaking when and each participant's relative sentiment throughout the discussion. We also identify the moments of the discussion where specific events occur, such as reading or quoting from source material or when questions are being asked, etc. Finally, we conduct a topic analysis to characterize the amount of time spent on various themes in the discussion. We then demonstrate that these tools are reasonably accurate at locating the desired content and can, in fact, provide a unique window to into the dynamics of group discussions.

Program evaluation is an essential practice for providers of behavior analytic services, as it helps providers understand the extent to which they are achieving their intended mission to the community they serve. A proposed method for conducting such evaluations, is through the use of a consecutive case series design, for which cases are sequentially gathered following the onset of a specific occurrence. Given the sequential nature in which data are collected within a consecutive case series, analytic techniques that adopt a time-series framework may be particularly advantageous. Although such methods are commonly used for program evaluation in medicine and economics, their application within the field of applied behavior analysis is largely absent. To serve as a model for providers undertaking evaluation efforts, I conducted a program evaluation of an outpatient severe behavior clinic, in which I employed quasi-experimental methods using an interrupted time-series analysis.

The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called item-level data with cross-classified structure. An example of such data structure is the routinely collected student evaluation of teaching (SET) data. Motivated by the lack of research on multilevel IRT modeling with crossed random effects and the need of an approach that can properly handle SET data, this study proposed a cross-classified IRT model, which takes into account both the cross-classified data structure and properties of multiple items in an assessment instrument. A new variant of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm was introduced to address the computational complexities in estimating the proposed model. A preliminary simulation study was conducted to evaluate the performance of the algorithm for fitting the proposed model to data. The results indicated that model parameters were well recovered. The proposed model was also applied to SET data collected at a large public university to answer empirical research questions. Limitations and future research directions were discussed.

I study student characteristics and academic performance at the University of California, where consideration of an applicant's ethnicity has been banned since 1996 and SAT scores were used in admitting students to the university until fall 2021. I show the following: (1) SAT scores were more important than high school grades in predicting first-year university GPA; (2) the use of SAT scores alone or with high school grades in determining admission is biased in favor of admitting underrepresented minorities and students who are socioeconomically disadvantaged; (3) SAT scores are more important and high school grades are less important in predicting GPA for underrepresented minorities and/or those students from low-income families than they are for those students who are white and/or from high-income families; and (4) the University of California found ways to admit a significant number of underrepresented minorities despite many of them having low SAT scores.

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.

Drawing on annual, nationally representative survey data from 2007-2022, I demonstrate that partisan gaps - the average differences in public opinion between Democrats and Republicans - have widened on many education issues. This pattern consistently exceeds what would be expected due to the changing demographic compositions of the parties alone. Widening partisan gaps are primarily attributable to sorting (alignment of one's party affiliation and one's issue positions) rather than polarization (increasing support for more extreme positions relative to more moderate positions). However, polarization is also increasing on some of the most divisive issues. Among those who are sorting, individuals are overwhelmingly switching their issue positions to align with their party affiliations rather than switching their party affiliations to align with their issue positions.

The present study used quantitative and qualitative measures to examine Iranian applied linguists' (mis-) conceptions of ethical issues in research. For this purpose, one hundred and twelve applied linguists completed a research ethics questionnaire constructed and validated by the researchers. In the follow-up qualitative phase, 15 applied linguists who were faculty members participated in semi-instructed interviews. Data were analyzed using exploratory factors analyses for the first phase and theme analyses for the second phase. Quantitative results showed that the most important misconceptions among Iranian applied linguists lingered on working with data (data collection and data analyses). For example, removing an outlier was a prevalent act conducted by applied linguists in the present study. Teachers using their students as participants of their own research as well as how they treated those students after a study were other controversial issues. The qualitative results revealed several themes including "lack of knowledge, conflict with real-world practices, a product-oriented approach to education and a publish or perish mentality" as the reasons for misconceptions of ethics in applied linguistics among Iranian researchers. On general terms, there was an implicit agreement regarding the lack of training on research ethics among Iranian applied linguists.

Situated in the education reform launched by the Egyptian Ministry of Education (MOE), which called for a socially-foreign shift away from memorization-based mathematics instruction, this paper explores the role of school-based teacher professional networks in implementing reform. Adopting the Goodson Change Model as a theoretical framework, we map out the power play between external agency (national society), internal agency (teacher professional networks) and the personal agency of individual teachers. Using an embedded case study model and a dataset consisting of the recordings of two full-day focus group discussions with teacher professional networks at two national schools, we contrast the two cases from the perspective of teacher group tensions and power dynamics in relation to the reform implementation. In one case, the teacher professional network (internal agency) was dominantly embracing the change, thus empowering the local teacher (personal agency) and enforcing its dogma on its stationed national society (external agency). In the other case, the teacher as a personal agent was solely attempting to break through both layers of internal and external agency. Our findings cast light on the role of teacher professional networks in curricular reform contexts, particularly within a collective cultural setup such as Egypt. Implications for the role of teacher professional networks in the diffusion of reform initiatives are discussed in light of the findings.

While a substantial body of work has shown that higher-SES students tend to apply to more selective colleges than their lower-SES counterparts, we know relatively less about "why" students differ in their application behavior. In this study, we draw upon a sociological approach to educational stratification to unpack the SES-based gap in college application selectivity. Using data from the "High School Longitudinal Study of 2009," we examine the contribution of theoretical factors to the class-based gap in the selectivity of college applications. Namely, from the rational action model we estimate the contribution of "performance differentials" and "choice differentials," while from the status attainment model we look at the level and type of "educational expectations" as well as the "number of applications" submitted. Through a series of Heckman selection models, as well as a Blinder-Oaxaca decomposition analysis, we can explain 85% of the gap in college application selectivity between students in the top and bottom SES quintiles. In turn, we estimate that 60% of this explained portion is due to rational action mechanisms such as grades and test scores while 35% is due to status attainment mechanisms. Finally, we reveal that SES moderates the relationship between type of expectations and application selectivity. We find that the payoff to higher expectations (in terms of selective applications) disproportionately accrues to higher-SES students.

This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN models were trained using the data for phonological awareness and print awareness of children. According to the findings, the created ANN model had an excellent fit to the actual data (R[superscript 2] = 0.934 and 0.940). Furthermore, the ANN model results were tested with a traditional analysis technique, Pearson correlation analysis. The ANN models yielded similar results to the Pearson correlation analysis but with more detail as expected. The ANN models were run for user-generated synthetic datasets and the relationships between the dependent and independent variables were discussed using model results. Demographic variables, namely, children's age, mother's age, mother's education, and family income were found to be not effective on children's print and phonological awareness skills. On the other hand, home literacy environment-related variables were found to be very effective. In conclusion, this paper introduces a methodology for implementing ANN modeling in educational data. A novel and powerful approach is provided to assess and estimate essential components of early literacy skills. The study has important implications for advancing our understanding of potential benefits of employing an AI-based modeling techniques in the field of education. The utilization of machine learning methods in educational research, as presented in this paper, has the potential to fundamentally reshape our approaches in categorizing and analyzing educational data.