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With businesses increasingly prioritizing data-driven decision making, the demand for business analysts is high and expected to grow. In response, many universities and institutions have developed courses and programs related to business analytics to prepare more graduates for careers in this field. Business analytics programs and educators consistently strive to achieve a high level of student learning success, ensuring competence in working in the business analytics field after graduation. In this study, we aim to examine key factors influencing student learning in business analytics, focusing on performance expectancy and satisfaction. We examined specific factors, including personal interest, career relevance expectancy, learning effort, and perceived course structure effectiveness, from perspectives related to both students and instructors. A research model was developed and empirically tested. The results showed that all factors significantly influenced both perceived academic performance and learning satisfaction. Additionally, personal interest and career relevance expectancy could significantly impact learning effort.

E-learning has become an emerging approach to delivering knowledge and information to students. It presents significant challenges to sharing and understanding learners' knowledge, inquiries, interest, and needs. Educational organizations are expected to apply the appropriate practice to address knowledge-sharing challenges in the e-learning environment. In this study, through a systematic literature review, we intend to identify and synthesize knowledge-sharing challenges and practices as well as classify the most discussed challenges and practices in various contextual settings. The findings classify the knowledge-sharing challenges and practices in e-learning from four perspectives: organization, individual, knowledge and technology. This study aims to build a knowledge base to support future research and effective knowledge-sharing practices in the e-learning environment.

Within the eukaryotic cell, the actin cytoskeleton is a crucial structural framework that maintains cellular form, regulates cell movement and division, and facilitates the internal transportation of proteins and organelles. External cues induce alterations in the actin cytoskeleton primarily through the activation of Rho GTPases, which then bind to a diverse array of effector proteins to promote the local assembly or disassembly of actin. We have harnessed the extensively studied functions of RhoA in the dynamics of the actin cytoskeleton to craft a practical series for Stage 2 Biology students. This series not only imparts essential tissue culture laboratory skills but also reinforces them through repetition. These activities are presented in a scenario designed for students to explore the function of a hypothetical RhoA family member. Students produce slides from transfected cells, undertake fluorescence microscopy, process the images using ImageJ, and compile their findings in a comprehensive scientific report. The composition of the report requires independent acquisition of new knowledge and synoptic learning. According to student feedback, this early experience greatly aids in solidifying and honing the skills required to report on more extensive and intricate research projects, such as capstone projects.

The focus of this study was to assess the skill requirements for data analytics positions and to understand data analysis employment expectations for new graduates. Furthermore, this work seeks to highlight issues relevant to curriculum management in university degree programs. 786 job postings were analyzed for domain-related, soft skills, as well as degree requirements. Soft skills, often referred to as people skills, comprised the largest part of the results (11 of the top 21 skills). Results revealed the most frequent soft skills were related to communication and teams or teamwork. The most frequent domain skills were related to visualization, data cleaning, data extraction and programming. Implications for curriculum based on results are discussed, and suggestions for future research are provided.

Exploratory data analysis (EDA), data visualization, and visual analytics are essential for understanding and analyzing complex datasets. In this project, we explored these techniques and their applications in data analytics. The case discusses Tableau, a powerful data visualization tool, and Google BigQuery, a cloud-based data warehouse that enables users to store, query, and analyze large datasets. It also explored the benefits and applications of both tools and their integration with other platforms and services. The project offers an introductory insight into Tableau's functionalities, employing a data file from the US Census Bureau via Google BigQuery.

We examined how Learning Analytics literature represents participants from diverse societies by comparing the studies published with samples from WEIRD (Western, Industrialized, Rich, Democratic) nations versus non-WEIRD nations. By analyzing the Learning Analytics studies published during 2015-2019 (N = 360), we found that most of the studies were on WEIRD samples, with at least 58 percent of the total studies on WEIRD samples. Through keyword analysis, we found that the studies on WEIRD samples' research topics focused on self-regulated learning and feedback received in learning environments. The studies on non-WEIRD samples focused on the collaborative and social nature of learning. Our investigation of the analysis tools used for the studies suggested the limitations of some software in analyzing languages in diverse countries. Our analysis of theoretical frameworks revealed that most studies on both WEIRD and non-WEIRD samples did not identify a theoretical framework. The studies on WEIRD and non-WEIRD samples convey the similarities of Learning Analytics and Educational Data Mining. We conclude by discussing the importance of integrating multifaceted elements of the participant samples, including cultural values, societal values, and geographic areas, and present recommendations on ways to promote inclusion and diversity in Learning Analytics research.

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.

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.

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.

The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes. To tackle these challenges, we present a novel approach, called DeepLM, that considers a series of time windows representing both sequences of learners' actions during gameplay and estimation of their current competencies (using stealth assessment) to model learners and accordingly predict their future performance. The approach employs a variant of deep neural networks to early predict learners' midterm and final scores simultaneously. The results show that using 20-50% of learners' action sequences can early predict their final scores, with a cross-validated convolutional neural network (CNN) achieving an area under the curve (AUC) and accuracy of 0.879 and 85.3%, respectively. The same model can also achieve high accuracy in predicting midterm and final scores at the same time, with an AUC and accuracy of 0.848 and 77.9%. Overall, the CNN model outperforms recurrent neural network, long short-term memory, and baseline multilayer perceptron (MLP) models in predicting learners' final performance and performs better than the baseline MLP model in predicting learners' midterm and final performance using both cross-validation and independent datasets. These findings show the potential of the proposed approach in accurately early predicting learners' performance, allowing educators and game designers to tailor interventions and support mechanisms that could lead to optimized learning outcomes.

Reasoning for complexity is a fundamental competency in these complex times for solutions to social problems and decision-making. The purpose of this paper is to demonstrate the validity and reliability of the eComplexity instrument by presenting its psychometric properties. The instrument consists of a Likert-type scale questionnaire designed to measure college students' perceptions of their mastery levels of complex reasoning competency as well as the subcompetencies that comprise it. The instrument was applied to higher education students in different countries of Latin America and Spain. The questionnaire articulates four types of thinking: systemic, scientific, critical and innovative and consists of 25 items. The methodology used was instrumental and psychometric, which seeks to demonstrate the validity and reliability of the eComplexity questionnaire. The results obtained from the research were as follows: The exploratory factor analysis indicated a Kaiser-Meyer-Olkin index (KMO) > 0.80, a significance of p< 0.05 and a Cronbach's Alpha value of 0.93. Likewise, a Confirmatory Factor Analysis was carried out and was possible to corroborate the internal structure validity of the instrument. In addition to Cronbach's Alpha coefficient, McDonald's Omega, and Guttman's Lambda coefficients were calculated to calculate reliability. With the results obtained it was possible to conclude that the instrument is valid and reliable, can be used in various university contexts to support integrated training necessary to address current challenges and contribute to educational research. It is recommended for future studies that the research can be expanded by using an instrument that can move from perceptual terms to measuring levels of complex reasoning mastery, but it is valuable to contrast with the students' perception to have a broader vision.

While early literature in accessibility services in postsecondary education largely focused on physically disabled students, their presence in the peer-reviewed literature from 1951-2012 was described as sparse by Gelbar et al. (2015). The studies that existed were largely descriptive, and no studies investigated the effectiveness of accommodations or services for physically disabled students. The present study extends this prior analysis and examines the professional literature on physically disabled students published from 2013-2021. Results indicated there was an increase in articles during this time that featured physically disabled students in the overall sample, and of articles focused on this cohort specifically. There was also an increase in the number of articles based in countries outside of the United States, Canada, and Great Britain, as well as a rise in the number of articles featuring qualitative designs. However, there remains a minimal number of articles featuring research designs that allow the field of postsecondary education and disability to determine research-based practices in providing access to the population, as well as articles that focus on 2-year and career/technical institutions and on faculty and institutional staff. Implications for research and practice are presented.

The advent of Career Enrolment Data (CED) in Higher Education is an important development for graduate employability practitioners and other stakeholders seeking to understand, analyse and enhance students' career readiness. CED, collected annually from all students in participating universities, requires students to self-report on their own career readiness, and is described as having 'the potential to be a component of a standardised measure of learning gain in relation to student employability' (Cobb, 2019, p.23). Reflections are offered by two graduate employability practitioners currently tasked with introducing CED to educators and recommending its use to support careers-focused components of their curricula. Drawing on different disciplinary backgrounds to inform their approaches, both practitioners are keen to explore opportunities to capitalise on the strengths of data insights. However, they advocate for a carefully nuanced and contextualised approach, openly acknowledging relevant limitations and risks when presenting the data to educators and other stakeholders. Drawing on one author's background in psychology, it considers the process of self-reporting on which the data wholly relies, noting potential influences and biases that may be relevant. The second author offers a sociological perspective, using the concept of neoliberalism as a framework for engaging with concerns about an employability agenda driven by corporate and economic interests, and a culture of auditing sometimes associated with hardship for academics and a detrimental effect on pedagogy. While it should be noted that experts in CED such as Gilworth and Cobb take care to acknowledge limitations and risks (Cobb, 2019; Gilworth, 2023) this reflective account grapples with the challenge of ensuring that these important nuances are not lost as the context shifts from theory to practice.

The prediction of student performance and the analysis of students' learning behaviour play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behaviour, educators can gain valuable insights into the factors that influence students' academic outcomes and identify areas of improvement in courses. In this study, we developed ClickTree, a tree-based methodology, to predict student performance in mathematical problems in end-unit assignments based on students' clickstream data. Utilising extensive clickstream data, we extracted a novel set of features at three levels, including problem-level, assignment-level and student-level, and we trained a CatBoost tree to predict whether a student will successfully answer a problem in an end-unit assignment or not. The developed method achieved an Area under the ROC Curve (AUC) of approximately 79% in the Educational Data Mining Cup 2023 and ranked second in the competition. Our results indicate that students who performed well in end-unit assignment problems engaged more with in-unit assignments and answered more problems correctly, while those who struggled had higher tutoring request rates. We also found that students face more difficulties with "check all that apply" types of problems. Moreover, Algebra II was the most difficult subject for students. The proposed method can be utilised to improve students' learning experiences, and the insights from this study can be integrated into mathematics courses to enhance students' learning outcomes.

Knowledge tracing aims to model and predict students' knowledge states during learning activities. Traditional methods like Bayesian Knowledge Tracing (BKT) and logistic regression have limitations in granularity and performance, while deep knowledge tracing (DKT) models often suffer from lacking transparency. This paper proposes a Transformer-based framework that emphasizes both accuracy and interpretability. It captures the relationship between student behaviors and learning outcomes considering the associations between exam and exercise problems. We participated in the EDM Cup 2023 Contest using the proposed framework and achieved first place on the task of predicting students' performance on end-of-unit test problems using clickstream data from previous assignments. Furthermore, the framework provides meaningful insights by analyzing user actions and visualizing attention weight matrices. These insights enable targeted interventions and personalized support, enhancing online learning experiences. We have uploaded our code, saved models, and predictions to an OSF repository: https://osf.io/mdpzc/.

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream data, Wide & Deep IRT provides precise predictions of answer correctness while enabling the exploration of behavioral patterns among different ability groups. Our experimental results based on a real-world dataset (EDM Cup 2023) demonstrate that Wide & Deep IRT outperforms conventional IRT models and state-of-the-art knowledge tracing models while maintaining the ease of interpretation associated with IRT models. Our model performed very well in the EDM Cup 2023 competition, placing second on the public leaderboard and third on the private leaderboard. Additionally, Wide & Deep IRT identifies distinct behavioral patterns across ability groups. In the EDMCup2023dataset, low-ability students were more likely to directly request an answer to a question before attempting to respond, which can negatively impact their learning outcomes and potentially indicates attempts to game the system. Lastly, the Wide & Deep IRT model consists of significantly fewer parameters compared to traditional IRT models and deep knowledge tracing models, making it easier to deploy in practice. The source code is available via Open Science Framework.

Effect sizes are important because they are an accessible way to indicate the practical importance of observed associations or differences. Standardized mean difference (SMD) effect sizes, such as Cohen's d, are widely used in education and the social sciences -- in part because they are relatively easy to calculate. However, SMD effect sizes assume normally distributed data, whereas most data in these fields are ordinal and/or non-normal. In these situations, SMD effect sizes can be biased, and a non-parametric measure such as Cliff's [delta] is more appropriate. This paper provides a practical guide on how to calculate Cliff's [delta]. First, we present a conceptual overview and a worked example. Then we present two methods of calculating Cliff's [delta]: (1) a web-based Shiny application developed to accompany this paper (https://cliffdelta.shinyapps.io/calculator; suitable for all users), and (2) an R tutorial (suitable for R users). This is intended to provide researchers and practitioners with an appropriate and accessible effect size measure for non-normal data.

This article introduces researchers in the science concerned with developing and studying research methods, measurement, and evaluation (RMME) to the educational data mining (EDM) community. It assumes that the audience is familiar with traditional priorities of statistical analyses, such as accurately estimating model parameters and inferences from those models. Instead, this article focuses on data mining's adoption of statistics and machine learning to produce cutting-edge methods in educational contexts. It answers three questions: (1) What are the primary interests of EDM and RMME researchers?; (2) What is their discipline-specific vocabulary?; and (3) What are the similarities and differences in how the EDM and RMME communities analyze similar types of data?

The purpose of this study was to analyze the activities in the Problem-Based Learning (PBL) methodology among high school students when solving problems related to the field of plane figures, both in extra-mathematical and intra-mathematical environments. The research was qualitative and constituted a case study. The technique used to select the research subjects was intentional sampling, which involved the selection of a group of sixth-grade students. The data were collected through tests and interviews. The data analysis techniques employed included data reduction, data presentation, and drawing conclusions. The results showed that the students used perimeter calculation as an erroneous representation of area calculation, and they exhibited a lack of argumentation during problem-solving. Furthermore, they expressed a lack of recognition of certain plane figures and their properties, a characteristic of students at the visualization level, also known as the first level of the Van Hiele levels. Another significant finding in this study was the use of problems involving extra-mathematical contexts, which had a greater impact on the problem-solving process and the understanding of mathematical concepts.

This manuscript presents an exploratory mixed-methods case study examining the impact of artificial intelligence (AI) in the form of generative pretrained transformers (GPTs) and large language models on special education administrative practices in one school district in the Northeast United States. AI holds tremendous potential to positively impact administrative practices, reducing the time administrators spend collecting and synthesizing data in special education settings (Center for Innovation, Design, and Digital Learning, 2024; Marino, Vasquez, Dieker, Basham, & Blackorby, 2023). Prior to the case study, a description of systematic data collection, cleansing, and traditional methods of data analysis are presented. This is followed by a discussion of data analysis using GPTs. The importance of data synthesis and effective communication is also included. Finally, the manuscript presents findings from a school district piloting GPTs to enhance administrative effectiveness during staffing considerations, program development, and budget allocation for staff.

We utilize a difference-in-difference design to examine the effect of adopting a statewide promise program on the enrollment of community college students across socio-economic status. Limited by a small sample size for treated units, we find inferential evidence that the adoption of a state-wide, last-dollar promise program for community colleges with no merit-based or need-based criteria raised the enrollment of in-state first-time-in-college, full-time students in their first year of college from families that earned between $0 and $75,000 in adjusted gross income. Effect sizes were largest for students from the lowest SES group ($0 to $30,000) approximately 168 additional students enrolled per 2-year institution per year following Tennessee Promise program adoption. Findings controlled for year-to-year variations in unemployment and state price parities. We discuss benefits and concerns regarding scholarships such as the Tennessee Promise that increase enrollment for lower-income students but do not affect the amount of financial aid included in their award packages in practice.

Classroom sensing systems can capture data on teacher-student behaviours and interactions at a scale far greater than human observers can. These data, translated to multi-modal analytics, can provide meaningful insights to educational stakeholders. However, complex data can be difficult to make sense of. In addition, analyses done on these data are often limited by the organization of the underlying sensing system, and translating sensing data into meaningful insights often requires custom analyses across different modalities. We present Edulyze, an analytics engine that processes complex, multi-modal sensing data and translates them into a unified schema that is agnostic to the underlying sensing system or classroom configuration. We evaluate Edulyze's performance by integrating three sensing systems (Edusense, ClassGaze, and Moodoo) and then present data analyses of five case studies of relevant pedagogical research questions across these sensing systems. We demonstrate how Edulyze's flexibility and customizability allow us to answer a broad range of research questions made possible by Edulyze's translation of a breadth of raw sensing data from different sensing systems into relevant classroom analytics.

This research focuses on how designing, implementing, and evaluating didactic strategies and activities based on scaffolding with visualization and the use of graphic organizers guide the process of reading comprehension in an eighth-grade public school course placed at A1 level. The research methodology was framed as action research with a mixed-method approach and a quasi-experimental design with no control group. There were 31 participants aged 12 to 18. The research design implied (1) a pretest, (2) a diagnostic analysis, (3) intervention, (4) a posttest, and (5) final analysis. After gathering information from the pretest initial diagnostic data, the pedagogical intervention with ten task-like scaffolding activities was applied and assessed with rubrics. Posttest data were collected and accounted for from the researcher's journal notes. Both reading comprehension tests, the pretest, and the posttest had 21 questions divided into literal, inferential, and evaluative levels. Results obtained using descriptive statistics exposed, from the posttest, some improvement in the literal and evaluative level questions but a slight decline in inferential level questions contrasted with the pretest results. Data demonstrated that some external factors like time, academic aids, and responsibility influenced the outcome positively or negatively.

As part of a study investigating research productivity of sociology PhD students in Australia, an application to the federal government's Department of Education, Skills and Employment (DESE) aimed to establish a baseline count of completed sociology doctorates by university. It was anticipated that university totals might be different from PhD completions within the main sociology teaching units. The government data furnished by this inquiry have proved to be problematic in several ways. The gaps in the data may be explained by the fact that universities report sociology PhDs to DESE in more generic rather than discipline-specific Field of Education (FoE) codes or as PhDs in other disciplines. DESE has either not required or been able to enforce consistent reporting. This raises questions about the time and cost value of these annual compliance efforts. It also limits the value of these data to researchers in charting current and potential future trends.

The purpose of this essay is to discuss the impact of the EdD experience on one teacher's understanding of data and research. From a first-person narrative, the author shares how learning to collect and analyze qualitative data has the potential to change the way teachers can engage with "data-driven decision making" in a high school setting. By exploring one scholar practitioner's personal evolution demonstrates the potential of qualitative research as a tool for both teachers and site administrators tackling stubborn challenges on our campuses. Includes examples of qualitative data sets collected on site and how data informed and inspired further work.

Using National Vital Statistics Birth and Fetal Death Data from 1995 to 2020 linked to county-level information on wildfires, we use variation in wildfire timing to examine how effects of wildfire exposure on infant health vary by maternal education. Results indicate that wildfire exposure increases the likelihood of low birth weight and fetal death, but effects vary by both trimester and maternal education. Mediation analyses suggest the variation by maternal education reflects selective survival and unequal sensitivity, rather than differential parental response to wildfires. In addition, mediation analyses suggest that maternal behaviors explain a greater share of the relationship between wildfire exposure and infant health than air quality. Wildfires may therefore reduce infant health through stress.

Floods cause extensive damage in high-income countries, including the United States, but problems are more severe in low- and middle-income countries (LMICs) that lack preventative and mitigating infrastructure. Marginalized children's education in LMICs might be particularly vulnerable. Using the Indian Human Development Survey, we investigate flood exposure implications for the education of school-age rural children, paying particular attention to children from marginalized groups. Results show that lower-caste Hindu, Muslim, and poorer children with less-educated parents in agricultural households are more likely to experience flooding. Interactions between flooding and marginalization characteristics indicate that flood exposure is associated with disproportionately negative learning outcomes for girls and that economic resources may mitigate flood exposure effects on delayed school progress. While greater exposures for marginalized groups are concerning, the limited number and modest magnitudes of documented negative effect heterogeneities for marginalized children are somewhat better news.

The preschool years, spanning from birth to age six, are crucial periods for acquiring social-emotional learning (SEL) skills. An effective way to address social-emotional learning deficits is to implement evidence-based intervention programs. The purpose of this article is to review specific evidencebased social-emotional learning intervention programs for preschool children, drawing from resources such as books, peer-reviewed journal articles, and conference proceedings. In this context, five social-emotional learning intervention programs, including First Step to Success, I Can Problem Solve, Incredible Years, PATHS, and Strong Start Pre-K, documented in the social-emotional learning literature, were reviewed. Each program was evaluated based on its target group, duration, focus, delivery method, and experimental effects. The reviews found that play-based activities, puppets and stories, role-playing, and family involvement were common components of these programs. The majority of programs focused on problem-solving skills, and all had positive effects on preschoolers' social-emotional learning skills. Based on the reviews, it was underlined that social-emotional learning intervention programs are effective when their content, process, and delivery methods are developmentally appropriate for the target group. Accordingly, it was seen that the widespread implementation of evidence-based social-emotional learning intervention programs is essential to improve the basic social-emotional learning skills of preschool children and to address their potential deficits. Continuous evaluation and refinement of social-emotional learning intervention programs, guided by feedback from educators, parents, and researchers, was also highlighted as crucial to increase their impact. By prioritizing the implementation of evidence-based social-emotional learning intervention programs with appropriate components and delivery methods, the stakeholders of education can facilitate the holistic development of preschool children and lay a strong foundation for their future social-emotional well-being.

The Titanic disaster is a topic that continues to fascinate. As the importance of analytics continues to grow in industry, data literacy skills have become increasingly important in business education. This project allows students to use the passenger data from the Titanic to build their data literacy skills using an engaging, experiential topic. The project requires students to extract, transform, describe, analyze, and draw conclusions regarding the factors that impacted survival on the Titanic. The project can be deployed using various application software and tools. We describe how the assignment can be completed using Excel, Tableau, and Python using the Pandas library.

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.

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.

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.

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.

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.

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.

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

This study 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.