Integrating Data Science in Educational Curricula

Financial market efficiency is significantly influenced by the availability and quality of information, with information asymmetry posing a major barrier to optimal market functioning. This article reviews the role of data science in mitigating information asymmetry and enhancing market efficiency, comparing traditional approaches with modern data-driven methods (e.g., machine learning, NLP, and blockchain). It systematically evaluates traditional approaches used to measure and mitigate information asymmetry and highlights their limitations in accurately capturing complex market dynamics. Traditional approaches such as statistical testing, price behavior analysis, and asset pricing models provide fundamental insights but often fail to capture complex, non-linear market dynamics, such as adverse selection, moral hazard, and asset mispricing, due to their reliance on historical data and linear assumptions. In contrast, data science has revolutionized financial market analysis by combining machine learning, natural language processing (NLP), big data analytics, and blockchain technology to solve information imbalances. It enables real-time analysis of unstructured data, improves predictive modeling, and enhances transparency through sentiment analysis, algorithmic trading, and decentralized ledgers. It concludes that integrating data science with traditional finance theory significantly reduces information gaps, offering policymakers and investors tools to foster fairer, more efficient markets. This bridges theoretical finance with computational innovations, demonstrating how data science addresses longstanding limitations in measuring and improving market efficiency.

Purpose This study aims to explore how middle-grade girls from predominantly underrepresented and underserved racially and ethnically minoritized (UUREM) backgrounds developed critical data literacy (CDL) through participation in a week-long residential science, technology, engineering and mathematics (STEM) camp. Given the increasing importance of data science education in a data-driven world, this research examines how informal learning environments can support CDL development among youth from historically marginalized groups. Design/methodology/approach The study draws on qualitative interview data from 11 participants and their group-produced artifacts to investigate how the camp experience supported engagement with data and the development of CDL. Interviews explored participants’ experiences with data collection, organization, analysis and interpretation within the context of real-world, community-relevant topics. Findings The findings demonstrate that participants engaged with data by collecting, organizing and analyzing information relevant to authentic problems. Meaning-making was facilitated through social interactions with peers and mentors, enabling participants to collaboratively interpret and critique data. Girls demonstrated critical examination of data by questioning sources, identifying limitations and considering potential biases. Through presentations, participants applied and communicated their findings, connecting data analyses to community-relevant concerns. Reflection and iteration emerged as participants reconsidered their approaches and interpretations over time. Originality/value This study contributes to research on informal learning, equity in STEM and data literacy by illustrating how informal STEM programs can intentionally foster CDL among UUREM girls. The findings highlight the role of collaborative meaning-making, critical reflection and community relevance in supporting youth to engage with data as a tool for understanding and addressing societal challenges.

NEWTON FAB LAB INITIATIVE: ATTRACTING K-12 EUROPEAN STUDENTS TO STEM EDUCATION THROUGH CURRICULUM-BASED FAB LABS

In the era of the Fourth Industrial Revolution (4IR), vast amounts of data are generated from sources such as IoT, cybersecurity, mobile, business, social media, and healthcare. Artificial intelligence (AI), particularly machine learning (ML), is essential for analyzing this data and developing smart, automated applications. Various machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, are used to enhance application intelligence. Additionally, deep learning, a subset of ML, can process large-scale data effectively. This paper provides an overview of these algorithms and their applications across fields like cybersecurity, smart cities, healthcare, e-commerce, and agriculture. It also discusses the challenges and research opportunities in the field, aiming to be a valuable resource for both academics and industry professionals, as well as decision-makers in various real-world scenarios. Highlights: Post-modern ceramics reflect features and connotations of body language. Research explores theoretical framework, variables, and applications in ceramic sciences. Results reveal key indicators, conclusions, and supporting references. Keywords: Machine learning, Deep learning, Artificial intelligence, Data science, Data-driven decision-making, Predictive analytics, Intelligent applications

This thesis examines the intersection of citizen science, serious games, and artificial intelligence, focusing on the methodological challenges that arise when participatory projects generate large and complex datasets. Citizen science has broadened public involvement in research, but recurring issues such as missing data, inconsistencies, observer variation, and limited reproducibility continue to affect data quality. Serious games also offer opportunities for engagement and learning, yet their behavioural data is often underused due to analytical constraints. By applying data science, machine learning, and explainable AI, this thesis shows how these techniques can improve the reliability and interpretation of citizen science data.<br/><br/>The research was carried out in collaboration with JGM Serious eXperiences, a serious-gaming organisation in Leeuwarden that develops escape-room-based games for professional training. These settings provided a controlled environment for observing real-time team behaviour under pressure. The collaboration reflects a form of participatory citizen science: JGM provided data and practical questions, while academic analysis refined behavioural indicators and strengthened scientific validity.<br/><br/>The thesis consists of three interconnected studies. The first, a systematic literature review, synthesises 90 publications on the use of AI in citizen science and highlights challenges such as data imbalance, bias, and limited transparency. The second study develops a data science framework to clean and validate JGM’s behavioural dataset, addressing missing and inconsistent entries. The third study applies machine learning and explainable AI to identify behavioural predictors of team success.<br/><br/>Together, these studies demonstrate how data science can enhance methodological rigour in citizen science by improving data preprocessing, analysis, and interpretation. The findings offer practical guidance on integrating data science and AI techniques into participatory projects to support more reliable, transparent, and impactful research.

A vast majority of Science, Technology, Engineering, Mathematics (STEM) courses and pedagogical frameworks concentrate on teaching the fundamental concepts and theoretical underpinnings of the tools related to the subject. While this aspect is important, we recognize that the teaching methods in a majority of the STEM courses today are broken; there is a major discrepancy between the skills and mindsets in technical classes and the ones that are useful to solve actual problems in “the real world”. Therefore, we suggest a new teaching framework called Data-X where entrepreneurial teaching methods developed in the Berkeley Method of Entrepreneurship are applied to advanced technical topics. Through inductive learning and by practicing story creation, stakeholder generation, adaptation, ideation, innovation processes, and by having a diverse mix of students being coached by a network of expert advisors, this highly applied teaching method empowers students to pursue and find solutions to open-ended projects and problems. The Data-X framework has been implemented and tested for three semesters in a UC Berkeley course called Applied Data Science for Venture Applications. In the class the students pick up, become comfortable, and utilize state-of-the-art tools in Data Science, Machine Learning, and Artificial Intelligence. The results, feedback, and testimonials we have received upon offering the class have been overwhelmingly positive, and we propose that the ideas and concepts behind Data-X can help fix many problems in modern STEM education.

The National Academies recommend academic institutions foster a basic understanding of data science in all undergraduates. However, data science education is not currently a graduation requirement at most colleges and universities. As a result, many graduates lack even basic knowledge of data science. To address the shortfall, academic institutions should incorporate introductory data science into general education courses. A general education IT course provides a unique opportunity to integrate data science education. Modules covering databases, spreadsheets, and presentation software, already present in many survey IT courses, teach concepts and skills needed for data science. As a result, a survey IT course can provide comprehensive introductory data science education by adding a data science module focused on modeling and evaluation, two key steps in the data science process. The module should use data science software for application, avoiding the complexities of programming and advanced math, while enabling an emphasis on conceptual understanding. We implemented a course built around these ideas and found that the course helps develop data savvy in students.

The fields of Science, Technology, engineering, and mathematics (STEM) have advanced significantly in Education. In various research in the world of education, STEM growth is frequently associated with project-based learning (PjBL). Combining STEM learning with PjBL is worth researching. This study’s objective is to comprehensively investigate the link between STEM and PjBL, which covers the most advanced PjBL-STEM idea in academic units, implementation in various nations and educational levels, and the impact of STEM and PjBL relationships on the caliber of students. This analysis identified up to 168 Scopus-recognized papers between 2007 and 2022. This study used a systematic review with a four-step PRISMA-P. Finally, there were 35 articles included in this research. The analysis’s findings demonstrate that STEM PjBL has successfully fostered active learning and student-centered instruction while enhancing 21st-century abilities (creativity, collaboration, problem-solving, creative use of technology, and higher-order thinking). The notion of STEM Subject, or the incorporation of STEM PjBL into activities, is the one that is researched the most in schools. The country that used PjBL STEM learning the most was the United States. In 2018, mixed research methodologies were used to study STEM-PjBL research the most. Secondary education STEM-PjBL studies and teachers are critical for children to become more interested in a STEM profession.

Chemistry learning, industrial needs and the existing problems in society can be discussed and integrated into the classroom by implementing the STEM (Science, Technology, Engineering, and Mathematics) approach. The focus of the STEM research conducted previously was on improving the skills possessed by students. Only few paid attentions to the perceptions of teachers in the chemistry class. The purpose of this research was to determine the chemistry teacher's perception of the implementation of the STEM approach in chemistry learning. The purposed-design survey method was used in this research. A total of 37 chemistry teachers were involved as respondents. The sampling technique used was random sampling by giving questionnaires to chemistry teachers from various level of education in around central java, Indonesia. The questionnaire instrument was used to determine teachers' perceptions of STEM learning in chemistry learning. The result of the survey conducted was chemistry teachers had a positive perception of the implementation of chemistry learning using the STEM approach. The main obstacle faced by the teachers to implement the STEM approach is that they are not accustomed to applying the STEM approach and there is limited time available for the learning process with STEM. Meanwhile, according to the teachers, by applying the STEM approach in the chemistry class, it can improve the students' skills needed in the 21st century.

Data Science for Local Government

Decisions related to career choice are influenced by person’s interactions with his or her environment. The purpose of this study was to examine the characteristics of a STEM (Science-Technology, Engineering, and Mathematics) career choice. The study included two stages in which two different questionnaires were used. The purpose of the first questionnaire, which was replied by 1162 people, was to examine their attitudes towards STEM, and their sense of employment capability in this field. The data were analyzed according to three age groups: 12th graders, young adults aged 21–35, and adults over the age of 35. The second stage focused on three sub-groups: young high school graduates with a high affinity to STEM studies, whom have not yet chosen career; adults who have chosen career as researchers in the STEM field; and adults who have chosen career as researchers in the social sciences and humanities. The second questionnaire examined environmental characteristics and attitudes towards the factors potentially influencing a career choice in STEM. The results indicate that the attitudes towards STEM and the sense of employment capability in this field become significantly more positive with age. The number of family members who have a STEM career was not found to be a significant factor influencing a career choice; however, a significant correlation was found between high school major and the career chosen by graduates. The research findings point to important trends that shed light on the process of choosing a career in STEM and concludes that young people’s interest in science during school is a key variable to sparking interest in science careers.

The growing reliance on data-driven decision-making across sectors has positioned predictive analytics as a core application of data science. Despite significant advancements in artificial intelligence and machine learning, existing predictive analytics studies often suffer from fragmented analytical pipelines and insufficient validation, limiting model reliability and real-world applicability. This study proposes and validates a structured data science framework for predictive analytics that integrates data preparation, feature engineering, model development, and validation within a unified methodological architecture. Adopting a quantitative, modeldriven research design, the study develops multiple predictive models and evaluates their performance through systematic validation procedures. The findings demonstrate that predictive models developed within the proposed framework exhibit improved accuracy, robustness, and generalizability compared to models developed using ad hoc approaches. The results further highlight the importance of continuous validation and comparative model assessment in mitigating overfitting and enhancing predictive stability. By emphasizing methodological coherence and validation rigor, the framework supports transparent and reliable decision-support systems applicable across domains such as education, healthcare, business analytics, logistics, and sustainability. The study contributes to data science literature by offering an empirically grounded framework that bridges theoretical principles with practical implementation, providing a replicable foundation for advancing predictive analytics research and application

Background The public often perceives STEM disciplines as objective and neutral from human and societal factors. However, recent movements in STEM education have increasingly challenged this value-free assumption, advocating for the integration of empathy into STEM education reform. How STEM education leaders view the role of empathy profoundly influences STEM education policy and curriculum design, yet our knowledge of this remains very limited globally. We interviewed 26 STEM education leaders in Hong Kong—comprising university faculty, secondary and primary school science/STEM teachers, and out-of-school STEM education providers. We develop and employ a novel analytical framework of two-fold orientations in STEM education (utilitarian-oriented and humanist-oriented) to interpret our empirical data. Results Thematic analysis revealed that empathy serves dual roles in STEM education: as a “tool” (utilitarian orientation) and as an “aim” (humanist orientation), with leaders referencing the latter twice more frequently. As a tool, participants identified empathy’s utility for (1) capturing student attention and (2) facilitating problem-solving in design activities, with notable consensus across all panel groups regarding its procedural value. As an aim, participants positioned empathy as (1) a fundamental educational value embodied in the creed for education and value-based pedagogy, (2) an essential component in defining STEM’s disciplinary boundaries, and (3) an ultimate goal focused on social service and transformative outcomes. These views vary according to their professional roles—school-based educators strongly emphasized empathy’s role as an educational value and goal, while university faculty and out-of-school STEM education providers expressed concerns about the potential dilution of disciplinary boundaries. Conclusions While leaders demonstrated consensus regarding empathy’s instrumental role in STEM education, notable disagreements emerged concerning its incorporation as an educational aim, particularly regarding disciplinary boundaries. The pattern of responses suggests that leaders’ professional contexts strongly influence their view of empathy’s role in STEM education. Our two-fold orientation framework provides a more nuanced understanding of how leaders position empathy in STEM education, revealing both tensions and synergies and evoking further discussion regarding the defining characteristics of STEM education. A cohesive collective understanding of empathy’s role remains underway, highlighting the need for enhanced professional dialogue among diverse STEM education stakeholders.

The positive impact of Science, Technology, Engineering, and Mathematics (STEM) programs on student achievement, attitude, interest, communication skills and problem-solving has alerted the education community to reform instructional approaches in STEM subjects. This meta-analysis study aims to analyze previous studies’ results of STEM program impacting students’ mathematics achievement. The criteria for inclusion of literature in the meta-analysis were: published between 1998 and 2017, employed experimental research design, and reported data necessary for computing effect sizes. Based on the Hedges-g effect size values, three of the studies were categorized into the large effect category (n=3), two studies were classified under the medium effect group (n=2), and twelve studies were considered to be small effect sizes (n=12). The overall weighted average effect size was 0.242 with a corresponding p-value of 0.023 demonstrating that STEM had an impact on student mathematics achievement. However, no evidence was found concerning the impact of STEM program on students’ mathematics achievement based on the three moderator variables: education level, publication source, and length of intervention. Limitations and implications of the study were discussed.

The affordable housing crisis has become a critical socio-economic challenge, disproportionately affecting underserved communities and exacerbating inequalities. Outdated data, reactive strategies, and inefficiencies in resource allocation have often hindered traditional policy interventions. This paper explores the role of smart data-driven analysis in addressing housing affordability challenges through predictive analytics, big data integration, and artificial intelligence. By leveraging large-scale datasets, including census data, rental market trends, and socioeconomic indicators, data-driven methodologies can provide real-time insights into housing affordability, enabling policymakers to design targeted and proactive interventions. The study reviews existing research on housing affordability, highlighting the limitations of conventional approaches and the emerging role of machine learning, geospatial analysis, and blockchain in improving transparency and efficiency in housing markets. A methodological framework is proposed that integrates predictive modeling, geographic information systems, and real-time data processing to assess housing crises at both macro and micro levels. Furthermore, the paper discusses policy implications and technology-enabled solutions such as AI-driven rent control models, smart subsidies, and blockchain-based housing registries. Key findings emphasize the necessity of public-private collaboration, ethical AI implementation, and the development of dynamic affordability tracking systems. Future research directions include refining predictive modeling techniques, enhancing real-time monitoring capabilities, and exploring AI-driven decision-support systems for urban planning. This paper provides a foundation for more effective, equitable, and sustainable housing interventions by integrating data science and housing policy.