Search Results (7)

This study assesses how Data-Driven Decision-Making (DDDM) impacts marketing practices and research. Using the PRISMA 2020 protocol, this research conducted systematic reviews of 94 peer-reviewed articles and utilized bibliometric and thematic analyses. From this, four major themes emerged: improvement in the customer experience via the personalization of marketing; marketing driven by innovation through data resource versatility, Machine Learning, analytics, and Artificial Intelligence; performance enhancement through the optimal allocation of resources; and the data governance and ethical use of such resources, and the use of such data resources. This study illustrates how the combination of multi-level theory and methodical stricture accounts for the systemic influence of DDDM in marketing. This study adds to these theories by proposing a cohesive and synthesized understanding of the interplay of the technological, organizational, and governance elements in data-driven marketing. This research provides organizations with actionable guidance aimed at increasing effective analytics-driven decision-making, while also ensuring the responsible use of data. Full article

This study validates a Spanish-language instrument designed to assess self-efficacy, digital competence, and anxiety in data-driven decision-making (DDDM) among pre-service teachers. Based on the 3D-MEA and the Beliefs about Basic ICT Competencies scale, the instrument was culturally adapted for Chile and Spain. A sample of 512 participants underwent exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Given the ordinal nature of the data and the assumption of non-normality, appropriate estimation methods were utilized. Results supported a well-defined four-factor structure: Interpretation and Application, Technology, Identification, and Anxiety. Factor loadings ranged from 0.678 to 0.869, and internal consistency was strong (α = 0.802–0.888). The CFA confirmed good model fit (χ² (129) = 189.25, p < 0.001; CFI = 0.985; TLI = 0.981; RMSEA = 0.041; SRMR = 0.061). Measurement invariance was established across gender and nationality, reinforcing the validity of cross-group comparisons. The study is framed within an educational context aligned with socioformative principles and sustainable education goals, which support reflective and ethical data use. This validated tool addresses the lack of culturally adapted and psychometrically validated instruments for assessing DDDM competencies in Spanish-speaking contexts, offering a culturally and linguistically relevant instrument with strong internal consistency and a well-supported factor structure. It supports the design of formative strategies in teacher education, enabling the identification of training needs and promoting evidence-based pedagogical decision-making in diverse Hispanic contexts. Future studies should test factorial invariance across additional contexts and explore longitudinal applications. Full article

This entry presents the findings of a bibliographic review in Differentiated Instruction (DI), emphasizing its importance in classrooms with diverse student abilities. DI encourages teachers to adjust their instructional methods based on students’ learning profiles and needs. It has been identified as a crucial strategy for fostering inclusion and equal opportunities in education. Overall, the document underscores the importance of DI in fostering personalized learning and equal opportunities, especially in diverse classrooms. It also highlights ongoing challenges, such as teacher preparation, time constraints, and the need for the effective use of technology and data. Full article

The higher education (HE) system is witnessing immense transformations to keep pace with the rapid advancements in digital technologies and due to the recent COVID-19 pandemic compelling educational institutions to completely switch to online teaching and assessments. Assessments are considered to play an important and powerful role in students’ educational experience and evaluation of their academic abilities. However, there are many stigmas associated with both “traditional” and alternative assessment methods. Rethinking assessments is increasingly happening worldwide to keep up with the shift in current teaching and learning paradigms due to new possibilities of using digital technologies and a continuous improvement of student engagement. Many educational decisions such as a change in assessment from traditional summative exams to alternate methods require appropriate rationale and justification. In this paper, we adopt data-driven decision-making (DDDM) as a process for rethinking assessment methods and implementing assessment transformations innovatively in an HE environment. We make use of student performance data to make an informed decision for moving from exam-based assessments to nonexam assessment methods. We demonstrate the application of the DDDM approach for an educational institute by analyzing the impact of transforming the assessments of 13 out of 27 subjects offered in a Bachelor of Information Technology (BIT) program as a case study. A comparison of data analysis performed before, during, and after the COVID-19 pandemic using different student learning measures such as failure rates and mean marks provides meaningful insights into the impact of assessment transformations. Our implementation of the DDDM model along with examining the influencing factors of student learning through assessment transformations in an HE environment is the first of its kind. With many HE providers facing several challenges due to the adoption of blended learning, this pilot study based on a DDDM approach encourages innovation in classroom teaching and assessment redesign. In addition, it opens further research in implementing such evidence-based practices for future classroom innovations and assessment transformations towards achieving higher levels of educational quality. Full article

Data-driven modeling, action, and strategies have become popular, and the education community has witnessed increased interest in data-driven decision-making (DDDM). DDDM values and prioritizes decisions supported by high-quality, verifiable data that has been effectively processed and analyzed. The objective of our study is to describe the design, development, and implementation of a data-driven, evidence-based model of academic development in the context of CUNY Graduate School of Public Health and Health Policy (CUNY SPH) utilizing SMAART (Sustainability Multisector Accessible Affordable Reimbursable Tailored) model. The alignment of academic and student affairs within CUNY SPH brought with it several challenges. Defining roles and responsibilities across different student and academic affair units with a goal of collaborative leadership model and lack of meaningfulness were key challenges. It was important to listen to the experiences and recommendations of various individuals performing various functions in different capacities. A unified framework of key data indicators was needed to create a transparent and equitable model. An innovative interactive SMAART SPH dashboard designed, developed, and implemented to guide data-driven, evidence-based decision-making. Institutions can use a large amount of data from various sources to improve students’ learning experience, enhance research initiatives, support effective community outreach, and develop campus infrastructure to bring in sustainability. Full article

An important issue today for industries is optimizing their processes. Therefore, it is necessary to make the right decisions to carry out these activities, such as increasing the profit of businesses, improving the commercial strategies, and analyzing the industrial processes performance to produce better goods and services. This work proposes an intelligent system approach to prescribe actions and reduce the chemical oxygen demand (COD) in an equalizer tank of a wastewater treatment plant (WWTP) using machine learning models and genetic algorithms. There are three main objectives of this data-driven decision-making proposal. The first is to characterize and adapt a proper prediction model for the decision-making scheme. The second is to develop a prescriptive intelligent system based on expert’s rules and the selected prediction model’s outcomes. The last is to evaluate the system performance. As a novelty, this research proposes the use of long short-term memory (LSTM) artificial neural networks (ANN) with genetic algorithms (GA) for optimization in the WWTP area. Full article

Over recent decades, the world has experienced a growing demand for and reliance upon unmanned aerial systems (UAS) to perform a broad spectrum of applications to include military operations such as surveillance/reconnaissance and strike/attack. As UAS technology matures and capabilities expand, especially with respect to increased autonomy, acquisition professionals and operational decision makers must determine how best to incorporate advanced capabilities into existing and emerging mission areas. This research seeks to predict which autonomous UAS capabilities are most likely to emerge over the next 20 years as well as the key challenges for implementation for each capability. Employing the Delphi method and relying on subject matter experts from operations, acquisitions and academia, future autonomous UAS mission areas and the corresponding level of autonomy are forecasted. The study finds consensus for a broad range of increased UAS capabilities with ever increasing levels of autonomy, but found the most promising areas for research and development to include intelligence, surveillance, and reconnaissance (ISR) mission areas and sense and avoid and data link technologies. Full article