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This AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques being used to identify new forms of data, measures, descriptives, predictions, and experiments in education. Not only are old research questions being analyzed in new ways but also new questions are emerging based on novel data and discoveries from EDS techniques. This overview defines the emerging field of education data science and discusses 12 articles that illustrate an AERA-angle on EDS. Our overview relates a variety of promises EDS poses for the field of education as well as the areas where EDS scholars could successfully focus going forward.
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Randomized Experiments in Education, with Implications for Multilevel Causal Inference
Education research has experienced a methodological renaissance over the past two decades, with a new focus on large-scale randomized experiments. This wave of experiments has made education research an even more exciting area for statisticians, unearthing many lessons and challenges in experimental design, causal inference, and statistics more broadly. Importantly, educational research and practice almost always occur in a multilevel setting, which makes the statistics relevant to other fields with this structure, including social policy, health services research, and clinical trials in medicine. In this article we first briefly review the history that led to this new era in education research and describe the design features that dominate the modern large-scale educational experiments. We then highlight some of the key statistical challenges in this area, including endogeneity of design, heterogeneity of treatment effects, noncompliance with treatment assignment, mediation, generalizability, and spillover. Though a secondary focus, we also touch on promising trial designs that answer more nuanced questions, such as the SMART design for studying dynamic treatment regimes and factorial designs for optimizing the components of an existing treatment.
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- Award ID(s):
- 1659935
- PAR ID:
- 10300972
- Date Published:
- Journal Name:
- Annual Review of Statistics and Its Application
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2326-8298
- Page Range / eLocation ID:
- 177 to 208
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Teachers, schools, districts, states, and technology developers endeavor to personalize learning experiences for students, but definitions of personalized learning (PL) vary and designs often span multiple components. Variability in definition and implementation complicate the study of PL and the ways that designs can leverage student characteristics to reliably achieve targeted learning outcomes. We document the diversity of definitions of PL that guide implementation in educational settings and review relevant educational theories that could inform design and implementation. We then report on a systematic review of empirical studies of personalized learning using PRISMA guidelines. We identified 376 unique studies that investigated one or more PL design features and appraised this corpus to determine (1) who studies personalized learning; (2) with whom, and in what contexts; and (3) with focus on what learner characteristics, instructional design approaches, and learning outcomes. Results suggest that PL research is led by researchers in education, computer science, engineering, and other disciplines, and that the focus of their PL designs differs by the learner characteristics and targeted outcomes they prioritize. We further observed that research tends to proceed without a priori theoretical conceptualization, but also that designs often implicitly align to assumptions posed by extant theories of learning. We propose that a theoretically guided approach to the design and study of PL can organize efforts to evaluate the practice, and forming an explicit theory of change can improve the likelihood that efforts to personalize learning achieve their aims. We propose a theory-guided method for the design of PL and recommend research methods that can parse the effects obtained by individual design features within the “many-to-many-to-many” designs that characterize PL in practice.more » « less
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In recent years, Network-on-Chip (NoC) has emerged as a promising solution for addressing a critical performance bottleneck encountered in designing large-scale multi-core systems, i.e., data communication. With advancements in chip manufacturing technologies and the increasing complexity of system designs, the task of designing the communication sub- systems has become increasingly challenging. The emergence of hardware accelerators, such as GPUs, FPGAs and ASICs, together with heterogeneous system integration of the CPUs and the accelerators creates new challenges in NoC design. Conventional NoC architectures developed for CPU-based multi- core systems are not able to satisfy the traffic demands of heterogeneous systems. In recent years, numerous research efforts have been dedicated to exploring the various aspects of NoC design in hardware accelerators and heterogeneous systems. However, there is a need for a comprehensive understanding of the current state-of-the-art research in this emerging research area. This paper aims to provide a summary of research work conducted in heterogeneous NoC design. Through this survey, we aim to present a comprehensive overview of the current related research, highlighting key findings, challenges, and future directions in this field.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-statistics-031219-041205
