Education and Learning Technologies
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In the domain of Natural Language Processing (NLP), the English Writing Fluency Improvement for non-native speakers, particularly in academic contexts, poses significant challenges. While Sentence-level Revision (SentRev) endeavors to address this concern, the existing evaluation corpus, SMITH, falls short in offering a robust and comprehensive assessment of the task. To bridge this gap, our research offers a novel evaluation corpus generation scheme, leading to the creation of Ten-Country Non-native Academic English Corpus (TCNAEC).
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Simulated dataset for deriving parametric constraints for Bayesian Knowedge Tracing. The classical Expectation-Maximization method results in degenerate parameters (i.e., parameters that violate the conceptual interpretation of the model, such as by saying that a learner with no knowledge of a skill is more likely to get an answer correct than a learner with knowledge). A novel approach based on Newton's method rescues these paramters using mathematically derived constraints on the parameter space.
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This dataset comprises detailed evaluations of web accessibility features across 20 major MOOC platforms, based on the Web Content Accessibility Guidelines (WCAG) 2.1. It is intended for researchers, educators, web developers, and policymakers interested in understanding and improving the accessibility of online learning environments. Despite the ongoing advancements in this field, MOOCs platforms still present considerable accessibility challenges for users with disabilities.
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The CF algorithm is combined to generate personalized English text reading recommendations for various long-tail user groups. By optimizing the recommendation generation process, the recommendation accuracy of the model is enhanced, and the recommendation performance and user satisfaction of the English text reading recommendation system are improved. The Top-N algorithm model is compared with the algorithm model based on matrix decomposition in terms of recommendation accuracy and F-Measure value, and the advantages of the proposed algorithm model are proved.
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This dataset records the assessment of the effectiveness of learning objects in statistical education within nursing degree programs. It includes observations from 54 students with the following variables: - diagnostico_institucional: Assessment by the educational institution. - pre_test: Knowledge assessment prior to the educational intervention. - post_test: Knowledge assessment following the educational intervention. - edad: Age of the students. - campus: Campus of the institution where education is conducted. - sede: University site grouping several campuses together.
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This survey dataset delves into the diverse experiences and perspectives of individuals, focusing on key aspects of their educational journey and subsequent career choices. Comprising more than 60 questions or attributes,respondents were asked to share insights into their personal background, educational history, university preferences, and current professional status. The questionnaire covers a range of topics, including high school experiences, university decision-making criteria, major selection influences, and post-graduation outcomes.
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The Numerical Latin Letters (DNLL) dataset consists of Latin numeric letters organized into 26 distinct letter classes, corresponding to the Latin alphabet. Each class within this dataset encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in color, size, writing style, thickness, background, orientation, luminosity, and other attributes, making the dataset highly comprehensive and rich.
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