1,720,975 research outputs found
Escenarios de aprendizaje para evaluaciones cortas
Get PDFThis article presents an assessment experience in the course “Agile Web Development” of the Information Technology Management program at the Costa Rica Institute of Technology. An educational tool was used to implement short assessments both in-class and at-home. The aim was to analyze how different sequences of activities (scenarios) influence students’ learning, motivation, and participation. By combining quantitative and qualitative data collection, we identified student preferences and potential pedagogical implications for courses in blended learning environments.Este artículo presenta una experiencia de evaluación en el curso “Desarrollo ágil de aplicaciones web” de la carrera de Administración de Tecnología de Información (ATI) del Instituto Tecnológico de Costa Rica, en la que se utilizó una herramienta educativa para implementar evaluaciones cortas en el aula y en la casa. El objetivo fue analizar cómo diferentes secuencias de actividades (escenarios) impactan el aprendizaje, la motivación y la participación del estudiantado. A través de una combinación de recolección de datos cuantitativos y cualitativos, se identificaron preferencias estudiantiles y posibles implicaciones pedagógicas para cursos en entornos mixtos
Extracción de modelos de conocimiento a partir de libros de texto y su aplicación en los negocios
Get PDFEste artículo describe un enfoque innovador para la extracción de modelos de conocimiento a partir de libros de texto y su aplicación en los negocios. A través de un proceso detallado, que incluye la extracción, vinculación, enriquecimiento, análisis y formalización, se crean modelos de conocimiento de alta calidad. Este proceso es el resultado de una investigación doctoral, y en este artículo se ofreceun resumen ejecutivo de la misma. Los modelos creados tienen aplicaciones en la educación, pero también pueden ser usados potencialmente en el ámbito empresarial. En ese sentido, este artículo explora también las posibilidades de adaptar este proceso para interpretar y procesar documentos empresariales esenciales
La Inteligencia Artificial Generativa y cómo podemos abordarla en nuestra enseñanza
Get PDFLa GenAI (Inteligencia Artificial Generativa) está transformando la educación en computación, y como docentes, debemos adaptarnos y actualizar nuestras prácticas de enseñanza. Este artículo explora una serie de acciones básicas que las personas docentes pueden implementar para integrar la GenAI en sus cursos, destaca una experiencia práctica en un curso específico y presenta un proyecto de investigación en curso que investiga las percepciones y el impacto de la GenAI en el aula. Se enfatiza la importancia de establecer políticas claras, enseñar los aspectos técnicos y éticos de la IA, y ajustar las prácticas y métodos de evaluación para mejorar el aprendizaje y abordar los desafíos que plantea la GenAI
Extraction of Knowledge Models from Textbooks
Get PDFMany adaptive educational systems and other artificial intelligence applications rely on high-quality knowledge representations. Still, knowledge acquisition remains the primary bottleneck hindering large-scale deployment and adoption of knowledge-based systems. One path to scalable knowledge extraction is using digital textbooks, given their domain-oriented content, structure, and availability. This dissertation presents a unified approach for automatically extracting high-quality and domain-specific knowledge models from digital textbooks. The proposed approach leverages the authors’ knowledge encoded in the textbooks’ elements that facilitate navigation and understanding of the material (table of contents, index, formatting styles) to create knowledge models. The proposed workflow first extracts initial information elements from the textbooks: the structure of chapters and subchapters using the Table of Content, the content of each section, and domain terminology from the back-of-the-book index. Then, new information is added: domain terms are linked to external entities in a knowledge graph (DBpedia) and are enriched with semantic content (e.g., abstracts and categories). Finally, the knowledge about the domain is refined by identifying the relevance of concepts to the target domain. The extracted knowledge is represented in a model using the Text Encoding Initiative. Multiple evaluations show that the extracted knowledge models have high levels of quality across several properties: accuracy, semantics, coverage, specificity, cognitive validity, and granularity. Additionally, the approach is effective in multiple domains—for example, statistics, ancient philosophy, and Python programming. Finally, there are many potential applications for the extracted knowledge models. This dissertation presents three different educational systems supported by the knowledge models
Knowledge models from PDF textbooks
Get PDFTextbooks are educational documents created, structured and formatted by domain experts with the primary purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extensible approach towards automated extraction of knowledge models from textbooks and enrichment of their content with additional links (both internal and external). The textbooks themselves essentially become hypertext documents where individual pages are annotated with important concepts in the domain. The evaluation experiments examine several aspects and stages of the approach, including the accuracy of model extraction, the pragmatic quality of extracted models using one of their possible applications— semantic linking of textbooks in the same domain, the accuracy of linking models to external knowledge sources and the effect of integration of multiple textbooks from the same domain. The results indicate high accuracy of model extraction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level
Interlingua: Linking textbooks across different languages
No full textIncreasing numbers of students enrol in formal and informal courses taught in a foreign language. Studying a course from an unfamiliar university/program is difficult enough, but the difficulties multiply when the transition to new course requirements is exacerbated by the necessity to learn course material in a foreign language. This paper describes Interlingua a platform where students can study textbooks in a foreign language supported by on-demand access to relevant reading material in their mother tongue. Interlingua automatically recognises important terminology within textbooks content, extracts structural models of textbooks and links sections and subsections across textbooks in different languages covering the same academic subject. The interface and architecture of Interlingua as well as the technologies underlying the platform are described
Order out of Chaos: Construction of Knowledge Models from PDF Textbooks
No full textTextbooks are educational documents created, structured and formatted by domain experts with the main purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extendable approach towards automated extraction of this knowledge from textbooks taking into account their formatting rules and internal structure. We focus on PDF as the most common textbook representation format; however, the overall method is applicable to other formats as well. The evaluation experiments examine the accuracy of the approach, as well as the pragmatic quality of the obtained knowledge models using one of their possible applications - semantic linking of textbooks in the same domain. The results indicate high accuracy of model construction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level
Extraction of Knowledge Models from Textbooks
No full textMany adaptive educational systems and other artificial intelligence applications rely on high-quality knowledge representations. Still, knowledge acquisition remains the primary bottleneck hindering large-scale deployment and adoption of knowledge-based systems. One path to scalable knowledge extraction is using digital textbooks, given their domain-oriented content, structure, and availability. This dissertation presents a unified approach for automatically extracting high-quality and domain-specific knowledge models from digital textbooks. The proposed approach leverages the authors’ knowledge encoded in the textbooks’ elements that facilitate navigation and understanding of the material (table of contents, index, formatting styles) to create knowledge models. The proposed workflow first extracts initial information elements from the textbooks: the structure of chapters and subchapters using the Table of Content, the content of each section, and domain terminology from the back-of-the-book index. Then, new information is added: domain terms are linked to external entities in a knowledge graph (DBpedia) and are enriched with semantic content (e.g., abstracts and categories). Finally, the knowledge about the domain is refined by identifying the relevance of concepts to the target domain. The extracted knowledge is represented in a model using the Text Encoding Initiative. Multiple evaluations show that the extracted knowledge models have high levels of quality across several properties: accuracy, semantics, coverage, specificity, cognitive validity, and granularity. Additionally, the approach is effective in multiple domains—for example, statistics, ancient philosophy, and Python programming. Finally, there are many potential applications for the extracted knowledge models. This dissertation presents three different educational systems supported by the knowledge models
Harnessing Textbooks for High-Quality Labeled Data: An Approach to Automatic Keyword Extraction
Get PDFAs textbooks evolve into digital platforms, they open a world of opportunities for Artificial Intelligence in Education (AIED) research. This paper delves into the novel use of textbooks as a source of high-quality labeled data for automatic keyword extraction, demonstrating an affordable and efficient alternative to traditional methods. By utilizing the wealth of structured information provided in textbooks, we propose a methodology for annotating corpora across diverse domains, circumventing the costly and time-consuming process of manual data annotation. Our research presents a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) fine-tuned on this newly labeled dataset. This model is applied to keyword extraction tasks, with the model’s performance surpassing established baselines. We further analyze the transformation of BERT’s embedding space before and after the fine-tuning phase, illuminating how the model adapts to specific domain goals. Our findings substantiate textbooks as a resource-rich, untapped well of high-quality labeled data, underpinning their significant role in the AIED research landscape
Generation of assessment questions from textbooks enriched with knowledge models
Get PDFAugmenting digital textbooks with assessment material improves their effectiveness as learning tools. It can be a laborious task requiring considerable amount of time and expertise. This paper presents an automated assessment generation tool that works as a component of the Intextbooks platform. Intextbooks extracts fine-grained knowledge models from PDF textbooks and converts them into semantically annotated learning resources. With the help of the developed assessment components, these textbooks become interactive educational tools capable to assess students' knowledge of relevant concepts. The results of an expert-based pilot evaluation show that generated questions are properly worded and have a good range in term of difficulty. From the point of assessment value, some generated questions types fall behind manually constructed assessment, while others obtain comparable results
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