Estrategia de visibilidad de recursos educativos abiertos para el sistema de educación costarricense a través de un repositorio institucional

El material didáctico generado en la universidad es de gran calidad, y con mucho potencial para ser utilizado por un gran número de estudiantes y profesores. Sin embargo, estos elementos educativos por si solos no pueden ser aprovechados por estudiantes de escuelas y colegios, pues ellos no tienen una forma de acceder y de buscar estos recursos educativos. Para solucionar este problema, el Tecnológico de Costa Rica diseño una estrategia de visibilidad de material didáctico y recursos digitales producidos en la universidad, para que los estudiantes puedan acceder a ellos a través de Recursos Educativos Abiertos. La estrategia contempla la creación de Recursos Educativos abiertos, el uso del Repositorio Institucional para dar visibilidad a estos recursos, y la utilización de un estándar de metadatos especializado. Como resultados esperamos contribuir con la educación de estudiantes y profesores, potenciar el repositorio, y dar mayor visibilidad al trabajo de calidad producido en la universidad.The educational materials generated at the university have high quality and a huge potential to be useful to a large number of students and teachers. However, these educational resources themselves can not be used by students in schools because they don’t have a way to access and search for these educational resources. To solve this problem, the Tecnológico de Costa Rica designed a visibility strategy to transform all this educational content into Open Educational Resources to be used by students. The strategy includes the creation of OER, the use of the institutional repository to give visibility to these resources, and the use of a specialized metadata standard. As results we expect to contribute to the education of students and teachers, strengthen the repository, and give greater visibility to the quality work produced at the university.Ibero-American Science and Technology Education Consortium (ISTEC

Harnessing Textbooks for High-Quality Labeled Data: An Approach to Automatic Keyword Extraction

As 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

Extraction of Knowledge Models from Textbooks

Many 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

Extraction of Knowledge Models from Textbooks

Many 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

Order out of Chaos: Construction of Knowledge Models from PDF Textbooks

Textbooks 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

Knowledge models from PDF textbooks

Textbooks 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

Increasing 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

Knowledge models from PDF textbooks

Textbooks 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

Generation of assessment questions from textbooks enriched with knowledge models

Augmenting 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

Generation of assessment questions from textbooks enriched with knowledge models

Augmenting 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