Conference Paper

Automatic creation of Moodle activities out of the Web of Data to link formal and informal learning contexts

Conference Paper

Automatic creation of Moodle activities out of the Web of Data to link formal and informal learning contexts

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... Casual Learn app provides students with an interactive map of geolocated learning tasks, obtained from the Casual Learn dataset, thus hiding all the details related to Semantic Web technologies. Another example of the use of LOD is the approach presented in [6], which generates questions to be carried out in Virtual Learning Environments (such as Moodle). ...
... If a suggestion is used, as in the case of the Listing 1.1, an attempt is made to retrieve the information in multiple languages so that CHEST can be used by as many people as possible. In 1.1(1) a unique identifier is defined for the resource to be added, in addition to providing a label (1.1 [3][4][5]), a short description (1.1 [6][7][8]), the location (1.1[9-10]) and the POI categories (1.1[11-12] Learning tasks added by teachers will also be stored as LOD (Fig. 1c). The Listing 1.2 shows the data that is saved in the triple store when the teacher add a learning task to the POI of Saint-Sernin. ...
Chapter
There is a deluge of Cultural Heritage Linked Open Data containing detailed information (e.g., location, architectural styles, etc.). Teachers could use this Open Data to generate meaningful learning tasks. However, most teachers are not using this information. This may be because they are not aware of these information sources or because they have technological difficulties in accessing the information (as they need to know Semantic Web related technologies). To overcome that limitation, this demonstration paper presents Cultural Heritage Educational Semantic Tool (CHEST), a distributed application aimed at supporting teachers in authoring Cultural Heritage learning tasks based on existing Cultural Heritage Linked Open Data. Teacher annotations are published as Linked Open Data, thus facilitating the reuse of such data by other teachers (using CHEST or other applications). This new application also supports students in the completion of the learning tasks created and/or reused by their teachers. CHEST can be used by both teachers and students using a web-based desktop interface or Android/iOS mobile apps.KeywordsLinked open dataCultural HeritageLearning tasksSemantic annotation
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DBpedia is a large-scale and multilingual knowledge base generated by extracting structured data from Wikipedia. There have been several attempts to use DBpedia to generate questions for trivia games, but these initiatives have not succeeded to produce large, varied, and entertaining question sets. Moreover, latency is too high for an interactive game if questions are created by submitting live queries to the public DBpedia endpoint. These limitations are addressed in Clover Quiz, a turn-based multiplayer trivia game for Android devices with more than 200K multiple choice questions (in English and Spanish) about different domains generated out of DBpedia. Questions are created off-line through a data extraction pipeline and a versatile template-based mechanism. A back-end server manages the question set and the associated images, while a mobile app has been developed and released in Google Play. The game is available free of charge and has been downloaded by more than 5K users since the game was released in March 2017. Players have answered more than 614K questions and the overall rating of the game is 4.3 out of 5.0. Therefore, Clover Quiz demonstrates the advantages of semantic technologies for collecting data and automating the generation of multiple choice questions in a scalable way.
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This paper discusses the key characteristics of smart learning and the main challenges to be overcome when designing smart educational environments to support personalisation. In order to integrate smart learning environments into the learning ecosystem and educational contexts, innovative uses and new pedagogical approaches need to be implemented to orchestrate formal and informal learning. This contribution describes the main characteristics of smart learning and smart learning environments and sustains the relevance of taking the participation of future users into account during the design process to increase knowledge of the design and the implementation of new pedagogical approaches in smart learning environments.
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Multiple choice questions (MCQs) are considered highly useful (being easy to take or mark) but quite difficult to create and large numbers are needed to form valid exams and associated practice materials. The idea of re-using an existing ontology to generate MCQs almost suggests itself and has been explored in various projects. In this project, we are applying suitable educational theory regarding assessments and related methods for their evaluation to ontology-based MCQ generation. In particular, we investigate whether we can measure the similarity of the concepts in an ontology with sufficient reliability so that this measure can be used to control the difficulty of the MCQs generated. In this report, we provide an overview of the background to this research, and describe the main steps taken and insights gained.
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The Semantic Web offers many opportunities for reusing datasets and domain models in the field of education and assessment in particular. We have conducted research to generate test questions from Semantic resources. The reuse of semantic resources raises however challenges, since all data and models have not been conceived to be directly used for educational purposes. We have therefore analysed existing domain models created specifically for educational contexts to identify structures and relations of the model that can help deem the relevance of a particular domain model for automatic item generation. We present an initial set of conditions which can help identifying a relevant domain model to be used in an educational context. We also suggest a mechanism to relate them to levels of knowledge to be assessed in generated items.
Chapter
The rapidly changing world requires that lifelong learning must become an integral part of people’s life. This challenge is especially apparent in the small countries with limited human resources. Open educational resources and practices should be seen as a part of larger learning ecosystem to improve access and increase the quality of education. This chapter presents the current situation of open education in Estonia. The development of digital learning resources infrastructure which also cover open educational resources is currently among the priorities in Estonia. In addition to the infrastructure development, there have been large projects for creating open educational resources for schools. However, Estonia does not have a specific open educational policy and a functional open education community. Also there is very little research on the actual use and impact of open educational resources.
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Smart Learning Environments hold promise of adapting learning processes to the individual context of students and connecting formal with non-formal learning. To do so, SLEs need to know the current context of the students, regardless of the physical or virtual space where learning takes place. This paper presents an architecture that assists in the deployment and enactment of learning situations across-spaces, able to sense and react to changes in the students’ context in order to adapt the learning process.
Conference Paper
In introductory programming courses, proficiency is typically achieved through substantial practice in the form of relatively small assignments and quizzes. Unfortunately, creating programming assignments and quizzes is both, time-consuming and error-prone. We use Automatic Item Generation (AIG) in order to address the problem of creating numerous programming exercises that can be used for assignments or quizzes in introductory programming courses. AIG is based on the use of test-item templates with embedded variables and formulas which are resolved by a computer program with actual values to generate test-items. Thus, hundreds or even thousands of test-items can be generated with a single test-item template. We present a semantic-based AIG that uses linked open data (LOD) and automatically generates contextual programming exercises. The approach was incorporated into an existing self-assessment and practice tool for students learning computer programming. The tool has been used in different introductory programming courses to generate a set of practice exercises different for each student, but with the same difficulty and quality.
Linked Data - Design Issues
  • T Berners-Lee