Development of Educational Ontology for C-Programming

Development of educational ontologies is a step towards creation of sharable and reusable adaptive educational systems. Ontology as a conceptual courseware structure may work as a mind tool for effective teaching and as a visual navigation interface to the learning objects. The paper discusses an approach to the practical ontology development and presents the designed ontology for teaching/learning C programming

Ontology-Based Open-Corpus Personalization for E-Learning

Conventional closed-corpus adaptive information systems control limited sets of documents in predefined domains and cannot provide access to the external content. Such restrictions contradict the requirements of today, when most of the information systems are implemented in the open document space of the World Wide Web and are expected to operate on the open-corpus content. In order to provide personalized access to open-corpus documents, an adaptive system should be able to maintain modeling of new documents in terms of domain knowledge automatically and dynamically. This dissertation explores the problem of open-corpus personalization and semantic modeling of open-corpus content in the context of e-Learning. Information on the World Wide Web is not without structure. Many collections of online instructional material (tutorials, electronic books, digital libraries, etc.) have been provided with implicit knowledge models encoded in form of tables of content, indexes, headers of chapters, links between pages, and different styles of text fragments. The main dissertation approach tries to leverage this layer of hidden semantics by extracting and representing it as coarse-grained models of content collections. A central domain ontology is used to maintain overlay modeling of students’ knowledge and serves as a reference point for multiple collections of external instructional material. In order to establish the link between the ontology and the open-corpus content models a special ontology mapping algorithm has been developed. The proposed approach has been applied in the Ontology-based Open-corpus Personalization Service that recommends and adaptively annotates online reading material. The domain of Java programming has been chosen for the proof-of-concept implementation. A controlled experiment has been organized to evaluate the developed adaptive system and the proposed approach overall. The results of the evaluation have demonstrated several significant learning effects of the implemented open-corpus personalization. The analysis of log-based data has also shown that the open-corpus version of the system is capable of providing personalization of similar quality to the close-corpus one. Such results indicate that the proposed approach successfully supports open-corpus personalization for e-Learning. Further research is required to verify if the approach remains effective in other subject domains and with other types of instructional content

Providing Students With Mobile Access to an Assessment Platform: Lessons Learned

The growing ubiquity, rich functionality, and relative affordability of mobile devices have been seen as opportune factors for implementing mobile learning solutions that can be used in a variety of contexts and domains. Plenty of successful mobile educational applications have been built. This paper describes an attempt to build on this success. The authors have investigated the use of mobile devices by students accessing assessment and self-assessment quizzes in the context of a university course. Two experiments were conducted with undergraduate students. The results of the first experiment were not successful, and initially, very few students used mobile devices. After several adjustments, during the second experiment, the usage of the system increased. However, the numbers were still much lower when compared to desktop access. This paper reports an investigation into the lack of mobile usage of the developed platform despite the educational affordances brought by mobile devices

The return of intelligent textbooks

The advancement of computational Artificial Intelligence (AI) in the recent decade has been transformative for many domains, including AI in Education. One direction, where it has caused a noticeable increase in research activity, is application of AI technologies to enhance digital textbooks by making them more interactive, engaging, adaptive, and intelligent. For many researchers coming into this field, it would have seemed as if an intelligent textbook is a completely new idea. We would like to provide a historic outlook on this field and outline the important phases that it went through over the last three decades. We hope that such an account can inform interested readers and help them better understand the problems and the approaches of intelligent textbooks

Addictive links: The motivational value of adaptive link annotation

Adaptive link annotation is a popular adaptive navigation support technology. Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. In this paper, we present our exploration of a lesser known effect of adaptive annotation, its ability to significantly increase students' motivation to work with non-mandatory educational content. We explored this effect and confirmed its significance in the context of two different adaptive hypermedia systems. The paper presents and discusses the results of our work

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

Enriching Intelligent Textbooks with Interactivity: When Smart Content Allocation Goes Wrong

One of the main directions of increasing the educational value of a digital textbook is its enrichment with interactive content. Such content can come from outside the textbooks - from multiple existing repositories of educational resources. However, finding the right place for such external resources is not always a trivial task. There exist multiple sources of potential problems: from mismatching metadata to mutually contradicting prerequisite-outcome structures of underlying resources, from differences in granularity and coverage to ontological conflicts. In this paper, we make an attempt to categorize these problems and give examples from our recent experiment on automated assignment of smart interactive learning content to the chapters of an intelligent textbook in a programming domain

Using E-Learning Tools to Enhance Students- Mathematicians’ Competences in the Context of International Academic Mobility Programmes

Introduction. The article is concerned with the use of special electronic teaching tools to increase the students’ understanding of the subject and adaptation to the professional language environment of the host country, taking into account the mathematical education. Our purpose is to develop a methodology of multilingual support of mathematical courses in the host country to improve the effectiveness of students’ academic mobility using e-learning tools. Materials and Methods. The basis of the research was methods of system analysis and descriptive and analytical methods, primarily experimental. To identify advantages of the proposed approach the methods of empirical research were used (observation and comparison). To prove the efficiency, classical methods of measurement were used. Results. We analyzed the existing electronic learning environments and defined an e-learning environment Math-Bridge that allows for creating mathematical courses in several languages in parallel. For example, the e-training course “Optimization Methods” was developed in three languages for training Russian-speaking Master programme students. The comparative analysis of the target and control student’s groups showed that 100 % of the students in the target group achieved an excellent level of mastering competencies, while the control group has only 75 %. For the control group, the degree of motivation to mathematical studying has not virtually changed (increase by 0,86 %). In the target group the level of student interest to the mathematics increased from 0,9 % to 8,9 % (mean 2.21 %). Discussion and Conclusion. The results described in the article will be useful for the staff of international departments, administrations and deans, as well as teachers of those universities that participate in the students’ international academic mobility programmes