Technologies to enhance self-directed learning from hypertext

With the growing popularity of the World Wide Web, materials presented to learners in the form of hypertext have become a major instructional resource. Despite the potential of hypertext to facilitate access to learning materials, self-directed learning from hypertext is often associated with many concerns. Self-directed learners, due to their different viewpoints, may follow different navigation paths, and thus they will have different interactions with knowledge. Therefore, learners can end up being disoriented or cognitively-overloaded due to the potential gap between what they need and what actually exists on the Web. In addition, while a lot of research has gone into supporting the task of finding web resources, less attention has been paid to the task of supporting the interpretation of Web pages. The inability to interpret the content of pages leads learners to interrupt their current browsing activities to seek help from other human resources or explanatory learning materials. Such activity can weaken learner engagement and lower their motivation to learn. This thesis aims to promote self-directed learning from hypertext resources by proposing solutions to the above problems. It first presents Knowledge Puzzle, a tool that proposes a constructivist approach to learn from the Web. Its main contribution to Web-based learning is that self-directed learners will be able to adapt the path of instruction and the structure of hypertext to their way of thinking, regardless of how the Web content is delivered. This can effectively reduce the gap between what they need and what exists on the Web. SWLinker is another system proposed in this thesis with the aim of supporting the interpretation of Web pages using ontology based semantic annotation. It is an extension to the Internet Explorer Web browser that automatically creates a semantic layer of explanatory information and instructional guidance over Web pages. It also aims to break the conventional view of Web browsing as an individual activity by leveraging the notion of ontology-based collaborative browsing. Both of the tools presented in this thesis were evaluated by students within the context of particular learning tasks. The results show that they effectively fulfilled the intended goals by facilitating learning from hypertext without introducing high overheads in terms of usability or browsing efforts

Innovative Techniques for the Implementation of Adaptive Mobile Learning Using the Semantic Web

Adaptive Mobile Learning has constantly faced many challenges in order to make course learning more adaptive. This research presents a conceptual framework for using the Semantic Web to obtain students’ data from other educational institutions, enabling the educational institutions to communicate and exchange students’ data. We then can use this information to adjust the students’ profiles and modify their learning paths. Semantic Web will create a more personalized dynamic course for each student according to his/her ability, educational level, and experience. Through the Semantic Web, our goal is to create an adaptive learning system that takes into consideration previously completed courses, to count the completed topics, and then adjust the leaning path graph accordingly to get a new shortest path. We have applied the developed model on our system. Then, we tested the students on our system and a control system to measure the improvements in the students’ learning. We also have analyzed the results collected from the AML Group and the Control Group. The AML system provided a 44.80% improvement over the Control System. The experimental results demonstrate that Semantic Web can be used with adaptive mobile learning system (AML) in order to enhance the students’ learning experience and improve their academic performance

Developing a Semantic Question Answering System for E-learning Environments using Linguistic Resources

The Question answering (QA) system plays a basic role in the acquisition of information and the e-learning environment is considered to be the field that is most in need of the question-answering system to help learners ask questions in natural language and get answers in short periods of time. The main problem in this context is how to understand the questions without any doubts in meaning and how to provide the most relevant answers to the questions. In this study, a question-answering system for specific courses has been developed to support the learning environment. The research outcomes indicate that the proposed method helps to solve the problem of ambiguities in meaning through the integration of natural language processing tools and semantic resources that can help to overcome several problems related to the natural language structure. This method also helps improve the capability to understand students’ needs and, consequently, to retrieve the most suitable answers

Virtual Telescopes in Education

Virtual Telescopes in Education is providing the services required to operate a virtual observatory comprising distributed telescopes, including an interactive, constraint-based scheduling service, data and resource archive, proposal preparation and review environment, and a VTIE Journal. A major goal of VTIE is to elicit from learners questions about the nature of celestial objects and the physical processes that give rise to the spectacular imagery that catches their imaginations. Generation of constrained science questions will assist learners in the science process. To achieve interoperability with other NSDL resources, our approach follows the Open Archives Initiative and the W3C Semantic Web activity

Learning ontology aware classifiers

Many applications of data-driven knowledge discovery processes call for the exploration of data from multiple points of view that reflect different ontological commitments on the part of the learner. Of particular interest in this context are algorithms for learning classifiers from ontologies and data. Against this background, my dissertation research is aimed at the design and analysis of algorithms for construction of robust, compact, accurate and ontology aware classifiers. We have precisely formulated the problem of learning pattern classifiers from attribute value taxonomies (AVT) and partially specified data. We have designed and implemented efficient and theoretically well-founded AVT-based classifier learners. Based on a general strategy of hypothesis refinement to search in a generalized hypothesis space, our AVT-guided learning algorithm adopts a general learning framework that takes into account the tradeoff between the complexity and the accuracy of the predictive models, which enables us to learn a classifier that is both compact and accurate. We have also extended our approach to learning compact and accurate classifier from semantically heterogeneous data sources. We presented a principled way to reduce the problem of learning from semantically heterogeneous data to the problem of learning from distributed partially specified data by reconciling semantic heterogeneity using AVT mappings, and we described a sufficient statistics based solution

Integrated Multimodal Copy-Paste Checking

Although the use of advanced computing and communications technology has made learning a significantly richer experience for learners, it also introduces greater ease in committing academic cheating. There is a growing increase of internet usage in educational communities. Powerful search engines and media gateways gives an ease of access to wide spreading and different types of information sources, this in turn reduces the need for learners to perform diligent research or study. A learner can easily copy information, found on the internet, relevant to the task at hand. E-learning systems would then need to incorporate extended functionalities to enable multimodal copy-paste checking. Besides the mere detection of text copy-paste activities, there is a need to determine deeper similarity relations in text and various other media formats. Furthermore, the introduction of a facility to monitor and check the student's ways of searching, reading and writing can improve the learning process. Our previous paper [1] describes the architecture and design of an integrated Copy-Paste system aimed to provide a platform addressing these concerns. This paper extends the work and shows our pioneering explorations in extending student modeling capability of e-learning systems. We discuss the use of layered similarity assessment techniques to add value to conventional copy-paste detection systems