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User modelling in Exploratory Learning Environments (ELEs) is an emerging field with several challenges to be addressed. Due to the freedom given to learners, the amount of information generated is very large, making the modelling process very challenging. Consequently, only relevant information should be used in the user modelling process. This, however, leads to other challenges such as identification of relevant information, finding an optimal knowledge representation and defining an inference mechanism by which this knowledge is used in diagnosing the learner. This thesis addresses the challenges of user modelling in ELEs by monitoring learners' behaviour and taking into account only relevant actions in the context of an ELE for the domain of mathematical generalisation. An iterative approach was used, in line with the iterative design of the ELE. The modelling mechanism employed a modified version of Case-based Reasoning (CBR) and was evaluated using pedagogical scenarios and data from simulated and real students. This approach has the advantage of storing only relevant information and allows learner diagnosis during as well as at the end of a task. The user model was further exploited to support learning related activities, such as prioritising feedback and grouping for collaboration. For feedback prioritisation, a mechanism based on Multi-criteria Decision Making was developed and tested with the help of educational experts. The grouping for collaboration approach was inspired from Group Technology, a method from cellular manufacturing systems, and its testing showed it produces meaningful groups. Both the feedback prioritisation and the grouping for collaboration mechanisms propose solutions that are particularly relevant for ELEs by considering pertinent criteria for this type of learning. To ensure optimal coverage of the knowledge base, the user modelling approach was enhanced with adaptive mechanisms for expanding the knowledge base, which was tested on real and simulated data. This approach ensures that learner diagnostic is possible when the initial knowledge base is small and/or new behaviours are encountered over time.

The work presented in this thesis focuses on exploring the potential of the use and development of mobile location-based services at outdoor cultural heritage sites. This PhD research investigated how people use mobile and wearable technologies for learning purposes with respect to cultural heritage sites. A user-centred design approach was adopted in this thesis using the socio-cognitive engineering methodology. Three empirical studies (field studies) were conducted with the aim of capturing users’ requirements adopting mixed methods. The studies were conducted sequentially using focus group, questionnaire and interview techniques; the focus group and questionnaire were conducted with potential end-users (learners), and the interviews were conducted with officials of cultural heritage and potential end-users. The studies with end-users were carried out to investigate their habits, behaviours and attitudes when using mobile and wearable technologies at outdoors cultural heritage sites. The official staff were interviewed to extract their opinions regarding using such services at their sites as well as find out what technologies they actually used to present information to their visitors. The results of the field studies led to the development of a theoretical framework, FoSLE, supported by the learning theories. FoSLE is introduced for designing smart and ubiquitous learning environments based on mobile and wearable technologies for outdoor cultural heritage sites. The framework was further analysed to pull out general requirements (GRs) (high-level requirements – more abstract) to be adopted in developing new technology supported artefacts. Four scenarios were developed based on the identified requirements to depict the context of use as well as to draw out a list of low-level requirements (LRs), i.e. detailed requirements. The LRs informed the design of a proof-of-concept, a smart and ubiquitous learning environment based on mobile and wearable technologies, SmartC. SmartC was evaluated in the field in two cycles using experts of human-computer interaction and potential end-users (learners). A combination of observation and interview techniques were used in the evaluation studies alongside the cognitive walkthrough method in the expert study and a usability questionnaire in the user study. The results of the evaluation studies revealed that SmartC is user-friendly and suitable for learning. The results of the evaluation studies contributed to the enhancement of the list of LRs, which consequently led to devise a list of design recommendations. The list of the design recommendations was designed to assist researchers and designers in designing and developing smart and ubiquitous learning environments based on mobile and wearable technologies. This PhD research introduces two main contributions to add to the academic knowledge, which are: 1. FoSLE: a theoretical framework for smart and ubiquitous learning environments utilising mobile location-based services and wearable computing. 2. A list of design recommendations for designing smart and ubiquitous learning environments utilising mobile location-based services and wearable computing.

Geographical Information Systems (GIS) are very useful systems in capturing, storing, manipulating, analysing, managing, and presenting spatial data. GIS systems can be used for solving problems and making decisions in various applications. Data mining is the automated process of discovering patterns in data. This thesis outlines the issues and challenges of GIS data to advance the use of data mining techniques in the context of GIS applications. This thesis focuses mainly on two domains of applications: first is the digital vector map copyright protection and second is the digital vector map partitioning. Further more, this thesis presents an efficient approach for identifying the resilient locations for embedding the watermark; improving the robustness of the watermarking approach against a defined set of attacks; investigating the impact of clustering approaches on the application of vector map protection; defining an effective metric for measuring the topological distortion in the watermarked GIS maps; and developing a spatial clustering approach that takes into consideration the GIS map properties. The experimental results show the reliability of using data mining techniques in combination with GIS map properties in advancing the GIS applications with more focus on spatial data protection and partitioning.

In real-life classification problems, prior information about the problem and expert knowledge about the domain are often used to obtain reliable and consistent solutions. This is especially true in fields where the data is ambiguous, such as text, in which the same words can be used in seemingly similar texts but have a different meaning. Many of the proposed approaches rely on the bag-of-words representation, which loses the information about the structure of the text. In this thesis, a literature review of related works in text classification is provided which includes an overview of text classification methods. In addition, detailed review of related works of two text classification domains; search engines and question answering systems. The core contribution is divided into three main parts. The first contribution is the Customizable Grammar Framework for user-intent text classification (CGF) which employs a formal grammar approach and exploits domain-related information in a new way to represent text as a series of syntactic categories forming syntactic patterns. In addition, the proposed framework has been applied to different domains which resulted in the second and third contribution. The second contribution is the Grammar-Based Framework for Query Classification (GQC) which helped in the improvement of query identification and classification. The third contribution is the Grammar-Based Framework for Question Categorization and Classification (GQCC) which helped in the enhancement of question identification and classification. In addition, using different machine learning algorithms the overall results show that the proposed approach outperforms previous ones in terms of classification performance for query and question classifications. Finally, comparison of the classification performance with the state-of-the-art approaches has been conducted, results validate that the proposed approach improves the classification accuracy and the identification of the different types of queries and questions.

Previous literature identifies that students’ real-time feedback is important in the learning process. There are numerous studies that have collected students’ feedback in real time. However, they include several limitations of which the most important is analysing the feedback. In this thesis, we address these limitations by proposing a system that will automatically analyse students’ feedback in real time and present the analysis results to the lecturer. To create such a system, we propose the use of sentiment analysis. The extensive literature highlights the importance of this research in the sentiment analysis field, as there is no research exploring sentiment analysis for students’ real-time feedback and research in the educational domain has been focused mainly on e-learning. The literature shows that emotions are also important in learning and could be detected using sentiment analysis. Consequently, this research is also important as there is little research in detecting emotion from students’ feedback. Polarity detection is explored and two experiments were done to identify the best combination of preprocessing, features and machine learning techniques to create an optimal model for polarity detection of students’ feedback. As a result, we found that the optimal model was to use a lower level of preprocessing, unigrams for features and Complement Naive Bayes for the classifier. Emotion detection in students’ feedback is also explored. Two experiments were done to find the optimal model to detect emotions related to learning from students’ feedback. The results showed that models which detect a single emotion have a better performance than multiple emotion models. Three emotions (i.e. Amused, Bored, and Excited) were more easily detected than others. The optimal model to detect emotion included a low level preprocessing, unigrams as features and Complement Naive Bayes as the classifier. Sarcasm detection in students’ feedback is explored. Our results showed that the lower level of preprocessing led to the best performance. Moreover, by adding other features, such as polarity and emotions, to the unigrams, sarcasm detection increased. Lastly, the best classifier to detect sarcasm was Complement Naive Bayes. Visualisation of the sentiment analysis models results is also investigated. Six visualisations were created and evaluated by lecturers. The findings indicated that there lecturers had no strong preference for a specific visualisation. The system evaluation was performed in real-life settings. As a result, we found that the lecturers were highly satisfied with the system, while the students had a neutral towards negative view of it.

This thesis introduces a unified framework for design of rule based systems for classification tasks, which consists of the operations of rule generation, rule simplification and rule representation. This thesis also stresses the importance of combination of different rule learning algorithms through ensemble learning approaches. For the three operations mentioned above, novel approaches are developed and validated by comparing with existing ones for advancing the performance of using this framework. In particular, for rule generation, Information Entropy Based Rule Generation is developed and validated through comparing with Prism. For rule simplification, Jmid-pruning is developed and validated through comparing with J-pruning and Jmax-pruning. For rule representation, rule based network is developed and validated through comparing with decision tree and linear list. The results show that the novel approaches complement well the existing ones in terms of accuracy, efficiency and interpretability. On the other hand, this thesis introduces ensemble learning approaches that involve collaborations in training or testing stage through combination of learning algorithms or models. In particular, the novel framework Collaborative and Competitive Random Decision Rules is created and validated through comparing with Random Prisms. This thesis also introduces the other novel framework Collaborative Rule Generation which involves collaborations in training stage through combination of multiple learning algorithms. This framework is validated through comparing with each individual algorithm. In addition, this thesis shows that the above two frameworks can be combined as a hybrid ensemble learning framework toward advancing overall performance of classification. This hybrid framework is validated through comparing with Random Forests. Finally, this thesis summarises the research contributions in terms of theoretical significance, practical importance, methodological impact and philosophical aspects. In particular, theoretical significance includes creation of the framework for design of rule based systems and development of novel approaches relating to rule based classification. Practical importance shows the usefulness in knowledge discovery and predictive modelling and the independency in application domains and platforms. Methodological impact shows the advances in generation, simplification and representation of rules. Philosophical aspects include the novel understanding of data mining and machine learning in the context of human research and learning, and the inspiration from information theory, system theory and control theory toward methodological innovations. On the basis of the completed work, this thesis provides suggestions regarding further directions toward advancing this research area.