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Shame is a universal but painful emotion which is known to most people. Major research reported in the shame literature has focused on the response to shame within the behavioral tendencies of withdrawal (Dickerson, Gruenewald & Kemeny, 2004; Kemeny, Gruenewald & Dickerson, 2004; Tangney, Mashek & Stuewig, 2005), and of externalization (Baumeister, Smart & Boden, 1996; Bushman & Baumeister, 1998; Tangney, Wagner, Fletcher & Gramzow, 1992). Recent discussion on shame response suggests that shame also leads to a restorative tendency through self-improvement and enhancement (Gausel, Leach, Vignoles & Brown, 2012). This is a result of most previous studies being conducted within a Western context where cultures devalue shame and see it as a totally negative emotion which should be avoided. Conversely, Eastern cultures are shame-affirming cultures and often see shame as a motivation for improvement. These different attitudes towards shame and different behavioral tendencies have recently been discussed in the shame literature (Skeikh, 2014). However, Sheikh (2014) provides only a conceptual idea of the shame response of restorative tendency, while lacking support from empirical evidence. This gap is important, yet poorly studied by researchers. To bridge the gap, this research aims to examine shame and its response of restorative tendency by providing qualitative evidence with linguistic examples of a shame restorative tendency. Further, though existing studies pay significant attention to the role of shame in regulating one’s behavior, most consider it in terms of the negativity of withdrawal and externalizing tendencies (Cohen, Wolf, Panter & Insko, 2011; Tangney, et al., 1992; Tangney & Dearing, 2002). Few recent studies focus on shame’s restorative role in regulating behavior (Tangney, Stuewig & Martinez, 2014). Additionally, most of these studies limit their focus within fields such as psychology (Scheff, 2014) and sociology (Gilbert, 2003). Research on the impact of shame in the consumption field is scarce. Therefore, following recent discussion on shame restorative tendency, and aiming to supplement existing knowledge on shame in consumption behavior, this research adapts self-affirmation theory and self-regulation theory in investigating the influence of shame in the context of status consumption. The research employs a mixed method using both qualitative and quantitative approaches. Incorporating a linguistic corpus analysis, the qualitative approach allows the investigation of the concept of shame and its causes and responses with Chinese and English corpus. Linguistic evidence supports the mainstream view of shame (Tangney, Miller, Flicker & Barlow, 1996; Tangney & Dearing, 2002) where self-attack is a cause of shame, and withdrawal and externalizing behaviors are responses to shame. More importantly, it confirms the alternative response to shame with restorative tendency raised in recent discussions (Gausel, et al., 2012; Sheikh, 2014), and provides substantial examples of restorative behaviors. Following this, a paper-based survey questionnaire distributed to Chinese consumers (n=210), aims to test the impact of shame on the consumption field with its response of restorative tendency. Stepwise hierarchical regression results highlight the influence of shame on consumer status consumption. It shows that consumers with high shame proneness are more likely to participate in status consumption, but only under the condition of a high independent construal of the self. To further examine the linkage between shame and status consumption, we then conduct three experimental studies. Study 1 (n=106) aims to investigate the impact of feeling shame on status consumption. Results suggest that the group with aroused feelings of shame demonstrates a higher intention to purchase status products. Study 2 (n=114) aims to test whether shame impacts consumer’s preferences for conspicuousness when both products are considered as status products. Results in this study found that consumers with primed shame feelings show a higher preference for status products with high conspicuousness, i.e., loud products. Study 3 (n=125) explores the moderating effect on shame and status consumption due to the relationships between consumers and those who accompany them, i.e., psychological proximity to others. Results suggest that when a consumer is accompanied by someone with whom they have a close relationship, the consumer’s feelings of shame are more likely to transfer into status consumption since they place high importance on those significant others with whom they share psychological proximity, rather than those with whom they have distant relationships. This research extends the existing literature by testing the influence of shame on status consumption with its restorative tendency. The corpus analysis helps to advance our understanding of shame responses and confirm a restorative tendency as an alternative response to shame, rather than merely the withdrawal or externalizing tendencies drawn from Western literature. This research fills this gap by providing linguistic evidence of restorative actions as a shame response, and provides the preconditions for testing the impact of shame on status consumption in the quantitative approach which follows. Most importantly, the quantitative part of this research adds to the existing consumption literature with status consumption as a restorative tendency responding to shame. By applying self-affirmation theory and self-regulation theory to build the conceptual model, this research also broadens the theorist’s discernment of shame on status consumption by explaining the mechanism of shame with response restorative tendency, which then leads to status consumption. Further, findings from extended studies on the impact of shame on status consumption with conspicuousness and psychological proximity offer insights for advertising agencies or marketing organizations demonstrating the practical implications of shame appeals.

Objectives: To explore multiple aspects of child maltreatment in China, including perceptions, risk factors, experiences and negative effects, with a focus on the role of parental aggression and parental childhood maltreatment, as well as the independent effects of different types of maltreatment on child emotional and behavioural problems. Methods: The study sites were urban and rural areas of Zhejiang Province, China. A mixed-method design was used: semi-structured interviews with 11 young adults, 21 parents and nine children, three focus group discussions with 22 children, and questionnaire surveys with 1,201 young adults, 576 parents and 791 children. Results: Physical and emotional maltreatment, before age 18, were reported by 81% and 82% of young adults respectively. Personal experience of emotional maltreatment was generally perceived as more harmful than physical. Lifetime prevalence of maltreatment reported by parents and children was - physical: 56% vs 50%; emotional: 75% vs 59%; non-contact punishment: 21% vs 18%. 21% of the children reported experiencing three or four types of maltreatment (including witnessing domestic violence). Parents with higher aggressive tendencies were more likely to maltreat children. Parental aggression was an explanatory factor for the intergenerational transmission of maltreatment. Emotional maltreatment was consistently associated with a higher risk of child emotional and conduct problems. Severe physical maltreatment showed the strongest association with abnormal conduct. Moderate physical maltreatment was independently associated with emotional problems. There was an increased risk with multiple types of maltreatment. The qualitative research adds useful insights into the perceptions of child maltreatment in China from different perspectives. Children’s and young adults’ perceptions of maltreatment experiences were focused on parents’ intentions. Most parents perceived physical punishment and verbal aggression as necessary in disciplining children. Some parents were more reflective of their aggressive behaviours towards children and were more willing to change their disciplinary methods. Conclusions: The pervasiveness of child maltreatment and the considerable harm caused to children and young adults suggest an urgent need for raising public awareness, educating parents and introducing a formal child protection system in China.

A novel colloidal model system is developed for the purpose of achieving simultaneous three-dimensional (3D) confocal imaging and optical tweezing of impurity probe particles embedded in a dense material of colloidal host particles. First, the colloidal host particles are developed from 3-trimethoxysilyl-propylmethacrylate (TPM), and the solvent mixture is tuned to match the refractive index and the density of the particles. A sedimentation-diffusion equilibrium profile of the TPM particles was imaged in 3D to establish suitability of the system for 3D confocal laser scanning microscopy, and to study its phase behaviour and particle dynamics. Then, core-shell particles, which consist of a high refractive index core and a TPM shell, are synthesised to be used as the impurities. The versatility of the two-step coating procedure with TPM has been demonstrated on several core materials, and the optical properties of the core-shell particle are established using digital holographic microscopy and their optical trapping strengths. Together with the TPM host particles, the core-shell particles are shown to be suitable impurity probes in dense colloidal materials, as they can be manipulated using optical tweezers in all three-dimensions, whilst the structure and dynamics of the surrounding host particles can be imaged simultaneously using fast confocal laser scanning microscopy. Subsequently, to demonstrate the capability of the TPM-based colloidal model system, the depletion potential of a pair of core-shell probe particles embedded in a sea of TPM host particles has been measured using optical tweezing. This is derived from comparing the direct pair potential between the core-shell particles in a TPM refractive index matching solvent, and the potential of mean force for the core-shell particles embedded in a dense fluid region of the TPM host particles. Direct visualisation of the liquid structures of the TPM host particles in the binary system around the probe particles has been linked to the form of the depletion potential, and its oscillatory nature as a function of the particle separation. Lastly, the formation of colloidal dumbbell particles is discussed. The dumbbell formation has been rationalised in terms of the total surface energy of droplet formation on spherical surfaces and the calculations are compared with experimental results from coating various seed particles with TPM. Using optically anisotropic dumbbell particles and tuning the refractive index of the dispersion medium, a preliminary two-trap experiment has been conducted which shows unusual trapping behaviour when a time-delayed feedback trapping trajectory has been applied.

Manufacturing more reliable and lighter body armour using the fabrics with high-performance fibres is the development trend of ballistic protection device. However,increasing the reliability of the body armour normally needs to increase weight. Thisinvestigation aims to develop better ballistic performance of body armour withoutaffecting weight. Inter-yarn friction in quasi-static state in fabrics constructed for bodyarmour is one of the important factors affecting ballistic performance. This researchfocuses on increasing inter-yarn friction by surface modification methods for superiorballistic protection of woven fabrics. Finite element (FE) simulation is employed toanalyse the effects of inter-yarn friction on ballistic performance theoretically. BothAPPCVD and sol-gel methods are used to achieve the purpose of practically increasinginter-yarn friction. Ballistic experiments are conducted to evaluate ballistic performanceof the fabrics with different levels of inter-yarn friction after treatments. Through both numerical and experimental investigation, it is confirmed that increasinginter-yarn friction in quasi-static state can improve ballistic performance of fabrics. Theoverall energy absorption will be increased with the increase of inter-yarn frictionbecause higher inter-yarn friction generates higher resistance to the projectile, makesfabric structure more stable, leads to more involvement of the secondary yarns andincreases both KE and FDE percentages. Moreover, higher levels of inter-yarn frictionwill flatten the trauma and make the fabric response more globalised owing to the lesstransverse deflection ability. However, over high inter-yarn friction is counterproductivebecause of stress concentration on the primary yarns. For the surface modification, one of the aramid yarns, Twaron® yarn and one of theUHMWPE yarns, Dyneema® yarn, and their fabric products are used as the substrates. SEM analyses are used to characterise the morphology changes. Both FTIR and EDXanalyses are conducted to identify the coated substance. Based on coefficients of friction test and yarn pull-out test, the APPCVD treatment and sol-gel treatment have been proved as two effective ways to increase inter-yarn friction and at the same time the tensile properties of the yarns and the weight are almost unaffected. Moreover, sol-gel treatment has been established as an effective method for improvingballistic performance without significant weight increase, where the energy absorption ofthe Dyneema® fabric can be increased by 6.74%, and the trauma depth can be decreased by16.99% for Twaron® fabric panel and by10.73% for Dyneema® fabric panel.

Non-coaxiality of the principal stress direction and principal strain increment direction has been observed in both numerical modelling and experimental studies. The importance of non-coaxiality has been widely recognised in the geomechnical engineering. Without considering the non-coaxiality in the design may lead to an unsafe soil structure. Therefore, it is essential to understand the non-coaxial soil behaviour better and take it into account in the numerical modelling. A new Hollow Cylinder Apparatus in Nottingham Centre of Geomechanics (NCG) has been employed in this study. A series of preliminary tests have been carried out to validate the reliability and repeatability of the testing results. Three series of tests, including 24 tests on Portaway sand and 2 tests on Leighton Buzzard sand, were conducted to study the non-coaxial soil behaviour of granular materials. The three stress paths followed were monotonic loading along fixed principal stress direction, pure rotation of the principal stress axes with constant deviator stress and combined rotation of principal stress axes with increasing deviator stress. Portaway sand was chosen because it has been used in NCG to investigate granular soil behaviour. Therefore, stress-strain behaviour including non-coaxial behaviour can be observed and used by the other researchers in NCG to develop or verify numerical models. The evidence of non-coaxiality has been obtained from the tests. In general, the non-coaxiality is relatively small in monotonic loading tests, but is more significant in the pure rotation tests and combined loading tests. The degree of non-coaxiality is affected by the density of the specimen, the stress path followed, the stress level and the material particle properties.

With the development of artificial intelligence, computer pattern recognition has also been applied in different scenarios. This thesis focuses on facial expression recognition to improve the generalisation ability of the model, and the accuracy of the facial expression recognition system. Various facial expression recognition models and systems have been proposed. However, when applied to other untrained datasets, the performance is often suboptimal. The innovation of this thesis lies in the processing of the dataset to improve the generalisation ability of the model by using local abstract features. By extracting the main local features, analysing the facial expression information contained in the local regions and sorting the local regions, better input data choices can be provided for other facial expression recognition systems. By using local binary patterns and support vector machine experiments, I found that the mouth contains more facial expression information. Local regions can effectively improve the generalisation ability of the model. The regions selected are the eyes and eyebrow region, the nose and mouth region and the mouth and chin region. The eyes region and the mouth and chin region seem to 'hold' most clues to a person's mood. Three local facial expression features were experimentally combined. When the features of the eyes, nose and mouth were combined, the recognition accuracy was 50.23%. This result indicates a significant improvement in recognition results compared with the crossdatabase FER results reported in the literature, and the 5.71% increase in recognition accuracy is a significant improvement. The results of three applied algorithms for local regions of different sizes are analysed. The general trend is that as facial expression information is lost in most regions, the recognition accuracy also decreases. For machine-based recognition systems, the eye region and mouth region carry most of the useful information about a person's emotional state. However, the relatively low importance of the nose region for the facial expression recognition task can sometimes interfere with the experimental results, particularly when the number of training images is limited. When the input data are improved without changing the research model and are preprocessed, the effective facial expression area is extracted instead of using the full face for input. This process can effectively improve the accuracy of facial expression recognition and improve the method ability of the model. That is, when using untrained data for testing, better effective results can be obtained, which is conducive to the use of the model in reality.

The number of connected devices of the Internet of Things (IoT) rise rapidly per person and will continue to do so predicting the amount of over 25 billion connected IoT devices until 2030, which broadens the range of crime-enabled as well as dependant crimes. Additionally, due to the fast-moving nature of such developments, digital forensic, specifically, IoT forensic, struggle to keep up with this evolution effectively and sufficiently. Some research address these issues by proposing device management methods as well as improved investigation procedures and tools, however, unfortunately, fail to address that device management can support forensic investigations. Furthermore, proposed platforms do not consider efficient national and international case investigations and only provide frameworks utilising single servers without further consideration of storage capacity and that the impact of investigators and their training, or lack thereof, may impact a court case's outcome. This thesis addresses the stated problems by proposing a DNA for devices, the Hybrid Forensic Server (HFS) as a server structure to be used as a dynamic platform for national and international cases as well as Confidence Values Models (CVM). Such evaluates and calculates the skills of investigators in relation to their evidence retrieval rate which affects the value of their final case report and demonstrates to other jurisdictions and investigators the confidence's influence on the process which may affect the sharing of further evidence during the case. This research proved that letting these aspect work together can significantly improve forensic investigation for police, especially when considering cases where evidence cannot be retrieved in a straightforward manner and the procedure being more challenging. Demonstrated cases showed that the proposed DNA, HFS and CVM effortlessly work together within the multilayered framework, showing that proposed investigation routines work more efficiently due to less effects on the overall server storage, allowing IoT forensic to be improved greatly.

This thesis advances the understanding of the link between the concepts of Sustainable Development (SD) and women's choice and attraction to engineering. Such understanding is particularly important in the present world where a combination of emerging SD challenges, the shortage of engineers and underrepresentation of women remain a challenge to the profession. As a way of addressing these engineering problems, previous research identified the importance of broad themes associated with such SD challenges of the profession in influencing women's choice of engineering. However, knowledge on the degree of importance of more detailed themes within the social, environmental and economic SD pillars and how these themes influence such career decisions, remains unknown. Moreover, these SD challenges not only require skilled engineers but also those who have the interests to address them. Thus, identifying more heterogeneous engineering associated SD themes that can influence women's choice of engineering is likely to attract more women who are interested in solving the numerous SD challenges. Consequently, the aim of this research is to develop a model that explains the link between SD themes and women's career choice and attraction to engineering. A mixed-methods research (MMR) design comprising the Analytical Hierarchy Process (AHP), Sensitivity analysis and Grounded Theory (GT) was adopted to address this aim, which reflects the primary research question. Hence, a unique gamified/AHP structurally designed quantitative survey and qualitative GT semi-structured interviews were utilized to gather and analyse data from a sample of 414 respondents and 15 participants respectively in the UK and Nigeria to contribute rich insights from both developed and developing nations 'perspectives. The quantitative results revealed the relative importance of specific SD-themed factors that influence women's choice of engineering. While this result is vital, knowledge about how this decision is reached is of utmost importance to inform strategies for recruiting and attracting women to engineering. For this reason, the qualitative study was conducted and the results revealed comprehensive insights indicating that the process of how SD themes influence women's engineering career choice involves a dynamic interaction of four primary interrelated factors- Exposure, Awareness, Interest and Direct involvement (EAID). The study outcome is a novel EAID process indicating that women's interest and direct involvement in engineering could be influenced through two main routes-either by creating exposure or by awareness of engineering roles that cause or address sustainability challenges. The combined results of this MMR provided useful insights that informed the development of the three- dimensional Gender diversity and Sustainability in Engineering (GDSE) model, showing the link between SD themes, women's choice and attraction to engineering. Significantly, these findings offered some practical implications for engineering education stakeholders including HEIs and engineering organisations. These include the potential of utilizing the GDSE model to enhance their recruitment strategies by focusing on the most salient areas that influence women's career choice. This could widen the options and pathways of making the profession attractive to women and other underrepresented groups.

Both the quantity and complexity of course-related information available to students are rapidly increasing on the Web. This potential information overload challenges standard information retrieval models as users find it increasingly difficult to locate relevant information. The education domain is one of the main domains that has been influenced by this problem. Choosing a higher education course at university can be incredibly tedious and extremely complicated for students. A personalised recommendation system can be an effective way of suggesting relevant courses to prospective students. The existing methods which are mainly based on keywords fail to address the individual user's needs in the recommendation process. Although models use collaborative filtering there is often a lack of historical information. Another shortcoming is that they do not provide comprehensive knowledge about the course that is most relevant to the student. This research presents a novel ontology-based hybrid approach to recommend personalised courses to match student's individual needs by integrating all available information about courses and supporting students to choose courses towards their career goals. This thesis makes three major contributions: firstly, it proposes a comprehensive Ontology based Personalised Course Recommendation framework, called OPCR, by combining several artificial intelligence techniques including collaborative filtering, content-based filtering, ontological representation and management of knowledge. A set of ontology based recommendation algorithms are developed for personalised recommendation. The framework is capable of automatic data extraction, integration to provide students with suitable recommendations to meet their needs. It not only reduces information overloading but also improves recommendation accuracy. Secondly, it proposes ontology models to extract and integrate information from multiple sources, which contributes to improving the quality of the recommendations by overcoming the heterogeneity of course information. In addition, it has properties such as generality which enables it to be used in different recommendation system domains which change with the user's interests and the object's attributes. Finally, a personalised recommendation system based on the OPCR framework is developed and evaluated. The system is available online as open access for researchers and developers. Results show that the ontology based recommendation algorithms that use hierarchically related concepts produce better outcomes compared to a filtering method that considers only keyword similarity. In addition, the system's performance is improved when the ontology similarity between the items' profiles and the users' profiles is utilised.

The explosive growth in online learning materials has generated an urgent need for new techniques and access mechanisms in order to reduce information overload. E-learning recommender systems have become very popular as a means of delivering learning materials. The main challenge is how to recommend quality learning materials to students based on their different backgrounds, knowledge, interests and needs. This thesis presents an integrated approach to recommending online video materials through sentiment analysis and hybrid filtering algorithms being used to support the development of personalised e-learning recommender systems and to address the information overloading and cold start problems. This thesis makes five major contributions. Firstly, it proposes a personalised e-learning recommender system framework. PREFERS combines the techniques of student profiling, knowledge estimation, recommendation approaches and classification. Secondly, it proposes a hybrid recommendation algorithm integrated with sentiment analysis to provide personalised educational online video learning materials to students based on students' profiles and the students' feedback and interactions with the system. Thirdly, it develops a student profiling method using data mining techniques in order to construct a student profile and a student knowledge level from multiple feedback. Fourthly, it develops a dynamic hierarchical classification method/algorithm based on students' feedback and the textual features of the videos to classify online video learning material using machine learning and information retrieval techniques. Finally, it designs and implements a personalised e-learning recommender system based on the PREFERS framework. The prototype system has been tested and evaluated with real users. Results show that the system can effectively recommend relevant learning materials to students based on their needs.

The amount of music available digitally is overwhelmingly increasing. The main purpose of music recommendation systems is to suggest quality relevant songs that fit with the user's preferences. Currently, most of the streaming music systems recommend songs based on Collaborative Filtering and Content-Based filtering techniques. However these systems fail in dealing with the Cold-Start problem. This thesis presents user-based hybrid algorithms for music recommendation systems to address the Cold-Start problem and to recommend music for both new and existing users based on their context by integrating the social information to provide context aware personalized music recommendation. This thesis makes two major contributions: First, hybrid recommendation algorithms are developed using multi-strategy approach to give more accurate recommendations by combining collaborative filtering, content based and the user's context obtained from social network in order to provide both new and existing users with an easy way to discover new songs. In this way, the system is able to estimate what artist/song would match user preferences. Second, a generic Context-Aware Personalised Music (CAPM) framework is proposed for supporting the rapid development of context-aware music recommendation systems and for clarifying the whole process of recommendation. As there are myriad approaches of recommendation, there is a need for a generic framework not only to gather these approaches, but also to interpret them under the proposed framework. Recommendation algorithm types differ by the input structure. For example, social recommendation algorithm uses social information, collaborative filtering uses users rating data, whereas content based recommendation uses item's characteristics. This difference affects enormously the representation of data and consequently the process of recommendation. CAPM is able to present different input data and uniforms the recommendation process. The proposed algorithms and the framework have been successfully evaluated via practical experiments by real users. The practical experiments are carried out by presenting a Context-Aware Personalised Music (CAPMusic) application in Google Play which helps users to discover new artists, albums or songs. Satisfactory results have been obtained which indicate that using the proposed hybrid recommendation algorithms leads to better results compared with using the pure content based and collaborative filtering techniques.

Planning a project with proper considerations of all necessary factors and managing a project to ensure its successful implementation are facing a lot challenges. Initial stage in planning a project is costly, time consuming and usually with poor accuracy on cost and effort predictions. On the other hand, detailed information for previous projects may be buried in piles of archived documents, which make it increasingly difficult to learn from the previous experiences. Current information sharing methods to support project management focus on activity based project operation and processes but lack some granulations on project deliverables, especially when project context and customer requirements are varied. This research develops a product based information sharing (PBIS) framework, which attempts to serve in general project planning and lead to properly and effectively benchmarking and recommending product portfolios for project management purposes. PBIS made contributions in various areas. It introduced a new product based approach to capture and reuse the project information that tackles the issue of information sharing from a very different perspective. The Project Analyser part articulates requirement information at both project and product levels. The analysed results can be used to assist the product based breakdown process which is validated by product refinement rules. The Project Planner part enables project plan to be generated accurately and efficiently through a novel product benchmarking and recommendation mechanism. This mechanism integrated with the strengths of Quartile, Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP) to ensure the right products are selected based on products of which attributes are important for customer criteria during the project planning stage. A novel software system iPAS based on PBIS has been developed to bridge the gap between PBIS main principles and its application, with providing the user with automated planning, monitoring, reports and human resource allocation. PBIS has been trialled with cases studies in two organisations, which clearly shows the business benefits of autonomic project management. It reduced effort to plan new projects and manage project portfolio and decreased estimation bias thereby reducing operational risk. It also automatically benchmarked performance against company best practices. As a result, the PBIS can be used to solve other real world problems in standardised industries such as manufacture, education, medicine, construction and rail industries etc.

Designing an Intelligent Tutoring System (ITS) that simulates human learning with regard to different knowledge levels is a challenge. Most developed ITSs typically focus on either the building or the testing phase without paying appropriate consideration to the design phase. The result is that these systems offer specific choices in isolation, and can thus prove difficult to apply in situations where multiple factors interact. Researchers have therefore developed tutoring models that did include the design phase in their implementation. However these models do not consider the cognitive factors of students. Such ITS models lack the ability to provide accurate estimations as they do not analyse the students' individual skills against the item skills, particularly when the learning items require multiple skills, which thus reduce student's learning efficiency due to an incomplete representation of the student's knowledge. This thesis proposes a novel tutoring model, called 'Cognitive Factor Analysis' (CFA), which adapts student cognitive factors, such as guessing/slipping parameters and student proficiency levels, together with each item's parameters to produce a better estimation of student latent performance. CFA also introduces the concept of the Q-matrix from psychometrics and connects this to the students' prior scores. The model does not only predict the students' performance, but helps students to target the strengths and weaknesses in their knowledge levels. Therefore, CFA has an adverse impact on the student's learning curve and reduce the student's learning time by controlling the amount of time spent practicing the skill several times. It assumes the role of modelling the student's learning by making inferences about their latent performance with multiple skills assessments. CFA also contributes to cognitive learning psychology by exploring how computational models can be used to understand human behaviour. It shows how data generated from tutoring systems can be analysed and modelled to create and improve a unified computational theory of human learning. Furthermore, it encapsulates psychological findings in a format that can be used by instructional designers and educational scientists to support the development of tutoring systems.

Tackling information overloading in online shopping is a significant challenge for e-shops. The current solutions demand that consumers become educated before they are able to distinguish which products are good for them. This research suggests a novel recommender system, namely ACIBa, which attempts to do part of the market research on behalf of the consumer and offer only a handful of products as recommendations. The novelty of ACIBa is not just in its approach of limiting rather than widening the product alternatives, but also in that it bases its reasoning partially on artificial intelligence and partially on the collective intelligence of an arbitrary crowd of people. This work aimed to create a framework which provides the guidelines and basic tools to produce an ACIBa-like recommender system, as well as to create a live proof-of-concept system to showcase its use. ACIBa made contributions in various areas. First and foremost it introduced a new recommender system that tackles the issue of information overloading from a very different perspective. The individual subcomponents of ACIBa have also extended the knowledge of their respective fields. The classifier used to cover the need for artificial intelligence, led to the creation of a novel ensemble classification methodology that allows training from typically unusable training sources. The software developed to interface between ACIBa and the crowd has been published as open-source software, effectively allowing other developers to create their own crowdsourcing systems. Finally, a new methodology has been introduced (namely ANA), that allows for an easy way to reduce the number of answers required from the crowd before conclusions can be reached. Both these parts, i.e. the ensemble classifier and ANA, have undergone separate testing as standalone components. The ensemble classifier was shown to drastically outperform individual member classifiers. ANA has been found to consistently outperform the fixed number of answers distribution. An ACIBa based recommender system has been developed and demonstrated live to consumers. We used online questionnaires to gather feedback on the quality of results. Despite its infant stage, the response was generally positive and the system well received.

Context-awareness has become an essential part in various personalised applications such as mobile recommeder systems and mobile information retrieval. Much progress has been made in context-aware applications. However there is a lack of general framework for supporting the rapid development of context-aware applications and enabling the sharing and dissemiation of context information across different applications