Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System

This thesis presents research that combines the benefits of intelligent tutoring systems (ITS), conversational agents (CA) and learning styles theory by constructing a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS aims to imitate a human tutor by implicitly predicting individuals’ learning style preferences and adapting its tutoring style to suit them during a tutoring conversation. ITS are computerised learning systems that intelligently personalise tutoring based on learner characteristics such as existing knowledge and learning style. ITS are traditionally student-led, hyperlink-based learning systems that adapt the presentation of learning resources by reordering or hiding links. Research suggests that students learn more effectively when instruction matches their learning style, which is typically modelled explicitly using questionnaires or implicitly based on behaviour. Learning is a social process and natural language interfaces to ITS, such as CAs, allow students to construct knowledge through discussion. Existing CITS adapt tutoring according to student knowledge, emotions and mood, however no CITS adapts to learning styles. Oscar CITS models a human tutor by directing a tutoring conversation and automatically detecting and adapting to an individual’s learning styles. Original methodologies and architectures were developed for constructing an Oscar Predictive CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured from a learning styles model to dynamically predict learning styles from an individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation algorithm to select the best tutoring style for each tutorial question. The Oscar CITS methodologies and architectures are independent of the learning styles model and subject domain. Empirical studies involving real students have validated the prediction and adaptation of learning styles in a real-world teaching/learning environment. The results show that learning styles can be successfully predicted from a natural language tutoring dialogue, and that adapting the tutoring style significantly improves learning performance

Embracing Social Media for Stakeholder Management within NPOs – A Case Study

Social media became highly popular over the last fifteen years since the introduction of Web 2.0 and is now used as a successful marketing and communication tool by many companies. The benefits include the cheap, easy and instantaneous posting of messages and the incomparable coverage of various platforms as well as smart features, like the promotion of events on Facebook. Hence, the embedding of social media into the overall business strategy is a great chance to achieve a better performance in terms of stakeholder management. This is especially promising for small- and medium-sized non-profit organizations (NPOs), who do not have the resources to invest in more expensive forms of marketing or stakeholder management. Nevertheless, companies often struggle with the implementation of social media strategies, especially due to the spread of diverse platforms and the lack of resources to maintain the accounts. This paper asks how small- and medium-sized NPOs can use social media to achieve stakeholder commitment. A study using a mixed-method approach was conducted in cooperation with a voluntary organization of the Catholic Church sector in Germany, called KjG Diözesanverband Essen. The study captured information on KjG’s stakeholders’ use and preferences of popular social media platforms. Interviews were conducted with the organization’s key stakeholders to examine the existing social media and stakeholder strategy. The results of 139 questionnaires and five interviews revealed important stakeholders’ usage patterns of social media platforms and the top-ranked ones for receiving information, which makes them highly interesting for stakeholder communication. A framework was designed to support NPOs with the strategic use of social media for stakeholder management

Exploiting social media for Stakeholder Engagement in Non-Profit Sporting Organisations

Social media has proliferated almost every type of organisation. The most important factor is, how social media is used effectively and efficiently within the organisation. Non-profits are already at a disadvantage compared to those that set out to make money, who have more resources, capabilities and money at their disposal. Social media, therefore, can act as a conduit to engage stakeholders and develop and maintain these relationships to help with organisational productivity, image and growth. This paper describes a study to understand the relationship social media has with stakeholders and a non-profit sporting organisation, called Eoghan Rua Gaelic Athletic Club, and investigate how social media can be advantageous to non-profits when used in an efficient and productive manner. This is an area of research that has remained relatively untouched and where a narrative gap is very prevalent. It is hoped that this research will be of use to those investigating social media and to non-profit organisations within sport. The ultimate aim of the study was the creation of a social media framework of policies and recommendations for the non-profit sports organisation to follow. The framework was developed to help the organisation utilise their social media activities and develop and maintain stakeholder relationships within the organisation itself. The study consisted of primary and secondary research, using a mixed methods approach, which involved interviews and questionnaires with various stakeholder groups in the organisation. Results revealed that motivations for using social media were different within the organisation and different ages and stakeholder groups use different social media applications for different purposes. Ultimately the most telling result was the positive effect social media had on the club and its exposure to stakeholders inside and prospective stakeholders outside of the organisation

Peoples Panel for Artificial Intelligence

Citizen trust in Artificial Intelligence (AI) applications and data driven technologies is at the forefront of ethical guidelines, principles, and future AI legalisation. The creation of successful products and services which benefit people and society requires many diverse citizen voices which are often absent from R&D processes and wider public discourse. Citizens need to have the opportunity and confidence to engage with researchers and innovators through a shared language, understanding and relationship between data and AI. Through funding obtained from the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence (AI), through its Public Engagement Grant award 2022 we established a Greater Manchester (GM) People’s Panel for AI (PPfAI) to empower marginalised communities to contribute to AI research and development. We reached out to two communities: The Tatton in Salford and Inspire in Levenshulme and neither community had previously engaged with the research and development sector previously. A key motivation for this project was to build people’s confidence to ask questions about how their data and AI is being used by businesses through an increased understanding of what AI is and how it is used, Starting in July 2022, we ran 3 community interactive roadshows to explore how AI impacted people’s everyday lives, debating technology, exploring a range of applications, and obtaining very diverse opinions. 9 citizens were recruited to the People’s Panel and completed two days of training about key aspects of data, AI and ethics, including learning the Open Data Institutes Consequence Scanning toolkit. Four live People’s Panel sessions were held where tech businesses and researchers pitched their ideas and were subject to intensive questioning by panel members. To sustain the panel, we have developed with panel members, businesses and the Greater Manchester Equality Alliance (GM=EqAl) (which works with marginalised communities to influence regional policy making) to develop a Peoples Panel for AI Terms of Reference which is freely available. After taking part, panel members reported an increase in confidence in being able to question businesses and researchers. Businesses heard a diverse stakeholder voice on the ethical impacts of their products / services which have and are leading to many changes from product design considerations to ethical practices

A Heuristic Based Pre-processing Methodology for Short Text Similarity Measures in Microblogs

Short text similarity measures have lots of applications in online social networks (OSN), as they are being integrated in machine learning algorithms. However, the data quality is a major challenge in most OSNs, particularly Twitter. The sparse, ambiguous, informal, and unstructured nature of the medium impose difficulties to capture the underlying semantics of the text. Therefore, text pre-processing is a crucial phase in similarity identification applications, such as clustering and classification. This is because selecting the appropriate data processing methods contributes to the increase in correlations of the similarity measure. This research proposes a novel heuristicdriven pre-processing methodology for enhancing the performance of similarity measures in the context of Twitter tweets. The components of the proposed pre-processing methodology are discussed and evaluated on an annotated dataset that was published as part of SemEval-2014 shared task. An experimental analysis was conducted using the cosine angle as a similarity measure to assess the effect of our method against a baseline (C-Method). Experimental results indicate that our approach outperforms the baseline in terms of correlations and error rates

An Empirical Performance Evaluation of Semantic-Based Similarity Measures in Microblogging Social Media

Measuring textual semantic similarity has been a subject of intense discussion in NLP and AI for many years. A new area of research has emerged that applies semantic similarity measures within Twitter. However, the development of these measures for the semantic analysis of tweets imposes fundamental challenges. The sparsity, ambiguity, and informality present in social media are hampering the performance of traditional textual similarity measures as “tweets”, have special syntactic and semantic characteristics. This paper reviews and evaluates the performance of topological, statistical, and hybrid similarity measures, in the context of Twitter analysis. Furthermore, the performance of each measure is compared against a naïve keyword-based similarity computation method to assess the significance of semantic computation in capturing the meaning in tweets. An experiment is designed and conducted to evaluate the different measures through examining various metrics, including correlation, error rates, and statistical tests on a benchmark dataset. The potential weaknesses of semantic similarity measures in relation to Twitter applications of textual similarity assessment and the research contributions are discussed. This research highlights challenges and potential improvement areas for the semantic similarity of tweets, a resource for researchers and practitioners

Near real-time comprehension classification with artificial neural networks: decoding e-Learner non-verbal behaviour

Comprehension is an important cognitive state for learning. Human tutors recognise comprehension and non-comprehension states by interpreting learner non-verbal behaviour (NVB). Experienced tutors adapt pedagogy, materials and instruction to provide additional learning scaffold in the context of perceived learner comprehension. Near real-time assessment for e-learner comprehension of on-screen information could provide a powerful tool for both adaptation within intelligent e-learning platforms and appraisal of tutorial content for learning analytics. However, literature suggests that no existing method for automatic classification of learner comprehension by analysis of NVB can provide a practical solution in an e-learning, on-screen, context. This paper presents design, development and evaluation of COMPASS, a novel near real-time comprehension classification system for use in detecting learner comprehension of on-screen information during e-learning activities. COMPASS uses a novel descriptive analysis of learner behaviour, image processing techniques and artificial neural networks to model and classify authentic comprehension indicative non-verbal behaviour. This paper presents a study in which 44 undergraduate students answered on-screen multiple choice questions relating to computer programming. Using a front-facing USB web camera the behaviour of the learner is recorded during reading and appraisal of on-screen information. The resultant dataset of non-verbal behaviour and question-answer scores has been used to train artificial neural network (ANN) to classify comprehension and non-comprehension states in near real-time. The trained comprehension classifier achieved normalised classification accuracy of 75.8%

Trust in Computational Intelligence Systems: A Case Study in Public Perceptions

The public debate and discussion about trust in Computational Intelligence (CI) systems is not new, but a topic that has seen a recent rise. This is mainly due to the explosion of technological innovations that have been brought to the attention of the public, from lab to reality usually through media reporting. This growth in the public attention was further compounded by the 2018 GDPR legislation and new laws regarding the right to explainable systems, such as the use of “accurate data”, “clear logic” and the “use of appropriate mathematical and statistical procedures for profiling”. Therefore, trust is not just a topic for debate – it must be addressed from the onset, through the selection of fundamental machine learning processes that are used to create models embedded within autonomous decision-making systems, to the selection of training, validation and testing data. This paper presents current work on trust in the field of Computational Intelligence systems and discusses the legal framework we should ascribe to trust in CI systems. A case study examining current public perceptions of recent CI inspired technologies which took part at a national science festival is presented with some surprising results. Finally, we look at current research underway that is aiming to increase trust in Computational Intelligent systems and we identify a clear educational gap

Building Trustworthy AI Solutions: A Case for Practical Solutions for Small Businesses

Building trustworthy AI solutions, whether in academia or industry, must take into consideration a number of dimensions including legal, social, ethical, public opinion and environmental aspects. A plethora of guidelines, principles and toolkits have been published globally, but have seen limited grassroots implementation, especially among small and medium sized enterprises (SME), mainly due to lack of knowledge, skills, and resources. In this paper, we report on qualitative SME consultations over two events to establish their understanding of both data and AI ethical principles and to identify the key barriers SMEs face in their adoption of ethical AI approaches. We then use independent experts to review and code 77 published toolkits designed to build and support ethical and responsible AI practices, based on 33 evaluation criteria. The toolkits were evaluated considering their scope to address the identified SME barriers to adoption, human-centric AI principles, AI lifecycle stages, and key themes around responsible AI and practical usability. Toolkits were ranked based on criteria coverage and expert inter-coder agreement. Results show that there is not a one-size-fits-all toolkit that addresses all criteria suitable for SMEs. Our findings show few exemplars of practical application, little guidance on how to use/apply the toolkits and very low uptake by SMEs. Our analysis provides a mechanism for SMEs to select their own toolkits based on their current capacity, resources, and ethical awareness levels focusing initially at the conceptualization stage of the AI lifecycle and then extending throughout

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data