A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.

The advent of Massive Open Online Courses (MOOCs) allows massive volume of registrants to enrol in these MOOCs. This research aims to offer MOOCs registrants with automatic content related feedback to fulfil their cognitive needs. A framework is proposed which consists of three modules which are the subject ontology learning module, the short text classification module, and the question answering module. Unlike previous research, to identify relevant concepts for ontology learning a regular expression parser approach is used. Also, the relevant concepts are extracted from unstructured documents. To build the concept hierarchy, a frequent pattern mining approach is used which is guided by a heuristic function to ensure that sibling concepts are at the same level in the hierarchy. As this process does not require specific lexical or syntactic information, it can be applied to any subject. To validate the approach, the resulting ontology is used in a question-answering system which analyses students' content-related questions and generates answers for them. Textbook end of chapter questions/answers are used to validate the question-answering system. The resulting ontology is compared vs. the use of Text2Onto for the question-answering system, and it achieved favourable results. Finally, different indexing approaches based on a subject's ontology are investigated when classifying short text in MOOCs forum discussion data; the investigated indexing approaches are: unigram-based, concept-based and hierarchical concept indexing. The experimental results show that the ontology-based feature indexing approaches outperform the unigram-based indexing approach. Experiments are done in binary classification and multiple labels classification settings . The results are consistent and show that hierarchical concept indexing outperforms both concept-based and unigram-based indexing. The BAGGING and random forests classifiers achieved the best result among the tested classifiers

Mining association language patterns using a distributional semantic model for negative life event classification

AbstractPurposeNegative life events, such as the death of a family member, an argument with a spouse or the loss of a job, play an important role in triggering depressive episodes. Therefore, it is worthwhile to develop psychiatric services that can automatically identify such events. This study describes the use of association language patterns, i.e., meaningful combinations of words (e.g., <loss, job>), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work).MethodsThis study proposes a framework that combines a supervised data mining algorithm and an unsupervised distributional semantic model to discover association language patterns. The data mining algorithm, called association rule mining, was used to generate a set of seed patterns by incrementally associating frequently co-occurring words from a small corpus of sentences labeled with negative life events. The distributional semantic model was then used to discover more patterns similar to the seed patterns from a large, unlabeled web corpus.ResultsThe experimental results showed that association language patterns were significant features for negative life event classification. Additionally, the unsupervised distributional semantic model was not only able to improve the level of performance but also to reduce the reliance of the classification process on the availability of a large, labeled corpus

The social sciences and the web : From ‘Lurking’ to interdisciplinary ‘Big Data’ research

Acknowledgements This research is supported by the award made by the RCUK Digital Economy theme to the dot.rural Digital Economy Hub (award reference: EP/G066051/1) and the UK Economic & Social Research Council (ESRC) (award reference: ES/M001628/1).Peer reviewedPublisher PD

Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature

The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that coin the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research

Exploring university students’ engagement in learning through gamification, transmedia and virtual reality

The advent of the 5th Internet generation and the evolution of university students’ behaviour leads professors, educators and researchers to search for and investigate new tools to engage students in course topics and content. The purpose of this thesis is to explore university students’ engagement for learning through gamification, transmedia and virtual reality. Although several studies have been conducted, as far as we know, the current thesis is the first to employ three tools to motivate and engage students: gamification, transmedia and virtual reality. Thus, the aims of the thesis are: (i) to comprehensively review relationship marketing and service marketing research fields, including gamification, virtual reality and education; (ii) to investigate gamification in higher education through a text mining approach; (iii) to explore transmedia effects in higher education using a mixed approach; (iv) to propose and validate a model portraying the influence of virtual reality experience on student engagement, extending the S-O-R framework. To develop this thesis and seeking to ensure its execution and results, we started with a comprehensive literature review followed by the development of three independent studies based on distinct research methodologies. From the comprehensive literature review, 115 scientific articles emerge, giving and understanding of the use of new technologies in education and, providing access to other relevant information on the topic. The first study reveals that through the application of the Kahoot! a gamification-based tool, students expressed positive emotions when asked about its use in the classroom as a learning tool. The results also show that gamification-based tools can be considered an important asset in the teaching-learning process, being able to motivate and engage students in their learning activities. The second study shows that use of Moodle as a complement to the traditional class allows students to go further in understanding the content of the course and be more engaged with the whole group of colleagues and professors. The level of student engagement and academic success seems to be higher as a result of activities based on information research, sharing and interaction through online discussion tools (such as the online forum), and analysis and discussion of case studies. The third study shows that memories are activated and stored through emotions and so, these are two key elements in virtual reality experiences that help students to become more engaged with course content. It also seems that less mindful students can benefit more than mindful ones from using virtual reality tools to become more creative and enhance their memories about the course content. Based on our findings, some theoretical contributions and managerial implications are also presented.O surgimento da 5ª geração da Internet e a evolução do comportamento dos estudantes universitários leva professores, educadores e investigadores a pesquisar e investigar novas ferramentas para envolver os alunos nos tópicos e no conteúdo dos cursos. O objetivo desta tese é explorar o envolvimento de estudantes universitários na aprendizagem através da gamificação, transmedia e realidade virtual. Embora vários estudos tenham já sido realizados, segundo sabemos, a tese atual é a primeira a utilizar três ferramentas para motivar e envolver os alunos: gamificação, transmedia e realidade virtual. Assim, os objetivos da tese são: (i) rever de forma abrangente a investigação nas áreas de marketing de relacionamento e marketing de serviços, incluindo gamificação, realidade virtual e educação; (ii) investigar a gamificação no ensino superior por meio de uma abordagem de mineração de texto; (iii) explorar efeitos transmedia no ensino superior usando uma abordagem mista; (iv) propor e validar um modelo que retrate a influência da experiência em realidade virtual no envolvimento dos alunos, alargando a estrutura S-O-R. Para desenvolver esta tese e procurar garantir a sua execução e resultados, iniciamos com uma revisão abrangente da literatura, seguida pelo desenvolvimento de três estudos independentes, baseados em metodologias distintas de pesquisa. Da revisão abrangente da literatura, emergem 115 artigos científicos, que permitem entender o uso de novas tecnologias na educação, obter acesso a outras informações relevantes sobre o tema e realizar a revisão da literatura. O primeiro estudo revela que, através da aplicação do Kahoot!, ferramenta baseada na gamificação, os alunos expressaram emoções positivas, quando questionados sobre o seu uso na sala de aula, como uma ferramenta de aprendizagem. Os resultados também mostram que as ferramentas baseadas na gamificação podem ser consideradas um ativo importante no processo de ensino-aprendizagem, podendo motivar e envolver os alunos nas suas atividades de aprendizagem. O segundo estudo mostra que o uso do Moodle, como um complemento da aula tradicional, permite que os alunos compreendam o conteúdo do curso e se envolvam com o seu grupo de colegas e professores. O nível de envolvimento e sucesso académico dos alunos parece ser maior face à realização de atividades baseadas em pesquisa de informações, partilha e interação por meio de ferramentas de discussão on-line (como o fórum on-line) e análise e discussão de estudos de caso. O terceiro estudo mostra que as memórias são ativadas e armazenadas através das emoções, logo esses são dois elementos-chave nas experiências de realidade virtual que contribuem para aprimorar e ajudar os alunos a envolverem-se mais com o conteúdo dos cursos. Parece também que os alunos menos atentos podem beneficiar mais do que os atentos, ao usar ferramentas de realidade virtual, para se tornarem mais criativos e melhorar as suas memórias sobre o conteúdo dos cursos. Com base nos nossos resultados, também são apresentadas algumas contribuições teóricas e implicações para a gestão

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity