The Seven Layers of Complexity of Recommender Systems for Children in Educational Contexts

Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward–we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context

Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience studying during COVID 19 confinement periods matched the findings in recent research about the importance of digital accessibility through more e-learning intensive academic experiences. Narrative and systematic literature reviews enabled providing context in World Health Organization’s International Classification of Functioning, Disability and Health, legal and standards framework and information technology and communication state-of-the art. Assessing Portuguese higher education institutions’ web sites alerted to the fact that only outlying institutions implemented near perfect, accessibility-wise, websites. Therefore a gap was identified in how accessible the Portuguese higher education websites are, the needs of all students, including those with disabilities, and even the accessibility minimum legal requirements for digital products and the services provided by public or publicly funded organizations. Having identified a problem in society and exploring the scientific base of knowledge for context and state of the art was a first stage in the Design Science Research methodology, to which followed development and validation cycles of an Inclusive Intelligent Learning Management System Framework. The framework blends various Data Science study fields contributions with accessibility guidelines compliant interface design and content upload accessibility compliance assessment. Validation was provided by a focus group whose inputs were considered for the version presented in this dissertation. Not being the purpose of the research to deliver a complete implementation of the framework and lacking consistent data to put all the modules interacting with each other, the most relevant modules were tested with open data as proof of concept. The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute Scientific Repository and is to be completed with the publication of this thesis and the already started PhD’s findings in relevant journals and conferences

Learning dispositions and transferable competencies: pedagogy, modelling and learning analytics

Theoretical and empirical evidence in the learning sciences substantiates the view that deep engagement in learning is a function of a complex combination of learners’ identities, dispositions, values, attitudes and skills. When these are fragile, learners struggle to achieve their potential in conventional assessments, and critically, are not prepared for the novelty and complexity of the challenges they will meet in the workplace, and the many other spheres of life which require personal qualities such as resilience, critical thinking and collaboration skills. To date, the learning analytics research and development communities have not addressed how these complex concepts can be modelled and analysed, and how more traditional social science data analysis can support and be enhanced by learning analytics. We report progress in the design and implementation of learning analytics based on a research validated multidimensional construct termed “learning power”. We describe, for the first time, a learning analytics infrastructure for gathering data at scale, managing stakeholder permissions, the range of analytics that it supports from real time summaries to exploratory research, and a particular visual analytic which has been shown to have demonstrable impact on learners. We conclude by summarising the ongoing research and development programme and identifying the challenges of integrating traditional social science research, with learning analytics and modelling

Deep Learning for Recommender Systems

The widespread adoption of the Internet has led to an explosion in the number of choices available to consumers. Users begin to expect personalized content in modern E-commerce, entertainment and social media platforms. Recommender Systems (RS) provide a critical solution to this problem by maintaining user engagement and satisfaction with personalized content. Traditional RS techniques are often linear limiting the expressivity required to model complex user-item interactions and require extensive handcrafted features from domain experts. Deep learning demonstrated significant breakthroughs in solving problems that have alluded the artificial intelligence community for many years advancing state-of-the-art results in domains such as computer vision and natural language processing. The recommender domain consists of heterogeneous and semantically rich data such as unstructured text (e.g. product descriptions), categorical attributes (e.g. genre of a movie), and user-item feedback (e.g. purchases). Deep learning can automatically capture the intricate structure of user preferences by encoding learned feature representations from high dimensional data. In this thesis, we explore five novel applications of deep learning-based techniques to address top-n recommendation. First, we propose Collaborative Memory Network, which unifies the strengths of the latent factor model and neighborhood-based methods inspired by Memory Networks to address collaborative filtering with implicit feedback. Second, we propose Neural Semantic Personalized Ranking, a novel probabilistic generative modeling approach to integrate deep neural network with pairwise ranking for the item cold-start problem. Third, we propose Attentive Contextual Denoising Autoencoder augmented with a context-driven attention mechanism to integrate arbitrary user and item attributes. Fourth, we propose a flexible encoder-decoder architecture called Neural Citation Network, embodying a powerful max time delay neural network encoder augmented with an attention mechanism and author networks to address context-aware citation recommendation. Finally, we propose a generic framework to perform conversational movie recommendations which leverages transfer learning to infer user preferences from natural language. Comprehensive experiments validate the effectiveness of all five proposed models against competitive baseline methods and demonstrate the successful adaptation of deep learning-based techniques to the recommendation domain

E-Learner Recommendation Model Based on Level of Learning Outcomes Achievement

Students in any learning environment differ in their level of knowledge, achieved learning outcomes, learning style, preferences, misunderstand and attempts in solving and addressing problems when their expectations are not met. When a student searches the web as an attempt to solve a problem, he suffers from the large number of resources which are, in most cases, not related to his “needs”, or may be related but complex and advance. The result of his search might make him more confused, scattered, depressed and finally result in wasting his time which – in some cases -may have negative effects on his achievements. From here comes the need for an intelligent learning system that can guide studentsbased on their needs. This research attempts to design and build an educational recommender system for a web-based learning environment in order to generate meaningful recommendations of the most interested and relevant learning materials that suit students’ needs based on their profiles1 . This can be achieved by accessing students’ history, exploring their learning navigation patterns and making use of similar students’ experiences and their success stories. The study proposed a design for a hybrid recommender system architecture which consists of two recommendation approaches: the content and collaborative filtering. The study concentrates on the collaborative recommender engine which will recommend learning materials based on students’ level of knowledge, looking at active students' profiles, and achievements in both learning outcomes and learning outcomes levels making use of similar students’ success stories and reflecting their good experience on active student who are in the same level of knowledge. The design of the collaborative recommender engine includes the “learning” module from which the engine learns past students’ access pattern and the “advising” module from which the engine reflects the experience of similar success stories on active students. The content base recommender engine with its suggested stages is considered as future work, the research used the k-mean cluster algorithm to find out similar students where five distance function are used: Euclidean, Correlation. Jaccard,cosine and Manhattan. The cosine function shows to be the most accurate distance function with the minimum SSE but the highest processing time that doesn’t differ a lot when compared the rest functions. The best number of clusters for the selected dataset was determined using three methods Elbow, Gap-statistic and average Silhouette approach where the best number of cluster shows to be three. The research used the two result rating matrices of similar good and good students with Learnings material in order to calculate learning material weights and rank them based on highest weights which results in a final recommendation list