The promotion of desirable behaviours, such as socially appropriate or health-promoting actions, can be bolstered through a deeper understanding and awareness of the values that underpin the associated behavioural choices. Various implementations for promoting behaviour change based on goals already exist in Human-Robot Interaction, but, since values are the building blocks for our behaviour an agent can use values for behaviour change. By reflecting on value-related choices in a conversation, a conversational agent can assist in identifying the values at stake. This is particularly significant as these agents serve as accessible and non-judgemental platforms for discussing various concerns in a private setting. Such reflective conversations are time-consuming and may span multiple sessions, necessitating some form of memory (e.g., to reflect on earlier statements and compare the choices made for different situations). While various robotic agents have been developed to provide behavioural support (e.g., for human health), the absence of a comprehensive memory structure and dialogue strategies capable of fostering personalised, reflective conversations based on the appreciation of certain values and actions in various scenarios through contextualised values remains a challenge. To address this, this study introduces the Contextualised Value Model – a dynamic memory model designed to facilitate value-based reflection and support personalised interactions between humans and robotic agents. To realise this robotic memory, a conversational agent was designed that could elicit values from participants by discussing various scenarios that happen in daily life and reflecting on said values using perspective-taking and other dialogue strategies. The evaluation of the Contextualised Value Model focused on three primary aspects: the model's accuracy, the influence on likeability and intelligence, and the effect on participants' value awareness. The model was evaluated during a between-subjects experiment (N=54), consisting of two conditions, one where the robot was able to update and use the Contextualised Value Model, and another one where the Contextualised Value Model was random throughout the conversation. The outcome measures indicated that the integration of the memory model in conversations led to a personalised and relevant conversation, highlighting the potential of the Contextualised Value Model in enhancing conversation personalisation. Although participants' value awareness and perception of the robot's likeability and intelligence did not significantly differ based on the memory model, the study emphasised the need for extended observation to thoroughly evaluate long-term impacts. Overall, the Contextualised Value Model presents a promising framework for enhancing personalised interactions in various real-world applications, emphasising the need for further research in this area. The ePartner4all project could be further developed to complement the efforts of primary school teachers and parents in supporting children's self-learning of socially, mentally, and physically desirable behaviours.

The cover of a children's book could play a crucial role in attracting the attention of young readers and influencing their decision to pick up and read the book. In this study, we aim to identify the factors that contribute to the appeal of children's book covers. Given that the target audience for children's books changes as children grow older, we divide our analysis into five age groups. We conduct a series of experiments in which we analyze three distinct aspects of covers: the visual attributes, visible objects, and implied story. Our results show that visual attributes play an important role in determining the appeal of a book. We also find that objects and themes present on a book cover vary between age groups. By understanding the elements that contribute to the appeal of children's book covers, we hope to improve recommendations for young readers and encourage a lifelong love of reading.

Although existing work demonstrates that the usage of Scratch strengthens programming skills, little is known about the effect of children encouraging or criticizing each other when programming on the development of their programming skills. To address this gap, we conducted a data analysis to reveal the correlation (or lack thereof) between the sentiment of comments on a Scratch project and the creator's inclination to continue producing projects. In particular, using a known dataset of Scratch projects, which we enriched with new projects, we examined several independent variables that capture sentiment on comments from different perspectives, e.g. the absolute or relative number of comments with a particular sentiment score that a user receives, or the ratio of comments that fall in a certain category. We also examined several dependent variables that capture the creator's inclination to continue producing projects, e.g. the number of projects they create or the total time that they are active on the platform. The results of our experiments reveal that the absolute and relative number of comments with a specific sentiment score given to a user have a low correlation to the time that a user is active on the platform. Additionally, the ratio of the number of positive and negative comments over the total number of comments given to a user has a low correlation to the time that a user is active on the platform. Finally, the number of comments that a user receives has a low correlation to the number of projects a user creates.

As music video streaming occupies a significant market share in how people consume music, gaining an understanding of user behavioural patterns becomes increasingly crucial. This understanding can enable better music video streaming experiences by tailoring them towards more personalized and user-centric designs. Though prior works have emphasized user behaviour during solely listening to music, understanding user actions/clicks while consuming music videos remains largely unexplored. Given the unique experience offered by the combination of audio and visual elements, there is a need for focused research in this area. Therefore this study attempts to bridge this research gap by collecting and analysing a large dataset of streaming sessions from a music video streaming company - XITE. In total, we analyzed 1.8 million sessions from approximately 270,000 unique users. The behaviour exhibited during those sessions is interpreted as a language and modelled using the Language Model - Doc2Vec. This facilitated the conversion of session action sequences into embeddings. Our findings suggest that music video streaming sessions exhibit cohesive user interaction patterns, which can be grouped into distinct clusters, thereby enabling the detection of distinct behavioural patterns across user sessions. Furthermore, previous studies have indicated that user interactions with multimedia streaming platforms can be influenced by the context in which content is consumed. Extending these findings, our analysis of behavioural clusters revealed that certain user behaviours while consuming music videos are associated with specific music video genres and temporal factors. For instance, we discovered that passive sessions predominantly commence around 10 am, while sessions requiring more active engagement typically start in the evening. The insights derived from this study are valuable for improving user-centric design in music video streaming platforms and providing businesses with data-driven recommendations for strategic planning.

Reading is an essential skill for any child to learn, and finding enjoyment in it can greatly contribute to developing proper reading comprehension. Finding the books they like could prove to be difficult. Utilizing collaborative filtering recommender systems to recommend books to children is a tricky task, the lack of user feedback makes it difficult to accurately recommend books they would enjoy. Using content based recommender systems might be preferable, but what book features could a recommender system like this base recommendations on? This research explores the idea of utilizing the textual complexity of books and their descriptions as such a possible feature. By evaluating how accurate readability formulas can predict the age a book is intended for, how the variability and length of sentences vary per age and analysing the difficulty of words used, this paper finds that the descriptions of books intended for younger audiences might not be aimed at them, but instead at their parents. These findings imply that basing recommendations of the textual complexity of book descriptions might not be the most useful feature to base recommendations of.

Recommender systems are a useful tool for match- ing readers with books. However, the lack of user data from children, both due to privacy concerns as well as a low incentive to leave reviews, results in existing systems proving inadequate at recom- mending to the youth. It has been shown that chil- dren have a different relation regarding emotion at different ages. We also theorize that the perspec- tive that a book is told from varies depending on target audience age. In this paper we examine traits that could be used for recommendation instead of review data. By examining both the description of books as well as the fulltext we obtain a set of traits relating to perspective and sentiment. Com- paring these traits among books written for differ- ent age groups we observe a subset of these traits that show potential significance in age categoriza- tion. By performing a combination of empirical examination and significance testing we find that both book descriptions and book fulltext contain perspective and sentiment traits that show signifi- cance when comparing books written for different ages. Because traits obtained from book fulltext present a higher quantity of significant instances when compared those obtained from descriptions, we conclude that analysing fulltext for traits shows promise when considering a recommender system that aims to use book content.

This study examines traits that may be derived from book metadata and identifies statistically signifi- cant patterns and trends in order to assist recom- mender system designers for children’s books in selecting the most important traits to take into ac- count. This research focuses on descriptive such as publish year and structural metadata such as the chapters’ count. We rely on data from two different data sets, Goodreads and Wikisource. The findings of this study, such as the fact that there is a relationship between the number of pages and the reader’s age, may have an impact on the design of recommender systems for children’s books. This is all with the respect to the fact that children are a vulnerable audience, making it challenging to not infringe on their privacy while gathering infor- mation that can be used in statistical experiments in many fields such as education and health.

Scratch is a block-based programming language. It is designed to be simple and syntax error avoidant. This makes Scratch an accessible platform for cultivating coding skills. Many young learners are taught about different programming skills using various project types as examples. For instance, games are used as an engagement tool, and various games can motivate new learners to make their own. This influx of new published projects are manually classified into different types by using tags in the project descriptions. However, this manual classification only happens when the user adds the appropriate tags. This calls for an extension of that feature, which could help classify all projects published on the Scratch website. This has the goal of improving the browsing process, especially for new projects. To address this gap, in this initial iteration we leverage the fact that there are likely similarities, or even various project type defining features that would help improve the accuracy of classification through machine learning. Filling this gap also opens the possibility of automatic classification, depending on the accuracy of the results. Within this study, various machine learning models were tested with quantifiable project features as input data. The accuracy scores were compared to draw conclusions on how well various features extracted from Scratch projects performed for classification.