Who are the Open Learners? A Comparative Study Profiling non-Formal Users of Open Educational Resources

Open educational resources (OER) have been identified as having the potential to extend opportunities for learning to non-formal learners. However, little research has been conducted into the impact of OER on non-formal learners. This paper presents the results of a systematic survey of more than 3,000 users of open educational resources (OER). Data was collected between 2013 and 2014 on the demographics, attitudes and behaviours of users of three repositories. Questions included a particular focus on the behaviours of non-formal learners and the relationship between formal and non-formal study. Frequency analysis shows that there are marked differences in patterns of use, user profiles, attitudes towards OER, types of materials used and popularity of different subjects. The experience of using OER is fairly consistent across platforms in terms of satisfaction and impact on future behaviour. On the whole, non-formal learners surveyed were highly positive about their use of OER and believe they will continue to use them. With regards to this making formal study more likely some degree of polarization was observed: some believed formal study was now more likely, while others felt it made this less likely. On the whole, while non-formal learners are enthusiastic about using free and online resources, the language and concept of OER does not seem to be well understood in the groups surveyed. A range of findings relating to OER selection and use as well as differences between repositories are explored in the discussion

User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

Expertise Profiling in Evolving Knowledgecuration Platforms

Expertise modeling has been the subject of extensiveresearch in two main disciplines: Information Retrieval (IR) andSocial Network Analysis (SNA). Both IR and SNA approachesbuild the expertise model through a document-centric approachproviding a macro-perspective on the knowledge emerging fromlarge corpus of static documents. With the emergence of the Webof Data there has been a significant shift from static to evolvingdocuments, through micro-contributions. Thus, the existingmacro-perspective is no longer sufficient to track the evolution ofboth knowledge and expertise. In this paper we present acomprehensive, domain-agnostic model for expertise profiling inthe context of dynamic, living documents and evolving knowledgebases. We showcase its application in the biomedical domain andanalyze its performance using two manually created datasets

The Wolf of SUTD (TWOS): A dataset of malicious insider threat behavior based on a gamified competition

In this paper we present open research questions and options for data analysis of our previously designed dataset called TWOS: The Wolf of SUTD. In specified research questions, we illustrate the potential use of the TWOS dataset in multiple areas of cyber security, which does not limit only to malicious insider threat detection but are also related to authorship verification and identification, continuous authentication, and sentiment analysis. For the purpose of investigating the research questions, we present several state-of-the-art features applicable to collected data sources, and thus we provide researchers with a guidance how to start with data analysis. The TWOS dataset was collected during a gamified competition that was devised in order to obtain realistic instances of malicious insider threat. The competition simulated user interactions in/among competing companies, where two types of behaviors (normal and malicious) were incentivized. For the case of malicious behavior,we designed two types of malicious periods that was intended to capture the behavior of two types of insiders – masqueraders and traitors. The game involved the participation of 6 teams consisting of 4 students who competed with each other for a period of 5 days. Their activities were monitored by several data collection agents and producing data for mouse, keyboard, process and file-system monitor, network traffic, emails, and login/logout data sources. In total, we obtained 320 hours of active participation that included 18 hours of masquerader data and at least two instances of traitor data. In addition to expected malicious behaviors, students explored various defensive and offensive strategies such as denial of service attacks and obfuscation techniques, in an effort to get ahead in the competition. The TWOS dataset was made publicly accessible for further research purposes. In this paper we present the TWOS dataset that contains realistic instances of insider threats based on a gamified competition. The competition simulated user interactions in/among competing companies, where two types of behaviors (normal and malicious) were incentivized. For the case of malicious behavior, we designed sessions for two types of insider threats (masqueraders and traitors). The game involved the participation of 6 teams consisting of 4 students who competed with each other for a period of 5 days, while their activities were monitored considering several heterogeneous sources (mouse, keyboard, process and file-system monitor, network traffic, emails and login/logout). In total, we obtained 320 hours of active participation that included 18 hours of masquerader data and at least two instances of traitor data. In addition to expected malicious behaviors, students explored various defensive and offensive strategies such as denial of service attacks and obfuscation techniques, in an effort to get ahead in the competition. Furthermore, we illustrate the potential use of the TWOS dataset in multiple areas of cyber security, which does not limit to malicious insider threat detection, but also areas such as authorship verification and identification, continuous authentication, and sentiment analysis. We also present several state-of-the-art features that can be extracted from different data sources in order to guide researchers in the analysis of the dataset. The TWOS dataset is publicly accessible for further research purposes. © 2018, Innovative Information Science and Technology Research Group. All rights reserved

Personalized information retrieval based on time-sensitive user profile

Les moteurs de recherche, largement utilisés dans différents domaines, sont devenus la principale source d'information pour de nombreux utilisateurs. Cependant, les Systèmes de Recherche d'Information (SRI) font face à de nouveaux défis liés à la croissance et à la diversité des données disponibles. Un SRI analyse la requête soumise par l'utilisateur et explore des collections de données de nature non structurée ou semi-structurée (par exemple : texte, image, vidéo, page Web, etc.) afin de fournir des résultats qui correspondent le mieux à son intention et ses intérêts. Afin d'atteindre cet objectif, au lieu de prendre en considération l'appariement requête-document uniquement, les SRI s'intéressent aussi au contexte de l'utilisateur. En effet, le profil utilisateur a été considéré dans la littérature comme l'élément contextuel le plus important permettant d'améliorer la pertinence de la recherche. Il est intégré dans le processus de recherche d'information afin d'améliorer l'expérience utilisateur en recherchant des informations spécifiques. Comme le facteur temps a gagné beaucoup d'importance ces dernières années, la dynamique temporelle est introduite pour étudier l'évolution du profil utilisateur qui consiste principalement à saisir les changements du comportement, des intérêts et des préférences de l'utilisateur en fonction du temps et à actualiser le profil en conséquence. Les travaux antérieurs ont distingué deux types de profils utilisateurs : les profils à court-terme et ceux à long-terme. Le premier type de profil est limité aux intérêts liés aux activités actuelles de l'utilisateur tandis que le second représente les intérêts persistants de l'utilisateur extraits de ses activités antérieures tout en excluant les intérêts récents. Toutefois, pour les utilisateurs qui ne sont pas très actifs dont les activités sont peu nombreuses et séparées dans le temps, le profil à court-terme peut éliminer des résultats pertinents qui sont davantage liés à leurs intérêts personnels. Pour les utilisateurs qui sont très actifs, l'agrégation des activités récentes sans ignorer les intérêts anciens serait très intéressante parce que ce type de profil est généralement en évolution au fil du temps. Contrairement à ces approches, nous proposons, dans cette thèse, un profil utilisateur générique et sensible au temps qui est implicitement construit comme un vecteur de termes pondérés afin de trouver un compromis en unifiant les intérêts récents et anciens. Les informations du profil utilisateur peuvent être extraites à partir de sources multiples. Parmi les méthodes les plus prometteuses, nous proposons d'utiliser, d'une part, l'historique de recherche, et d'autre part les médias sociaux. En effet, les données de l'historique de recherche peuvent être extraites implicitement sans aucun effort de l'utilisateur et comprennent les requêtes émises, les résultats correspondants, les requêtes reformulées et les données de clics qui ont un potentiel de retour de pertinence/rétroaction. Par ailleurs, la popularité des médias sociaux permet d'en faire une source inestimable de données utilisées par les utilisateurs pour exprimer, partager et marquer comme favori le contenu qui les intéresse. En premier lieu, nous avons modélisé le profil utilisateur utilisateur non seulement en fonction du contenu de ses activités mais aussi de leur fraîcheur en supposant que les termes utilisés récemment dans les activités de l'utilisateur contiennent de nouveaux intérêts, préférences et pensées et doivent être pris en considération plus que les anciens intérêts surtout que de nombreux travaux antérieurs ont prouvé que l'intérêt de l'utilisateur diminue avec le temps. Nous avons modélisé le profil utilisateur sensible au temps en fonction d'un ensemble de données collectées de Twitter (un réseau social et un service de microblogging) et nous l'avons intégré dans le processus de reclassement afin de personnaliser les résultats standards en fonction des intérêts de l'utilisateur.En second lieu, nous avons étudié la dynamique temporelle dans le cadre de la session de recherche où les requêtes récentes soumises par l'utilisateur contiennent des informations supplémentaires permettant de mieux expliquer l'intention de l'utilisateur et prouvant qu'il n'a pas trouvé les informations recherchées à partir des requêtes précédentes.Ainsi, nous avons considéré les interactions récentes et récurrentes au sein d'une session de recherche en donnant plus d'importance aux termes apparus dans les requêtes récentes et leurs résultats cliqués. Nos expérimentations sont basés sur la tâche Session TREC 2013 et la collection ClueWeb12 qui ont montré l'efficacité de notre approche par rapport à celles de l'état de l'art. Au terme de ces différentes contributions et expérimentations, nous prouvons que notre modèle générique de profil utilisateur sensible au temps assure une meilleure performance de personnalisation et aide à analyser le comportement des utilisateurs dans les contextes de session de recherche et de médias sociaux.Recently, search engines have become the main source of information for many users and have been widely used in different fields. However, Information Retrieval Systems (IRS) face new challenges due to the growth and diversity of available data. An IRS analyses the query submitted by the user and explores collections of data with unstructured or semi-structured nature (e.g. text, image, video, Web page etc.) in order to deliver items that best match his/her intent and interests. In order to achieve this goal, we have moved from considering the query-document matching to consider the user context. In fact, the user profile has been considered, in the literature, as the most important contextual element which can improve the accuracy of the search. It is integrated in the process of information retrieval in order to improve the user experience while searching for specific information. As time factor has gained increasing importance in recent years, the temporal dynamics are introduced to study the user profile evolution that consists mainly in capturing the changes of the user behavior, interests and preferences, and updating the profile accordingly. Prior work used to discern short-term and long-term profiles. The first profile type is limited to interests related to the user's current activities while the second one represents user's persisting interests extracted from his prior activities excluding the current ones. However, for users who are not very active, the short-term profile can eliminate relevant results which are more related to their personal interests. This is because their activities are few and separated over time. For users who are very active, the aggregation of recent activities without ignoring the old interests would be very interesting because this kind of profile is usually changing over time. Unlike those approaches, we propose, in this thesis, a generic time-sensitive user profile that is implicitly constructed as a vector of weighted terms in order to find a trade-off by unifying both current and recurrent interests. User profile information can be extracted from multiple sources. Among the most promising ones, we propose to use, on the one hand, searching history. Data from searching history can be extracted implicitly without any effort from the user and includes issued queries, their corresponding results, reformulated queries and click-through data that has relevance feedback potential. On the other hand, the popularity of Social Media makes it as an invaluable source of data used by users to express, share and mark as favorite the content that interests them. First, we modeled a user profile not only according to the content of his activities but also to their freshness under the assumption that terms used recently in the user's activities contain new interests, preferences and thoughts and should be considered more than old interests. In fact, many prior works have proved that the user interest is decreasing as time goes by. In order to evaluate the time-sensitive user profile, we used a set of data collected from Twitter, i.e a social networking and microblogging service. Then, we apply our re-ranking process to a Web search system in order to adapt the user's online interests to the original retrieved results. Second, we studied the temporal dynamics within session search where recent submitted queries contain additional information explaining better the user intent and prove that the user hasn't found the information sought from previous submitted ones. We integrated current and recurrent interactions within a unique session model giving more importance to terms appeared in recently submitted queries and clicked results. We conducted experiments using the 2013 TREC Session track and the ClueWeb12 collection that showed the effectiveness of our approach compared to state-of-the-art ones. Overall, in those different contributions and experiments, we prove that our time-sensitive user profile insures better performance of personalization and helps to analyze user behavior in both session search and social media contexts

The ethics of algorithms: mapping the debate

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms

A Multimedia Approach to Enhancing School Leaders’ Reflective Thinking and Decision Making

This article presents an overview of one multimedia project—the Administrator Case Simulation (ACS) Multimedia Library—focusing on the professional development of school administrative leaders involved in collaborative school leadership. The article provides an overview of the multimedia case simulation concept and describes multimedia case design features. The ACS simulations’ integrated professional learning approach to enhancing the reflective thinking and decision making abilities of school leaders is highlighted.This article presents an overview of one multimedia project—the Administrator Case Simulation (ACS) Multimedia Library—focusing on the professional development of school administrative leaders involved in collaborative school leadership. The article provides an overview of the multimedia case simulation concept and describes multimedia case design features. The ACS simulations’ integrated professional learning approach to enhancing the reflective thinking and decision making abilities of school leaders is highlighted

Labour law: the medium and the message

From the contract of employment to the ‘gig economy’, attention has been fixed on the medium through which personal work relationships have been facilitated. The medium and message in labour law are explored here using the example of the United Kingdom. Fragmentation of and the associated decline in trade unions (the most evident medium in labour law) have been noticeable. Labour law’s preoccupation with the medium warrants further consideration with regards to the ‘gig economy’. ‘Gig’ work does not warrant ‘game changer’ standing because it is a continuation of the long-standing issue of employment status. Still, innovations in information technology prompt further reflection, such as the lure of app-based work. Conversely, algorithms as a tool of workplace management may be themselves ‘game changers’. Appealing to the notion of ‘scientific management’, algorithms ostensibly promise an objective means through which to measure workplace performance

A Multimedia Approach to Enhancing School Leaders’ Reflective Thinking and Decision Making

This article presents an overview of one multimedia project—the Administrator Case Simulation (ACS) Multimedia Library—focusing on the professional development of school administrative leaders involved in collaborative school leadership. The article provides an overview of the multimedia case simulation concept and describes multimedia case design features. The ACS simulations’ integrated professional learning approach to enhancing the reflective thinking and decision making abilities of school leaders is highlighted.This article presents an overview of one multimedia project—the Administrator Case Simulation (ACS) Multimedia Library—focusing on the professional development of school administrative leaders involved in collaborative school leadership. The article provides an overview of the multimedia case simulation concept and describes multimedia case design features. The ACS simulations’ integrated professional learning approach to enhancing the reflective thinking and decision making abilities of school leaders is highlighted