Towards a social learning space for open educational resources

We identify a number of meanings of “Open”, as part of the motivating rationale for a social media space tuned for learning, called SocialLearn. We discuss why online social learning seems to be emerging so strongly at this point, explore features of social learning, and identify some of the dimensions that we believe characterize the social learning design space, before describing the emerging design concept and implementation

Equality of Learning Opportunity via Individual Fairness in Personalized Recommendations

Online education platforms play an increasingly important role in mediating the success of individuals’ careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform principles, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we shape a blueprint of the decisions and processes to be considered in the context of equality of recommended learning opportunities, based on principles that need to be empirically-validated (no evaluation with live learners has been performed). To this end, we first provide a formalization of educational principles that model recommendations’ learning properties, and a novel fairness metric that combines them to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. This paper provides a theoretical foundation for future studies of learners’ preferences and limits concerning the equality of recommended learning opportunities

Advances in MASELTOV – Serious Games in a Mobile Ecology of Services for Social Inclusion and Empowerment of Recent Immigrants

Immigration imposes a range of challenges with the risk of social exclusion from the information society (Halfman 1998), such as, getting into communication with the local society and understanding the culture of their host nation. Failure to address these challenges can lead to difficulties in the frame of integrating into the society of the host country, leading to fragmented communities and a range of social issues. As part of a comprehensive suite of services for immigrants, the European project seeks to provide both practical tools and learning services via mobile devices, providing a readily usable resource for immigrants. We introduce recent results, such as the game-based learning aspect of the MASELTOV project is introduced, with the rationale behind its design presented. In doing so, the benefits and implications of mobile platforms and emergent data capture techniques for game-based learning are discussed, as are methods for putting engaging gameplay at the forefront of the experience whilst relying on rich data capture and analysis to provide an effective learning solution

Mobile Incidental Learning to Support the Inclusion of Recent Immigrants

Social inclusion of recent immigrants is a challenge in many countries for both immigrants and the host communities. To harness the potential of social, situated and opportunistic mobile interactions for the social inclusion of immigrants in a host country, we have developed an Incidental Learning Framework. This supports the design and evaluation of MApp, a suite of smartphone tools and services for recent immigrants. Developed within the European Union's MASELTOV project (http://www.maseltov.eu), the MApp delivers language learning activities, image-to-text translation, context-aware and interest-based recommendations, local information, game-based cultural learning and social support to immigrants in cities. Preliminary field trials in Vienna, Madrid and London have highlighted issues of mobile literacy, affordability, ethics and privacy challenges, as well as insights into motivations and possible measures of success. Incidental learning implemented on a smartphone app has implications for the relationship between formal and informal learning; new systems of learner support by other immigrants, mentors and volunteers; the design of learning materials that combine immediate assistance with longer term learner development; and potential conflicts between technological affordances, e.g. context awareness and learner tracking, and user preferences among vulnerable groups such as recent immigrants

STUDY: Socially Aware Temporally Causal Decoder Recommender Systems

Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to use social network information effectively in recommender systems. This is especially true for demographic groups with interests that differ from the majority. This paper introduces STUDY, a Socially-aware Temporally caUsal Decoder recommender sYstem. STUDY introduces a new socially-aware recommender system architecture that is significantly more efficient to learn and train than existing methods. STUDY performs joint inference over socially connected groups in a single forward pass of a modified transformer decoder network. We demonstrate the benefits of STUDY in the recommendation of books for students who are dyslexic, or struggling readers. Dyslexic students often have difficulty engaging with reading material, making it critical to recommend books that are tailored to their interests. We worked with our non-profit partner Learning Ally to evaluate STUDY on a dataset of struggling readers. STUDY was able to generate recommendations that more accurately predicted student engagement, when compared with existing methods.Comment: 15 pages, 5 figure

Meaningful XAI Based on User-Centric Design Methodology

This report explores the concept of explainability in AI-based systems, distinguishing between "local" and "global" explanations. “Local” explanations refer to specific algorithmic outputs in their operational context, while “global” explanations encompass the system as a whole. The need to tailor explanations to users and tasks is emphasised, acknowledging that explanations are not universal solutions and can have unintended consequences. Two use cases illustrate the application of explainability techniques: an educational recommender system, and explainable AI for scientific discoveries. The report discusses the subjective nature of meaningfulness in explanations and proposes cognitive metrics for its evaluation. It concludes by providing recommendations, including the inclusion of “local” explainability guidelines in the EU AI proposal, the adoption of a user-centric design methodology, and the harmonisation of explainable AI requirements across different EU legislation and case law.Overall, this report delves into the framework and use cases surrounding explainability in AI-based systems, emphasising the need for “local” and “global” explanations, and ensuring they are tailored toward users of AI-based systems and their tasks

Digital education governance:Data visualization, predictive analytics, and ‘real-time’ policy instruments

Educational institutions and governing practices are increasingly augmented with digital database technologies that function as new kinds of policy instruments. This article surveys and maps the landscape of digital policy instrumentation in education and provides two detailed case studies of new digital data systems. The Learning Curve is a massive online data bank, produced by Pearson Education, which deploys highly sophisticated digital interactive data visualizations to construct knowledge about education systems. The second case considers ‘learning analytics’ platforms that enable the tracking and predicting of students’ performances through their digital data traces. These digital policy instruments are evidence of how digital database instruments and infrastructures are now at the centre of efforts to know, govern and manage education both nationally and globally. The governing of education, augmented by techniques of digital education governance, is being distributed and displaced to new digitized ‘centres of calculation’, such as Pearson and Knewton, with the technical expertise to calculate and visualize the data, plus the predictive analytics capacities to anticipate and pre-empt educational futures. As part of a data-driven style of governing, these emerging digital policy instruments prefigure the emergence of ‘real-time’ and ‘future-tense’ techniques of digital education governance

Towards Bridging Skill Gaps for the Future Industrial Workforce

Industry faces a multitude of disrupting economic, social, and environmental challenges. To overcome those, a skilled workforce is key. The aim of this thesis is to define, measure, and bridge the skill gaps of industry professionals who primarily have engineering backgrounds. In short, the research carried out and summarized in this thesis contributed to understanding the problem of skill gaps. The “skill gap” was generically defined by synthesizing definitions from the literature. Approaches to measuring skill gaps were compiled. Challenges and success factors when bridging skill gaps were found and potential solutions to bridge those challenges were identified. Those challenges are connected to motivation, managing change, identifying skill gaps, and tailor learning for everyone. The research resulted in a clear definition of skill gaps in an industrial context, a summary of approaches that have been used to measure skill gaps and key topics for employers, employees, and education providers to address when bridging skill gaps. However, the mission isn’t completed until the vision of having the right skills with the right person at the right time is fulfilled. So far, an understanding of the problem and potential actions to bridge skill gaps have been achieved. It remains to implement and test solutions to bridge the skill gap to be able to roll out an impactful solution

The design and study of pedagogical paper recommendation

For learners engaging in senior-level courses, tutors in many cases would like to pick some articles as supplementary reading materials for them each week. Unlike researchers ‘Googling’ papers from the Internet, tutors, when making recommendations, should consider course syllabus and their assessment of learners along many dimensions. As such, simply ‘Googling’ articles from the Internet is far from enough. That is, learner models of each individual, including their learning interest, knowledge, goals, etc. should be considered when making paper recommendations, since the recommendation should be carried out so as to ensure that the suitability of a paper for a learner is calculated as the summation of the fitness of the appropriateness of it to help the learner in general. This type of the recommendation is called a Pedagogical Paper Recommender.In this thesis, we propose a set of recommendation methods for a Pedagogical Paper Recommender and study the various important issues surrounding it. Experimental studies confirm that making recommendations to learners in social learning environments is not the same as making recommendation to users in commercial environments such as Amazon.com. In such learning environments, learners are willing to accept items that are not interesting, yet meet their learning goals in some way or another; learners’ overall impression towards each paper is not solely dependent on the interestingness of the paper, but also other factors, such as the degree to which the paper can help to meet their ‘cognitive’ goals.It is also observed that most of the recommendation methods are scalable. Although the degree of this scalability is still unclear, we conjecture that those methods are consistent to up to 50 papers in terms of recommendation accuracy. The experiments conducted so far and suggestions made on the adoption of recommendation methods are based on the data we have collected during one semester of a course. Therefore, the generality of results needs to undergo further validation before more certain conclusion can be drawn. These follow up studies should be performed (ideally) in more semesters on the same course or related courses with more newly added papers. Then, some open issues can be further investigated. Despite these weaknesses, this study has been able to reach the research goals set out in the proposed pedagogical paper recommender which, although sounding intuitive, unfortunately has been largely ignored in the research community. Finding a ‘good’ paper is not trivial: it is not about the simple fact that the user will either accept the recommended items, or not; rather, it is a multiple step process that typically entails the users navigating the paper collections, understanding the recommended items, seeing what others like/dislike, and making decisions. Therefore, a future research goal to proceed from the study here is to design for different kinds of social navigation in order to study their respective impacts on user behavior, and how over time, user behavior feeds back to influence the system performance