Generic knowledge-based analysis of social media for recommendations

Recommender systems have been around for decades to help people find the best matching item in a pre-defined item set. Knowledge-based recommender systems are used to match users based on information that links the two, but they often focus on a single, specific application, such as movies to watch or music to listen to. In this presentation, we present our Interest-Based Recommender System (IBRS). This knowledge-based recommender system provides recommendations that are generic in three dimensions: IBRS is (1) domain-independent, (2) language-independent, and (3) independent of the used social medium. To match user interests with items, the first are derived from the user's social media profile, enriched with a deeper semantic embedding obtained from the generic knowledge base DBpedia. These interests are used to extract personalized recommendations from a tagged item set from any domain, in any language. We also present the results of a validation of IBRS by a test user group of 44 people using two item sets from separate domains: greeting cards and holiday homes

Assessing technical candidates on the social web

This is the pre-print version of this Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEThe Social Web provides comprehensive and publicly available information about software developers: they can be identified as contributors to open source projects, as experts at maintaining weak ties on social network sites, or as active participants to knowledge sharing sites. These signals, when aggregated and summarized, could be used to define individual profiles of potential candidates: job seekers, even if lacking a formal degree or changing their career path, could be qualitatively evaluated by potential employers through their online contributions. At the same time, developers are aware of the Web’s public nature and the possible uses of published information when they determine what to share with the world. Some might even try to manipulate public signals of technical qualifications, soft skills, and reputation in their favor. Assessing candidates on the Web for technical positions presents challenges to recruiters and traditional selection procedures; the most serious being the interpretation of the provided signals. Through an in-depth discussion, we propose guidelines for software engineers and recruiters to help them interpret the value and trouble with the signals and metrics they use to assess a candidate’s characteristics and skills

Thriving in the 21st century: Learning Literacies for the Digital Age (LLiDA project): Executive Summary, Conclusions and Recommendations

LLiDA set out to: review the evidence of change in the contexts of learning, including the nature of work,nknowledge, social life and citizenship, communications media and other technologies review current responses to these challenges from the further and higher education sectors, in terms of: a) the kinds of capabilities valued, taught for and assessed (especially as revealed through competence frameworks); b) the ways in which capabilities are supported ('provision') c) the value placed on staff and student 'literacies of the digital' collect original data concerning current practice in literacies provision in UK FE and HE, including 15 institutional audits and over 40 examples of forward thinking practice offer conclusions and recommendations, in terms of the same issues reviewed in

Knowledge Graph semantic enhancement of input data for improving AI

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability

Assessing collaborative learning: big data, analytics and university futures

Traditionally, assessment in higher education has focused on the performance of individual students. This focus has been a practical as well as an epistemic one: methods of assessment are constrained by the technology of the day, and in the past they required the completion by individuals under controlled conditions, of set-piece academic exercises. Recent advances in learning analytics, drawing upon vast sets of digitally-stored student activity data, open new practical and epistemic possibilities for assessment and carry the potential to transform higher education. It is becoming practicable to assess the individual and collective performance of team members working on complex projects that closely simulate the professional contexts that graduates will encounter. In addition to academic knowledge this authentic assessment can include a diverse range of personal qualities and dispositions that are key to the computer-supported cooperative working of professionals in the knowledge economy. This paper explores the implications of such opportunities for the purpose and practices of assessment in higher education, as universities adapt their institutional missions to address 21st Century needs. The paper concludes with a strong recommendation for university leaders to deploy analytics to support and evaluate the collaborative learning of students working in realistic contexts

Investigating the learning transfer of genre features and conceptual knowledge from an academic literacy course to business studies: Exploring the potential of dynamic assessment

Academic literacy courses aim to enable higher education students to participate in their chosen academic fields as fully as possible. However, the extent to which these students transfer the academic skills taught in these courses to their chosen disciplines is still under-researched. This article reports on a study that investigated the potential of dynamic assessment (an assessment approach that blends instruction into assessment) in the transfer of genre features and conceptual knowledge among undergraduate business studies students in a UK public university. The data includes three students’ written assignments (N = nine), interviews (N = three) and business studies tutor (N = three) feedback. Drawing on Vygotskian sociocultural theory of learning and a genre theory based on Systemic Functional Linguistics, the data were analysed. The findings suggest that dynamic assessment may contribute to the transfer of genre features and conceptual knowledge to a new assessment context. Implications of this for academic literacy instruction and assessment design are presented