Exploring engagement profiling in MOOCs through Learning Analytics: The Open edX Case

The enormous amount of data being generated daily, requires effective and efficient ways of processing and analysing in order to extract useful information and form meaningful conclusions. Learning Analytics is a set of methodologies and practices that uncover such information from educational data. The research in this thesis explores the addition of a Learning Analytics feature to the context of a Learning Analytics tool that aids instructors using the online Massive Open Online Course (MOOC) platform, Open edX. This is done through the development and evaluation of a working artefact that supports profiling of students according to their activity throughout the course, alongside the visualizations, which represent said activity. As a result, the thoroughly demonstrated process of the artefact creation and feedback collection from the instructors shows the potential of Learning Analytics methods when applied to Open edX tracking data. Several practical features for creating different engagement groups, together with the visualizations, are conceptualized, implemented and evaluated, and are positively assessed by the target group of instructors. In addition, the challenges that were encountered in the period of the development, are presented, together with the suggestions to overcome them. Finally, a few extra features are outlined for future work, which could expand the existing functionality even more and bring additional knowledge to this research area.Master's Thesis in Information ScienceINFO390MASV-INF

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

In-Secure Identities: on the Securitization of Abnormality

Highly securitized sites, such as airports, are increasingly using screening methods designed to purge racial profiling from their practices. In these contexts, not only are profiling methods seen as unlawful, but are also perceived as ineffective from a security perspective. Instead of basing security screenings on a perceived ‘dangerousness’ of social categories, these new screening methods aim to rely on automatic and objective criteria. This paper examines the shaping and effects of these security procedures, claiming that this redesigning of security technologies in accordance with practices which are presumably scientific, measurable and objective, has resulted in the creation of new categories of ‘threatening’ persons. Specifically, we show how the category of ‘normal’ has become central to security sorting and how, therefore – unintentionally yet necessarily – these procedures and technologies have become apparatuses of social normalization. People who deviate from given norms are thus singled out as potential security threats and are subjected to extended security probing, if not to outright violence. Tracing the effects of the increasing centrality of normalization processes to the management of securitized sites, this paper examines this reconfiguration of (ab)normality and explores the consequences of the securitization of social deviance

Algorithmic Prosumers

Today almost everything we do in our everyday life is datafied and fed into an algorithm, i.e. reduced to an input that recursive computational systems process and transform into behavioural models. How algorithms sort, classify and propose contents have a striking impact on how people make sense of the world and derive their sense of self. Despite their powerful social presence, however, algorithms remain mainly invisible to individuals, as well as difficult to examine for researchers. By drawing on auto-ethnographic diaries, prepared following a critical pedagogy approach, this contribution discusses the results of an empirical research that aim to analyse media consumption, content production and sharing practices on digital platforms, in order to shed light on how individuals relate to algorithmic media and how they critically reflect on their apparently innocuous daily online practices. In accordance with the results, we argue that users on digital platforms can be framed as algorithmic prosumers. Indeed, the consumption, as well as the production of contents on digital platforms are algorithmic practices that foster datafication and capitalist surveillance logics, with users feeding algorithmic media while they are contemporarily fed by them within a recursive loop. In this context, it emerges an individual whose subjectivity is strictly connected to and enacted by computational procedures

Gender prediction from tweets: Improving neural representations with hand-crafted features

Author profiling is the characterization of an author through some key attributes such as gender, age, and language. In this paper, a RNN model with Attention (RNNwA) is proposed to predict the gender of a twitter user using their tweets. Both word level and tweet level attentions are utilized to learn ’where to look’. This model1 is improved by concatenating LSA-reduced n-gram features with the learned neural representation of a user. Both models are tested on three languages: English, Spanish, Arabic. The improved version of the proposed model (RNNwA + n-gram) achieves state-of-the-art performance on English and has competitive results on Spanish and Arabic

Unobtrusive Assessment Of Student Engagement Levels In Online Classroom Environment Using Emotion Analysis

Measuring student engagement has emerged as a significant factor in the process of learning and a good indicator of the knowledge retention capacity of the student. As synchronous online classes have become more prevalent in recent years, gauging a student\u27s attention level is more critical in validating the progress of every student in an online classroom environment. This paper details the study on profiling the student attentiveness to different gradients of engagement level using multiple machine learning models. Results from the high accuracy model and the confidence score obtained from the cloud-based computer vision platform - Amazon Rekognition were then used to statistically validate any correlation between student attentiveness and emotions. This statistical analysis helps to identify the significant emotions that are essential in gauging various engagement levels. This study identified emotions like calm, happy, surprise, and fear are critical in gauging the student\u27s attention level. These findings help in the earlier detection of students with lower attention levels, consequently helping the instructors focus their support and guidance on the students in need, leading to a better online learning environment

Multilingual Cross-domain Perspectives on Online Hate Speech

In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content. Analysis of the multilingual corpora shows that the different contexts share certain characteristics in their hateful rhetoric. To expose the main features, we have focused on text classification, text profiling, keyword and collocation extraction, along with manual annotation and qualitative study.Comment: 24 page