216 research outputs found

    Predicting Paid Certification in Massive Open Online Courses

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    Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

    Security and Privacy for Modern Wireless Communication Systems

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    The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

    Disinformation and Fact-Checking in Contemporary Society

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    Funded by the European Media and Information Fund and research project PID2022-142755OB-I00

    Exploring Sentiment Analysis on Twitter: Investigating Public Opinion on Migration in Brazil from 2015 to 2020

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    openTechnology has reshaped societal interaction and the expression of opinions. Migration is a prominent trend, and analysing social media discussions provides insights into societal perspectives. This thesis explores how events between 2015 and 2020 impacted Brazilian sentiment on Twitter about migrants and refugees. Its aim was to uncover the influence of key sociopolitical events on public sentiment, clarifying how these echoed in the digital realm. Four key objectives guided this research: (a) understanding public opinions on migrants and refugees, (b) investigating how events influenced Twitter sentiment, (c) identifying terms used in migration-related tweets, and (d) tracking sentiment shifts, especially concerning changes in government. Sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) was employed to analyse tweet data. The use of computational methods in social sciences is gaining traction, yet no analysis has been conducted before to understand the sentiments of the Brazilian population regarding migration. The analysis underscored Twitter's role in reflecting and shaping public discourse, offering insights into how major events influenced discussions on migration. In conclusion, this study illuminated the landscape of Brazilian sentiment on migration, emphasizing the significance of innovative social media analysis methodologies for policymaking and societal inclusivity in the digital age

    Internet Mediated NGO Activity: How Environmental NGOs use Weibo in China

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    This thesis uses an interdisciplinary approach that draws on both the political science and media and communication fields to analyse how Chinese environmental NGOs use the microblogging site, Sina Weibo, in their online activism. The study of NGOs and how they use the internet in China is widespread. However, in many cases, the way that NGOs in China work, both online and offline, has been analysed through the lens of traditional civil society and internet studies literature, which has mostly focused on the ability of NGOs, and the internet, to give rise to significant political change, and even democratisation.Through a mixture of thematic, network, and organisational analysis, this thesis investigates the communicative functions, themes, and use of interactive features in posts on Weibo, including the use of hashtags, retweets and @mentions. At the organisational level, the ways that NGOs engage with different actors, both online and offline, including fellow NGOs, government departments, their followers, and potential donors are interrogated using four case studies. These analyses found that although the political space afforded to environmental NGOs in China is severely constrained, and the operations of the NGOs could not be seen as overtly activist or confrontational in the traditional sense, the NGOs do in fact retain a certain amount of autonomy and are able to carve out some political space for themselves. The findings of this thesis therefore challenge the notion that NGOs in China are co-opted organisations without autonomy from the state and suggests that there is scope for digital activism by NGOs in an authoritarian context, even though the online and offline political space they inhabit may be tightly regulated and controlled

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    (Un)filial daughters and digital feminisms in China: The stories of awakening, resisting, and finding comrades

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    This thesis sets out to understand Chinese feminist struggles in a so-called digital era by looking at the experiences and practices of an emerging generation of digital feminists that came into light in Chinese feminist movements. Conceptually and methodologically, this research took inspirations from an interdisciplinary body of literature including feminist theory, sociology, media and cultural studies, girlhood studies and gender studies. Inspired by online ethnography and feminist participatory methodologies, it combined an online tracking of feminist events on Weibo with semi-structured interviews and social media diary study with 21 Chinese girls and young women. This thesis explores the embedded and embodied experiences of these participants as they discover and learn about feminism, resist and challenge gender and sexual inequalities, and try to build connections with like-minded people within and beyond the digital sphere. By charting feminist responses and resistance to familial discourses and norms around girlhood and young femininity, I show the emergence of feminist subjectivities of (un)filial daughters that arises from but also comes to reconfigure gender and sexuality within a neoliberal and postsocialist context of patriarchal familism in China. I build upon the concepts of networked counterpublics and networked affects to explore how these (un)filial daughters are networked to carve out spaces for feminist discussion in social media. Employing an affective-discursive analysis, I also tune into how networked feminist resistance and alliances are formed not merely on the basis of how women and feminists talk about these issues but also how they feel

    Event Detection from Social Media Stream: Methods, Datasets and Opportunities

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    Social media streams contain large and diverse amount of information, ranging from daily-life stories to the latest global and local events and news. Twitter, especially, allows a fast spread of events happening real time, and enables individuals and organizations to stay informed of the events happening now. Event detection from social media data poses different challenges from traditional text and is a research area that has attracted much attention in recent years. In this paper, we survey a wide range of event detection methods for Twitter data stream, helping readers understand the recent development in this area. We present the datasets available to the public. Furthermore, a few research opportunitiesComment: 8 page
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