1,393 research outputs found
Predicting Paid Certification in Massive Open Online Courses
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 âthe Year of the MOOCsâ, 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 4.5â13% and 1â3%, 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 learnerâs 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 learnersâ 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
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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 learnersâ discussion forums to predict paid certification after annotating learnersâ 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 â specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learnersâ interaction in discussion forums can predict learnersâ 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 learnersâ 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
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Ditransitives in germanic languages. Synchronic and diachronic aspects
This volume brings together twelve empirical studies on ditransitive constructions in Germanic languages and their varieties, past and present. Specifically, the volume includes contributions on a wide variety of Germanic languages, including English, Dutch, and German, but also Danish, Swedish, and Norwegian, as well as lesser-studied ones such as Faroese. While the first part of the volume focuses on diachronic aspects, the second part showcases a variety of synchronic aspects relating to ditransitive patterns. Methodologically, the volume covers both experimental and corpus-based studies. Questions addressed by the papers in the volume are, among others, issues like the cross-linguistic pervasiveness and cognitive reality of factors involved in the choice between different ditransitive constructions, or differences and similarities in the diachronic development of ditransitives. The volumeâs broad scope and comparative perspective offers comprehensive insights into well-known phenomena and furthers our understanding of variation across languages of the same family
REVERTING TO GREATNESS: WHITE -AMERICAN TRAUMA AND THE OCCLUSION OF MUSLIMS IN THE POST-9/11 âGREAT AMERICAN NOVELâ
Don DeLillo, in his December 2001 Harperâs article, âIn the Ruins of the Future: Reflections on Terror and Loss in the Shadow of September,â urged fellow American writers to create âthe counternarrativeâ that would take back control of culture from terrorists who threatened it. DeLilloâs call for nation-rebuilding cultural production hearkens back to John William de Forestâs original post-Civil War coinage of the term and concept of the âGreat American Novelâ. Examining four seminal post-9/11 novels through the conceptual framework of a ânewâ Great American Novel oeuvre, I demonstrate a concerted effort by the authors to address what I have termed the âMuslim Questionâ. Jonathan Safran Foerâs Extremely Loud & Incredibly Close (2005), DeLilloâs own Falling Man (2007), Amy Waldmanâs The Submission (2011), and John Updikeâs Terrorist (2006) all feature traumatized white Americans creating a variety of mechanisms with which to mitigate the trauma of 9/11 as it resurges at even the thought of Muslims existing in America after 9/11. By examining the mechanisms of repression, appropriation, adversarial othering, and enforced secularization, I critically analyze the iterations of âsolutionsâ while also demonstrating the abandonment of American ideals by the traumatized white Americans. The spectral, fluid, and slippery notion of the so-called Great American Novel looms in the background as a tradition within which each of these novels operates; and it provides the lens necessary to see literary concerns and depictions shifting in America after the terrorist attack. While the original concept of the Great American Novel featured novels with multifaceted explorations of the American Dream, the renewed interest in creating nation-rebuilding texts is threatening to stagnate and congeal particularly around examining the relative success of the mechanisms of occluding Muslims and Islam within and from the United States
Digital agriculture: research, development and innovation in production chains.
Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil
Moving usable security research out of the lab: evaluating the use of VR studies for real-world authentication research
Empirical evaluations of real-world research artefacts that derive results from observations and experiments are a core aspect of usable security research. Expert interviews as part of this thesis revealed that the costs associated with developing and maintaining physical research artefacts often amplify human-centred usability and security research challenges. On top of that, ethical and legal barriers often make usability and security research in the field infeasible. Researchers have begun simulating real-life conditions in the lab to contribute to ecological validity. However, studies of this type are still restricted to what can be replicated in physical laboratory settings. Furthermore, historically, user study subjects were mainly recruited from local areas only when evaluating hardware prototypes. The human-centred research communities have recognised and partially addressed these challenges using online studies such as surveys that allow for the recruitment of large and diverse samples as well as learning about user behaviour. However, human-centred security research involving hardware prototypes is often concerned with human factors and their impact on the prototypesâ usability and security, which cannot be studied using traditional online surveys.
To work towards addressing the current challenges and facilitating research in this space, this thesis explores if â and how â virtual reality (VR) studies can be used for real-world usability and security research. It first validates the feasibility and then demonstrates the use of VR studies for human-centred usability and security research through six empirical studies, including remote and lab VR studies as well as video prototypes as part of online surveys.
It was found that VR-based usability and security evaluations of authentication prototypes, where users provide touch, mid-air, and eye-gaze input, greatly match the findings from the original real-world evaluations. This thesis further investigated the effectiveness of VR studies by exploring three core topics in the authentication domain: First, the challenges around in-the-wild shoulder surfing studies were addressed. Two novel VR shoulder surfing methods were implemented to contribute towards realistic shoulder surfing research and explore the use of VR studies for security evaluations. This was found to allow researchers to provide a bridge over the methodological gap between lab and field studies. Second, the ethical and legal barriers when conducting in situ usability research on authentication systems were addressed. It was found that VR studies can represent plausible authentication environments and that a prototypeâs in situ usability evaluation results deviate from traditional lab evaluations. Finally, this thesis contributes a novel evaluation method to remotely study interactive VR replicas of real-world prototypes, allowing researchers to move experiments that involve hardware prototypes out of physical laboratories and potentially increase a sampleâs diversity and size.
The thesis concludes by discussing the implications of using VR studies for prototype usability and security evaluations. It lays the foundation for establishing VR studies as a powerful, well-evaluated research method and unfolds its methodological advantages and disadvantages
An Epidemiological and Pharmacokinetic-pharmacodynamic Investigation into the Impact of Carbapenem-resistant Enterobacterales
Background: According to the 2019 CDC Antibiotic Resistance Threats Report, more than 2.8 million antibiotic-resistant infections occur in the United States each year, leading to more than 35,000 deaths. Among the most urgent threats identified by the CDC are carbapenem-resistant Enterobacterales (CRE). Despite efforts to control the spread of these organisms, the number of estimated cases between 2012 and 2017 remained stable. In 2017, an estimated 13,100 hospitalized cases of CRE led to approximately 1,100 deaths and $130 million attributable healthcare costs. This dissertation seeks to address this issue from both a pharmacokinetic/pharmacodynamic and epidemiological perspective.
Methods: We evaluated the susceptibility of 140 CRE clinical isolates against novel agents eravacycline and plazomicin using techniques standardized by the Clinical and Laboratory Standards Institute. We performed in-vitro static time-kill assays in 8 Verona Integron-encoded metallo-beta-lactamase (VIM)-producing CRE using single and combination exposures of cefepime, meropenem, piperacillin/tazobactam, amikacin, and plazomicin along with aztreonam and aztreonam/avibactam. Additionally, we performed a 10-year, inverse probability of treatment weighting adjusted retrospective cohort study comparing the risk in observing a composite outcome of all-cause mortality or discharge to hospice in patients having CRE vs. carbapenem-susceptible Enterobacterales (CSE) infections after 14 and 30 days. In this cohort, we also reported on the prevalence of CRE across the decade. Additionally, we compared the organism composition and susceptibilities of isolates cultured in both the CRE and CSE groups.
Results: Plazomicin showed higher susceptibility than eravacycline against our CRE isolates. In time kill studies, plazomicin was bactericidal against 5/8 isolates as monotherapy. Meropenem/amikacin or meropenem/plazomicin were bactericidal in all experiments, except for one isolate which regrew against meropenem/plazomicin. Aztreonam/avibactam was bactericidal in all experiments tested. Neither cefepime nor piperacillin/tazobactam improved the activity of plazomicin against our isolates. Cefepime with amikacin showed inconsistent activity. In the retrospective cohort study, the overall incidence of CRE infections was 1.8%. CRE isolates exhibited higher resistance across all routinely tested antimicrobials classes compared to CSE. The CRE population appeared to be largely non-carbapenemase-producing given the high susceptibility of meropenem and the high prevalence of E. cloacae, a known AmpC-producer. Overall, the risk of composite outcome only appeared to be increased among patients with a bloodstream infection on the index date and could only be assessed when utilizing an exposure of carbapenem-non-susceptible Enterobacterales (CNSE) due to insufficient sample size. However, the results were inconclusive as they were not statistically significant.
Conclusions: Novel antimicrobial agents plazomicin and aztreonam/avibactam were highly active against a collection of CRE including both Klebsiella pneumoniae carbapenemase (KPC) and VIM. Aztreonam/avibactam, meropenem/amikacin, and meropenem/plazomicin all exhibited comparably bactericidal activity. Furthermore, at an academic medical center in a non-endemic region for CRE, it appears that CRE infection may have increased the risk of experiencing the composite outcome after both 14 and 30 days, but definitive conclusions may not be drawn given the lack of statistical significance and imprecision in the estimation of the effect. The difficulties in drawing definitive conclusions from this study owing to limited sample size in the CRE or CNSE group stresses the importance of developing novel strategies and performing larger, multicenter studies when investigating highly resistant infections with low prevalence
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Improved Asymptotics for Multi-armed Bandit Experiments under Optimism-based Policies: Theory and Applications
The classical multi-armed bandit paradigm is a foundational framework for online decision making underlying a wide variety of important applications, e.g., clinical trials, advertising, sequential assignments, assortment optimization, etc. This work will examine two salient aspects of decision making that arise naturally in settings with large action spaces.
The first issue pertains to the division of samples across arms at the level of a trajectory (or sample-path). Traditional bounds at the ensemble-level (or in expectation) only translate to meaningful pathwise guarantees (high probability bounds) when the separation between mean rewards is ``large,'' commonly referred to as the ``well-separated'' regime in the literature. On the other hand, applications with a large action space are intrinsically endowed with smaller separations between arm-means (e.g., multiple products of similar quality in e-retail). As a result, classical ensemble-level guarantees for such problems become vacuous at the sample-path level in several settings. This theoretical gap in the understanding of bandit algorithms in the ``small gap'' regime can be of significant consequence in applications where considerations such as fairness and post hoc inference play an important role. Our work provides the first systematic treatment and analysis of this aspect under the celebrated UCB class of optimism-based bandit algorithms, including a complete diffusion-limit characterization of its regret. The diffusion-scale lens also reveals profound insights and highlights distinctions between UCB and the popular posterior sampling-based method, Thompson Sampling, such as an ``incomplete learning'' phenomenon that is characteristic of the latter.
The second research question studied in this work concerns the complexity of decision making in problems where the action space is endowed with a large number of substitutable alternatives. For example, it is common in e-retail for multiple brands to offer similar products (in terms of quality-of-service) that compete for revenue within a given product segment. We model the platform's decision problem in this example as a bandit with countably many arms, and investigate limits of achievable performance under canonical bandit algorithms adapted to this setting. We also propose novel rate-optimal algorithms that leverage results for the ``small gap'' regime alluded to earlier, and show that these outperform aforementioned conventional adaptations. We extend the countable-armed bandit paradigm to also serve as a basal motif in sequential assignment and dynamic matching problems typical of settings such as online labor markets.
The last chapter of this thesis investigates achievable performance in the countable-armed bandit problem under non-stationarity that is attributable to vanishing arms. This characteristic abstracts away certain attrition and churn processes observable in online markets, e.g., a popular brand may retract its product from a platform owing to under-exposure within its category -- a potential negative externality of the exploration carried out by the platform's policy
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