2,782 research outputs found

    The impact of computer assisted auditing techniques in the audit process: an assessment of performance and effort expectancy

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    The rapid advancement of technology has had a significant impact on a wide range of industries, including the auditing industry. It is now obvious that employing Computer Assisted Auditing Techniques (CAATs) is a possible tactic for enhancing the effectiveness and efficiency of the audit process. This study evaluates how CAATs affect auditors' expectations for performance and effort in Jordan. Through a comprehensive survey of Jordanian auditors, this research provides insights into the factors that drive CAATs adoption. Utilizing structural equation modeling, the study confirms that both Effort Expectancy and Performance Expectancy positively influence CAATs adoption. These relationships are supported by robust path coefficients and low P-values, indicating statistical significance. The results of this study should clarify the possible advantages of including CAATs in the audit process and point out any difficulties auditors could encounter. Companies and professionals may choose wisely whether to embrace and use CAATs by comprehending Performance Expectancy and Effort Expectancy

    E-learning usage from a social constructivist learning approach: perspectives of Iraqi Kurdistan students in social studies classrooms

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    Background: Many schools in the Iraqi Kurdistan Region have incorporated information and communication technologies (ICT) into their environments. However, the results have shown that computer usage has had only a minimal effect on the classroom environment and learning outcomes. This minimal effect could be attributed to the teaching and learning of social studies subjects, which often rely on a traditional vision of teaching and an excessive inclusion of facts and dates in school textbooks. Consequently, students feel compelled to memorize all the information to pass tests. Yet, merely employing technology alongside traditional teaching and assessment approaches, such as lecturing or having students study in isolation without any form of collaborative learning, does not foster the development of students' higher-order thinking skills. It's time to revitalize school curricula and teaching practices to embrace a more contemporary, open-minded approach to social science education. This approach should incorporate a social constructivist perspective with technology to better instill international moral values such as democracy, respect for differences, and learning to live harmoniously with others. Aim: This cross-sectional study aims to investigate the impact of a social constructivist learning approach on the acceptance of technology and its influence on perceived e-learning outcomes among students in the Iraqi Kurdistan Region. Additionally, this study examines the differences in the effects of the social constructivist learning approach and dimensions of technology acceptance on perceived e-learning outcomes between students studying social studies in Arabic and those studying social studies in English. Setting and participants: Data were gathered from both public and private schools in Erbil governorate, situated in northern Iraq and affiliated with the Ministry of Education-Iraqi Kurdistan Regional Government. To select participants, a random sampling technique was employed, encompassing students in grades 8 through 12 of both genders. The data were obtained through a self-administered paper-based questionnaire. Instruments: Data were collected using a social constructivist learning environment survey (personal relevance, critical voice, shared control, uncertainty, student negotiation), dimensions of the attitude toward technology (attitude toward technology use, perceived usefulness, feeling ease of use, learning facility condition, and subjective norms), some additional external variables (investigation, respect for difference, student economic ability, and perceived e-learning outcomes), and socio-demographic data. Conclusion: This study is intended to emphasize the significance of employing constructivist pedagogy to enhance the technology acceptance model and improve learning outcomes. The findings of the study showed that a social constructivist learning environment had a favorable influence on perceived e-learning outcomes as well as ease of use, perceived usefulness, investigation, and respect for difference. Attitude towards technology use and perceived usefulness are contributory factors to the positive perceived e-learning outcomes. Furthermore, feeling ease of use technology has a positive effect on both attitude towards technology use and perceived usefulness. Perceived usefulness also has a direct positive impact on attitudes towards technology use. Finally, students’ technological experience is positively correlated with feeling ease of use but not with perceived usefulness. Additionally, regarding the comparison between students studying social studies in Arabic and those in English, the findings demonstrated that students studying social studies in English showed stronger positive effects from the social constructivist learning environment on their perceived e-learning outcomes. Conversely, students studying social studies in Arabic demonstrated a more potent positive effect of perceived usefulness on their attitudes towards technology. Moreover, the positive impact of an attitude towards technology use on perceived e-learning outcomes was more pronounced among the Arabic students compared to their English counterparts. Additionally, the influence of the learning facility on the perceived ease of use, as well as the perceived usefulness of technology, differed between the two groups. The English group experienced a more substantial positive impact. However, there was no significant difference observed in the effect of feeling ease of use on attitudes towards technology use between the English and Arabic student groups. Furthermore, no significant difference was observed in the effect of perceived usefulness on the social constructivist learning environment for either group. The findings from this research are expected to contribute to the development of effective and efficient counseling and support intervention programs. These programs can play a crucial role in transforming teachers

    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

    Digitalization and Development

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    This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents. The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term. This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies

    Sensitivity of NEXT-100 detector to neutrinoless double beta decay

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    Nesta tese estúdiase a sensibilidade do detector NEXT-100 á desintegración dobre beta sen neutrinos. Existe un gran interese na busca desta desintegración xa que podería respostar preguntas fundamentais en física de neutrinos. O detector constitúe a terceira fase do experimento NEXT, colaboración na que se desenrolou esta tese. A continuación inclúese un resumo de cada un dos capítulos nos que se divide a tese. Comézase introducindo o marco teórico e experimental nas seccións Física de neutrinos, A busca da desintegración dobre beta sen neutrinos e O experimento NEXT. Posteriormente descríbense a parte principal do análise da tese en Simulación do detector, Procesamento de datos e Sensibilidade do detector NEXT-100

    The Individual And Their World

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    Enhancing Recommender Systems with Causal Inference Methodologies

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    In the current era of data deluge, recommender systems (RSs) are widely recognized as one of the most effective tools for information filtering. However, traditional RSs are founded on associational relationships among variables rather than causality, meaning they are unable to determine which factors actually affect user preference. In addition, the algorithm of conventional RS continues to recommend similar items to users, resulting in user aesthetic fatigue and ultimately the loss of customer sources. Moreover, the generation of recommendations could be biased by the confounding effect, leading to inaccurate results. To tackle this series of challenges, causal inference for recommender systems (CI for RSs) has emerged as a new area of study. In this paper, we present four different propensity score estimation methods, namely hierarchical Poisson factorization (HPF), logistic regression, non-negative matrix factorization (NMF), and neural networks (NNs), and five causal effect estimation methods, namely linear regression, inverse probability weighting (IPW), zero-inflated Poisson (ZIP) regression, zero-inflated Negative Binomial (ZINB) regression, and doubly robust (DR) estimation. Additionally, we propose a new algorithm for parameter estimation based on the concept of alternating gradient descent (AGD). Regarding the study's reliability and precision, it will be evaluated on two distinct categories of datasets. Our research demonstrates that the causal RS can correctly infer causality from user and item characteristics to the final rating with an accuracy of 96%. Moreover, according to the de-confounded and de-biased recommendations, ratings can be increased by an average of 1.6 points (out of 4) for the Yahoo! R3 dataset and 1.2 points (out of 2) for the Restaurant and Consumer data

    Effectiveness and safety of Levofloxacin containing regimen in the treatment of Isoniazid mono-resistant pulmonary Tuberculosis: a systematic review

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    BackgroundWe aimed to determine the effectiveness and safety of the Levofloxacin-containing regimen that the World Health Organization is currently recommending for the treatment of Isoniazid mono-resistant pulmonary Tuberculosis.MethodsOur eligible criteria for the studies to be included were; randomized controlled trials or cohort studies that focused on adults with Isoniazid mono-resistant tuberculosis (HrTB) and treated with a Levofloxacin-containing regimen along with first-line anti-tubercular drugs; they should have had a control group treated with first-line without Levofloxacin; should have reported treatment success rate, mortality, recurrence, progression to multidrug-resistant Tuberculosis. We performed the search in MEDLINE, EMBASE, Epistemonikos, Google Scholar, and Clinical trials registry. Two authors independently screened the titles/abstracts and full texts that were retained after the initial screening, and a third author resolved disagreements.ResultsOur search found 4,813 records after excluding duplicates. We excluded 4,768 records after screening the titles and abstracts, retaining 44 records. Subsequently, 36 articles were excluded after the full-text screening, and eight appeared to have partially fulfilled the inclusion criteria. We contacted the respective authors, and none responded positively. Hence, no articles were included in the meta-analysis.ConclusionWe found no “quality” evidence currently on the effectiveness and safety of Levofloxacin in treating HrTB.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022290333, identifier: CRD42022290333

    Hybrid human-AI driven open personalized education

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    Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

    A system for editing triangle mesh sequences with time-varying connectivity

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    Časově proměnné sekvence trojúhelníkových sítí (TVM sekvence) jsou častým produktem metod pro 3D skenování, které jsou využívány k reprezentování animovaných 3D modelů. Zpracování TVM sekvencí může být obtížné vzhledem k chybějící časové korespondenci mezi jejich snímky, kterou mnohé algoritmy vyžadují. S použitím existujícího systému pro sledování objemových prvků byla navržena metoda pro editování TVM sekvencí a implementována v interaktivní aplikaci využívající virtuální realitu. V rámci této práce jsou představeny teoretické podklady potřebné pro vyvinutí editačního systému a jeho vlastnosti jsou analyzovány. Na základě analýzy jsou pak navržena možná zlepšení použitého editačního algoritmu. Je poskytnuta také technická dokumentace implementace.ObhájenoTime-varying mesh (TVM) sequences are a common product of modern 3D scanning methods, which are used to represent animated 3D models. Processing TVM sequences can be challenging due to a lack of temporal correspondence between consecutive frames, which is required by many algorithms. Using an existing system for tracking volume elements, a method for editing TVM sequences was designed and implemented as an interactive application using virtual reality. In this work, the theoretical background required to develop the editing system is presented and its properties are analyzed. Based on this analysis, future improvements to the editing algorithm are proposed. Technical documentation of the implementation is also provided
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