2828 research outputs found
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Do low-skilled migrant remittances help achieve SDG 10?
No full textIn this paper we explore the role migrant skill composition plays in
remittances and income inequality’ relationships, using a panel
study of 53 African countries over the period 1990–2020 and refer
ring to two strands of literature that demonstrate that (1) highly
skilled migrants widen the income gap in the home country; and (2)
they have fewer incentives to remit compared to their less-skilled
compatriots. The instrumental variable technique was employed to
estimate a dynamic panel data model whilst rectifying endogeneity
issues. The findings reveal that a policy that shifts the migrant skill
composition toward the less-skilled workers could channel more
remittance funds to poor households, resulting in diminishing the
income disparity in the home country. Migration policies attempt
ing to mitigate the brain drain and facilitate the migration of low-
skilled workers would enable the attenuation of the income
inequality gap
Towards Scalable Process Mining Pipelines
No full textOver the past two decades, process mining has
proven to be a valuable approach to gain insights into or ganizations’ performance. The major sub-fields of discovery,
conformance, and improvement have witnessed substantial de velopment. Contributions have covered the spectrum of better
algorithms, richer comparison metrics, and movement towards
online analysis for process data. Mostly, these contributions were
addressing process mining guidelines from the process mining
manifesto. In this paper, we address the sixth guideline in the
process mining manifesto. That is, process mining should be a
continuous process. For this, we propose a pipelining approach
that is: configurable, scalable, modular, and automated. We
realize our proposal using Dask and evaluate it with different
architectures, process discovery, and evaluation metrics
Investigating the Use of Inflectional and Derivational Morphemes in Academic Written Essays in EFL Contexts in the UAE
No full textThe present study investigated the impact of derivational and inflectional morphemes on improving the quality of
English as a foreign language (EFL) learners’ essay writing and explored the perceptions of English teachers
regarding the challenges of using these morphemes in essay writing in the UAE. Using the phenomenological
mixed methods research approach, data were collected using document analysis of 30 written essays of grade 10
Arab EFL learners and semi-structured interviews with five English teachers. Quantitative data were analyzed using
descriptive and correlational statistics. However, qualitative semi-structured interviews were analyzed using
thematic analysis. The findings of the study demonstrated that the frequency of inflectional morphemes in learners’
writing was higher than the frequency of derivational morphemes. Additionally, the findings revealed that there was
a positive moderate correlation between the number of bound morphemes used and the quality of learners’ writing.
The qualitative findings showed a number of challenges that students encountered while using morphemes in their
writing
Implementation of Social Inclusion to Support Refugee Students’ Well-Being in Victoria, Australia: A Study of School Reports and Policies
No full textThis paper explores social inclusion approaches implemented by ten sec
ondary schools in Victoria, Australia, to support refugee students’ well-
being, as articulated in their policies, reports, and other published
documents. Using an exploratory, qualitative research design, we found
that all schools employed a holistic approach to implementing social
inclusion programs for refugee students. This paper reports on the best
practices and unique examples of social inclusion programs from all
schools involved in the study
The declarative–procedural knowledge of grammatical functions in higher education ESL contexts: Fiction and reality
No full textThe present article purported to gain insights about English as a second language (ESL) learners’
knowledge of grammatical functions at the declarative and the procedural levels in the higher education
context, and argued that the dialogue between the types of knowledge calls for more attention. The study
utilised a Words-in-Sentences Test that was administered to 841 ESL students in seven colleges and universities
in three Arab countries: United Arab Emirates, Jordan and Oman. The test was used to measure the partici
pants’ declarative knowledge of grammatical functions. The participants’ test scores were then correlated with
their essay writing scores to find if there is a significant correlation between the two, and thus gain insight into
the relationship between the declarative knowledge and the procedural knowledge of grammatical functions.
Finally, a qualitative analysis was conducted on nine essays to gain an in-depth understanding of this relation
ship. The findings indicated that the university participants’ declarative knowledge of grammatical functions
was below the expected level and that there was a significant correlation between the learners’ test scores and
writing scores. In addition, intriguing themes emerged from the qualitative analysis. Together, the findings are
anticipated to spark more research on grammatical function knowledge among university students in ESL
contexts
PSYCHOLOGICAL EMOTION RECOGNITION OF STUDENTS USING MACHINE LEARNING BASED CHATBOT
No full textAnxiety and depression can have a significant impact on students’ academic performance, however, these
mental health impacts were increased during the Covid-19 pandemic, and accordingly students and
parents need some people to share their feelings together; however, there are different types of social
media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter is one of the
most popular social application that people prefer to share their emotional states. Interestingly, the
psychologist and computer scientists are inspired to study these emotions. In this paper, we propose a
chatbot for detecting the students feeling by using machine-learning algorithms. The authors used a
dataset of tweets from Kaggle’s paltform, and it includes 41157 tweets that are all related to the COVID
19. The tweets are classified into categories based on the feeling: Positive and negative. The authors
applied Machine Learning algorithms, Support Vector Machines (SVM) and the Naïve Bayes (NB) and
accordingly they compared the accuracy between them. In addition to that, the classifiers were evaluated
and compared after changing the test split ratio. The result shows that the accuracy performance of SVM
algorithm is better than Naïve Bayes algorithm, but the speed is extremely slow compared to Naive Bayes
model. In future, other neural network algorithms such as the RNN, LSTM will be implemented, and Arabic
tweets will be included in the future
Heartbeat Abnormality Detection in Phonocardiogram Signals using Wavelet Time Scattering and Optimized KNN Classification
No full textHeart auscultation continues to play an essential role
in heart health diagnosis. However, many places
worldwide have a shortage of suitably qualified
medical practitioner’s adept at this ability. This
highlights the critical need to develop accurate
automated systems for evaluating Phonocardiogram
(PCG) data. PCGs are acoustic recordings that capture
the noises made by the heart during its systolic and
diastolic cycles. To solve this issue, we suggest using
Wavelet Time Scattering with an optimized XGBoost
classifier and K-Nearest Neighbors (KNN) classifier
to detect irregular heartbeats in PCG signals. The
results are promising, as the optimized KNN classifier
obtains an impressive accuracy rate of 92.5% when
combined with five-fold cross-validation, which is
better than XGBoost classifier, which gains 87.93%.
This demonstrates the efficacy of the optimized KNN
in improving the automated interpretation of PCG data
and assisting in the early diagnosis of heart-related
problems
How Does Studying Online Affect the Well-Being of Students with Special Educational Needs and Disabilities?
No full textThe current paper aimed to explore the impact of studying online during the coronavirus pandemic on the
well-being of a sample of students (n=74) who were classified as students with Special Educational Needs and
Disabilities (SEND) in five Dubai-based schools. An online 18-item Likert scale survey was developed and distributed
among participants; however, this resulted in no statistically significant findings. However, there was a slight negative
trend, suggesting that the students’ well-being was somewhat affected by studying alone in an online context. Ultimately,
the study recommended the need to provide counselling programs to improve the well-being of the students with SEND
due to the circumstances imposed on them by the pandemic; and highlights the need for more support services for these
students when they study online
Experiences of Mothers on Maternity Leave Policies in the UAE: A Research Study in the UAE
No full textThis research explores the experiences of mothers on maternity leave policies in the UAE, focusing on the effects of maternity leave length, financial support, and quality on mothers' well-being and general health. While extensive research on this topic has been conducted in Western countries, limited research has been carried out in the UAE, requiring further investigation. The study's participants encompassed 37 working mothers aged 25 to 44 who had recently returned from maternity leave. The research questions sought to determine the effect of maternity leave length, financial support, and quality on maternal well-being and general health.
Hypotheses were formulated, suggesting positive associations between maternity leave length, financial support, and quality with well-being and general health. The results indicated no significant relationship between maternity leave length and financial support with well-being and general health. These findings contrast with previous studies conducted in other countries, indicating that there may be unique contextual factors in the UAE influencing these relationships. However, a notable finding emerged, revealing a significant positive correlation between the quality of maternity leave and well-being. The results provide valuable insights into the complex dynamics of maternity leave policies and their impact on maternal outcomes. Research highlights the need for further research in the UAE to better understand the specific factors influencing mothers’ well-being and general health during the maternity leave period
Summarising a Twitter Feed Using Weighted Frequency
No full textThis open access book presents contributions on a wide range of scientific areas originating from the BUiD Doctoral Research Conference (BDRC 2022)Data is growing exponentially every day, with 500 million tweets sent on Twitter alone (Desjardins 2021). Twitter feeds are long, take time to understand, are multilingual, and have multimedia. This makes it difficult to analyse in its raw form so the data needs to be extracted, cleaned, and structured, to be able to be used in research. This paper proposes summarising twitter feeds as a manner of structuring them. The objectives we sought to achieve are: (1) Use the Twitter API to retrieve tweets successfully, (2) Efficiently detect the language of text, and tokenize it to then analyse their content (in its language), (3) Use live tweets as the input instead of a database of tweets, (4) Create the interface as a plugin to make it accessible for computer scientists, and others, alike. We also aimed to test whether using weighted frequency to construct summaries of tweets would be successful, and by conducting a survey to test our results, we have found that our program is seen to be useful, accessible, and efficient at giving summarizations of twitter accounts. Weighted frequency also proved to be good at summarising text of any language, inputted