1,720,958 research outputs found
AGENT-BASED SIMULATION FOR UNIVERSITY STUDENTS ADMISSION: MEDICAL COLLEGES IN JORDAN UNIVERSITIES
No full textMedical colleges are considered one of the most competitive schools compared to other university
departments. Most countries adopted the particular application process to ensure maximum fairness
between students. For example, in UK students apply through the UCAS system, and most of USA
universities use either Coalition App or Common App, on the other hand, some universities use their own
websites. In fact, a Unified Admission Application process is adopted in Jordan for allocating the students
to the public universities. However, the universities and colleges in Jordan are evaluating the applicants by
using merely the centralized system without considering the socioeconomics factor, as the high school GPA
is the essential player their selection mechanism. In this paper, the authors will use an Agent Based model
(ABM) to simulate different scenarios by using Netlogo software (v. 6.3). The authors used different
parameters such as the family-income and the high school GPA in order to maximize the utilities of the
fairness and equalities of universities admission. The model is simulated into different scenarios. For
instance, students with low family income and high GPA given them the priority in studying medicine
comparing with same high school GPA and higher family-income, as a results, after several rotations of
the simulation the reputation of medical schools are identified based on students’ preferences and seats’
allocated as it shows that high ranking universities are mainly allocated with have high cut-off GPA score
A Generative AI Chatbot in High School Advising: A Qualitative Analysis of Domain-Specific Chatbot a ChatGPT
No full textDue to the variety of chatbot types and classifications, students and advisers may experience confusi
when trying to select the right chatbot that can more trust it, however, the classification of chatbo
depends on different factors including, the complexity of the task, the response-based approach and the
type of the domain. Since selecting the most effective chatbot is crucial for high schools and students, a
semi-structured interviews in qualitative research were conducted with eight high school students in order
to investigate the students ‘perspectives on different seven responses of generative questions from the
domain-specific chatbot named HSGAdviser, comparing it with the ChatGPT. All questions were related
students’ advising interests including university applications, admission tests, majors and more. The
transcribed data were reviewed and examined by using the thematic analysis. However, the results reveal
that most students found that HSGAdviser chatbot is easier, shorter, faster and more concise compared
to ChatGPT, especially for Yes/No questions as students expect brief answers. However, some students
found that certain crucial questions that can have a significance impact on their future, they would pref
the ChatGPT for more detailed information. The limitation of this study is the limited size of the
participants. Nevertheless, in the future research, other high school students from different regions will
participate in the study
Artificial Intelligence based Chatbot for promoting Equality in High School Advising
No full textThe sustainable development goal 4 (SDG4) aims to
“ensure inclusive and equitable quality education and promote
lifelong learning opportunities for all”. Therefore, researchers
are inspired to study the fairness and equality in different
aspects of education. Some studies are focused on social and
academic initiatives and others on developing state-of-the-art
technology to enhance the education between students equally.
Henceforth, in this paper a novel affordable chatbot
implemented by using a neural network model and natural
language processing (NLP) to assist students in high schools
particularly, since high school is one of the most essential
stages in students’ lives, as in this stage, students have the
option to select their academic streams and advanced courses
that can shape their career with their passions and interests.
The dataset in this study collected from different academic
resources such as schools & universities websites, schools’
advisers, parents and students. It includes (968) pairs of
enquiries and tags. The first column represents the student’s
enquiry; the second column indicates the tag or the class of
each sentence. The model built by connecting the input data
into embedding layer, and then the data fed into the LSTM
layer with different number of neurons, then authors used
sigmoid function for the output layer. The result in this study
shows that the performance of the chatbot is improved by
increasing the number of neurons from 5 to 8, the model
achieved high accuracy ratio with score (96.5%). In future the
model will be developed with stacked LSTM layers with using
softmax activation function in the output layer, as different
classes will be added as well in the dataset
A Generative AI Chatbot in High School Advising: A Qualitative Analysis of Domain-Speci c Chatbot and ChatGPT
No full textDue to the variety of chatbot types and classi cations, students and advisers may experience confusion
when trying to select the right chatbot that can more trust it, however, the classi cation of chatbots
depends on different factors including, the complexity of the task, the response-based approach and the
type of the domain. Since selecting the most effective chatbot is crucial for high schools and students, a
semi-structured interviews in qualitative research were conducted with eight high school students in order
to investigate the students ‘perspectives on different seven responses of generative questions from the
domain-speci c chatbot named HSGAdviser, comparing it with the ChatGPT. All questions were related to
students’ advising interests including university applications, admission tests, majors and more. The
transcribed data were reviewed and examined by using the thematic analysis. However, the results reveal
that most students found that HSGAdviser chatbot is easier, shorter, faster and more concise compared
to ChatGPT, especially for Yes/No questions as students expect brief answers. However, some students
found that certain crucial questions that can have a signi cance impact on their future, they would prefer
the ChatGPT for more detailed information. The limitation of this study is the limited size of the
participants. Nevertheless, in the future research, other high school students from different regions will
participate in the study
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
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
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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