Addressing the Recruitment and Retention of Female Students in Computer
Science at Third Level
- URL: http://arxiv.org/abs/2110.06090v1
- Date: Tue, 12 Oct 2021 15:39:15 GMT
- Title: Addressing the Recruitment and Retention of Female Students in Computer
Science at Third Level
- Authors: Susan McKeever, Deirdre Lillis
- Abstract summary: The School of Computing at the Dublin Institute of Technology (DIT) implemented a five year strategy to address recruitment and retention issues of female undergraduate computer science (CS) students.
Since 2012, under CS4All we implemented a variety of reforms to improve student retention, set up a new CS program to attract more female students, and delivered changes to promote a sense of community amongst our female students.
For example, we have achieved a dramatic improvement in retention rising from 45% to 89% in first year progression rates.
Our new hybrid CS International program has more than double the percentage of females first year enrolments in comparison to our
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In the School of Computing at the Dublin Institute of Technology (DIT),
Ireland, we undertook our Computer Science for All (CS4All) initiative, a five
year strategy to implement structural reforms at Faculty level, to address
recruitment and retention issues of female undergraduate computer science (CS)
students. Since 2012, under CS4All we implemented a variety of reforms to
improve student retention, set up a new CS program to attract more female
students, and delivered changes to promote a sense of community amongst our
female students. We have made significant improvements. For example, we have
achieved a dramatic improvement in retention rising from 45% to 89% in first
year progression rates. Our new hybrid CS International program has more than
double the percentage of females first year enrolments in comparison to our
other undergraduate programs. As at 2018, we continue to roll out the remaining
parts of CS4All within our School.
Related papers
- GPT-4 as a Homework Tutor can Improve Student Engagement and Learning Outcomes [80.60912258178045]
We developed a prompting strategy that enables GPT-4 to conduct interactive homework sessions for high-school students learning English as a second language.
We carried out a Randomized Controlled Trial (RCT) in four high-school classes, replacing traditional homework with GPT-4 homework sessions for the treatment group.
We observed significant improvements in learning outcomes, specifically a greater gain in grammar, and student engagement.
arXiv Detail & Related papers (2024-09-24T11:22:55Z) - How are Primary School Computer Science Curricular Reforms Contributing
to Equity? Impact on Student Learning, Perception of the Discipline, and
Gender Gaps [0.7896843467339624]
Early exposure to Computer Science (CS) for all is critical to broaden participation and promote equity in the field.
But how does introducting CS into primary school curricula impact learning, perception, and gaps between groups of students?
We investigate a CS-curricular reform and teacher Professional Development (PD) program from an equity standpoint.
arXiv Detail & Related papers (2023-06-01T15:42:26Z) - Computer Science for Future -- Sustainability and Climate Protection in
the Computer Science Courses of the HAW Hamburg [0.0]
Computer Science for Future (CS4F) is an initiative in the Department of Computer Science at HAW Hamburg.
The aim of the initiative is a paradigm shift in the discipline of computer science, thus establishing sustainability goals as a primary leitmotif for teaching and research.
The change in teaching influences our research, the transfer to business and civil society as well as the change in our own institution.
arXiv Detail & Related papers (2023-01-17T13:43:57Z) - Better Balance in Informatics: An Honest Discussion with Students [3.9227642572344177]
The Department of Computer Science at UiT The Arctic University of Norway has a gender gap at all academic levels.
This paper presents the results of the discussions and the subsequent recommendations that we made to the administration of the department.
arXiv Detail & Related papers (2023-01-06T14:44:32Z) - Disadvantaged students increase their academic performance through
collective intelligence exposure in emergency remote learning due to COVID 19 [105.54048699217668]
During the COVID-19 crisis, educational institutions worldwide shifted from face-to-face instruction to emergency remote teaching (ERT) modalities.
We analyzed data on 7,528 undergraduate students and found that cooperative and consensus dynamics among students in discussion forums positively affect their final GPA.
Using natural language processing, we show that first-year students with low academic performance during high school are exposed to more content-intensive posts in discussion forums.
arXiv Detail & Related papers (2022-03-10T20:23:38Z) - Increasing Gender Balance Across Academic Staffing in Computer Science
-- case study [0.0]
Technological University Dublin is the top university in Ireland in terms of gender balance of female academic staff in computer science schools.
In an academic team of approximately 55 full-time equivalents, 36% of our academic staff are female.
75% of our School Executive are female.
arXiv Detail & Related papers (2021-10-12T15:43:35Z) - GradInit: Learning to Initialize Neural Networks for Stable and
Efficient Training [59.160154997555956]
We present GradInit, an automated and architecture method for initializing neural networks.
It is based on a simple agnostic; the variance of each network layer is adjusted so that a single step of SGD or Adam results in the smallest possible loss value.
It also enables training the original Post-LN Transformer for machine translation without learning rate warmup.
arXiv Detail & Related papers (2021-02-16T11:45:35Z) - Interleaving Computational and Inferential Thinking: Data Science for
Undergraduates at Berkeley [81.01051375191828]
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses.
These courses emphasize computational thinking, inferential thinking, and working on real-world problems.
These courses have become some of the most popular on campus and have led to a surging interest in a new undergraduate major and minor program in data science.
arXiv Detail & Related papers (2021-02-13T22:51:24Z) - Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI)
Curriculum [58.86139968005518]
The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education.
A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum.
The co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.
arXiv Detail & Related papers (2021-01-19T11:26:19Z) - What's the worth of having a single CS teacher program aimed at teachers
with heterogeneous profiles? [68.8204255655161]
We discuss the results of a 400-hour teacher training program conducted in Argentina aimed at K-12 teachers with no Computer Science background.
Our research aims at understanding whether a single teacher training program can be effective in teaching CS contents and specific pedagogy to teachers with very heterogeneous profiles.
arXiv Detail & Related papers (2020-11-09T15:03:31Z) - Gender Diversity in Computer Science at a Large Research University [0.0]
We explore the gender gap in Computer Science at a large public research university.
We find that a large percentage of women taking the Introductory CS1 course for majors do not intend to major in CS.
This finding implies that a large part of the retention task is attracting these women to further explore the major.
arXiv Detail & Related papers (2020-04-28T18:11:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.