Students Programming Competitions as an Educational Tool and a
Motivational Incentive to Students
- URL: http://arxiv.org/abs/2105.15136v2
- Date: Tue, 1 Jun 2021 01:10:33 GMT
- Title: Students Programming Competitions as an Educational Tool and a
Motivational Incentive to Students
- Authors: Youry Khmelevsky, Ken Chidlow
- Abstract summary: We report on student programming competition results by students from the Computer Science Department (COSC) of Okanagan College (OC)
We found that some freshmen and sophomore students in diploma and degree programs are very capable and eager to be involved in applied research projects as early as the second semester.
Students reported that participation in competitions give them motivation to effectively learn in their programming courses, inspire them to learn deeper and more thoroughly, and help them achieve better results in their classes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In this short paper we report on student programming competition results by
students from the Computer Science Department (COSC) of Okanagan College (OC)
and discuss the achieved results from an educational point of view. We found
that some freshmen and sophomore students in diploma and degree programs are
very capable and eager to be involved in applied research projects as early as
the second semester, and into local and international programming competitions
as well. Our observation is based on the last 2 educational years, beginning
2015 when we introduced programming competitions to COSC students. Students
reported that participation in competitions give them motivation to effectively
learn in their programming courses, inspire them to learn deeper and more
thoroughly, and help them achieve better results in their classes.
Related papers
- Enhancing Student Engagement in Large-Scale Capstone Courses: An Experience Report [2.7629502923028944]
capstone courses offer students a valuable opportunity to gain hands-on experience in software development.
coordinating a capstone course, especially for a large student cohort, can be a daunting task for academic staff.
We outline the iterative development and refinement of our capstone course as it grew substantially in size over a span of six consecutive sessions.
arXiv Detail & Related papers (2024-04-03T23:59:35Z) - An Undergraduate Consortium for Addressing the Leaky Pipeline to Computing Research [1.9336815376402718]
This experience report describes a first-of-its-kind Undergraduate Consortium (UC)
The UC aims to broaden participation in the AI research community by recruiting students, particularly those from historically marginalized groups.
This paper presents our program design, inspired by a rich set of evidence-based practices, and a preliminary evaluation of the first years that points to the UC achieving many of its desired outcomes.
arXiv Detail & Related papers (2024-03-25T21:43:43Z) - Hierarchical Programmatic Reinforcement Learning via Learning to Compose
Programs [58.94569213396991]
We propose a hierarchical programmatic reinforcement learning framework to produce program policies.
By learning to compose programs, our proposed framework can produce program policies that describe out-of-distributionally complex behaviors.
The experimental results in the Karel domain show that our proposed framework outperforms baselines.
arXiv Detail & Related papers (2023-01-30T14:50:46Z) - Engaging, Large-Scale Functional Programming Education in Physical and
Virtual Space [0.0]
COVID-19 pandemic requires institutions to radically replace the traditional way of on-site teaching.
We report on our strategies and experience tackling these issues as part of a Haskell-based functional programming and verification course.
arXiv Detail & Related papers (2022-07-26T07:47:22Z) - 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) - Using Machine Learning to Predict Engineering Technology Students'
Success with Computer Aided Design [50.591267188664666]
We show how data combined with machine learning techniques can predict how well a particular student will perform in a design task.
We found that our models using early design sequence actions are particularly valuable for prediction.
Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.
arXiv Detail & Related papers (2021-08-12T20:24:54Z) - Latent Execution for Neural Program Synthesis Beyond Domain-Specific
Languages [97.58968222942173]
We take the first step to synthesize C programs from input-output examples.
In particular, we propose La Synth, which learns the latent representation to approximate the execution of partially generated programs.
We show that training on these synthesized programs further improves the prediction performance for both Karel and C program synthesis.
arXiv Detail & Related papers (2021-06-29T02:21:32Z) - Students Struggle to Explain Their Own Program Code [0.0]
We ask students to explain the structure and execution of their small programs after they submit them to a programming exercise.
One third of the students struggled to explain their own program code.
Our results indicate that answering properly aligned QLCs correctly has stronger correlation with student success and retention than merely submitting a correct program.
arXiv Detail & Related papers (2021-04-14T09:13:05Z) - 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) - Latent Programmer: Discrete Latent Codes for Program Synthesis [56.37993487589351]
In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences.
We propose to learn representations of the outputs that are specifically meant for search: rich enough to specify the desired output but compact enough to make search more efficient.
We introduce the emphLatent Programmer, a program synthesis method that first predicts a discrete latent code from input/output examples, and then generates the program in the target language.
arXiv Detail & Related papers (2020-12-01T10:11:35Z) - Effects of Internship on Fresh Graduates: A case study on IIT, DU
students [2.6763498831034034]
The aim of any curriculum is to produce industry ready students.
The uniqueness of this SE syllabus is having a six month long internship semester inside the curriculum.
The result shows that the students having internship experiences, performed above the level of expectation from the industries.
arXiv Detail & Related papers (2020-08-03T07:31:51Z)
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.