Does This Even Matter in the Real World? Real World Problems in Foundational Theory Courses
- URL: http://arxiv.org/abs/2604.25082v1
- Date: Tue, 28 Apr 2026 00:30:20 GMT
- Title: Does This Even Matter in the Real World? Real World Problems in Foundational Theory Courses
- Abstract summary: We introduced homework questions throughout our introductory theory courses based on real world applications of the course content.<n>We surveyed students at the beginning and end of the term on their attitudes toward the relevance of the course material.<n>Our results, surprisingly, indicate that a small minority of students (less than 7%) expected the material to be irrelevant to them at the start of the term.<n>Our surveys and qualitative feedback also indicate students enjoyed having the problems and wanted them to continue being offered in future iterations of the courses.
- Score: 0.5076419064097734
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Discrete mathematics and probability theory contain foundational material for computer scientists. Despite their importance, instructors often worry that students will find these courses to be too abstract and seemingly disconnected from their future careers. For this research project, we introduced homework questions throughout our introductory theory courses based on real world applications of the course content. Areas of application included a court case, code correctness, and machine learning ethics. We surveyed students at the beginning and end of the term on their attitudes toward the relevance of the course material. Our results, surprisingly, indicate that a small minority of students (less than 7%) expected the material to be irrelevant to them at the start of the term, and a similarly small number believed that at the end of the term. Our surveys and qualitative feedback also indicate students enjoyed having the problems and wanted them to continue being offered in future iterations of the courses.
Related papers
- There Will Be a Scientific Theory of Deep Learning [62.690977616250954]
We make the case that a scientific theory of deep learning is emerging.<n>We identify five growing bodies of work that point toward such a theory.<n>We argue that the emerging theory is best thought of as a mechanics of the learning process.
arXiv Detail & Related papers (2026-04-23T13:58:12Z) - Which course? Discourse! Teaching Discourse and Generation in the Era of LLMs [37.14307877244518]
The field of NLP has undergone vast, continuous transformations over the past few years, sparking debates going beyond discipline boundaries.<n>This paper explores this question from the angle of discourse processing, an area with rich linguistic insights and computational models for the intentional, attentional, and coherence structure of language.<n>We present a new course, "Computational Discourse and Natural Language Generation"
arXiv Detail & Related papers (2026-02-02T22:35:29Z) - Artificial Intelligence in Everyday Life 2.0: Educating University Students from Different Majors [8.282180585560928]
misunderstandings regarding their capabilities, limitations, and associated advantages and disadvantages are widespread.
In this experience report, we present an overview of an introductory course that we offered to students coming from different majors.
We discuss the assignments and quizzes of the course, which provided students with a firsthand experience of AI processes.
arXiv Detail & Related papers (2024-04-12T08:10:42Z) - Experiences with Research Processes in an Undergraduate Theory of
Computing Course [0.30458514384586394]
Theory of computing (ToC) courses are a staple in many undergraduate CS curricula.
We emulated a common research environment within our ToC course by creating a mock conference assignment.
arXiv Detail & Related papers (2023-10-03T11:37:06Z) - Does Starting Deep Learning Homework Earlier Improve Grades? [63.20583929886827]
Students who start a homework assignment earlier and spend more time on it should receive better grades on the assignment.
Existing literature on the impact of time spent on homework is not clear-cut and comes mostly from K-12 education.
We develop a hierarchical Bayesian model to help make principled conclusions about the impact on student success.
arXiv Detail & Related papers (2023-09-30T09:34:30Z) - Towards a Holistic Understanding of Mathematical Questions with
Contrastive Pre-training [65.10741459705739]
We propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo.
We first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes.
Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy.
arXiv Detail & Related papers (2023-01-18T14:23:29Z) - A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade.
Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
arXiv Detail & Related papers (2022-12-20T18:46:16Z) - The interdisciplinary quantum information classroom: Themes from a
survey of quantum information science instructors [0.0]
Interdisciplinary introduction to quantum information science (QIS) courses are proliferating at universities across the US.
We report on the findings of a survey of instructors teaching introduction to QIS courses at institutions across the US.
arXiv Detail & Related papers (2022-02-12T00:37:58Z) - Identifying Hubs in Undergraduate Course Networks Based on Scaled
Co-Enrollments: Extended Version [2.0796330979420836]
This study uses undergraduate student enrollment data to form networks of courses where connections are based on student co-enrollments.
The networks are analyzed to identify "hub" courses often taken with many other courses.
arXiv Detail & Related papers (2021-04-27T16:26:29Z) - 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) - Heterogeneous Representation Learning: A Review [66.12816399765296]
Heterogeneous Representation Learning (HRL) brings some unique challenges.
We present a unified learning framework which is able to model most existing learning settings with the heterogeneous inputs.
We highlight the challenges that are less-touched in HRL and present future research directions.
arXiv Detail & Related papers (2020-04-28T05:12:31Z)
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.