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Teaching Computer Science Fundamentals to Business Students: A SoTL Experience Report on How to Increase Student Engagement via Practical Programming Tasks and AI
Journal
Gemeinschaften in Neuen Medien (GeNeMe) 2024
Type
conference paper
Date Issued
2024-09
Author(s)
Abstract
The current hype about artificial intelligence (AI) attracts many researchers and students to computer science (CS) education. New course offerings in various study programs emerge that teach concepts and applications of AI and data science. At the same time, interest in more fundamental theoretical computer science topics declines, even though basic knowledge in data structures and algorithms is the foundation of many modern AI and software applications. In this work we investigate an elective course teaching fundamentals of computer science to business students. The course is covering mostly theoretical CS concepts including data structures, sorting and search algorithms, computational complexity, and graphs, accompanied by small Python code examples. It used to be taught in a traditional front-of-class style to the non-CS students, which is among the reasons why it suffers from decreasing numbers of students and low student engagement.
In this experience report, we describe the implementation of an 18 months-long teaching innovation project following the Scholarship of Teaching and Learning (SoTL) methodology to modernize the course with interactive programming tasks and the application of AI. We analyze the initial situation of student engagement based on lecturer experience, expert interviews, and observations. Then we design new interactive course elements in the form of short in-class programming exercises that are aligned with the lecture contents to increase student engagement. These exercises cover the aforementioned theoretical topics and revolve around analyzing different real world datasets collected from a smart factory. The tasks involve learning about typical data analysis and pre-processing tasks such as data cleaning, reordering, filtering, and visualization that can all be linked to the theoretical CS concepts. We combine these practical tasks with the use of AI to generate source code such that students can directly apply the lecture contents in an interactive, introductory programming setting.
The analysis of this new course format based on observations and a small user study among participating students shows an increase of student engagement and an overall improvement of the learning atmosphere. The practical tasks and opportunity to work with a real world dataset from a smart factory were well received by the students. These insights may be beneficial for other computer science educators outside of the computer science majors to partially adopt the new interactive formats for an improved student engagement. We provide open access to the datasets and Python-based programming exercises in Jupyter notebooks via a public repository such that lecturers can adopt and embed the teaching materials both in digital or physical learning settings. We view the results of this teaching innovation project as a first successful step towards transitioning the course from classical teaching to a more interactive and engaging learning format.
In this experience report, we describe the implementation of an 18 months-long teaching innovation project following the Scholarship of Teaching and Learning (SoTL) methodology to modernize the course with interactive programming tasks and the application of AI. We analyze the initial situation of student engagement based on lecturer experience, expert interviews, and observations. Then we design new interactive course elements in the form of short in-class programming exercises that are aligned with the lecture contents to increase student engagement. These exercises cover the aforementioned theoretical topics and revolve around analyzing different real world datasets collected from a smart factory. The tasks involve learning about typical data analysis and pre-processing tasks such as data cleaning, reordering, filtering, and visualization that can all be linked to the theoretical CS concepts. We combine these practical tasks with the use of AI to generate source code such that students can directly apply the lecture contents in an interactive, introductory programming setting.
The analysis of this new course format based on observations and a small user study among participating students shows an increase of student engagement and an overall improvement of the learning atmosphere. The practical tasks and opportunity to work with a real world dataset from a smart factory were well received by the students. These insights may be beneficial for other computer science educators outside of the computer science majors to partially adopt the new interactive formats for an improved student engagement. We provide open access to the datasets and Python-based programming exercises in Jupyter notebooks via a public repository such that lecturers can adopt and embed the teaching materials both in digital or physical learning settings. We view the results of this teaching innovation project as a first successful step towards transitioning the course from classical teaching to a more interactive and engaging learning format.
Language
English (United States)
Keywords
Computer science
Computing education
Computer classrooms
Student engagement
Higher education
HSG Classification
contribution to education
Refereed
Yes
Publisher
TUDPress
Publisher place
Dresden
Event Title
Gemeinschaften in Neuen Medien (GeNeMe) 2024
Event Location
Dresden, Germany
Event Date
September 26-27, 2024
Subject(s)
Division(s)