Evaluating the Impact of Additional Examples and Explanation on Student Outcomes in a Free Online Python Course

Timothy James; Alejandra J. Magana

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Programming Education 2
Tagged Division: Computers in Education Division (COED)
Permanent URL: https://peer.asee.org/47354

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Paper Authors

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Timothy James Purdue University

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Timothy James is an instructor at the University of Pittsburgh Computer Science department, as well as a Doctor of Technology student at Purdue University. Previously, Tim has spent some time in a variety of industries including Internet consulting, finance, defense contracting, aviation maintenance, telecommunications, capital markets, and sandwich artistry. Timothy hopes to continue actively engaging the community in technical training and CS education efforts.

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Alejandra J. Magana Purdue University orcid.org/0000-0001-6117-7502

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Alejandra J. Magana, Ph.D., is the W.C. Furnas Professor in Enterprise Excellence in the Department of Computer and Information Technology and Professor of Engineering Education at Purdue University.

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Abstract

Helping students to learn a new programming language in a voluntary online course can be time consuming and difficult. Students in such a noncredit course face many challenges in learning; the content must keep their attention, and these students also need to quickly achieve competency in analysis, evaluation, and application of the concepts. As explanation and examples can help in student understanding, the amount of explanation and the number of examples to support these concepts may be a factor in successful learning.

Colaboratory (typically called "Colab") is a free software-as-a-service product provided by Google. It can be quickly accessed through a browser, allowing users to create, modify, and execute Jupyter Notebooks. This environment removes many setup and configuration obstacles for students, and can be used to deliver interactive instructional activities. Jupyter Notebooks can intersperse instruction and explanation with modifiable, executable Python code. These features make it an excellent environment for students to study, learn, experiment, and write their own code, which can be executed through the browser. Students can see the results of running their code almost instantly.

In 2023, the authors taught an online introductory programming course using Colab with similar approaches to two cohorts of students. For both cohorts, students around the world signed up for the course using a public Google Form that was shared on LinkedIn and Twitter. In the first cohort, one group of 174 students received content based on worked examples and try-modify-create pedagogical approaches; the other group of 112 received the same content, but with more explanation and additional examples. A portion of the students were given a choice between shorter lessons and longer lessons in order to compare student preferences to outcomes. The remaining students were randomly assigned to either longer lessons or shorter lessons. Student performance was evaluated through quizzes, assignments, reflection exercises, and a final exam. Other than the inclusion of more explanation and additional examples, the content in the two courses was identical.

In the second cohort, students were randomly assigned to one of three groups. All three groups received ungraded exercises with each lesson in order to evaluate the effect of solutions to these exercises.. The first group did not receive solutions to these. The second group received solutions to these exercises, but after a delay of more than 12 hours. The third group received solutions to these exercises immediately.

The purpose of this work is to attempt to understand the effect of additional examples and explanation in an online, voluntary Python programming course in an effort to understand how to improve student learning and engagement in the material.

Citation
Format

James, T., & Magana, A. J. (2024, June), Evaluating the Impact of Additional Examples and Explanation on Student Outcomes in a Free Online Python Course Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47354

TY - CPAPER
AB - Helping students to learn a new programming language in a voluntary online course can be time consuming and difficult. Students in such a noncredit course face many challenges in learning; the content must keep their attention, and these students also need to quickly achieve competency in analysis, evaluation, and application of the concepts. As explanation and examples can help in student understanding, the amount of explanation and the number of examples to support these concepts may be a factor in successful learning.

Colaboratory (typically called &quot;Colab&quot;) is a free software-as-a-service product provided by Google. It can be quickly accessed through a browser, allowing users to create, modify, and execute Jupyter Notebooks. This environment removes many setup and configuration obstacles for students, and can be used to deliver interactive instructional activities. Jupyter Notebooks can intersperse instruction and explanation with modifiable, executable Python code. These features make it an excellent environment for students to study, learn, experiment, and write their own code, which can be executed through the browser. Students can see the results of running their code almost instantly.

In 2023, the authors taught an online introductory programming course using Colab with similar approaches to two cohorts of students. For both cohorts, students around the world signed up for the course using a public Google Form that was shared on LinkedIn and Twitter. In the first cohort, one group of 174 students received content based on worked examples and try-modify-create pedagogical approaches; the other group of 112 received the same content, but with more explanation and additional examples. A portion of the students were given a choice between shorter lessons and longer lessons in order to compare student preferences to outcomes. The remaining students were randomly assigned to either longer lessons or shorter lessons. Student performance was evaluated through quizzes, assignments, reflection exercises, and a final exam. Other than the inclusion of more explanation and additional examples, the content in the two courses was identical.

In the second cohort, students were randomly assigned to one of three groups. All three groups received ungraded exercises with each lesson in order to evaluate the effect of solutions to these exercises.. The first group did not receive solutions to these. The second group received solutions to these exercises, but after a delay of more than 12 hours. The third group received solutions to these exercises immediately.

The purpose of this work is to attempt to understand the effect of additional examples and explanation in an online, voluntary Python programming course in an effort to understand how to improve student learning and engagement in the material.
AU - Timothy James
AU - Alejandra J. Magana
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Evaluating the Impact of Additional Examples and Explanation on Student Outcomes in a Free Online Python Course
UR - https://peer.asee.org/47354
ER -