Traditional Lecture Format vs. Active Teaching Format in an Online Freshman Engineering Course

Nina Kamath Telang; Nisha Abraham; Mohana Seelan; Ramakrishna Sai Annaluru

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Conference: 2021 ASEE Virtual Annual Conference Content Access
Location: Virtual Conference
Publication Date: July 26, 2021
Start Date: July 26, 2021
End Date: July 19, 2022
Conference Session: First-Year Programs: Virtual Instruction in the First Year II
Tagged Division: First-Year Programs
Tagged Topic: Diversity
Page Count: 17
DOI: 10.18260/1-2--37925
Permanent URL: https://strategy.asee.org/37925
Download Count: 305

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Paper Authors

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Nina Kamath Telang University of Texas at Austin

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Dr. Nina Telang is an associate professor of instruction in the ECE department at UT Austin. She has taught a variety of courses in the ECE department at the freshman, sophomore and junior undergraduate levels that include 4 required (core) courses, and 2 elective (tech-area) courses. Her repertoire of courses is from a range of areas such as circuit theory, digital logic design, solid state devices, computing systems, and embedded systems.
Her teaching style fosters an active learning classroom environment where student involvement is highly encouraged. Instructional tools based in technology are heavily used in the classroom to aid the learning process for all students, to strengthen student-faculty interaction, and to improve student engagement. She is passionately involved in supporting the success of at-risk students through the development of the general engineering course and supplemental instruction sessions for introductory ECE courses.

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Nisha Abraham University of Texas at Austin

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Nisha Abraham coordinates the Supplemental Instruction program. She received her B.S. in Cell and Molecular biology from The University of Texas at Austin in 2007, her M.S. in Biology from Texas A&M University in 2012 and her M.A. in STEM Education from The University of Texas at Austin in 2019. Additionally, she has over five years of combined industry and science research experience, has worked as a senior bioscience associate at UT’s Austin Technology Incubator, and has served as an adjunct faculty member in biology for South University. She was a teaching assistant for several undergraduate biology classes, created TA training modules for the Center for Teaching Excellence, and conducted research on improving student motivation and performance in science education. Additionally, Nisha has over five years of combined industry and science research experience, has worked recently as a senior bioscience associate at UT’s Austin Technology Incubator, and has served as an adjunct faculty member in biology for South University.

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Mohana Seelan University of Texas at Austin

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Mohana Seelan obtained her B.S. in Electrical and Computer Engineering at the University of Texas at Austin in Spring 2021. During her time at UT, she served as a STEM tutor for the Sanger Learning Center, as an undergraduate teaching assistant for EE306 (Intro to Computing) and EE109K (Enhancing Academic Success), and as a first-year mentor for Ramshorn students. Mohana is interested in computer science and engineering education, specifically in first-year student learning. She is now starting her M.S. at the University of California, San Diego in Computer Science Engineering.

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Ramakrishna Sai Annaluru University of Texas at Austin

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Ramakrishna (Sai) Annaluru is a 3rd Year MS/PhD student in Electrical and Computer Engineering at the University of Texas at Austin, researching at the intersection of machine learning and signal processing. Sai's educational background include 1 semester of graduate Teaching Assistant experience for Signals and Systems and Introduction to Computing, 2 semesters of head instructor experience for a 1 credit hour spatial visualization course, and 2 years of undergraduate tutoring experience in introductory electrical engineering and mathematics classes.

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Abstract

This paper analyzes the impact of active classroom learning on student performance in an introductory computing course. This course is a freshman level, foundational course in the Computer Engineering track in our engineering program. Student performance and overall experience in this course can influence their perception of the major, and therefore affect retention rates. A significant number of our freshman students enter our program with some computer programming experience through high school level courses in Java programming. Evidence gathered through a survey conducted at the start of the semester indicated that more than 50% of our incoming students have either completed AP Computer Science A or AP Computer Science Principles or both, about 20% have taken another computer programming course or learned programming through high school club activities, and about 20% of our students have no experience whatsoever. While this freshman level course does not require any prior knowledge of programming basics, students having some background are at an advantage due to their familiarity with the process of algorithmic thinking, and translation of an algorithm to a computer program. Many different instructional innovations have been implemented in science and engineering classrooms as a method of engaging students and improving learning outcomes. Active teaching is a method that has been implemented in a number of science and engineering courses taught in numerous universities [1-3]. While the results of these experiments indicate that there is a positive change in student engagement and learning in classrooms where active learning is the primary method of instruction, there is no data indicating the effectiveness of this pedagogical technique in an online classroom. One of the authors of this paper was the instructor of record for two sections of this course. One section (control section) was primarily taught using the traditional lecture format, and the second section (treatment section) was taught using a more active learning approach. Both sections were taught online with class meetings held twice weekly over Zoom. The predominant active learning approach used was a cooperative learning approach where students worked together in groups during class time. Students in both sections of the course had access to the same course materials and took the same course assessments. Measuring the effectiveness of an instructional technique is, in general, problematic, since the technique can have different levels of impact on different course objectives. For that reason, we have considered a broad range of student learning outcomes such as knowledge of foundational concepts in computing, basic logic blocks used in digital circuits, computer microarchitecture, and low-level or assembly programming. Students in both sections had access to Supplemental Instruction (SI) sessions where they had further exposure to active learning strategies. In this study we investigate the impact of this active learning experience (both in lecture for the treatment section, and the Supplemental Instruction section) on student performance in all aspects of the course such as weekly quizzes, programming assignments, and overall course GPA. Preliminary analysis of student performance in weekly quizzes indicates that there is a slight improvement in the mean quiz score for the treatment section. Specifically, the students in the treatment section with no prior experience in programming show a 3.5% to 13.3% improvement in their most recent quiz grades compared with their peers in the control section. The data analysis in our final paper will include a statistical analysis to find any significant correlations between the type of instruction and student performance. We can use these results to find what impact the type of instruction has on specific student populations, including students with minimal to no experience in computer programming. References: 1. Prince, M., Does Active Learning Work? A Review of the Research, Journal of Engineering Education, 93(3), July 2004, pp. 223-231. 2. Mazur, E. 1996. Peer Interaction, A User’s Manual. Prentice Hall. 3. Ruhl, K., Hughes, C., Schloss, P., Using the Pause Procedure to enhance lecture recall, Teacher Education and Special Education, Vol. 10, 1987, pp. 14-18.

Citation
Format

Telang, N. K., & Abraham, N., & Seelan, M., & Annaluru, R. S. (2021, July), Traditional Lecture Format vs. Active Teaching Format in an Online Freshman Engineering Course Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--37925

TY - CPAPER
AB - This paper analyzes the impact of active classroom learning on student performance in an introductory computing course. This course is a freshman level, foundational course in the Computer Engineering track in our engineering program. Student performance and overall experience in this course can influence their perception of the major, and therefore affect retention rates. A significant number of our freshman students enter our program with some computer programming experience through high school level courses in Java programming. Evidence gathered through a survey conducted at the start of the semester indicated that more than 50% of our incoming students have either completed AP Computer Science A or AP Computer Science Principles or both, about 20% have taken another computer programming course or learned programming through high school club activities, and about 20% of our students have no experience whatsoever. While this freshman level course does not require any prior knowledge of programming basics, students having some background are at an advantage due to their familiarity with the process of algorithmic thinking, and translation of an algorithm to a computer program.
Many different instructional innovations have been implemented in science and engineering classrooms as a method of engaging students and improving learning outcomes. Active teaching is a method that has been implemented in a number of science and engineering courses taught in numerous universities [1-3]. While the results of these experiments indicate that there is a positive change in student engagement and learning in classrooms where active learning is the primary method of instruction, there is no data indicating the effectiveness of this pedagogical technique in an online classroom.
One of the authors of this paper was the instructor of record for two sections of this course. One section (control section) was primarily taught using the traditional lecture format, and the second section (treatment section) was taught using a more active learning approach. Both sections were taught online with class meetings held twice weekly over Zoom. The predominant active learning approach used was a cooperative learning approach where students worked together in groups during class time. Students in both sections of the course had access to the same course materials and took the same course assessments.
Measuring the effectiveness of an instructional technique is, in general, problematic, since the technique can have different levels of impact on different course objectives. For that reason, we have considered a broad range of student learning outcomes such as knowledge of foundational concepts in computing, basic logic blocks used in digital circuits, computer microarchitecture, and low-level or assembly programming. Students in both sections had access to Supplemental Instruction (SI) sessions where they had further exposure to active learning strategies.
In this study we investigate the impact of this active learning experience (both in lecture for the treatment section, and the Supplemental Instruction section) on student performance in all aspects of the course such as weekly quizzes, programming assignments, and overall course GPA. Preliminary analysis of student performance in weekly quizzes indicates that there is a slight improvement in the mean quiz score for the treatment section. Specifically, the students in the treatment section with no prior experience in programming show a 3.5% to 13.3% improvement in their most recent quiz grades compared with their peers in the control section.
The data analysis in our final paper will include a statistical analysis to find any significant correlations between the type of instruction and student performance. We can use these results to find what impact the type of instruction has on specific student populations, including students with minimal to no experience in computer programming.
References:
1. Prince, M., Does Active Learning Work? A Review of the Research, Journal of Engineering Education, 93(3), July 2004, pp. 223-231.
2. Mazur, E. 1996. Peer Interaction, A User’s Manual. Prentice Hall.
3. Ruhl, K., Hughes, C., Schloss, P., Using the Pause Procedure to enhance lecture recall, Teacher Education and Special Education, Vol. 10, 1987, pp. 14-18.
AU - Nina Kamath Telang
AU - Nisha Abraham
AU - Mohana Seelan
AU - Ramakrishna Sai Annaluru
CY - Virtual Conference
DA - 2021/07/26
PB - ASEE Conferences
TI - Traditional Lecture Format vs. Active Teaching Format in an Online Freshman Engineering Course
UR - https://strategy.asee.org/37925
DO - 10.18260/1-2--37925
ER -