Analyzing Trends in Curricular Complexity and Extracting Common Curricular Design Patterns

Sushant Makarand Padhye; David Reeping; Nahal Rashedi

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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: Educational Research and Methods Division (ERM) Technical Session 26
Tagged Division: Educational Research and Methods Division (ERM)
Permanent URL: https://peer.asee.org/46580

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Paper Authors

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Sushant Makarand Padhye University of Cincinnati

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Sushant Padhye is a sophomore majoring in electrical engineering and an Undergraduate Research Assistant at the University of Cincinnati.

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David Reeping University of Cincinnati orcid.org/0000-0002-0803-7532

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Dr. David Reeping is an Assistant Professor in the Department of Engineering and Computing Education at the University of Cincinnati. He earned his Ph.D. in Engineering Education from Virginia Tech and was a National Science Foundation Graduate Research Fellow. He received his B.S. in Engineering Education with a Mathematics minor from Ohio Northern University. His main research interests include transfer student information asymmetries, threshold concepts, curricular complexity, and advancing quantitative and fully integrated mixed methods.

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Nahal Rashedi University of Cincinnati

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PhD Student of Engineering Education

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Abstract

This research paper explores how common curricular design patterns can be extracted from plan of study data in a systematic fashion. Analyzing curricula has become more and more data-driven, as is exemplified by the adoption of Curricular Analytics – a method of quantitatively determining the “complexity” of a plan of study (PoS). The technique represents the pre- and corequisite relationships in a curriculum as a network, which not only allows for degree requirements to be visualized but also analyzed using network analysis techniques. One of the additional advantages of representing curricula as networks is the ability to decompose them into smaller subnetworks representing curricular design patterns, such as the Calculus sequence and the core Mechanics sequence (i.e., Statics, Dynamics, and Strength of Materials). However, considering the steep data requirements and standardization across course names, there is little work exploring how these curricular design patterns manifest in engineering program across the United States.

We posed the following research question: “What are common curricular design patterns in programs across the United States for mechanical, electrical, civil, industrial, and chemical engineering?” We leveraged existing data collected as part of an ongoing project connected to the Multiple Institution Database for Engineering Longitudinal Development (MIDFIELD), a data sharing agreement between 21 US institutions. Unlike previous efforts with Curricular Analytics, these data spans 13 universities across 5 disciplines and for the past 10 years – comprising 494 PoS networks, one of the largest and most diverse samples to date.

We performed data mining using Python, facilitated by established APIs and libraries such as NetworkX and pandas. Network analysis, employing the NetworkX library, allowed us to create dependency graphs for courses and identify recurring patterns. However, due to the dataset’s vast scope, courses exhibited a multitude of names that varied across institutions and years. Thus, we categorized course chains based on subject matter and common sets of courses. Manual generalization across a large dataset was infeasible, so we turned to OpenAI’s large language model (LLM) API, focusing on the GPT-4 model. We used the API to categorize classes based on their subject matter – manually verified to offset issues with hallucinations. From mining the data, we were able to obtain results in the form of common chains with the counts and instances where they appear in networks across the POS dataset. There were 12 types of course sequences with counts higher than 50 across plans of study. These sequences provide the basis for exploring common curricular design patterns (e.g., Calculus, Mechanics) and were observed across multiple subjects like Mathematics, Physics and General Engineering.

There are multiple avenues to explore in the future using the PoS dataset. For example, the common sequences themselves have applications in curriculum planning. But the scope of the study can be changed to analyze changes in course sequencing over the years for a particular discipline, or across an institution. We will provide examples of these different design patterns in the paper.

Citation
Format

Padhye, S. M., & Reeping, D., & Rashedi , N. (2024, June), Analyzing Trends in Curricular Complexity and Extracting Common Curricular Design Patterns Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46580

TY - CPAPER
AB - This research paper explores how common curricular design patterns can be extracted from plan of study data in a systematic fashion. Analyzing curricula has become more and more data-driven, as is exemplified by the adoption of Curricular Analytics – a method of quantitatively determining the “complexity” of a plan of study (PoS). The technique represents the pre- and corequisite relationships in a curriculum as a network, which not only allows for degree requirements to be visualized but also analyzed using network analysis techniques. One of the additional advantages of representing curricula as networks is the ability to decompose them into smaller subnetworks representing curricular design patterns, such as the Calculus sequence and the core Mechanics sequence (i.e., Statics, Dynamics, and Strength of Materials). However, considering the steep data requirements and standardization across course names, there is little work exploring how these curricular design patterns manifest in engineering program across the United States.

We posed the following research question: “What are common curricular design patterns in programs across the United States for mechanical, electrical, civil, industrial, and chemical engineering?” We leveraged existing data collected as part of an ongoing project connected to the Multiple Institution Database for Engineering Longitudinal Development (MIDFIELD), a data sharing agreement between 21 US institutions. Unlike previous efforts with Curricular Analytics, these data spans 13 universities across 5 disciplines and for the past 10 years – comprising 494 PoS networks, one of the largest and most diverse samples to date.

We performed data mining using Python, facilitated by established APIs and libraries such as NetworkX and pandas. Network analysis, employing the NetworkX library, allowed us to create dependency graphs for courses and identify recurring patterns. However, due to the dataset’s vast scope, courses exhibited a multitude of names that varied across institutions and years. Thus, we categorized course chains based on subject matter and common sets of courses. Manual generalization across a large dataset was infeasible, so we turned to OpenAI’s large language model (LLM) API, focusing on the GPT-4 model. We used the API to categorize classes based on their subject matter – manually verified to offset issues with hallucinations.
From mining the data, we were able to obtain results in the form of common chains with the counts and instances where they appear in networks across the POS dataset. There were 12 types of course sequences with counts higher than 50 across plans of study. These sequences provide the basis for exploring common curricular design patterns (e.g., Calculus, Mechanics) and were observed across multiple subjects like Mathematics, Physics and General Engineering.

There are multiple avenues to explore in the future using the PoS dataset. For example, the common sequences themselves have applications in curriculum planning. But the scope of the study can be changed to analyze changes in course sequencing over the years for a particular discipline, or across an institution. We will provide examples of these different design patterns in the paper.

AU - Sushant Makarand Padhye
AU - David Reeping
AU - Nahal Rashedi
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Analyzing Trends in Curricular Complexity and Extracting Common Curricular Design Patterns
UR - https://peer.asee.org/46580
ER -