Comparative analysis of course prerequisite networks for five Midwestern public institutions

Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite ne...
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Gespeichert in:

Autor*in:	Yang, Bonan [verfasserIn] Gharebhaygloo, Mahdi [verfasserIn] Rondi, Hannah Rachel [verfasserIn] Hortis, Efrosini [verfasserIn] Lostalo, Emilia Zeledon [verfasserIn] Huang, Xiaolan [verfasserIn] Ercal, Gunes [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Course prerequisite network Curriculum graph Directed acyclic graph

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Applied network science - Springer International Publishing, 2016, 9(2024), 1 vom: 26. Juni
Übergeordnetes Werk:	volume:9 ; year:2024 ; number:1 ; day:26 ; month:06

Links:	Volltext

DOI / URN:	10.1007/s41109-024-00637-z

Katalog-ID:	SPR056377215

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allfields	10.1007/s41109-024-00637-z doi (DE-627)SPR056377215 (SPR)s41109-024-00637-z-e DE-627 ger DE-627 rakwb eng 300 VZ 300 VZ Yang, Bonan verfasserin aut Comparative analysis of course prerequisite networks for five Midwestern public institutions 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. Course prerequisite network (dpeaa)DE-He213 Curriculum graph (dpeaa)DE-He213 Directed acyclic graph (dpeaa)DE-He213 Gharebhaygloo, Mahdi verfasserin aut Rondi, Hannah Rachel verfasserin aut Hortis, Efrosini verfasserin aut Lostalo, Emilia Zeledon verfasserin aut Huang, Xiaolan verfasserin aut Ercal, Gunes verfasserin aut Enthalten in Applied network science Springer International Publishing, 2016 9(2024), 1 vom: 26. Juni (DE-627)860194647 (DE-600)2857425-4 2364-8228 nnns volume:9 year:2024 number:1 day:26 month:06 https://dx.doi.org/10.1007/s41109-024-00637-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2024 1 26 06
spelling	10.1007/s41109-024-00637-z doi (DE-627)SPR056377215 (SPR)s41109-024-00637-z-e DE-627 ger DE-627 rakwb eng 300 VZ 300 VZ Yang, Bonan verfasserin aut Comparative analysis of course prerequisite networks for five Midwestern public institutions 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. Course prerequisite network (dpeaa)DE-He213 Curriculum graph (dpeaa)DE-He213 Directed acyclic graph (dpeaa)DE-He213 Gharebhaygloo, Mahdi verfasserin aut Rondi, Hannah Rachel verfasserin aut Hortis, Efrosini verfasserin aut Lostalo, Emilia Zeledon verfasserin aut Huang, Xiaolan verfasserin aut Ercal, Gunes verfasserin aut Enthalten in Applied network science Springer International Publishing, 2016 9(2024), 1 vom: 26. Juni (DE-627)860194647 (DE-600)2857425-4 2364-8228 nnns volume:9 year:2024 number:1 day:26 month:06 https://dx.doi.org/10.1007/s41109-024-00637-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2024 1 26 06
allfields_unstemmed	10.1007/s41109-024-00637-z doi (DE-627)SPR056377215 (SPR)s41109-024-00637-z-e DE-627 ger DE-627 rakwb eng 300 VZ 300 VZ Yang, Bonan verfasserin aut Comparative analysis of course prerequisite networks for five Midwestern public institutions 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. Course prerequisite network (dpeaa)DE-He213 Curriculum graph (dpeaa)DE-He213 Directed acyclic graph (dpeaa)DE-He213 Gharebhaygloo, Mahdi verfasserin aut Rondi, Hannah Rachel verfasserin aut Hortis, Efrosini verfasserin aut Lostalo, Emilia Zeledon verfasserin aut Huang, Xiaolan verfasserin aut Ercal, Gunes verfasserin aut Enthalten in Applied network science Springer International Publishing, 2016 9(2024), 1 vom: 26. Juni (DE-627)860194647 (DE-600)2857425-4 2364-8228 nnns volume:9 year:2024 number:1 day:26 month:06 https://dx.doi.org/10.1007/s41109-024-00637-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2024 1 26 06
allfieldsGer	10.1007/s41109-024-00637-z doi (DE-627)SPR056377215 (SPR)s41109-024-00637-z-e DE-627 ger DE-627 rakwb eng 300 VZ 300 VZ Yang, Bonan verfasserin aut Comparative analysis of course prerequisite networks for five Midwestern public institutions 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. Course prerequisite network (dpeaa)DE-He213 Curriculum graph (dpeaa)DE-He213 Directed acyclic graph (dpeaa)DE-He213 Gharebhaygloo, Mahdi verfasserin aut Rondi, Hannah Rachel verfasserin aut Hortis, Efrosini verfasserin aut Lostalo, Emilia Zeledon verfasserin aut Huang, Xiaolan verfasserin aut Ercal, Gunes verfasserin aut Enthalten in Applied network science Springer International Publishing, 2016 9(2024), 1 vom: 26. Juni (DE-627)860194647 (DE-600)2857425-4 2364-8228 nnns volume:9 year:2024 number:1 day:26 month:06 https://dx.doi.org/10.1007/s41109-024-00637-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2024 1 26 06
allfieldsSound	10.1007/s41109-024-00637-z doi (DE-627)SPR056377215 (SPR)s41109-024-00637-z-e DE-627 ger DE-627 rakwb eng 300 VZ 300 VZ Yang, Bonan verfasserin aut Comparative analysis of course prerequisite networks for five Midwestern public institutions 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. Course prerequisite network (dpeaa)DE-He213 Curriculum graph (dpeaa)DE-He213 Directed acyclic graph (dpeaa)DE-He213 Gharebhaygloo, Mahdi verfasserin aut Rondi, Hannah Rachel verfasserin aut Hortis, Efrosini verfasserin aut Lostalo, Emilia Zeledon verfasserin aut Huang, Xiaolan verfasserin aut Ercal, Gunes verfasserin aut Enthalten in Applied network science Springer International Publishing, 2016 9(2024), 1 vom: 26. Juni (DE-627)860194647 (DE-600)2857425-4 2364-8228 nnns volume:9 year:2024 number:1 day:26 month:06 https://dx.doi.org/10.1007/s41109-024-00637-z X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2024 1 26 06
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author	Yang, Bonan
spellingShingle	Yang, Bonan ddc 300 misc Course prerequisite network misc Curriculum graph misc Directed acyclic graph Comparative analysis of course prerequisite networks for five Midwestern public institutions
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title	Comparative analysis of course prerequisite networks for five Midwestern public institutions
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title_sort	comparative analysis of course prerequisite networks for five midwestern public institutions
title_auth	Comparative analysis of course prerequisite networks for five Midwestern public institutions
abstract	Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. © The Author(s) 2024
abstractGer	Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. © The Author(s) 2024
abstract_unstemmed	Abstract We present the first formal network analysis of curricular networks for public institutions, focusing around five midwestern universities. As a first such study of public institutions, our analyses are primarily macroscopic in nature, observing patterns in the overall course prerequisite networks (CPNs) and Curriculum Graphs (CGs). An overarching objective is to better understand CPN variability and patterns across different institutions and how these patterns relate to curricular outcomes. In addition to computing well known network centrality measures to capture courses of importance in the CPNs studied, we have also formulated some newer methods with specific relevance to the curricular domains and corresponding graph types at hand. We have discovered that a new graph theoretic measure of node importance which we call reach, based on the well-known concept of reachability, is needed to more accurately express the critical nature of some introductory courses in a university. Another analytical novelty that we introduce and apply to the subject of CPNs is the Longest Paths Induced sub-Graph (LPIG) of the CPN, which yields information on relatively constrained programs and pathways. Finally, we have established a new connection between clustering of the CG and meta-majors at Southern Illinois University Edwardsville (SIUE), providing clusterings of the other public institution CGs as useful heuristics of major groupings as well. This work is borne from collaboration between academic units and academic advising with hopes of practical benefits towards aiding student advising. © The Author(s) 2024
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title_short	Comparative analysis of course prerequisite networks for five Midwestern public institutions
url	https://dx.doi.org/10.1007/s41109-024-00637-z
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author2	Gharebhaygloo, Mahdi Rondi, Hannah Rachel Hortis, Efrosini Lostalo, Emilia Zeledon Huang, Xiaolan Ercal, Gunes
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up_date	2024-07-03T22:01:45.370Z
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Comparative analysis of course prerequisite networks for five Midwestern public institutions

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