Academic performance and behavioral patterns
Abstract Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral a...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2018-04-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-018-0138-8 |
| _version_ | 1819001429426176000 |
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| author | Valentin Kassarnig Enys Mones Andreas Bjerre-Nielsen Piotr Sapiezynski David Dreyer Lassen Sune Lehmann |
| author_facet | Valentin Kassarnig Enys Mones Andreas Bjerre-Nielsen Piotr Sapiezynski David Dreyer Lassen Sune Lehmann |
| author_sort | Valentin Kassarnig |
| collection | DOAJ |
| description | Abstract Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students. |
| first_indexed | 2024-12-20T22:49:04Z |
| format | Article |
| id | doaj.art-ec5c930d0d4b462594e360a847c1784d |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-20T22:49:04Z |
| publishDate | 2018-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-ec5c930d0d4b462594e360a847c1784d2022-12-21T19:24:18ZengSpringerOpenEPJ Data Science2193-11272018-04-017111610.1140/epjds/s13688-018-0138-8Academic performance and behavioral patternsValentin Kassarnig0Enys Mones1Andreas Bjerre-Nielsen2Piotr Sapiezynski3David Dreyer Lassen4Sune Lehmann5Institute of Software Technology, Graz University of TechnologyDepartment of Applied Mathematics and Computer Science, Technical University of DenmarkDepartment of Economics, University of CopenhagenDepartment of Applied Mathematics and Computer Science, Technical University of DenmarkDepartment of Economics, University of CopenhagenDepartment of Applied Mathematics and Computer Science, Technical University of DenmarkAbstract Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students.http://link.springer.com/article/10.1140/epjds/s13688-018-0138-8Academic performanceData collectionHomophilyPeer effect |
| spellingShingle | Valentin Kassarnig Enys Mones Andreas Bjerre-Nielsen Piotr Sapiezynski David Dreyer Lassen Sune Lehmann Academic performance and behavioral patterns EPJ Data Science Academic performance Data collection Homophily Peer effect |
| title | Academic performance and behavioral patterns |
| title_full | Academic performance and behavioral patterns |
| title_fullStr | Academic performance and behavioral patterns |
| title_full_unstemmed | Academic performance and behavioral patterns |
| title_short | Academic performance and behavioral patterns |
| title_sort | academic performance and behavioral patterns |
| topic | Academic performance Data collection Homophily Peer effect |
| url | http://link.springer.com/article/10.1140/epjds/s13688-018-0138-8 |
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