Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/6/4/105 |
| _version_ | 1827641851832696832 |
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| author | Gomathy Ramaswami Teo Susnjak Anuradha Mathrani |
| author_facet | Gomathy Ramaswami Teo Susnjak Anuradha Mathrani |
| author_sort | Gomathy Ramaswami |
| collection | DOAJ |
| description | Learning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on <i>what-if</i> modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the <i>what-if</i> modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on <i>what-if</i> counterfactuals can be used to automate student feedback through prescriptive analytics. |
| first_indexed | 2024-03-09T17:20:31Z |
| format | Article |
| id | doaj.art-c9109add6fc4436abdd32830cb039d4f |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-09T17:20:31Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-c9109add6fc4436abdd32830cb039d4f2023-11-24T13:17:22ZengMDPI AGBig Data and Cognitive Computing2504-22892022-09-016410510.3390/bdcc6040105Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive AnalyticsGomathy Ramaswami0Teo Susnjak1Anuradha Mathrani2School of Mathematical and Computational Sciences, Massey University, Auckland 0632, New ZealandSchool of Mathematical and Computational Sciences, Massey University, Auckland 0632, New ZealandSchool of Mathematical and Computational Sciences, Massey University, Auckland 0632, New ZealandLearning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on <i>what-if</i> modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the <i>what-if</i> modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on <i>what-if</i> counterfactuals can be used to automate student feedback through prescriptive analytics.https://www.mdpi.com/2504-2289/6/4/105machine learninganchorscounterfactualsexplainable machine learning |
| spellingShingle | Gomathy Ramaswami Teo Susnjak Anuradha Mathrani Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics Big Data and Cognitive Computing machine learning anchors counterfactuals explainable machine learning |
| title | Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics |
| title_full | Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics |
| title_fullStr | Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics |
| title_full_unstemmed | Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics |
| title_short | Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics |
| title_sort | supporting students academic performance using explainable machine learning with automated prescriptive analytics |
| topic | machine learning anchors counterfactuals explainable machine learning |
| url | https://www.mdpi.com/2504-2289/6/4/105 |
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