### Abstract

In modern Universities, the timely identification of students at risk is valuable to limit failure and withdrawal rates. In this study we define the concept of students’ academic pathways, and use this and other variables to detect when students become at risk. We approach the problem modelling the latent variable “student learning” in the framework of transformation models, and use an indicator function readably available in this framework to detect change-points that depend on the observations already made but do not depend on the future which is not yet observed. We show how change-points can be detected as

quickly as possible after they have occurred, while keeping the false-positive rate at a low predefined level. The methodology is applied to a data base of students graduated with Science Degrees at Macquarie University in the last 10 years.

quickly as possible after they have occurred, while keeping the false-positive rate at a low predefined level. The methodology is applied to a data base of students graduated with Science Degrees at Macquarie University in the last 10 years.

Original language | English |
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Title of host publication | Proceedings of the 34th International Workshop on Statistical Modelling |

Subtitle of host publication | Volume II |

Editors | Luis Meira-Machado, Gustavo Soutinho |

Place of Publication | Guimaraes, Portugal |

Publisher | IWSM |

Pages | 212-215 |

Number of pages | 4 |

Volume | 2 |

ISBN (Electronic) | 9789892096308 |

Publication status | Published - 2019 |

Event | International Workshop on Statistical Modelling (34th : 2019) - Guimaraes, Portugal Duration: 7 Jul 2019 → 12 Jul 2019 |

### Conference

Conference | International Workshop on Statistical Modelling (34th : 2019) |
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Country | Portugal |

City | Guimaraes |

Period | 7/07/19 → 12/07/19 |

### Fingerprint

### Keywords

- transformation models
- change-point detection
- student learning

### Cite this

Manuguerra, M., & Sofronov, G. (2019). Which pathways lead to success? Transformation models and change-point detection for the early identification of students at risk. In L. Meira-Machado, & G. Soutinho (Eds.),

*Proceedings of the 34th International Workshop on Statistical Modelling: Volume II*(Vol. 2, pp. 212-215). Guimaraes, Portugal: IWSM.