Evaluation of the Bias of Student Performance Data with Assistance of Expert Teacher

Vegega, Cinthia; Pytel, Pablo; Straccia, Luciano; Pollo-Cattaneo, María Florencia

doi:10.1007/978-3-030-01535-0_2

Cited by 2 publications

(1 citation statement)

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“…In this context, an adaptation of the Repertory Grid has been proposed in [14] to evaluate the collected data with the assistance of the available domain experts and determine whether it can be used. Likewise, in [15] the proposed method has been applied to a higher education institution course data to predict the students' performance throughout the course and identify their strengths and weaknesses. In this work, the method is applied to another case study for the management of patient's appointments in a pediatric office in order to evaluate whether they are sufficiently representative to implement a future Intelligent System.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Bias in the Management of Patient’s Appointments in a Pediatric Office

2020

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The application of Machine Learning algorithms must always take into account the objectives set within the project, the characteristics of the domain where the project will be carried out and the data available to use. Given this, it is essential before collecting data considered as representative of the problem to be solved, because otherwise there may be hidden biases in the data and these may solve a different problem from the one intended. In this context, the aim of this work is to apply a process based on the Gridding method that allows the analysis of the features of the data to be used. This process is applied to the historical data of a pediatric medical office where it is sought to implement an intelligent system that allows to predict the number of normal and over-shift appointments for a particular date and time, since it is desired to hire, when necessary, another pediatric doctor to assist in the care of patients.

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Section: Introductionmentioning

confidence: 99%