Abstract
Students typically do not have practical tools to help them choose their target universities to apply. This work proposes a comprehensive analytics framework as a decision support tool that assists students in their admission process. As an essential element of the developed framework, a prediction procedure is developed to precisely determine the student's chance of admission to each university using various machine learning methods. It is concluded that random forest combined with kernel principal component analysis outperforms other prediction models. Besides, an online survey is built to elicit the utility of the student regarding each university. A mathematical programming model is then proposed to determine the best universities to apply among the candidates considering the probable limitations; the most important is the student's budget. The model is also extended to consider multiple objectives for making decisions. Last, a case study is provided to show the practicality of the developed decision support tool.
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Appendix A: Details on preprocessing
Appendix A: Details on preprocessing
Earlier in Sect. 4.1. it was discovered that the dataset used in this framework is clean and has no missing values, but what if this wasn’t the case? If a dataset is not clean and contains some missing data, there are multiple ways to solve the problem and handle this discrepancy. These solutions include deletion methods to eliminate missing data, replacing the missing data with the mean of that column, predicting the missing values, etc. To check the impact of the missing data on the prediction models, a new dataset is created from the original dataset by intentionally missing 10% of the data. Then, the first two abovementioned methods, i.e., replacing the missing data with the mean of that column and eliminating missing data methods, are used to handle these missing data. The results of the R-square values of the prediction models on the new dataset are reported in Table 11.
11.
Comparing Table 11 with the original Table IV shows that missing data in this dataset does not significantly change the results. Therefore, the Random Forest remains the selected method for prediction in this work, even in the presence of missing data since it has the best R-square value. Interested readers regarding handling missing data are referred to, e.g., Springuel et al. (2019) and Nissen et al. (2019). After scaling the data, the calculated mean of the chance of admitting column is 0.61, as seen in Table III, which indicates that the data is nearly balanced and there is no need to handle imbalanced data. What if this was not the case and the data were imbalanced as well? In that case, there are multiple ways to handle imbalanced data, for instance, resample the training set, under-sampling, over-sampling, and more; refer to Kaur and Gosain (2020) for more detail.
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Kiaghadi, M., Hoseinpour, P. University admission process: a prescriptive analytics approach. Artif Intell Rev 56, 233–256 (2023). https://doi.org/10.1007/s10462-022-10171-y
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DOI: https://doi.org/10.1007/s10462-022-10171-y