scholarly journals Survival Analysis Approach for Early Prediction of Student Dropout Using Enrollment Student Data and Ensemble Models

2020 ◽  
Vol 8 (9) ◽  
pp. 4036-4047
Author(s):  
Frederick F. Patacsil
Keyword(s):  
Survival Analysis ◽  
Analysis Approach ◽  
Early Prediction ◽  
Ensemble Models ◽  
Student Dropout ◽  
Student Data

scholarly journals Investigating Social Contextual Factors in Remaining-Time Predictive Process Monitoring—A Survival Analysis Approach

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 267 ◽  
Author(s):  
Niyi Ogunbiyi ◽  
Artie Basukoski ◽  
Thierry Chaussalet
Keyword(s):  
Survival Analysis ◽  
Process Monitoring ◽  
Contextual Factors ◽  
Survival Function ◽  
Analysis Approach ◽  
Contextual Features ◽  
Monitoring Framework ◽  
Predictive Process ◽  
The Impact ◽  
Remaining Time

Predictive process monitoring aims to accurately predict a variable of interest (e.g., remaining time) or the future state of the process instance (e.g., outcome or next step). The quest for models with higher predictive power has led to the development of a variety of novel approaches. However, though social contextual factors are widely acknowledged to impact the way cases are handled, as yet there have been no studies which have investigated the impact of social contextual features in the predictive process monitoring framework. These factors encompass the way humans and automated agents interact within a particular organisation to execute process-related activities. This paper seeks to address this problem by investigating the impact of social contextual features in the predictive process monitoring framework utilising a survival analysis approach. We propose an approach to censor an event log and build a survival function utilising the Weibull model, which enables us to explore the impact of social contextual factors as covariates. Moreover, we propose an approach to predict the remaining time of an in-flight process instance by using the survival function to estimate the throughput time for each trace, which is then used with the elapsed time to predict the remaining time for the trace. The proposed approach is benchmarked against existing approaches using five real-life event logs and it outperforms these approaches.

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