by
Abstract:
Massive Open Online Courses (MOOCs) appeared as a proper way to provide lifelong learning for potential learners of both professional and academic settings. Industry leaders would benefit from these courses because they can foster professional development in their employees in their industry. However, nowadays these online courses continue to register a high dropout rate and a vast number of their learners do not acquire the certificate at the end of the course. This article serves the purpose of presenting a tool for supporting the utilization of Machine Learning algorithms in the generation of Predictive Models for MOOCs in order to contribute research to this specific issue. The presented tool predicts two situations: which learners would pass the course (certificate acquisition) and which learners would left the course (dropout). The tool was tested in fifteen deliveries of seven MOOCs, initial results provide interesting information, for instance, that the precision of predicting certificate acquisition is higher than the precision of predicting dropout for all algorithms.
Reference:
A Learning Analytic Tool for Predictive Modeling of Dropout and Certificate Acquisition on MOOCs for Professional Learning (), In The 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM2018) (Thailand, ed.), 2018.
Bibtex Entry:
@inproceedings{CobosR.Olmos2018, abstract= {Massive Open Online Courses (MOOCs) appeared as a proper way to provide lifelong learning for potential learners of both professional and academic settings. Industry leaders would benefit from these courses because they can foster professional development in their employees in their industry. However, nowadays these online courses continue to register a high dropout rate and a vast number of their learners do not acquire the certificate at the end of the course. This article serves the purpose of presenting a tool for supporting the utilization of Machine Learning algorithms in the generation of Predictive Models for MOOCs in order to contribute research to this specific issue. The presented tool predicts two situations: which learners would pass the course (certificate acquisition) and which learners would left the course (dropout). The tool was tested in fifteen deliveries of seven MOOCs, initial results provide interesting information, for instance, that the precision of predicting certificate acquisition is higher than the precision of predicting dropout for all algorithms.}, author= {{Cobos, R., Olmos}, L.}, booktitle= {The 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM2018)}, editor= {Thailand}, file= {:D$\backslash$:/Usuarios/Ruth/articulos/Lara/IEEM18/ieem-edxmasplus-uam-cr.pdf:pdf}, keywords= {Educational Data Mining,Learning Analytics,Lifelong Learning,MOOC.,Machine Learning,Professional Learning}, title= {{A Learning Analytic Tool for Predictive Modeling of Dropout and Certificate Acquisition on MOOCs for Professional Learning}}, year= {2018}}