Abstract
Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few works have tried to discover under which circumstances a prediction model built on a source course can be used in other different but similar courses. Our motivation in this work is to study the portability of models obtained directly from Moodle logs of 24 university courses. The proposed method intends to check if grouping similar courses by the degree or the similar level of usage of activities provided by the Moodle logs, and if the use of numerical or categorical attributes affect in the portability of the prediction models. We have carried out two experiments by executing the well-known classification algorithm over all the datasets of the courses in order to obtain decision tree models and to test their portability to the other courses by comparing the obtained accuracy and loss of accuracy evaluation measures. The results obtained show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances.
Highlights
The use of web-based education systems or e-learning systems has grown exponentially in the last years, spurred by the fact that neither students nor teachers are bound to any specific location and that this form of computer-based education is virtually independent of a specific hardware platform.Adopting these e-learning systems in higher educational institution can provide us with enormous quantities of data that describe the behavior of students
Baker [10] considered that one of the challenges for the future of Educational Data Mining (EDM) is what he called the “Generalizability” problem or “The New York City and Marfa” problem. In his words, Learning Analytics models are mostly built on the samples that we have ready at hand, whether it is the current population of students at a university developing a model, the current user base of the adaptive learning system we are building the model for, or just students who are relatively easy to survey or observe
We present the Area Under the ROC Curve (AUC) and the loss of AUC in four different matrixes for each group of similar courses
Summary
The use of web-based education systems or e-learning systems has grown exponentially in the last years, spurred by the fact that neither students nor teachers are bound to any specific location and that this form of computer-based education is virtually independent of a specific hardware platform Adopting these e-learning systems in higher educational institution can provide us with enormous quantities of data that describe the behavior of students. Baker [10] considered that one of the challenges for the future of EDM is what he called the “Generalizability” problem or “The New York City and Marfa” problem In his words, Learning Analytics models are mostly built on the samples that we have ready at hand, whether it is the current population of students at a university developing a model, the current user base of the adaptive learning system we are building the model for, or just students who are relatively easy to survey or observe. What happens when the population changes? He defined this problem in three steps: (1) Build an automated detector for a commonly-seen outcome or measure; (2) Collect a new population distinct from the original population; and (3) Demonstrate that the detector works for the new population with degradation of quality under 0.1 in terms of AUC ROC
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
