Towards Portability of Models for Predicting Students’ Final Performance in University Courses Starting from Moodle Logs

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.

Transfer Learning from Deep Neural Networks for Predicting Student Performance

“…Further, there are key issues to be considered such as measuring the degree of similarity between two courses (i.e., the number and form of learning activities), the type of attributes and the duration of the course. Finally, it is similarly important to build both simple and interpretable transferable models that could be easily applied by educators from one course to another [29]. Therefore, more studies are required on the current topic for establishing these results.…”

“…Moreover, the classifier is adjusted to the new domain without using any labeled examples. Very recently, López-Zambrano et al [29] investigated the portability of learning models based on Moodle log data regarding the courses of different universities. The authors explored whether the grouping of similar courses (i.e., similarity level of learning activities) influence the portability of the prediction models.…”

Improving the portability of predicting students’ performance models by using ontologies

“…In our previous work (López-Zambrano et al, 2020 ), we obtained models generated from Moodle’s logs data and we studied the degree of portability of the models between subjects, grouped by area of knowledge and by the usage level of platform resources. We used Moodle’s native raw attributes which, in certain combinations of courses, led us to a certain loss in the portability of models since these low-level attributes are very dependent on each particular course.…”

“…Precisely, our research aims to evaluate the degree of portability of models built by using ontologies of interaction-with-the-platform attributes. To do so, we defined an ontology inspired by Bloom’s taxonomy and based on the work by Cerezo et al ( 2020 ), with the purpose of conducting a comprehensive study to measure the degree of portability of the models built based on that ontology (denoted as ontological models), compared with a previous similar study conducted by the authors López-Zambrano et al ( 2020 ) in which we did not use ontologies but instead employed low-level Moodle attributes (denoted as non-ontological models). The models have been built from students’ interactions with Moodle logs and the class attribute to predict is binary and represents whether or not the student will pass the course (Pass/Fail).…”

Developing a Motion Infographic-Based Learning System for Effective Learning

“…The advent of digital systems has changed the way we read and learn and has also become a future trend [3,4]. Digital systems are not constrained by time and place, so the use of web-based education or e-learning systems has grown exponentially [5]. Teachers are also aware that the current curriculum and teaching methods are designed for the previous generation and they are not suitable for the new generation of students [6].…”