Abstract
The objective of this work is to present a methodology that automates the prediction of students’ academic performance at the end of the course using data recorded in the first tasks of the academic year. Analyzing early student records is helpful in predicting their later results; which is useful, for instance, for an early intervention. With this aim, we propose a methodology based on the random Tukey depth and a non-parametric kernel. This methodology allows teachers and evaluators to define the variables that they consider most appropriate to measure those aspects related to the academic performance of students. The methodology is applied to a real case study obtaining a success rate in the predictions of over the 80%. The case study was carried out in the field of Human-computer Interaction.The results indicate that the methodology could be of special interest to develop software systems that process the data generated by computer-supported learning systems and to warn the teacher of the need to adopt intervention mechanisms when low academic performance is predicted.
Highlights
Recent technological innovations are currently reflected in the proliferation of groupware systems aimed at facilitating communication and coordination between users, as well as providing shared workspaces where users build artifacts that solve tasks
It is worth highlighting those that refer to providing better mechanisms to be aware of the interactions performed by other users [2], optimizing business processes to achieve strategic goals of organizations [3], and adapting academic processes supported by collaborative learning environments [4]
The objective of this section is to provide the results for the research problem proposed in Section 2, which consist in predicting the average grade of the students taking an academic course based on their early performance in the course and the performance of the students who took the course in previous academic years
Summary
Recent technological innovations are currently reflected in the proliferation of groupware systems aimed at facilitating communication and coordination between users, as well as providing shared workspaces where users build artifacts that solve tasks. Collaboration supported by groupware is characterized by a large number of interactions that each user performs to cooperate with other members of a common group. An analysis of these interactions can be used to improve these collective processes. (ii) to categorize and characterize the information collected and (iii) to intervene in the improvement of the collaborative activity Among these improvements, it is worth highlighting those that refer to providing better mechanisms to be aware of the interactions performed by other users [2], optimizing business processes to achieve strategic goals of organizations [3], and adapting academic processes supported by collaborative learning environments [4]. These processes are usually oriented towards solving academic problems using social interaction with classmates
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