DRAL: a tool for discovering relevant e-activities for learners

Web-based educational systems routinely collect vast quantities of data on students’ e-activity generating log files that offer researchers unique opportunities to apply data mining techniques and discover interesting information to improve the learning process. This paper proposes a friendly and intuitive tool called DRAL to detect the most relevant e-activities that a student needs to pass a course based on features extracted from logged data in an education web-based system. The method uses a more flexible representation of the available information based on multiple instance learning to prevent the appearance of a great number of missing values and is based on a multi-objective grammar guided genetic programming algorithm which obtains simple and clear classification rules which are markedly useful to identify the number, type and time of e-activities more relevant so that a student has a high probability to pass a course. To validate this approach, our proposal is compared with the most traditional proposals in multiple instance learning over the years. Experimental results demonstrate that the approach proposed successfully improves the accuracy of previous models by finding a balance between specificity and sensitivity values.