Educational data mining in the academic setting: employing the data produced by blended learning to ameliorate the learning process

PurposeThe purpose of this paper is to introduce an interactive system that relies on the educational data generated from the online Universities services to assess, correct and ameliorate the learning process for both students and faculty.Design/methodology/approachIn the presented research, data from the online services, provided by a Greek University, prior, during and after the COVID-19 outbreak, are analyzed and utilized in order to ameliorate the offered learning process and provide better quality services to the students. Moreover, according to the learning paths, their presence online and their participation in the services of the University, insights can be derived for their performance, so as to better support and assist them.FindingsThe system can deduce the future learning progression of each student, according to the past and the current performance. As a direct consequence, the exploitation of the data can provide a road map for the strategic planning of universities, can indicate how the learning process can be updated and amended, both online and in person, as well as make the learning experience more essential, effective and efficient for the students and aiding the professors to provide a more meaningful and to-the-point learning experience.Originality/valueNowadays, educational activities in academia are strongly supported by online services, information systems and online educational materials. The learning design in the academic setting is primarily facilitated in the University premises. However, the exploitation of the contemporary technologies and supporting materials that are available online can enrich and transform the educational process and its results.

An integrated system for predicting students' academic performance in smart universities

Due to the current situation with Coronavirus (COVID-19) the attendance of students in the academic life has changed and the educational process has been driven towards smart educational environments. Higher educational Institutes invest significant resources in reforming their educational programs so that it will support distance learning using asynchronous or synchronous methodologies and tools. In this work, we propose the development of a student profile using data from both asynchronous and synchronous e-learning platforms, using a multi-layered neural network in order to classify students' performance. A neural network is compared against Support Vector Machines, k-Nearest Neighbour and decision trees. The results indicate that the Neural network achieves better accuracy than the others, so using our methodology the instructors or the policy makers of the institute will be able to keep informed about the performance of the students, or take the appropriate actions in order to prevent student failure or low participation. © 2020 ACM.