Personal data broker instead of blockchain for students’ data privacy assurance

Data logs about learning activities are being recorded at a growing pace due to the adoption and evolution of educational technologies (Edtech). Data analytics has entered the field of education under the name of learning analytics. Data analytics can provide insights that can be used to enhance learning activities for educational stakeholders, as well as helping online learning applications providers to enhance their services. However, despite the goodwill in the use of Edtech, some service providers use it as a means to collect private data about the students for their own interests and benefits. This is showcased in recent cases seen in media of bad use of students’ personal information. This growth in cases is due to the recent tightening in data privacy regulations, especially in the EU. The students or their parents should be the owners of the information about them and their learning activities online. Thus they should have the right tools to control how their information is accessed and for what purposes. Currently, there is no technological solution to prevent leaks or the misuse of data about the students or their activity. It seems appropriate to try to solve it from an automation technology perspective. In this paper, we consider the use of Blockchain technologies as a possible basis for a solution to this problem. Our analysis indicates that the Blockchain is not a suitable solution. Finally, we propose a cloud-based solution with a central personal point of management that we have called Personal Data Broker.Peer ReviewedPostprint (author's final draft

Feedback Preferences of Students Learning in a Blended Environment: Worked Examples, Tutored and Untutored Problem-Solving

In contemporary technology-enhanced, learning platforms that combine the learning of new concepts with the practicing of newly learned skills, students are offered multiple feedback options. Typically, a problem-solving exercise allows the option to check the correctness of the answer, for calling hints that provide a partial help in the sequence of problem-solving steps, or calling a fully worked-out example. This opens new opportunities for research into student learning tactics and strategies, leaving the traditional context of lab-based research following experimental design principles behind, going into the research of revealed learning choices of students learning in authentic settings. In this empirical study, we apply multi-modal data consisting of logged trace data, self-report surveys and learning performance data, to investigate antecedents and consequences of learning tactics and strategies applied by students learning introductory mathematics and statistics. We do so by distinguishing different learning profiles, determined by the intensity of using the platform and the relative amounts of examples and hints called. These learning profiles are related to prior knowledge and learning dispositions, as antecedents, and course performance, as a consequence. One of our findings is that of ‘help abuse’: students who bypass the option to call for hints as concrete feedback in their problem-solving journey and instead opt for calling generic solutions of the problem: the worked examples. This help abuse is associated with prior knowledge and learning dispositions, but much less with course performance