Process-mining enabled feedback: “Tell me what I did wrong” vs. “tell me how to do it right”

Abstract

Fast advancement of technology has led to an increased interest for using information technology to provide feedback based on learning behavior observations. This work outlines a novel approach for analyzing behavioral learner data through the application of process mining techniques specifically targeting a complex problem solving process. We realize this in the context of one particular learning case, namely, domain modeling. This work extends our previous research on process-mining analysis of domain modeling behavior of novices by elaborating with new insights from a replication study enhanced with an extra observation on how novices verify/validate models. The findings include a set of typical modeling and validation patterns that can be used to improve teaching guidance for domain modeling courses. From a scientific viewpoint, the results contribute to improving our knowledge on the cognitive aspects of problem-solving behavior of novices in the area of domain modeling, specifically regarding process-oriented feedback as opposed to traditional outcome feedback (is a solution correct? Why (not)?) usually applied in this type of courses. Ultimately, the outcomes of the work can be inspirational outside of the area of domain modeling as learning event data is becoming readily available through virtual learning environments and other information systems.

Guiding the choice of learning dashboard visualizations: Linking dashboard design and data visualization concepts

“…Recent research also introduces the concept of process-oriented feedback for a broader learning process context outside university settings ("How can I do better? ", e.g., by looking for inefficient procedural and sequential aspects of learning processes) based on process analytics approaches (Sedrakyan, 2016;Sedrakyan, Snoeck, & De Weerdt, 2014;Sedrakyan, De Weerdt, & Snoeck, 2016).…”

Evaluating emotion visualizations using AffectVis, an affect-aware dashboard for students

“…Thus, expanding the dashboard visualizations with mechanisms to capture a broader scope of learning processes could be another future direction. For instance, process analytics driven approaches (Sedrakyan, 2016;Sedrakyan, De Weerdt and Snoeck, 2016; 121 Evaluating emotion visualizations Sedrakyan et al, 2014) targeting broader learning-related indicators (Glahn, 2009) will be a relevant future study. Exploring mechanisms for coupling visualizations with textual advice, such as cognitive feedback and behavioral feedforward (Sedrakyan, 2016;Sedrakyan and Snoeck, 2017;Sedrakyan, Järvelä and Kirschner, 2016) as well as a generalizing for different learning goals and tasks (e.g.…”

Knowledge Discovery from the Programme for International Student Assessment

“…As part of our future research, we intend to repeat our study using the individual students instead of the country-level aggregation as data for clustering. Furthermore, one of the recent trends in LA focuses on educational process mining (Sedrakyan et al 2016, Mukala et al 2015, Trčka et al 2010. For the traditional pen-and-paper PISA tests, this is not an option.…”