Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…

Traditional knowledge-component models describe students' content knowledge (e.g., their ability to carry out problem-solving procedures or their ability to reason about a concept). In many STEM domains, instruction uses multiple visual representations such as graphs, figures, and diagrams. The use of visual representations implies a…

Abstract electronics concepts are difficult to develop because the phenomena of interest cannot be readily observed. Visualisation skills support learning about electronics and can be applied at different levels of representation and understanding (observable, symbolic and abstract). Providing learners with opportunities to make transitions…

Rachlin (2012) makes two general assertions: (a) "To be human is to behave as humans behave, and to function in society as humans function," and (b) "essential human attributes such as consciousness, the ability to love, to feel pain, to sense, to perceive, and to imagine may all be possessed by a computer'. Although Rachlin's article is an…

Pedagogic approaches to TVET offer a limited range of teaching strategies which make use of experimental learning. Thus experiments were developed for teachers of structural engineering and timber processing technologies and were subject to empirical evaluation by a researcher at the Otto-von-Guericke-University Magdeburg and Kassel University.…

Mental models are basic cognitive constructs that are central for understanding phenomena of the world and predicting future events. Our comparison of two analysis approaches, SMD and QFCA, for measuring externalized mental models reveals different levels of abstraction and different perspectives. The advantages of the SMD include possibilities…

An important element of adaptive expertise involves stepping away from a routine to retool one's knowledge or environment. The current study investigated two forms of this adaptive pattern: fault-driven adaptations, which are reactions to a difficulty, and prospective adaptations, which are proactive reformulations. Graduate and undergraduate…

We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…