There is currently significant demand for training in how to use metals additive manufacturing (AM) machines. Such training is important not only for the technicians who run and maintain the machines, but also for engineers and strategic decision makers who need to support AM part fabrication. Furthermore, there are a variety of AM machines, each with different details to be learned and potential hazards to overcome, and it is difficult to train more than a handful of users at one time. To address these challenges, a prototype training system has been developed, the AM Training Tutor, which uses interactive virtual reality (VR) to train users on a specific AM machine - the EOS M290. To make the training technology more widely available and expand its use across a variety of different AM machines, efforts are underway to develop a modularized and generic version of the AM Training Tutor that can be customized with relatively little effort to train users to operate other AM machines. This work-in-progress paper details the progress to-date, challenges and proposed solutions with the aim to demonstrate how standalone VR-based training systems can be redesigned for relatively easy repurposing and generalization.

A common approach to the problem of predicting students' exam scores has been to base this prediction on the previous educational history of students. In this paper, we present a model that bases this prediction on students' performance on several tasks assigned throughout the duration of the course. In order to build our prediction model, we use data from a semi-automated peer-assessment system implemented in two undergraduate-level computer science courses, where students ask questions about topics discussed in class, answer questions from their peers, and rate answers provided by their peers. We then construct features that are used to build several multiple linear regression models. We use the Root Mean Squared Error (RMSE) of the prediction models to evaluate their performance. Our final model, which has recorded an RMSE of 2.93 for one course and 3.44 for another on predicting grades on a scale of 18 to 30, is built using 14 features that capture various activities of students. Our work has possible implications in the MOOC arena and in similar online course administration systems.