This paper aims to address two fundamental challenges in engineering education; i) the disconnect between digital and tactile learning activities in traditional Engineering Design curricula and ii) variations in standards used to assess digital activities completed by students. Digital 3D scanning technologies have the potential to mitigate the disconnect between digital and tactile learning activities by providing students with a real time understanding of the relationship between the digital and tactile design space in a real time, dynamic manner. In the process, students are introduced to the concept of reverse engineering as a means of understanding product assembly/disassembly as tactile activities, which can be then seamlessly represented/augmented in the digital space. The researchers of this work aim to understand the impact on the learning outcomes experienced by students when digital and tactile engineering activities are integrated in a real time dynamic manner. To mitigate variations in standards used to assess digital activities completed by students, the authors propose employing a 3D similarity metric that quantifies the differences between digital solutions created by students and a baseline solution from which student solutions are compared against. By establishing a quantitative similarity metric to assess student solutions, variations in grading across different instructors can be minimized and scores finalized in a more timely and efficient manner. The case study presented in this work is based on an Introduction to Engineering Design course, where freshmen students working both in individual and team based design projects are introduced to both digital and tactile activities. The research findings reveal students' perception of 3D scanning technologies as it relates to their experiences with digital and tactile learning activities. After being introduced to digital and tactile activities, students' performance are quantified through controlled design activities that are then assessed/graded using the proposed digital similarity metric.