Analyzing Engineering Design through the Lens of Computation

Abstract

Learning analytics and educational data mining are introducing a number of new techniques and frameworks for studying learning. The scalability and complexity of these novel techniques has afforded new ways for enacting education research and has helped scholars gain new insights into human cognition and learning. Nonetheless, there remain some domains for which pure computational analysis is currently infeasible. One such area, which is particularly important today, is open-ended, hands-on, engineering design tasks. These open-ended tasks are becoming increasingly prevalent in both K-12 and post-secondary learning institutions, as educators are adopting this approach in order to teach students real-world science and engineering skills (e.g., the "Maker Movement"). This paper highlights findings from a combined human-computer analysis of students as they complete a short engineering design task. The study uncovers novel insights and serves to advance the field's understanding of engineering design patterns. More specifically, this paper uses machine learning on hand-coded video data to identify general patterns in engineering design and develop a fine-grained representation of how experience relates to engineering practices. Finally, the paper concludes with ideas on how the specific findings from this study can be used to improve engineering education and the nascent field of "making" and digital fabrication in education. We also discuss how human-computer collaborative analyses can grow the learning analytics community and make learning analytics more central to education research.

Exploring the interplay between human and machine annotated multimodal learning analytics in hands-on STEM activities

“…The ability to use both the researcher codes of the different design stages [1] [3] and the students own classification of their activities suggests a rapid way to break down the data collected into appropriate parts for analysis and visualisation. Of course, questions and work remain to test further this process of using multimodal data and encoded annotations in comparison to more traditional hand coding of the video analysis in the project.…”

“…A finegrained analysis of the collaborative problem-solving process, using learning analytics tools, can provide insight into learning to support both teachers and learners. However, it is hard to track and interpret learner activity in engineering design problems, due to the hands-on and open-ended nature of such tasks [3]. This poster presents work that be-Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

“…The aim of PELARS is to develop learning analytics tools that support teachers and students. There is limited research on learning analytics within this context, due to the difficulty of tracking and interpreting student activity [3]. Researchers, in this area, have primarily focused on using learning analytics tools to gain a more fine-grained understanding of patterns of problem-solving between novices and experts, generally with a strong focus on individual rather than collaborative learning [1] [3].…”

Reasoning Strategies in the Context of Engineering Design with Everyday Materials

“…Specifically, we wish to answer the question of how students develop a solution when presented with an openended engineering design challenge. While some prior literature in engineering design cognition and analogical problem solving has examined elements of the student problem solving process, there is a need for additional work in this space, especially as it relates to studying engineering design cognition among high school students (Lammi & Gero, 2011;Worsley & Blikstein, 2014a). We will build on this prior literature to reconceptualize, interpret, and refine the language used for describing engineering design.…”

An Assessment Instrument of Technological Literacies in Makerspaces and FabLabs

“…These spaces also pave multiple pathways to learning twenty-firstcentury skills by facilitating collaborative and iterative projects, and constitute a space where constructionist learning (Papert, 1980) can take place with unprecedented sophistication Here we call these diverse spaces "digital fabrication facilities." They deploy a range of toolkits and machines for the pursuit of learning through making and construction, and are designed to encourage a broad range of social practices and pedagogical approaches that utilize contemporary fabrication technologies for design and prototyping (Blikstein, 2013;Kafai & Peppler, 2011;Remold, Verdugo, & Michalchik, 2013;Worsley & Blikstein, 2014;Smith, Cheruvelil, & Auvenshine, 2013). In this article, we describe our iterative development of and early findings on an assessment instrument intended to capture the learning that occurs in these settings.…”