Search for: All records

Involving undergraduate STEM majors in authentic research has been cited as being an imperative goal in advancing the field of science and preparing students for careers and post-graduate educational programs. An important component of authentic research that is often overlooked is student understanding of the Nature of Science (NOS) and how this relates to novel research. Previous research in these authentic settings appears to have depended upon an implicit approach to the teaching of NOS, and, not surprisingly, these studies revealed that students’ understandings only marginally improved. Research in authentic setting since indicates students develop deeper understandings of NOS in general, but struggle with more abstract concepts, such as the role of social and cultural influences as well as imagination and creativity in science. Therefore, the purpose of this qualitative study is to examine student understanding of these NOS concepts as they are engaged in novel research. NOS concepts were introduced using an explicit and reflective approach. Specifically, students were engaged with reflection questions, in-class discussions, historical narratives, and autobiographical stories of the instructor as they explored the NOS concepts and how these relate to scientific research. Student NOS understandings (n = 16) were measured pre/post using the SUSSI with semi-structured interviews taking place at the end of the course. The findings from the interviews revealed that students understanding of the NOS concepts improved. Students came to better understand how society and culture impact scientific research, and how imagination and creativity are used throughout the entire scientific process. Students largely cited the reflection questions and in-class discussions as contributing to their change in understanding in their responses to how their views changed. In discussing society and culture, students noted that they better understood how society impacts what and how research is conducted as well as noting instances where gender bias is still present in science today. Likewise, students indicated during the interviews how they came to understand how imagination and creativity can be found throughout the entire scientific process instead of just the stage where a research question is posed. This study shows the importance of discussing NOS using an explicit/reflective approach as it relates to authentic research in helping students develop deeper understandings. 

Abstract The COVID‐19 pandemic forced educators to teach in an online environment. This was particularly challenging for those teaching courses that are intended to support bench science research. This practitioner article tells the story of how an instructor transformed their Course‐based Undergraduate Research Experience (CURE) using the Backwards Design Method into a synchronous online course. Research objectives in this transformed course included: conducting a literature review, identifying research questions and hypotheses based on literature, and developing practical and appropriate research methodologies to test these hypotheses. We provide details on how assignments were created to walk students through the process of research study design and conclude with recommendations for the implementation of an online CURE. Recommendations made by the instructor include scaffolding the design, building opportunities for collaboration, and allowing students to fail in order to teach the value of iteration. The Backwards Design framework naturally lends itself to a scaffolded instructional approach. By identifying the learning objectives and final assessment, the learning activities can be designed to help students overcome difficult concepts by filling in the gaps with purposeful instruction and collaborative opportunities. This present course also practiced iteration through the extensive feedback offered by the instructor and opportunities for students to revise their work as their understanding deepened. Anecdotally, based on end of course reviews, students overall had a positive experience with this course. Future work will examine the efficacy of student learning in this online environment and is forthcoming. 

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users’ social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques. To tackle the above challenges, this work proposes a Knowledge-aware Coupled Graph Neural Network (KCGN) that jointly injects the inter-dependent knowledge across items and users into the recommendation framework. KCGN enables the high-order user- and item-wise relation encoding by exploiting the mutual information for global graph structure awareness. Additionally, we further augment KCGN with the capability of capturing dynamic multi-typed user-item interactive patterns. Experimental studies on real-world datasets show the effectiveness of our method against many strong baselines in a variety of settings. Source codes are available at: https://github.com/xhcdream/KCGN. 

There is a growing importance in characterizing 3D shape quality in additive manufacturing (a.k.a. 3D printing). To accurately define the shape deviation between the designed product and actual build, shape registration of scanned point cloud data serves as a prerequisite for a reliable measurement. However, manual registration is currently heavily involved, for example, in obtaining initial matching of the design and the scanned product based on landmark features. The procedure can be inefficient, and more importantly, introduce potentially large operator-to-operator variations for complex geometries and deformation. Finding a sparse shape correspondence before refined registration would be meaningful to address this problem. In that case, automatic landmark selection has been a challenging issue, particularly for complicate geometric shapes like teeth. In this work we present an automatic landmark selection method for complicated 3D shapes. By incorporating subject matter knowledge (e.g., dental biometric information), a 3D shape will be first segmented through a new density-based clustering method. The geodesic distance is proposed as the distance metric in the revised clustering procedure. Geometrically informative features in each segment are automatically selected through the principal component analysis and Hotelling's T2 statistic. The proposed method is demonstrated in dental 3D printing application and could serve as a basis of sparse shape correspondence.