Research Projects

CodeNC: Integrating Computational Thinking into K-12 Instructional Activities using Animated Videos

N. Rich Nguyen (PI), Iuliia Poliakova, Sahithi Meduri, Joshua Hutcheson, and Ryan Ke

Spring 2018 -- Summer 2018. Increasing the representation of minorities in computer science (CS) has become a national priority. One of the many reasons minority students nationwide choose not to study CS is that they often lack mentors and role models to encourage them early in their learning. However, a big issue facing this initiative is that many K-12 teachers finds themselves lack of (1) relevant materials, (2) systematic training, and (3) a supportive community. The democratization of media, such as photos and videos, has provided a great variety of options to educate a broad audience on myriad topics. In this poster, we will describe the challenges and successes of using animated videos including its beauty, soundness, and utility as critical elements in establishing a strong CT comprehension while engaging K-12 teachers in a non-threatening way.

Affective Peer Tutoring: Fostering a Sense of Belonging among Female CS students

N. Rich Nguyen (PI), Audrey Rorrer

Spring 2016 -- Fall 2017. One of the many reasons female students nationwide choose not to finish their study in CS is that they do not feel a deep sense of belonging in the major. To foster the sense of belonging among female CS students, the affective learning outcomes, which are adapted from Bloom's Taxonomy on human learning, are integrated into the context of peer tutoring as five successive stages. Through the five stages of affective peer tutoring, students gradually deepen their sense of belonging in CS by: (1) being aware of the tutoring services; (2) proactively seeking answers to their programming questions; (3) recognizing the value of self-efficacy; (4) discussing learning issues in a supportive environment; and (5) contributing to a peer-led learning program to help others. Therefore, this poster may be of interest to any CS educator who wishes to improve the interaction, performance, and retention among female CS students while sustaining a peer-led learning program at their institution.

Previous Projects

Detecting Social Insects in Videos

N. Rich Nguyen, Min C. Shin

Spring 2015 -- Spring 2017. The studies of the network formed by social insects require the motion analysis of their interactions and movements in videos over an extended period of time. Automated detection is an important field of interest because it enables the motion analysis in large-scale experiments. When an automated detection method is applied to various insect types, the training task often involves the collection of a large number of labels provided by human experts. To save the experts' time and effort, unlabeled data have been recently employed to supplement the training. In this paper, we utilize the spatiotemporal connectivity of the unlabeled data to regulate the training of a detector on a new insect type.

Improving Pollen Classification

N. Rich Nguyen, Matina Donaldson, Min C. Shin

Spring 2012 -- Spring 2014. This research focus mainly on the automated classification of pollen using computer vision and machine learning. Automated methods still requires a large number of training samples as the number of pollen types increases over time. We reduce the training effort of the classification with three main key points: a modified transfer learning framework, a new active selection criteria, and a new spike count feature.

Rapidly Adaptive Cell Detection

N. Rich Nguyen, Eric Norris, Mark Clemens, Min C. Shin

Spring 2010 -- Spring 2012. Different imaging protocol and cell type result in various cell appearances. We present a novel method of training a cell detection method on new datasets with minimal effort. We show that our method reduces the training effort up to 10 times.

Tracking Colliding Cells

N. Rich Nguyen, Steve Keller, Toan Huynh, Min C. Shin

Summer 2007 -- Fall 2009. It's the first tracking algorithm for colliding cells. The motion of leukocytes is significant in studying the inflammation response of the immune system. We model the collision states of cells and testing multiple hypotheses of their motion and appearance.