This Innovative-Practice Full Paper presents the design, implementation, and evaluation of the effectiveness of team contracts in a client-sponsored project-based learning course. A team contract is a collection of procedural rules, expectations, guidelines on team etiquette and task delegation, as well as penalties for violation that are mutually agreed upon by all members of a team working together to complete a project. Agreeing to adhere to an explicit set of expectations and rules at the beginning of the course has been found to reduce conflict, increase accountability, and provide a bench-mark for the obligations of every team member for effectively and efficiently completing a project. While we set down the framework of the contract, we encouraged every team to discuss the contract and its terms in detail amongst the members and make reasonable updates and amendments to any term if they deemed it necessary. The final amended version of the contract is signed by every team member as a sign of acknowledgment that they will adhere to the contract throughout the duration of the project. We implemented this team contract in a client-sponsored project-based data science course consisting of undergraduate, master's, and doctoral students. We collected feedback from the students on its effectiveness through a questionnaire that included questions requiring yes/no responses, responses on a Likert scale, and free-text responses. In general, the responses indicated a positive attitude and appreciation towards the usefulness of team contracts, especially in reducing social loafing, the explicit nature of the expectations, and in creating a professional working environment. We discuss how the implementation of the team contract has led to reduced conflicts, fewer complaints about non-contributing team members, increased engagement, a sense of ownership through the definition of rules and responsibilities, and a collectively enforced mechanism for the accountability of team members. Finally, we note how team contracts must be supplemented with other strategies, such as anonymized peer feedback and grading policies on individual contribution to teamwork, for more positive student learning outcomes in team-based learning courses.

In our Innovative-Practice Full Paper, we introduce a novel near-peer mentoring model that involves doctoral students for mentoring in a client-facing project-based learning (PBL) course involving teams of students working on interdisciplinary projects. Mentoring forms the backbone for any PBL framework and an increase in enrollment necessitates the mentoring of a growing number of teams for instructors. We introduced a competitive mentoring fellowship program that seeks applications from doctoral students for mentoring project teams and awards fellowships to the selected applicants. Every team in the course is assigned a dedicated fellow from the program as a mentor who provides mentoring to the team in addition to the instructors throughout an entire semester. The program is also designed to help doctoral students gain valuable experience mentoring a project end-to-end. Being an interdisciplinary course, we seek applications for mentoring from several departments across the university and evaluate the applications based on the expertise and knowledge of the applying doctoral students in a variety of relevant fields. To date, we have had 32 mentors in the program from five departments across the university. The selected fellows are tasked not only with mentoring their respective teams but also with attending meetings with the clients sponsoring the projects and understanding the needs of the clients for successfully completing the project. These responsibilities additionally provide the near-peer mentors with the experience of interacting with a client for the best possible outcome from the project. Evaluation of the program is done through separate surveys for enrolled students in the course (mentees) and the fellows (near-peer mentors) in the program. Results from our evaluation indicate that enrolled students appreciate the additional mentoring provided by the fellows while the fellows rate the program highly because of the experience they gain and the professional skills, such as team management, project management, and leadership skills, that they develop. We discuss strategies for improving the program in the future based on the recommendations of both the mentors and the mentees. Additionally, we discuss the instructor's perspective on designing and offering the program, which has benefits for instructors and departments as well.

In this Innovative-Practice Full Paper, we present the design and implementation of a novel strategy for the assessment of the individual contributions of students working in teams for an interdisciplinary project-based learning (PBL) course. Team-based and project-based learning have been the cornerstones of experiential learning in recent years. In such courses, the assessment of students is often performed solely at a team level, resulting in student discontent, concerns about fairness, and social loafing. The application of a single method for evaluating individual contributions has its shortcomings, prompting us to incorporate a combination of evidence-based approaches. Thus, to mitigate these problems, we developed an assessment strategy that involves three components - self and peer evaluation (SPA), instructor's evaluation of individual contribution, and class participation. Anonymized SPA was carried out at three different checkpoints over the semester for both formative and summative assessment. The individual contribution of the students is also evaluated by the instructor and/or other mentors based on their interactions with all the students in the team over the entire semester. Class participation is another component of individual contribution incorporated in our scheme, where instructors evaluate the participation of individual students in classroom activities, presentations, and meetings. We collected feedback about the perception of students towards our assessment policies for individual contribution. We observed overall satisfaction and positive attitudes toward our scheme. We noted positive student perceptions of fairness in grading through our scheme and reduced chances of social loafing. Further, in the feedback, the students noted the effectiveness of using SPA as an evaluation tool, the usefulness of instructor's evaluation, and the role of class participation in creating a more engaging classroom and a more enriching experience. We also emphasize the need to set clear expectations for students with regard to the grading policies early in the semester. Finally, we discuss our experiences in implementing our design and recommendations for incorporating our grading scheme in team-based PBL courses.

This Innovative-Practice Full Paper presents the curriculum development of an introductory course in programming and data science for postdoctoral researchers (PDRs) in the biosciences. The use of computing software has become ubiquitous and a working knowledge of data science has become increasingly essential for researchers in all domains. However, curriculum development focusing on imparting foundational programming skills and fundamentals of data science for researchers in domains other than computing has been scarce. Thus, there is an unmet need for curriculum development involving computational thinking, programming, and the fundamentals of data science for this audience. Recognizing these growing needs and demands of researchers to learn programming and data science that can then be applied to their area of research or practice, we developed an introductory course in programming and data science for PDRs in biology and medicine. The primary goal of the course was to develop computational thinking skills in PDRs who hail from backgrounds that have traditionally not focused on inculcating computational thinking. This course covered the fundamental concepts of programming using either Python or R - languages that researchers outside the computing community use in numerous ways including the statistical analysis of large datasets that are becoming increasingly common in biomedical research. Further, PDRs enrolled in the course were introduced to some of the broad categories of problems in data science - exploratory data analysis, classification, regression, and clustering - along with relevant algorithms and how they can be applied to real-world datasets in their respective domains using packages or libraries in Python or R. We also report the feedback from the enrolled PDRs, lessons learned, and recommendations for instructors interested in designing similar curricula. Our course focusing on computing and data science education for postdoctoral scholars from a non-computing background demonstrates a promising model for incorporating computing education in other areas of study that do not traditionally have a focus on computing education as well as in continuing education.

This Innovative-Practice Full Paper presents the curriculum development and our experiences in offering a client-facing consulting course in data science. Data science education has seen rapid growth over the past decade. To provide students with hands-on opportunities to work with real data, many data science programs have advocated for and implemented experiential learning opportunities throughout the curriculum, which has been shown in a wide variety of literature to have many benefits. Most experiential learning opportunities in STEM programs are provided through capstone and engineering design courses; this is becoming increasingly the case in data science programs as well where several universities have developed data science capstone programs in which students work with clients on the client’s real-world data sets. While client-sponsored capstone projects are an exemplar of experiential learning, they may pose major challenges to implement and can be particularly resource-intensive for institutions; this is especially the case in data science where the legalities of data sharing may come with additional hurdles. Because of this, we were motivated to develop a novel client-facing data science consulting course that provides a unique experiential learning scenario to both undergraduate and graduate students while requiring much fewer resources and legalities. In our novel data science consulting course, groups of students work directly with real clients in a consulting clinic setting to provide data science guidance and short-term help with data science challenges. Through this process, students learn about the diversity of real-world problems in data science, how to lead consultations with clients effectively as a team, how to frame data science challenges and research possible solutions, and how to communicate solutions to clients in reports and presentations. We leveraged best practices in consulting courses developed in business school settings to design our course. Additionally, the consulting course serves as a community service initiative whereby researchers, clinicians, non-profit and government workers, and industry professionals benefit from the advice and short-term help provided through consultation. In this paper, we report how our consulting course is set up, how clients from both within and outside the university can seek help at the consulting clinic, and how the structure of the course enables students to have firsthand experience working on many real-world data science problems with clients. Finally, we discuss how student performance is assessed in this course, the lessons learned from offering this course, and recommendations for other data science programs in universities that wish to design similar courses.

Monitoring of colonial waterbird nesting islands is essential to tracking waterbird population trends, which are used for evaluating ecosystem health and informing conservation management decisions. Recently, unmanned aerial vehicles, or drones, have emerged as a viable technology to precisely monitor waterbird colonies. However, manually counting waterbirds from hundreds, or potentially thousands, of aerial images is both difficult and time-consuming. In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone. By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast. Our experiments using Faster R-CNN and RetinaNet object detectors give mean interpolated average precision scores of 67.9% and 63.1% respectively.

Image matching and retrieval is the underlying problem in various directions of computer vision research, such as image search, biometrics, and person re-identification. The problem involves searching for the closest match to a query image in a database of images. This work presents a method for generating a consensus amongst multiple algorithms for image matching and retrieval. The proposed algorithm, Shortest Hamiltonian Path Estimation (SHaPE), maps the process of ranking candidates based on a set of scores to a graph-theoretic problem. This mapping is extended to incorporate results from multiple sets of scores obtained from different matching algorithms. The problem of consensus-based decision-making is solved by searching for a suitable path in the graph under specified constraints using a two-step process. First, a greedy algorithm is employed to generate an approximate solution. In the second step, the graph is extended and the problem is solved by applying Ant Colony Optimization. Experiments are performed for image search and person re-identification to illustrate the efficiency of SHaPE in image matching and retrieval. Although SHaPE is presented in the context of image retrieval, it can be applied, in general, to any problem involving the ranking of candidates based on multiple sets of scores.

Person re-identification is the problem of identifying a person over multiple cameras in video-based surveillance. In this paper, we propose a novel generalized optimization framework for combining results from different methods for person re-identification to significantly improve re-identification rates. The proposed framework evaluates the similarity of score distributions by means of Bhattacharyya distance to arrive at an optimum solution that minimizes a defined cost function. Using our framework, we employ similarity scores from existing algorithms to generate score aggregates, which are then used for ranking the gallery images based on their "closeness" to a given probe image. The optimization problem is solved using Genetic Algorithm, a heuristic optimization algorithm. Our results show significant improvement in performance for person re-identification using existing algorithms on challenging datasets - VIPeR, CUHK01, CUHK03 and QMUL-GRID.

Person re-identification is a challenge in video-based surveillance where the goal is to identify the same person in different camera views. In recent years, many algorithms have been proposed that approach this problem by designing suitable feature representations for images of persons or by training appropriate distance metrics that learn to distinguish between images of different persons. Aggregating the results from multiple algorithms for person re-identification is a relatively less-explored area of research. In this paper, we formulate an algorithm that maps the ranking process in a person re-identification algorithm to a problem in graph theory. We then extend this formulation to allow for the use of results from multiple algorithms to make a consensus-based decision for the person re-identification problem. The algorithm is unsupervised and takes into account only the matching scores generated by multiple algorithms for creating a consensus of results. Further, we show how the graph theoretic problem can be solved by a two-step process. First, we obtain a rough estimate of the solution using a greedy algorithm. Then, we extend the construction of the proposed graph so that the problem can be efficiently solved by means of Ant Colony Optimization, a heuristic path-searching algorithm for complex graphs. While we present the algorithm in the context of person reidentification, it can potentially be applied to the general problem of ranking items based on a consensus of multiple sets of scores or metric values.