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- Society for Information Technology & Teacher Education International Conference
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- National Science Foundation
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There is a currently a shortage of computer science professionals and this shortage is projected to continue into the foreseeable future as not enough students are selecting computer science majors. Researchers and policy-makers agree that development of this career pipeline starts in elementary school. Our study examined which collaborative programming setup, pair programming (two students collaborate on one computer) or side-by-side programming (two students collaborate on the same program from two computers), fifth-grade students preferred. We also sought to understand why students preferred one method over the other and explored ideas on how to effectively design a collaborative programming environment for this age group. Our study had participants first engage in five instructional days, alternating between pair and side-by-side programming, and then conducted focus groups. We found that students overwhelmingly preferred side-by-side programming. We explain this using self-determination theory which states that behavior is motivated by three psychological needs: autonomy, competence, and psychological relatedness which side-by-side programming was better able to meet.more » « less
SITE (Ed.)Persons with learning disabilities (LD) are underrepresented in computer science and information technology fields despite the explosion of related career opportunities and interest. In this study, we examine the use of pair programming as a collaborative intervention in with computer programming and compare students with learning disabilities to students who do not have learning disabilities. We concentrate on situational motivation constructs which tap into the desire to meet goals and acquire skills. We find that students with LD and similar students without LD fare the same. For the both groups, three of the four situational motivation subscales increase after the introduction of pair programming. The use of pair programming holds promise as an educational intervention for all students including those with learning disabilities.more » « less
This Innovative Practice Work-In-Progress paper presents a collaborative virtual computer lab (CVCL) environment to support collaborative learning in cloud-based virtual computer labs. With advances of cloud computing and virtualization technologies, a new paradigm of virtual computer labs has emerged, where students carry out labs on virtualized resources remotely through the Internet. Virtual computer labs bring advantages, such as anywhere, anytime, on-demand access of specialized software and hardware. However, with current implementations, it also makes it difficult for students to collaborate, due to the fact that students are assigned separated virtual working spaces in a remote-accessing environment and there is a lack of support for sharing and collaboration. To address this issue, we develop a CVCL environment that allows students to reserve virtual computers labs with multiple participants and support remote real-time collaboration among the participants during a lab. The CVCL environment will implement several well-defined collaborative lab models, including shared remote collaboration, virtual study room, and virtual tutoring center. This paper describes the overall architecture and main features of the CVCL environment and shows preliminary results.more » « less
Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and degradation at unpredictable times. Immediate medical care at these austere locations requires semi-autonomous teleoperated systems, which enable the completion of medical procedures even under interrupted networks while isolating the medics from the dangers of the battlefield. However, to achieve autonomy for complex surgical and critical care procedures, robots require extensive programming or massive libraries of surgical skill demonstrations to learn effective policies using machine learning algorithms. Although such datasets are achievable for simple tasks, providing a large number of demonstrations for surgical maneuvers is not practical. This article presents a method for learning from demonstration, combining knowledge from demonstrations to eliminate reward shaping in reinforcement learning (RL). In addition to reducing the data required for training, the self-supervised nature of RL, in conjunction with expert knowledge-driven rewards, produces more generalizable policies tolerant to dynamic environment changes. A multimodal representation for interaction enables learning complex contact-rich surgical maneuvers. The effectiveness of the approach is shown using the cricothyroidotomy task, as it is a standard procedure seen in critical care to open the airway. In addition, we also provide a method for segmenting the teleoperator’s demonstration into subtasks and classifying the subtasks using sequence modeling.
Materials and Methods
A database of demonstrations for the cricothyroidotomy task was collected, comprising six fundamental maneuvers referred to as surgemes. The dataset was collected by teleoperating a collaborative robotic platform—SuperBaxter, with modified surgical grippers. Then, two learning models are developed for processing the dataset—one for automatic segmentation of the task demonstrations into a sequence of surgemes and the second for classifying each segment into labeled surgemes. Finally, a multimodal off-policy RL with rewards learned from demonstrations was developed to learn the surgeme execution from these demonstrations.
The task segmentation model has an accuracy of 98.2%. The surgeme classification model using the proposed interaction features achieved a classification accuracy of 96.25% averaged across all surgemes compared to 87.08% without these features and 85.4% using a support vector machine classifier. Finally, the robot execution achieved a task success rate of 93.5% compared to baselines of behavioral cloning (78.3%) and a twin-delayed deep deterministic policy gradient with shaped rewards (82.6%).
Results indicate that the proposed interaction features for the segmentation and classification of surgical tasks improve classification accuracy. The proposed method for learning surgemes from demonstrations exceeds popular methods for skill learning. The effectiveness of the proposed approach demonstrates the potential for future remote telemedicine on battlefields.
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