@article{xiao_chatterjee_gehringer_2022, title={A New Era of Plagiarism the Danger of Cheating Using AI}, ISSN={["2380-1603"]}, DOI={10.1109/ITHET56107.2022.10031827}, abstractNote={Recent development in AI algorithms has benefited many industries, but they also brought some problems to fairness in academic evaluation. Plagiarism is one of them, and little research has been put into it. This paper examines AI tools that can be used to plagiarize and preliminary findings using existing plagiarism detection algorithms. We found that tools commonly used to detect plagiarism in the academic field are vulnerable to attacks by these AI-based tools.}, journal={2022 20TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY BASED HIGHER EDUCATION AND TRAINING (ITHET)}, author={Xiao, Yunkai and Chatterjee, Soumyadeep and Gehringer, Edward}, year={2022} }
 @article{cui_li_lou_liu_xiao_jia_gehringer_zhang_2022, title={Can Pre-class GitHub Contributions Predict Success by Student Teams?}, DOI={10.1109/ICSE-SEET55299.2022.9794264}, abstractNote={Over one million teachers, students, and schools around the world use GitHub to reach their learning goals. GitHub promotes team-work, and group or team projects are a necessary element of the software-engineering curriculum. Past studies on GitHub have explored how to integrate GitHub into teaching and how to mine information from GitHub to help students. To our knowledge, we are the first to study the previous contributions of students to GitHub in order to characterize student teams that perform well on team projects, compared to student teams that did not perform so well. We identify factors such as the number of public commits, number of repositories, and size of repositories in certain languages that are associated with the quality of team projects. We discuss the implications of this from the point of view of an educator and a student.}, journal={2022 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING EDUCATION AND TRAINING (ICSE-SEET 2022)}, author={Cui, Jialin and Li, Ruochi and Lou, Kaida and Liu, Chengyuan and Xiao, Yunkai and Jia, Qinjin and Gehringer, Edward and Zhang, Runqiu}, year={2022}, pages={40–49} }
 @article{xiao_gao_yue_gehringer_2022, title={Estimating Student Grades through Peer Assessment as a Crowdsourcing Calibration Problem}, ISSN={["2380-1603"]}, DOI={10.1109/ITHET56107.2022.10031993}, abstractNote={There is a trend to move education into an online environment, especially when offline learning is restricted by time, space, availability, or is impacted by issues such as a public health incident. Evaluating students’ performance in online education has always been challenging. Objective questions, which can be graded automatically, could only assess certain aspects of students’ mastery of knowledge. A grading problem appears if subjective questions exist, primarily when the class is taught at scale. Many online education platforms have been using peer assessment to resolve this problem. Aside from that, peer assessment also improves interactions between students, instructors, and peers. While peer assessment has some inherent weaknesses, reviewers may not have the same ability or attitude toward reviewing others, and the feedback generated by them shall not be taken at face value. Many algorithms have been developed to evaluate annotators’ trustworthiness and generate reliable labels in the crowdsourcing industry. We proposed an algorithm under the same concept that could provide accurate automated grading, an overview of students’ weaknesses from peer feedback, and identify reviewers who lack an understanding of certain concepts. This information allows instructors to offer targeted training and create data-driven lesson plans.}, journal={2022 20TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY BASED HIGHER EDUCATION AND TRAINING (ITHET)}, author={Xiao, Yunkai and Gao, Yinan and Yue, Chuhuai and Gehringer, Edward}, year={2022} }
 @inproceedings{xiao_wang_sun_li_song_cui_jia_liu_gehringer_2022, title={Modeling review helpfulness with augmented transformer neural networks}, ISSN={["2325-6516"]}, DOI={10.1109/ICSC52841.2022.00019}, abstractNote={The past two years have witnessed a dramatic change in the delivery of education, as most providers have pivoted to remote online learning. With the enrollment of some MOOCs platforms, for example, Coursera, going up by 444% between mid-March and mid-September 202011Coursera 2020 Impact Report: https://about.coursera.org/press/wpcontent/uploads/2020/09/Coursera-Impact-Report-2020.pdf, practical teaching on a large scale attracted significant attention from the public. For online educators, this was manifested as a significant increase in assessment workload. MOOC instructors have long used peer assessment to evaluate work submitted by online learners. Prior research has shown that when a student believes feedback is helpful, then the suggestion given by that feedback is more likely to be implemented. Researchers have studied machine learning models for detecting problem statements or suggestions in feedback. However, these models do not work as well in detecting secondary features like helpfulness. This paper introduces a new augmented model for detecting helpfulness and tests it against the original model that uses raw text, yielding a 7% increase in performance measured in F1 score.}, booktitle={2022 IEEE 16th International Conference on Semantic Computing (ICSC)}, author={Xiao, Yunkai and Wang, Tianle and Sun, Xinhua and Li, Licong and Song, Yang and Cui, Jialin and Jia, Qinjin and Liu, Chengyuan and Gehringer, Edward}, year={2022}, pages={83–90} }
 @article{hendry_harris_goodwin_neverov_xiao_tian_song_2021, title={PyRoboCar: A Low-cost Deep Neural Network-based Autonomous Car}, DOI={10.1109/FIE49875.2021.9637429}, abstractNote={In this innovative practice WIP paper, we present PyRoboCar, a low-cost deep neural network-based autonomous car project. PyRoboCar is a small-scale replication of a real self-driving car using a deep convolutional neural network (CNN), which takes images from a front fisheye camera as input and produces car steering angles as output. PyRoboCar uses a similar network architecture as industry-level autonomous cars and can drive itself in real-time using a camera, an additional Tensor Processing Unit, and a Raspberry Pi 4 platform. We have also made this project open online, including the code and instructions.}, journal={2021 IEEE FRONTIERS IN EDUCATION CONFERENCE (FIE 2021)}, author={Hendry, Gordon and Harris, Seth and Goodwin, Ryan and Neverov, Leonid and Xiao, Yunkai and Tian, David and Song, Yang}, year={2021} }
 @article{song_xiao_stephens_ruesch_roginski_layman_2020, title={Suitability of SCS1 as a Pre-CS2 Assessment Instrument: A Comparison with Short Deliberate-practice Questions}, DOI={10.1145/3374135.3385321}, abstractNote={In an entry-level programming course, the instructor needs to assess students' prior knowledge or to evaluate their learning at important milestones. The Second CS1 Assessment (SCS1) is one of the best-known validated tests of programming knowledge. It is a multiple-choice test written in pseudo-code, covering concepts commonly presented in Computer Science 1 (CS1) courses. However, SCS1 is known to be an unwieldy assessment - the questions tend to be difficult and thereby may not provide good discrimination between students with average competencies against the weak ones. In our project, we created a set of short deliberate-practice questions for CS1 topics and used them to evaluate students' prior knowledge at the beginning of a Computer Science 2 (CS2) course. This set of deliberate-practice questions consists of 127 multiple-choice questions. Both SCS1 questions and our short deliberate-practice questions were given to CS2 students as pre-CS2 assessments on an online practice system called HawkQB. Using Item Response Theory (IRT), we analyzed students' responses in both sets of questions as pre-CS2 assessments to examine whether the SCS1 questions are suitable to be used as a pre-CS2 assessment instrument. We found that the SCS1 questions are of greater difficulty and low discrimination compare with our short deliberate-practice questions based on the data from students in four sections of CS2 in fall 2019. Considering the time gap between CS1 and CS2 may vary, we question the suitability of SCS1 as a pre-CS2 assessment used in a formal test setup in the first or second week of CS2 courses.}, journal={ACMSE 2020: PROCEEDINGS OF THE 2020 ACM SOUTHEAST CONFERENCE}, author={Song, Yang and Xiao, Yunkai and Stephens, Jonathan and Ruesch, Emma and Roginski, Sean and Layman, Lucas}, year={2020}, pages={313–314} }