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Intelligent Tutoring Systems, Feedback (Response), Computer Science Education, Artificial Intelligence, Programming, Educational Technology, and Learning Analytics

Intelligent tutoring systems for programming education can support students by providing personalized feedback when a student is stuck in a coding task. We study the problem of designing a hint policy to provide a next-step hint to students from their current partial solution, e.g., which line of code should be edited next. The state of the art techniques for designing a hint policy use supervised learning approach, however, require access to historical student data containing trajectories of partial solutions written when solving the task successfully. These techniques are limited in applicability when needed to provide feedback for a new task without any available data, or to a new student whose trajectory of partial solutions is very different from that seen in historical data. To this end, we tackle the "zero-shot" challenge of learning a hint policy to be able to assist the very first student who is solving a task, without relying on any data. We propose a novel "reinforcement learning" (RL) framework to solve the challenge by leveraging recent advancements in RL-based neural "program synthesis." Our framework is modular and amenable to several extensions, such as designing appropriate reward functions for adding a desired feature in the type of provided hints and allowing to incorporate student data from the same or related tasks to further boost the performance of the hint policy. We demonstrate the effectiveness of our RL-based hint policy on a publicly available dataset from Code.org, the world's largest programming education platform. [For the full proceedings, see ED607784.]

Teamwork, Group Activities, Student Projects, Programming, College Students, Computer Science Education, Classification, Data Analysis, and Automation

Teamwork, often mediated by version control systems such as Git and Apache Subversion (SVN), is central to professional programming. As a consequence, many colleges are incorporating both collaboration and online development environments into their curricula even in introductory courses. In this research, we collected GitHub logs from two programming projects in two offerings of a CS2 Java programming course for computer science majors. Students worked in pairs for both projects (one optional, the other mandatory) in each year. We used the students' GitHub history to classify the student teams into three groups, "collaborative," "cooperative," or "solo-submit," based on the division of labor. We then calculated different metrics for students' teamwork including the total number and the average number of commits in different parts of the projects and used these metrics to predict the students' teamwork style. Our findings show that we can identify the students' teamwork style automatically from their submission logs. This work helps us to better understand novices' habits while using version control systems. These habits can identify the harmful working styles among them and might lead to the development of automatic scaffolds for teamwork and peer support in the future. [For the full proceedings, see ED607784.]

Programming, Prediction, Error Patterns, Models, Educational Technology, Technology Uses in Education, Feedback (Response), and Undergraduate Students

In colleges, programming is increasingly becoming a general education course of almost all STEM majors as well as some art majors, resulting in an emerging demand for scalable programming education. To support scalable education, teaching activities such as grading and feedback have to be automated. Recently, online judge systems have been extensively used for programming training, because they are able to automatically evaluate the correctness of programs in real time and thereby make grading work scalable. However, existing online judge systems lack of the ability to give effective feedback on logical programming errors. As such, instructors and teaching assistants are still overwhelmed by the work of helping students fix programs, especially for those novice students. To tackle the challenge, we develop "PIPE," a deep learning model that is able to "P"redict log"I"cal "P"rogramming "E"rrors in student programs. The model seamlessly integrates a representation learning model for obtaining the latent feature of a program and a multi-label classification model for predicting the error types in the program, thereby allowing end-to-end learning and prediction. We use the C programs submitted in our online judge system to train PIPE, and demonstrate its superior performance over the baseline models. We use PIPE to implement the error-feedback feature in our online judge system and enable automated feedback on logical programming errors to the students. [For the full proceedings, see ED607784.]

Educational Improvement, Teaching Methods, Information Retrieval, Data Processing, Data Analysis, Equal Education, Student Diversity, Cooperative Learning, Online Courses, Demography, Transformative Learning, Blended Learning, Learning Strategies, Large Group Instruction, Artificial Intelligence, Educational Technology, Privacy, Learner Engagement, Student Motivation, Programming, Models, Game Based Learning, Competence, Problem Solving, Information Sources, Critical Theory, At Risk Students, Elementary Secondary Education, Spatial Ability, Visualization, Intervention, Student Characteristics, Automation, Remedial Reading, Handheld Devices, Video Technology, Item Response Theory, Reinforcement, Student Behavior, Prediction, Success, College Students, Data Collection, Masters Programs, Reaction Time, Reading Comprehension, College Admission, Essays, Peer Teaching, Performance, Teamwork, Student Projects, Inquiry, Electronic Publishing, Textbooks, Tests, Ethics, Legal Responsibility, Higher Education, Measurement Techniques, Reading Achievement, Grade 9, Video Games, Peer Evaluation, Secondary Schools, Computer Science Education, Physics, Feedback (Response), Semitic Languages, Technical Writing, Telecommunications, Personal Autonomy, Computer Software, Plagiarism, Curriculum, Collaborative Writing, Dropouts, Educational Assessment, Gender Differences, Difficulty Level, Social Media, Mental Health, Innovation, Student Role, Eye Movements, Foreign Countries, Bayesian Statistics, Scores, Distance Education, Student Attitudes, Scoring, Natural Language Processing, Middle School Students, Social Networks, Employment Qualifications, Intelligent Tutoring Systems, Anxiety, Second Languages, Cues, Visual Aids, Uruguay, and Brazil

The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of presentations all paper presenters pre-recorded their presentation as a video and then hosted it on YouTube with closed-captioning (CC). The official theme of this year's conference is Improving Learning Outcomes for All Learners. The theme comprises two parts: (1) Identifying actionable learning or teaching strategies that can be used to "improve" learning outcomes, not just predict them; (2) Using EDM to promote more "equitable" learning across diverse groups of learners, and to benefit underserved communities in particular. This year's conference features three invited talks: Alina von Davier, Chief Officer at ACTNext; Abelardo Pardo, Professor and Dean of Programs (Engineering), at UniSA STEM, University of South Australia; and Kobi Gal, Associate Professor at the Department of Software and Information Systems Engineering at Ben-Gurion University of the Negev, and Reader at the School of Informatics at the University of Edinburgh.