This research full paper explores the optimal times for studying and learning Structured Query Language (SQL), a critical skill in managing relational databases across various domains, to improve learning, problem-solving, and academic performance. By identifying prime study times, productivity and retention can be enhanced, particularly by scheduling breaks when cognitive function wanes. This study analyzes over 129,000 SQL submissions from students in a Fall 2022 Database Systems course at the University of Illinois Urbana-Champaign, examining correlations between time of day and answer accuracy (correct, syntax error, or semantic error). Time series analysis was done to analyze the data collected over a set of intervals, and null-hypothesis significance testing was utilized to calculate the p-value, determining the statistical significance of the collected data for drawing confident conclusions.

Previous research has shown that students encounter difficulties when learning database systems and their corresponding languages. Researchers have categorized these challenges into syntax and semantic errors and have identified common error types and overall learning obstacles among students. However, most existing studies have primarily focused on quantitatively assessing students‘ overall performance in an aggregated manner’ which may overlook valuable insights into individual-level knowledge transfer. In this study, we scrutinized over 250,000 submissions to query language programming assignments, their corresponding error messages, and the performance data of 702 students who took a database course in the Fall 2022 semester at the University of Illinois Urbana-Champaign to gain a comprehensive overview of each student's performance. We followed each student's progress in semantic and syntax errors across three query languages to determine their overall learning experience and whether knowledge transfer had occurred. Consequently, we discovered that many students may still encounter difficulties when transferring their knowledge from one language to another, despite having already learned and practiced the same abstract data operation concepts in one language. On the other hand, the majority of students were able to reduce syntax errors through practice in one language, but the rate of improvement varied among individuals. This study seeks to investigate two key aspects: the potential transfer of abstract data operation concepts among different database languages, and the possibility of a decrease in syntax errors through consistent practice within a single query language.

Structured Query Language (SQL) plays a pivotal role in the effective management of relational databases and is a key skill across domains that engage with database systems, including research, development, and business management. However, mastering SQL can be challenging. To comprehend the approaches employed by students when solving SQL problems and address the challenges they faced during the learning process, our study analyzes submissions from the Database Systems course at the University of Illinois Urbana-Champaign during the Fall 2022 semester. We extend prior research involving line chart visualizations that facilitate instructors in identifying struggling students and understanding their submission behaviors. Yet, we acknowledge the limitations of this approach in providing timely feedback and actionable insights due to the sheer volume of visualizations. To address this, we developed an innovative technique using global sequence alignment scores and regular expression algorithms to compress student submission sequences. Our approach reveals submission patterns and pattern elements, leading to recommendations for instructors to enhance database education. By integrating student performance data, such as the number of submission attempts on a particular SQL problem and whether the student arrived at a correct final solution query, we aim to empirically support these recommendations, thereby enabling instructors to more accurately differentiate between struggling and excelling students.

Structured Query Language (SQL) is an essential skill to acquire for those who interact with databases, such as researchers, developers, and people involved in businesses. However, the challenges that these users face while learning SQL requires further research. In particular, the types of errors that students encounter on various assignment types or under exam conditions are an area that we are interested in to determine an optimal arrangement of coursework materials for improved learning. In this paper, we analyze 156,513 student SQL submissions to homework assignments, collaborative assignments, and exams of the Database Systems course available to 730 upper-level undergraduate and graduate students offered in the Fall 2022 semester at the University of Illinois Urbana-Champaign. We look at the ratio of syntax and semantic errors, and correct submissions for each of these assignment problem types as well as the most frequent syntax error codes. We visualize our data findings and draw recommendations for future coursework arrangements from the comparisons between the assignment types for a more effective acquisition of SQL as a skill. We found that although students most commonly encountered syntax error codes 1064 and 1054 regardless of the assignment type, they made more syntax errors (and fewer semantic errors) on exam problems compared with homework and collaborative assignment problems. We recommend instructors place a higher emphasis on non-timed SQL programming problems, targeted syntax drills during instruction, and syntax support during exams.

This Research Full Paper presents our findings of studying the effects of teaching modality on collaborative learning by comparing data from two sections of a Database Systems course offered simultaneously, with one offered fully face-to-face in a classroom setting while the other is offered online through a flipped-classroom model. Both sections utilized a collaborative learning approach where students work on group activities for part of the class meeting. Since the two sections were almost identical except for the teaching modality, we are provided with a unique opportunity to study the effect of teaching modalities on collaborative learning. As part of this study, we analyze four crucial data sources: 1) student performance data from the grade book 2) student performance data from the online learning management platform 3) an end-of-semester survey given by the instructor and 4) an end-of-semester survey given by the university. We extract insights on the impact of teaching modalities on collaborative learning in order to identify factors that can enhance collaborative learning. We also study the effect of teaching modalities on students’ performance. We visualize our findings to differentiate between the two modalities, and draw on the strengths of each section to establish recommendations for the instructors for course improvement efforts.

This tutorial will show you how to do visualization and build interactive dashboards using HoloViz, which is an open-source visualization ecosystem comprising seven packages. You will learn how to turn nearly any notebook into a deployable dashboard, how to build visualizations easily even for big, streaming, and multidimensional data, how to build interactive drill-down exploratory tools for your data and models without having to run a web-technology software development project, and finally how to deploy your dashboard.

Structured Query Language (SQL), the standard language for relational database management systems, is an essential skill for software developers, data scientists, and professionals who need to interact with databases. SQL is highly structured and presents diverse ways for learners to acquire this skill. However, despite the significance of SQL to other related fields, little research has been done to understand how students learn SQL as they work on homework assignments. In this paper, we analyze students' SQL submissions to homework problems of the Database Systems course offered at the University of Illinois at Urbana-Champaign. For each student, we compute the Levenshtein Edit Distances between every submission and their final submission to understand how students reached their final solution and how they overcame any obstacles in their learning process. Our system visualizes the edit distances between students' submissions to a SQL problem, enabling instructors to identify interesting learning patterns and approaches. These findings will help instructors target their instruction in difficult SQL areas for the future and help students learn SQL more effectively.