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SQL is a crucial language for managing relational database systems, and is an essential skill for individuals in roles such as researchers, developers, and business professionals who work with databases. However, learning SQL can be a challenge, presenting an opportunity to study the various methods students use to arrive at semantically equivalent SQL queries. In this study, we examined students’ SQL submissions to homework assignments in the Database Systems course offered to upper-level undergraduate and graduate students at the University of Illinois Urbana-Champaign during the Fall 2022 semester. Our goal was to understand how students arrive at SQL solutions and overcome challenges in the learning process by building on prior research on line chart visualizations that instructors can use to increase visibility on students who are struggling. However, a major limitation of this approach was the difficulty for instructors to sift through a large number of visuals representing each student’s performance on a SQL problem and generate action items at scale, especially when dealing with enrollments of over 700 students. To overcome this limitation, we developed a novel technique to generate textual representations of the student submission sequence using global sequence alignment scores and regular expression algorithms to further compact these submission sequences. This allows instructors to gain insights quickly, on an aggregate level, and in an automated manner, enabling them to identify students who may be struggling with SQL based on their submission sequence characteristics and take appropriate action to improve database education. Our study discovered common textual submission patterns and pattern elements, and we present our recommendations to instructors to improve database education based on these findings. 

This full research paper explores how second-chance testing can be used as a strategy for mitigating students’ test anxiety in STEM courses, thereby boosting students’ performance and experiences. Second-chance testing is a testing strategy where students are given an opportunity to take an assessment twice. We conducted a mixed-methods study to explore second-chance testing as a potential solution to test anxiety. First, we interviewed a diverse group of STEM students (N = 23) who had taken courses with second-chance testing to ask about the stress and anxiety associated with testing. We then administered a survey on test anxiety to STEM students in seven courses that offered second-chance tests at Midwestern University (N = 448). We found that second-chance testing led to a 30% reduction in students’ reported test anxiety. Students also reported reduced stress throughout the semester, even outside of testing windows, due to the availability of second-chance testing. Our study included an assortment of STEM courses where second-chance testing was deployed, which indicates that second-chance testing is a viable strategy for reducing anxiety in a variety of contexts. We also explored whether the resultant reduction in test anxiety led to student complacency, encouraged procrastination, or other suboptimal student behavior because of the extra chance provided. We found that the majority of students reported that they worked hard on their initial test attempts even when second-chance testing was available. 

In this full research paper, we examine various grading policies for second-chance testing. Second-chance testing refers to giving students the opportunity to take a second version of a test for some form of grade replacement. Second-chance testing as a pedagogical strategy bears some similarities to mastery learning, but second-chance testing is less expensive to implement. Previous work has shown that second-chance testing is associated with improved performance, but there is still a lack of clarity regarding the optimal grading policies for this testing strategy. We interviewed seven instructors who use second-chance testing in their courses to collect data on why they chose specific policies. We then conducted structured interviews with some students (N = 11) to capture more nuance about students’ decision making processes under the different grading policies. Afterwards, we conducted a quasi-experimental study to compare two second-chance testing grading policies and determine how they influenced students across multiple dimensions. We varied the grading policies used in two similar sophomore-level engineering courses. We collected assessment data and administered a survey that queried students (N = 513) about their behavior and reactions to both grading policies. Surprisingly, we found that the students’ preference between these two policies were almost perfectly split. We conclude that there are likely many policies that perform well by being simple and encouraging serious attempts on both tests. 

We present a psychometric evaluation of the Cybersecurity Curriculum Assessment (CCA), completed by 193 students from seven colleges and universities. The CCA builds on our prior work developing and validating a Cybersecurity Concept Inventory (CCI), which measures students' conceptual understanding of cybersecurity after a first course in the area. The CCA deepens the conceptual complexity and technical depth expectations, assessing conceptual knowledge of students who had completed multiple courses in cybersecurity. We review our development of the CCA and present our evaluation of the instrument using Classical Test Theory and Item-Response Theory. The CCA is a difficult assessment, providing reliable measurements of student knowledge and deeper information about high-performing students. 

We present a psychometric evaluation of a revised version of the Cybersecurity Concept Inventory (CCI) , completed by 354 students from 29 colleges and universities. The CCI is a conceptual test of understanding created to enable research on instruction quality in cybersecurity education. This work extends previous expert review and small-scale pilot testing of the CCI. Results show that the CCI aligns with a curriculum many instructors expect from an introductory cybersecurity course, and that it is a valid and reliable tool for assessing what conceptual cybersecurity knowledge students learned. 

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.