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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. 

This full research paper explores students’ attitudes toward second-chance testing and how second-chance testing influences students’ behavior. Second-chance testing refers to giving students the opportunity to take a second instance of each exam for some sort of grade replacement. Previous work has demonstrated that second-chance testing can lead to improved student outcomes in courses, but how to best structure second-chance testing to maximize its benefits remains an open question. We complement previous work by interviewing a diverse group of 23 students that have taken courses that use second-chance testing. From the interviews, we sought to gain insight into students’ views and use of second-chance testing. We found that second-chance testing was almost universally viewed positively by the students and was frequently cited as helping to reduce test takers’ anxiety and boost their confidence. Overall, we find that the majority of students prepare for second-chance exams in desirable ways, but we also note ways in which second-chance testing can potentially lead to undesirable behaviors including procrastination, over-reliance on memorization, and attempts to game the system. We identified emergent themes pertaining to various facets of second-chance test-taking, including: 1) concerns about the time commitment required for second-chance exams; 2) a belief that second-chance exams promoted fairness; and 3) how second-chance testing incentivized learning. This paper will provide instructors and other stakeholders with detailed insights into students’ behavior regarding second-chance testing, enabling instructors to develop better policies and avoid unintended consequences. 

In this paper, we study a computerized exam system that allows students to attempt the same question multiple times. This system permits students either to receive feedback on their submitted answer immediately or to defer the feedback and grade questions in bulk. An analysis of student behavior in three courses across two semesters found similar student behaviors across courses and student groups. We found that only a small minority of students used the deferred feedback option. A clustering analysis that considered both when students chose to receive feedback and either to immediately retry incorrect problems or to attempt other unfinished problems identified four main student strategies. These strategies were correlated to statistically significant differences in exam scores, but it was not clear if some strategies improved outcomes or if stronger students tended to prefer certain strategies. 

We explore how course policies affect students' studying and learning when a second-chance exam is offered. High-stakes, one-off exams remain a de facto standard for assessing student knowledge in STEM, despite compelling evidence that other assessment paradigms such as mastery learning can improve student learning. Unfortunately, mastery learning can be costly to implement. We explore the use of optional second-chance testing to sustainably reap the benefits of mastery-based learning at scale. Prior work has shown that course policies affect students' studying and learning but have not compared these effects within the same course context. We conducted a quasi-experimental study in a single course to compare the effect of two grading policies for second-chance exams and the effect of increasing the size of the range of dates for students taking asynchronous exams. The first grading policy, called 90-cap, allowed students to optionally take a second-chance exam that would fully replace their score on a first-chance exam except the second-chance exam would be capped at 90% credit. The second grading policy, called 90-10, combined students' first- and second-chance exam scores as a weighted average (90% max score + 10% min score). The 90-10 policy significantly increased the likelihood that marginally competent students would take the second-chance exam. Further, our data suggests that students learned more under the 90-10 policy, providing improved student learning outcomes at no cost to the instructor. Most students took exams on the last day an exam was available, regardless of how many days the exam was available. 

Computational methods have become increasingly used in both academia and industry. At the University of Illinois Urbana Champaign, the Department of Materials Science and Engineering (MSE), as part of a university-funded educational innovation program, has integrated computation throughout its undergraduate courses since 2014. Within this curriculum, students are asked to solve practical problems related to their coursework using computational tools in all required courses and some electives. Partly in response to feedback from students, we have expanded our current curriculum to include more computational modules. A computational module was added to the freshman Introduction to Materials Science and Engineering class; thus, students will be expected to use computational tools from their first year onwards. In this paper, we survey students who are currently taking courses with integrated computation to explore the effects of gradually introducing students to programming as well as both macro- and micro-scale simulations over multiple years. We investigate the improving confidence level of students, their attitude towards computational tools, and their satisfaction with our curriculum reform. We also updated our survey to be more detailed and consistent between classes to aid in further improvements of our MSE curriculum. 

A computational approach has become an indispensable tool in materials science research and related industry. At the University of Illinois, Urbana-Champaign, our team at the Department of Materials Science and Engineering (MSE), as part of a Strategic Instructional Initiatives Program (SIIP), has integrated computation into multiple MSE undergraduate courses over the last years. This has established a stable environment for computational education in MSE undergraduate courses through the duration of the program. To date, all MSE students are expected to have multiple experiences of solving practical problems using computational modules before graduation. In addition, computer-based techniques have been integrated into course instruction through iClicker, lecture recording, and online homework and testing. In this paper, we seek to identify the impact of these changes beyond courses participating in the original SIIP project. We continue to keep track of students' perception of the computational curriculum within participating courses. Furthermore, we investigate the influence of the computational exposure on students' perspective in research and during job search. Finally, we collect and analyze feedback from department faculty regarding their experience with teaching techniques involving computation.