Mastery learning, randomized assessments and instant feedback

Introducing PrairieLearn

This paper introduces PrairieLearn, an online assessment system designed to facilitate learning to mastery. The objectives of this system are to: (1) enable students to practice solving randomized problem variants repeatedly until mastery, (2) incentivize students to repeat questions until mastery is achieved, and (3) provide immediate feedback about their current mastery level to the student. The results from using PrairieLearn over several semester in a large engineering course include gains in student mastery, improved student satisfaction when compared to other existing assessment systems and high instructor satisfaction.

M. West, G. L. Herman, and C. Zilles, 'PrairieLearn: Mastery-based online problem solving with adaptive scoring and recommendations driven by machine learning', ASEE 2015.

Improving learning via frequent testing: a CS study case

Research studies have shown that frequent testing improves learning, with bigger impact than rehearsal strategies such as re-reading a textbook or re-watching lectures. This study presents a quasi-experimental study to examine the effect of using frequent, automated examinations in an advanced computer science course: in the first semester students were given traditional paper-based exams, and in the following semester students took frequent computer-based tests, while other aspects of the course were held constant. It was observed a significant change in the distribution of students' grades with fewer students failing the final examination, and proportionately more students earning grades of B and C.

T. Nip, E. Gunter, G. Herman, J. Morphew, M. West, 'Using a Computer-based Testing Facility to Improve Student Learning in a Programming Languages and Compilers Course, (SIGCSE 2018).

Improving learning via frequent testing: a ME study case

This study compares final exam performance from two different semesters in a mechanical engineering course, the first offering including two midterms and a final exam, and the other with seven bi-weekly quizzes and the same final exam. The bi-weekly quizzes were auto-graded and offered at a computer-based testing facility where students had immediate feedback of their performance. Results indicated that students who completed seven short assessments over the course of the semester scored higher on the final exam than students who completed two longer mid-term examinations, and they were twice as likely to receive a perfect score.

Morphew J., Silva M., Herman G., West M., 'Frequent mastery testing with second-chance exams leads to enhanced student learning in undergraduate STEM', Applied Cognitive Psychology 2019.

Estimating exam difficulty

To design good assessments, it is useful to have an estimate of the difficulty of a novel exam question before running an exam. This study uses a collection of a few hundred automatic item generators and show that their exam difficulty can be roughly predicted from student performance on the same generator during pre-exam practice. Specifically, we show that the rate that students correctly respond to a generator on an exam is on average within 5% of the correct rate for those students on their last practice attempt.

B. Chen, M. West, and C. Zilles, 'Predicting the difficulty of automatic item generators on exams from their difficulty on homeworks', L@S 2019.

Comparing second-chance exams grading policies

Second-chance testing, where students are allowed to take a second instance of an exam for some form of grade replacement, is a less expensive approximation of mastery-based learning that can be easily integrated into a broad range of college course structures. This paper analyzes three different grading policies, where all of them encourage the students to prepare adequately for the first-chance exam and review the material again before the second-chance exam, if they elect to take it. By comparing these different course policies, we show that grading policies have a significant effect on whether students take second-chance exams. The data also suggests that adding a second-chance exam had no effect on student performance or study habits for the first-chance exam.

G. L. Herman, K. Varghese, and C. Zilles, 'Second-chance testing course policies and student behavior', FIE 2019.

Second-chance exams and impact on student learning

This quasi-experimental study in a single course compares 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. 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.

G. L. Herman, Z. Cai, T. Bretl, C. Zilles, and M. West, 'Comparison of grade replacement and weighted averages for second-chance exams', ICER 2020.

Student perceptions on second-chance exams

This study complements previous work by including interviews from 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, overreliance on memorization, and attempts to game the system.

C. Emeka, T. Bretl, G. Herman, M. West, C. Zilles, 'Students Perceptions and Behavior Related to Second-Chance Testing', FIE 2021.

Creating fair randomized asynchronous exams

When exams are run asynchronously, a student can potentially gain an advantage by receiving information about the exam from someone who took it earlier. Generating random exams from pools of problems mitigates this potential advantage, but has the potential to introduce unfairness if the problems in a given pool are of significantly different difficulty. This study presents an algorithm that takes a collection of problem pools and historical data on student performance on these problems and produces exams with reduced variance of difficulty (relative to naive random selection) while maintaining sufficient variation between exams to ensure security.

P. Sud, M. West, and C. Zilles, 'Reducing difficulty variance in randomized assessments', ASEE 2019.

Investigating fairness in randomized asynchronous exams

This study investigates fairness when adopting exam versioning and randomization to mitigate cheating during asynchronous online exams. It uses a Generalized Partial Credit Model (GPCM) Item-Response Theory (IRT) model to fit exams from a for-majors data structures course and non-majors CS0 course, both of which used randomly generated exams. For all exams, students' estimated ability and exam score are strongly correlated (ρ ≥ 0.7), suggesting that the exams are reasonably fair. Through simulation, we find that most of the variance in any given student's simulated scores is due to chance and the worst of the score impacts from possibly unfair permutations is only around 5 percentage points on an exam.

M. Fowler, D. Smith, C. Emeka, M. West and C. Zilles, 'Are We Fair? Quantifying Score Impacts of Computer Science Exams with Randomized Question Pools', SIGCSE 2022.

Student's strategies when completing exams with immediate feedback

When taking a computer-based exam using PrairieLearn, students have the option to receive immediate feedback on their submitted answer or they can defer the feedback and grade questions in bulk. This study analyzes data from three courses across two semesters, and finds that only a small minority of students used the deferred feedback option. Moreover, four main submission strategies were identified and they were correlated to statistically significant differences in exam scores, however it was not clear if some strategies improved outcomes or if stronger students tended to prefer certain strategies.

A. Verma, T. Bretl, M. West, and C. Zilles, 'A quantitative analysis of when students choose to grade questions on computerized exams with multiple attempts', L@S 2020.

Auto-grading and immediate feedback of mechanics drawings

This paper presents an algorithmic framework for auto-grading of computer-drawn mechanics diagrams including key functionality requirements: (1) ability to provide students with meaningful feedback about errors in their diagram, (2) easy to understand for problem authors, and require only data which is readily available to authors, (3) adaptable to different types of drawings or sketches, (4) fast to execute, and (5) robust to unexpected or unusual inputs.

M. Silva and M. West, 'Algorithmic grading strategies for computerized drawing assessments', ASEE 2017.

Including a markup tool for drawing-based assessments in PrairieLearn

This paper introduces a simple HTML markup language added to PrairieLearn to create automated drawing-based questions, allowing students to draw diagrams, graphs and design solutions on the computer that are instantly auto-graded by the computer. A key advantage of this new tool over previous work is that the question author does not need to write any explicit programming code. We present results from student interaction data with the system, student surveys, and feedback from instructors and question authors.

N. Nytko, M. West, and M. Silva, 'A simple and efficient markup tool to generate drawing-based online assessments', ASEE 2020.

Machine-graded questions for complex engineering problems

Assessing students' knowledge of complex engineering systems often requires administering long-form multi-part questions with copious extra credit. Creating and grading these questions can be time consuming. In this paper, we describe our efforts to convert multi-part pencil-and-paper questions into parameterized, machine-gradable questions in PrairieLearn. Questions were built and parameterized by creating a simulator for the engineering system in the back-end of PrairieLearn. A comparison of machine-graded PrairieLearn variants of a question with human-graded, pencil-and-paper variants of a question revealed comparable student performance and partial credit awarded. Students revealed an overwhelming preference for the machine-graded questions to the pencil-and-paper questions. This work provides proof-of-concept for creating meaningful, complex assessments in PrairieLearn.

S. Mahmood, M. Zhao, O. Khan and G. Herman, 'Caches as an example of machine-gradable exam questions for complex engineering systems', FIE 2020.

Creating robust and randomized assessments to reduce cheating

Performance trends on asynchronous exams

Using a data set from 29,492 asynchronous exams in CBTF, we observed correlations between when a student chooses to take their exam within the exam period and their score on the exam. Somewhat surprisingly, instead of increasing throughout the exam period, which might be indicative of widespread collaborative cheating, we find that exam scores decrease throughout the exam period. While this could be attributed to weaker students putting off exams, this effect holds even when accounting for student ability as measured by a synchronous exam taken during the same semester.

B. Chen, M. West, and C. Zilles, 'Do performance trends suggest wide-spread collaborative cheating on asynchronous exams?', L@S 2017.

Understanding the performance trends during asynchronous exams

This study presents a hypothesis that the average exam scores decline over the exam period in asynchronous testing is primarily due to self-selection effects, where weaker students tend to choose exam times later in the exam period, while stronger students are more likely to choose earlier times. We used data from 31,673 exams over four semesters from six undergraduate engineering and computing courses that had both synchronous and asynchronous exams. We analyzed student exam time choice and asynchronous exam scores, using synchronous exam scores in the same course as a control variable. We conclude that self-selection effects are primarily responsible for exam score declines over time, that exam time selection is unlikely to be a useful target for interventions to improve performance, and that there is no evidence for widespread collaborative cheating in the dataset used in this research.

B. Chen, M. West, and C. Zilles, 'Analyzing the decline of student scores over time in self‐scheduled asynchronous exams', Journal of Engineering Education, 2019.

How much randomization is needed to reduce cheating?

This paper investigates randomization on asynchronous exams as a defense against collaborative cheating. Collaborative cheating occurs when one student (the information producer) takes the exam early and passes information about the exam to other students (the information consumers) that are taking the exam later. Using a dataset from 425 students, we identified 5.5% of students (on average) as information consumers. These information consumers had a significant advantage (13% on average) when every student was given the same exam problem but that advantage dropped to almost negligible levels (2-3%) when students were given a random problem from a pool of two or four problems.

B. Chen, M. West, and C. Zilles, 'How much randomization is needed to deter collaborative cheating on asynchronous exams?', L@S 2018.

Measuring score advantage during unproctored exams

This study investigates the score advantage of unproctored exams versus proctored exams using a within-subjects design for 510 students in a CS1 course with 5 proctored exams and 4 unproctored exams. We found that students scored 3.3 percentage points higher on questions on unproctored exams than on proctored exams. More interestingly, however, we discovered that this score advantage on unproctored exams grew steadily as the semester progressed, from around 0 percentage points at the start of semester to around 7 percentage points by the end, indicating that students were "learning to cheat". The data suggests that both more individuals are cheating and the average benefit of cheating is increasing over the course of the semester.

B. Chen, S. Azad, M. Fowler, M. West, and C. Zilles, 'Learning to cheat: Quantifying changes in score advantage of unproctored assessments over time', L@S 2020.

Cheating effect in computer-based testing

This was a controlled crossover experiment designed to measure the score advantage that students have when taking the quizzes asynchronously at a computer-based testing facility (i.e., students could select a time to take the exam in a period of 4 days) compared to synchronous quizzes (when all students took the quiz during lecture time). The results indicated that when students took exams asynchronously their scores were, on average, only 3% higher.

Silva M., Zilles C., West M., 'Measuring the score advantage on asynchronous exams in an undergraduate CS course', SIGCSE 2020.

Computer-based testing facilities (CBTF)

A vision for computer-based testing

This paper describes our first experience building a computerized testing lab and running the bulk of a 200-student class's exams using computerized testing. It discusses the mechanics of operating the testing lab, the work required by the instructor to enable this approach, and the student response, which has been strongly positive: 75% prefer computerized testing, 12% prefer traditional written exams, and 13% had no preference.

C. Zilles, R. T. Deloatch, J. Bailey, B. B. Khattar, W. Fagen, C. Heeren, D. Mussulman, and M. West, 'Computerized testing: A vision and initial experiences', ASEE 2015.

Students and instructors perceptions regarding computer-based testing

In this work we explore how the large-scale introduction of computer-based testing has impacted students and instructors. Specifically we discuss the results of multiple rounds of surveys completed by students and faculty.

C. Zilles, M. West, D. Mussulman, and C. Sacris, 'Student and instructor experiences with a computer-based testing facility', (EDULEARN 2018).

Lessons learned after running a CBTF for 4 years

This paper discusses five main aspects of the CBTF: 1) basic operations; 2) precautions taken to maintain secure exam environments; 3) support of students that require testing accommodations like extra time and/or a distraction-reduced environment; 4) policies to handle exceptional circumstances with minimal intervention by faculty; and 5) cost of operating the CBTF and how it compares to traditional exams and online services.

C. Zilles, M. West, D. Mussulman, and T. Bretl, 'Making testing less trying: Lessons learned from operating a computer-based testing facility', FIE 2018.

Every university should have a computer-based testing facility

This paper summarizes research studies performed over several years in a broad collection of STEM-oriented classes using a computer based-testing facility, indicating improved quality of assessment, ability to test computational skills, and reduced recurring burden of creating assessments. We find the CBTF to be secure, cost-effective, and well liked by faculty, who choose to use it semester after semester. We believe that there are many institutions that would similarly benefit from having a Computer-Based Testing Facility.

C. Zilles, M. West, G. Herman, and T. Bretl, 'Every university should have a computer-based testing facility', (CSEDU 2019).

Review sessions post-exam hosted at CBTF

Two major concerns reported by students taking computer-based testing are: (1) limited access to the assessment after completion, and (2) the lack of partial credit. To address these concerns, the CBTF adopted a new exam-review service to provide in-person feedback to students after the completion of computerized exams. These review sessions are conducted by course staff and hosted at the CBTF to ensure the integrity of exam problems for future use. In this paper, we present the design of this review system, including the scheduling logistics, software support, course staff training, and guidance to students. Detailed data from student usage is reported, including survey data of student affect and learning outcome changes after review sessions.

W. L. Chang, M. West, C. Zilles, D. Mussulman, and C. Sacris, 'Computerized exam reviews: In-person and individualized feedback to students after a computerized exam', ASEE 2020.

Scheduling of asynchronous exams in a CBTF

How do students schedule their asynchronous exams?

This paper explores the times students choose to take an asynchronous exam at CBTF, when students make and change their reservations, and the correlation between when students choose to take exams and their exam performance. Among our results, we find that students prefer to take exams in late afternoon/early evening towards the end of the exam period. In addition, we find that students frequently re-schedule when they take exams. Finally, we find that there is a correlation between how early in the exam period a student takes an exam and their score on the exam.

C. Zilles, M. West, and D. Mussulman, 'Student behavior in selecting an exam time in a computer-based testing facility', ASEE 2015.

Modeling students scheduling preferences

When undergraduate students are allowed to choose a time slot in which to take an exam from a large number of options (e.g., 40), the students exhibit strong preferences among the times. We found that students can be effectively modelled using constrained discrete choice theory to quantify these preferences from their observed behavior. The resulting models are suitable for load balancing when scheduling multiple concurrent exams and for capacity planning given a set schedule.

M. West and C. Zilles, 'Modeling student scheduling preferences in a computer-based testing facility', L@S 2016.

How to forecast demand at a computer-based testing facility?

For planning and resource scheduling purposes it is important to be able to forecast demand, and thus it is important to understand what drives student preferences for particular scheduling time slots. This paper presents a general framework for measuring revealed student preferences from actual reservation or scheduling data. The proposed method accurately captures student preferences in real-world scheduling data.

J. Bailey, M. West, and C. Zilles, 'Measuring revealed student scheduling preferences using constrained discrete choice models', ASEE 2017

Auto-grading of open-ended questions

Validated score rubric for "Explain in plain English" questions

In this paper, we describe a 7-point rubric developed for scoring student responses to "Explain in plain English" questions, reporting four different ways to validate the rubric.

B. Chen, S. Azad, R. Haldar, M. West, and C. Zilles, 'A validated scoring rubric for Explain-in-Plain-English questions', SIGCSE 2020.

Auto-grading "Explain in plain English" questions

Previous research suggests that "Explain in Plain English" (EiPE) code reading activities could play an important role in the development of novice programmers, but EiPE questions aren't heavily used in introductory programming courses because they (traditionally) required manual grading. We present what we believe to be the first automatic grader for EiPE questions and its deployment in a large-enrollment introductory programming course. Based on a set of questions deployed on a computer-based exam, we find that our implementation has an accuracy of 87–89%, which is similar in performance to course teaching assistants trained to perform this task and compares favorably to automatic short answer grading algorithms developed for other domains.

M. Fowler, B. Chen, S. Azad, M. West, and C. Zilles, 'Autograding Explain in Plain English questions using NLP', SIGCSE 2021.

Peer-grading "Explain in plain English" questions

This paper presents the use of peer grading for "Explain in Plain English" (EipE) questions in a large enrollment introductory programming course, where students were asked to categorize other students' responses. We developed a novel Bayesian algorithm for performing calibrated peer grading on categorical data, and we used a heuristic grade assignment method based on the Bayesian estimates. The peer-grading exercises served both as a way to coach students on what is expected from EipE questions and as a way to alleviate the grading load for the course staff.

B. Chen, M. West, and C. Zilles, 'Peer-grading Explain in plain English questions: A Bayesian calibration method for categorical answers, SIGCSE 2022.

Contribute to this page

Do you have a paper that should be included on this page? Please send us the appropriate information at