From 5e542b4e3e3062b48286341dc39f378bf69eac49 Mon Sep 17 00:00:00 2001 From: Mariana Silva Date: Mon, 18 Mar 2024 22:38:21 -0700 Subject: [PATCH 1/7] updated research page --- src/pages/research/index copy.tsx | 629 +++++++++++++++++++ src/pages/research/index.tsx | 986 ++++++++++++++---------------- 2 files changed, 1071 insertions(+), 544 deletions(-) create mode 100644 src/pages/research/index copy.tsx diff --git a/src/pages/research/index copy.tsx b/src/pages/research/index copy.tsx new file mode 100644 index 0000000..b1a71b0 --- /dev/null +++ b/src/pages/research/index copy.tsx @@ -0,0 +1,629 @@ +import React from "react"; +import classnames from "classnames"; +import Head from "next/head"; + +import { PageBanner } from "../../components/Banner"; +import { BannerCTA } from "../../components/CallToActionBanner"; +import { Heading } from "../../components/Heading"; +import { ResearchCard } from "../../components/ResearchCard"; +import Stack from "../../components/Stack"; + +import styles from "./index.module.scss"; + +export default function Research() { + return ( + + + Case Studies | PrairieLearn + + + + +
+
+ + + + Mastery learning, randomized assessments and instant feedback + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + + + + + + + Creating robust and randomized assessments to reduce cheating + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + + Computer-based testing facilities (CBTF) + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + 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. + + + + + Scheduling of asynchronous exams in a CBTF + + + 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. + + + + 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. + + + + 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. + + + + + Auto-grading of open-ended 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. + + + + 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. + + + + 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. + + + + This papers presents survey and interview results that recorded + students’ interactions with a short-answer AI autograder for + “Explain in Plain English” code reading problems. Using causal + modeling, they inferred the learning impacts of wrong answers + marked as right (false positives, FPs) and right answers marked + as wrong (false negatives, FNs). + + + + + Open Educational Resources (OER) + + This paper aims to understand what motivates and dissuades + instructors to contribute to and adopt OERs. In addition, they + investigate what would increase the likelihood of instructors + contributing their work to OER repositories. + + + + + CS1 Research Studies + + This study aims to replicate a slightly simplified hierarchy of + skills in CS1 using a larger body of students in a non-major + introductory Python course with computer-based exams. We also + explore the validity of other possible hierarchies. + + + + +

Contribute to this page

+

+ Do you have a paper that should be included on this page? Please + send us the appropriate information at{" "} + + hello@prairielearn.com + + . +

+ + +
+
+ + + + ); +} diff --git a/src/pages/research/index.tsx b/src/pages/research/index.tsx index 5329d37..901face 100644 --- a/src/pages/research/index.tsx +++ b/src/pages/research/index.tsx @@ -1,14 +1,429 @@ import React from "react"; -import classnames from "classnames"; +// import classnames from "classnames"; import Head from "next/head"; +import Accordion from "react-bootstrap/Accordion"; +import Link from "next/link"; import { PageBanner } from "../../components/Banner"; import { BannerCTA } from "../../components/CallToActionBanner"; -import { Heading } from "../../components/Heading"; -import { ResearchCard } from "../../components/ResearchCard"; -import Stack from "../../components/Stack"; +// import { Heading } from "../../components/Heading"; +// import { ResearchCard } from "../../components/ResearchCard"; +// import Stack from "../../components/Stack"; -import styles from "./index.module.scss"; +// import styles from "./index.module.scss"; + +export interface ResearchCardProps { + title: string; + reference: string; + referenceHref: string; +} + +export const ResearchCard: React.FC = ({ + title, + referenceHref, + reference, +}) => ( + +); + +const Papers = [ + { + title: "Computer-based assessments with randomization and instant feedback", + contents: ( + + + + + + + + + + + + + + + + + + + + ), + }, + { + title: "Retrieval practice and second-chance testing", + contents: ( + + + + + + + + + + + + + + + + + + ), + }, + + { + title: "Computer-based testing facilities", + contents: ( + + + + + + + + + + + + + + + + + + + + + + ), + }, + { + title: "Investigating cheating during computer-based testing", + contents: ( + + + + + + + + + + + + + + + + + + ), + }, + { + title: "Auto-grading open-ended questions", + contents: ( + + + + + + + + + + ), + }, + { + title: "Computer-based collaborative learning", + contents: ( + + + + + + ), + }, + { + title: "Open Educational Resources (OER)", + contents: ( + + + + ), + }, + { + title: "Applications in CS1 courses", + contents: ( + + + + + + + + ), + }, + { + title: "Application in Discrete Math and Algorithms courses", + contents: ( + + + + + + + + + + + + + + + + ), + }, + { + title: "Application in Database Systems courses", + contents: ( + + + + + + ), + }, +]; export default function Research() { return ( @@ -22,546 +437,29 @@ export default function Research() { subtitle="Collection of educational research and case studies using PrairieLearn" /> -
+
+
+ + {Papers.map((faq, i) => ( + + + {faq.title} + + {faq.contents} + + ))} + +
+
+ +
- - - - Mastery learning, randomized assessments and instant feedback - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - - - Creating robust and randomized assessments to reduce cheating - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - - Computer-based testing facilities (CBTF) - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - - Scheduling of asynchronous exams in a CBTF - - - 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. - - - - 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. - - - - 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. - - - - - Auto-grading of open-ended 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. - - - - 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. - - - - 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. - - - - -

Contribute to this page

-

- Do you have a paper that should be included on this page? Please - send us the appropriate information at{" "} - - hello@prairielearn.com - - . -

- - +

Contribute to this page

+

+ Do you have a paper that should be included on this page? Please + send us the appropriate information at{" "} + hello@prairielearn.com. +

From efbd3afe55d2f1cd4ad8f38b0de8f26136076869 Mon Sep 17 00:00:00 2001 From: Mariana Silva Date: Mon, 18 Mar 2024 22:38:55 -0700 Subject: [PATCH 2/7] removed unused import --- src/pages/research/index.tsx | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/pages/research/index.tsx b/src/pages/research/index.tsx index 901face..e212ac0 100644 --- a/src/pages/research/index.tsx +++ b/src/pages/research/index.tsx @@ -1,16 +1,10 @@ import React from "react"; -// import classnames from "classnames"; import Head from "next/head"; import Accordion from "react-bootstrap/Accordion"; import Link from "next/link"; import { PageBanner } from "../../components/Banner"; import { BannerCTA } from "../../components/CallToActionBanner"; -// import { Heading } from "../../components/Heading"; -// import { ResearchCard } from "../../components/ResearchCard"; -// import Stack from "../../components/Stack"; - -// import styles from "./index.module.scss"; export interface ResearchCardProps { title: string; From 2003544f122c804ffa296932578ac2d6fcdf29c0 Mon Sep 17 00:00:00 2001 From: Mariana Silva Date: Mon, 18 Mar 2024 22:39:28 -0700 Subject: [PATCH 3/7] deleted research card component --- src/components/ResearchCard.tsx | 25 ------------------------- 1 file changed, 25 deletions(-) delete mode 100644 src/components/ResearchCard.tsx diff --git a/src/components/ResearchCard.tsx b/src/components/ResearchCard.tsx deleted file mode 100644 index c4f3009..0000000 --- a/src/components/ResearchCard.tsx +++ /dev/null @@ -1,25 +0,0 @@ -import React from "react"; -import { Link } from "./Link"; - -export interface ResearchCardProps { - title: string; - children: React.ReactNode; - reference: string; - referenceHref: string; -} -export const ResearchCard: React.FC = ({ - title, - children, - referenceHref, - reference, -}) => ( -
-
-

{title}

-

{children}

- - {reference} - -
-
-); From 6747ad440f74564688e4c5a167bd39fa4df8ecac Mon Sep 17 00:00:00 2001 From: Mariana Silva Date: Mon, 18 Mar 2024 22:59:10 -0700 Subject: [PATCH 4/7] added sigcse 2024 papers --- src/pages/research/index.tsx | 32 +++++++++++++++++++++++++++++++- 1 file changed, 31 insertions(+), 1 deletion(-) diff --git a/src/pages/research/index.tsx b/src/pages/research/index.tsx index e212ac0..94f32cb 100644 --- a/src/pages/research/index.tsx +++ b/src/pages/research/index.tsx @@ -85,10 +85,16 @@ const Papers = [ /> + + ), }, @@ -294,6 +300,12 @@ const Papers = [ reference="T. Li, S. Hsu, M. Fowler, Z. Zhang, C. Zilles, and K. Karahalios. ICER 2023." referenceHref="https://doi.org/10.1145/3568813.3600124" /> + + ), }, @@ -312,6 +324,12 @@ const Papers = [ reference="M. Fong, L. Butler, A. Alawini, G. Herman, and M. Silva. ASEE 2023." referenceHref="https://peer.asee.org/42700" /> + + ), }, @@ -396,6 +414,18 @@ const Papers = [ reference="S. Poulsen, S. Kulkarni, G. Herman, and M. West. AIED 2023." referenceHref="https://doi.org/10.1007/978-3-031-36272-9_41" /> + + + + ), }, From 501f948e948858def1bb4432a119ff6d3e1ff477 Mon Sep 17 00:00:00 2001 From: Mariana Silva Date: Mon, 18 Mar 2024 23:04:19 -0700 Subject: [PATCH 5/7] removed old version of the file --- src/pages/research/index copy.tsx | 629 ------------------------------ 1 file changed, 629 deletions(-) delete mode 100644 src/pages/research/index copy.tsx diff --git a/src/pages/research/index copy.tsx b/src/pages/research/index copy.tsx deleted file mode 100644 index b1a71b0..0000000 --- a/src/pages/research/index copy.tsx +++ /dev/null @@ -1,629 +0,0 @@ -import React from "react"; -import classnames from "classnames"; -import Head from "next/head"; - -import { PageBanner } from "../../components/Banner"; -import { BannerCTA } from "../../components/CallToActionBanner"; -import { Heading } from "../../components/Heading"; -import { ResearchCard } from "../../components/ResearchCard"; -import Stack from "../../components/Stack"; - -import styles from "./index.module.scss"; - -export default function Research() { - return ( - - - Case Studies | PrairieLearn - - - - -
-
- - - - Mastery learning, randomized assessments and instant feedback - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - - - - - - - Creating robust and randomized assessments to reduce cheating - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - - Computer-based testing facilities (CBTF) - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - 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. - - - - - Scheduling of asynchronous exams in a CBTF - - - 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. - - - - 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. - - - - 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. - - - - - Auto-grading of open-ended 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. - - - - 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. - - - - 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. - - - - This papers presents survey and interview results that recorded - students’ interactions with a short-answer AI autograder for - “Explain in Plain English” code reading problems. Using causal - modeling, they inferred the learning impacts of wrong answers - marked as right (false positives, FPs) and right answers marked - as wrong (false negatives, FNs). - - - - - Open Educational Resources (OER) - - This paper aims to understand what motivates and dissuades - instructors to contribute to and adopt OERs. In addition, they - investigate what would increase the likelihood of instructors - contributing their work to OER repositories. - - - - - CS1 Research Studies - - This study aims to replicate a slightly simplified hierarchy of - skills in CS1 using a larger body of students in a non-major - introductory Python course with computer-based exams. We also - explore the validity of other possible hierarchies. - - - - -

Contribute to this page

-

- Do you have a paper that should be included on this page? Please - send us the appropriate information at{" "} - - hello@prairielearn.com - - . -

- - -
-
- - - - ); -} From 9962df5ea4e87d1c970dd353de64451780d9fe2e Mon Sep 17 00:00:00 2001 From: Mariana Silva Date: Tue, 19 Mar 2024 12:50:54 -0700 Subject: [PATCH 6/7] added 2 references and inverted sorting --- src/pages/research/index.tsx | 304 ++++++++++++++++++----------------- 1 file changed, 159 insertions(+), 145 deletions(-) diff --git a/src/pages/research/index.tsx b/src/pages/research/index.tsx index 94f32cb..7778297 100644 --- a/src/pages/research/index.tsx +++ b/src/pages/research/index.tsx @@ -33,25 +33,27 @@ const Papers = [ contents: ( + + ), @@ -102,30 +104,6 @@ const Papers = [ title: "Retrieval practice and second-chance testing", contents: ( - - - - - - - - + + + + + + + + ), }, { - title: "Computer-based testing facilities", + title: "Computer-based testing centers", contents: ( - - + + ), @@ -224,21 +226,21 @@ const Papers = [ contents: ( ), @@ -278,15 +280,15 @@ const Papers = [ contents: ( ), @@ -314,9 +316,9 @@ const Papers = [ contents: ( ), @@ -349,12 +351,6 @@ const Papers = [ title: "Applications in CS1 courses", contents: ( - - + + ), }, @@ -374,27 +376,15 @@ const Papers = [ contents: ( - - - - + + + + ), @@ -433,17 +435,29 @@ const Papers = [ title: "Application in Database Systems courses", contents: ( - - + + + + + + ), }, From 323672eb847c0ae35c8be70c3c69853f644fbf53 Mon Sep 17 00:00:00 2001 From: Nathan Sarang-Walters Date: Wed, 20 Mar 2024 13:33:55 -0700 Subject: [PATCH 7/7] Remove unused file --- src/pages/research/index.module.scss | 6 ------ 1 file changed, 6 deletions(-) delete mode 100644 src/pages/research/index.module.scss diff --git a/src/pages/research/index.module.scss b/src/pages/research/index.module.scss deleted file mode 100644 index 411d66a..0000000 --- a/src/pages/research/index.module.scss +++ /dev/null @@ -1,6 +0,0 @@ -@import "~bootstrap/scss/_functions.scss"; -@import "~bootstrap/scss/_variables.scss"; - -.container { - background-color: $gray-200; -}