arXiv subjects

Seth Poulsen

Publications and source records attributed to Seth Poulsen.

17 recordsLinked to original sources

arXiv2026

A Survey On Digital Reading Materials and Personal Study of Christian Religious Texts

Religion is an important part of many people's lives, with reading from religious texts being among the most common and important regular practices. Modern technology has in many ways changed the way this religious reading takes place, but the effects of these changes have not yet been studied. While there have been plenty of studies examining the effects of digital media on reading comprehension and similar topics, the study of religious texts is often primarily focused on achieving a religious experience, so the existing research is not sufficient to explore these changes. In our study, we used surveyed students from two universities to ask individuals how they use technology in their personal study of religious texts and religious education courses. Our respondents were predominately Christian. Through qualitative and quantitative analysis of the results, we aim to learn how technology interacts with study of religious texts, in what situations technology is preferred over print and vice versa, and the rationale for choosing the medium for a given task. In particular, we have found that paper scriptures are still frequently used even with the prevalence of technology. Additionally, individuals do not strictly use one particular medium during study; they tend to choose different mediums depending on the circumstances.

cs.HC↗

arXiv2026

Student programming behavior with and without phone notification suppression

Background and Context. Computer programming often involves extended periods of sustained activity and mobile phone notifications introduce frequent opportunities for interruption. Prior work demonstrates that suppressing phone notifications may reduce these disruptions. Objectives. Our primary research question is: How does suppressing phone notifications affect students' task engagement and productivity while programming? Method. We report on a replication and methodological extension study conducted in a CS1 course involving 22 students. Using a within-subject design, selected programming assignments were randomly designated for enabling notification suppression. Phone state logs were synchronized with millisecond-resolution IDE keystroke data to measure student attention and focus when in the control and notification-suppression conditions. Findings. Assignments completed with notification suppression enabled significantly lower break rates and longer intervals of focus compared to assignments completed in the control condition for many, but not all, students. This study provides evidence that notification suppression is associated with measurable differences in programming engagement and behavior. We also find a remarkable bimodality in the effect across students -- many students are positively affected, a small number are negatively affected, and very few experience little or no effect. This finding is consistent with other studies in diverse disciplines. Implications. Our results show that, for many students, phone notification suppression tools, such as Do Not Disturb, can improve attention and focus. Implications apply to educational settings (do-not-disturb as an intervention) and scholarship (understanding the effects of phone distraction).

cs.HC↗

arXiv2026

Combating Harms of Generative AI in CS1 with Code Review Interviews and a Flipped Classroom

Background and Context: Large Language Models (LLMs) are more accessible and accurate than ever before, raising significant concerns for computing educators. One major concern is students using LLMs to bypass the effort needed to understand concepts and metacognitive strategies essential for success in computer science. Objectives: We contribute a unique approach to assessing and building up student understanding through weekly oral code review assessments. These formative assessments incentivize students to understand their submitted code, regardless of whether or not the code was generated by AI tools. We also use a flipped classroom to provide time for students to learn concepts outside of class and provide ample time for students to schedule code review interviews. Methods: For this paper, we collected data from three semesters. We analyze student exam scores, keystroke logs, and surveys to understand how the new course policies affected student learning, behavior, and attitudes. Findings: Pairwise comparison of exam results reveals a statistically insignificant increase in average scores for Fall 2025 compared to previous semesters. Keystroke logs show a significant increase in characters pasted per total characters input into coding assignments in Fall 2025, pointing towards higher AI usage. Survey results show positive student sentiment towards code reviews at the end of Fall 2025, with nearly all negative feedback being addressable through better scheduling and more rigorous TA training. Implications: Oral code reviews with a flipped classroom appear to be effective at mitigating harms of LLM use while providing space for students to freely experiment with these tools. Our work suggests that students in Fall 2025 still show adequate understanding of material covered in written exams, despite dramatic increases in LLM usage for coding assignments.

cs.HC↗

arXiv2026

Assessing Student Ability to Select an Algorithmic Paradigm

Computer science students are expected to be able to look at a problem and select an appropriate algorithm design paradigm to use to produce a solution. However, there is little research on how students determine which algorithmic paradigm to use. Historically, researchers have relied on free-response questions or interviews to assess students' knowledge of algorithmic paradigm selection. To successfully evaluate and scale teaching interventions for selecting an algorithmic design paradigm, we need to efficiently test a student's ability to select among different design paradigms. Here, we present the first attempts to assess student knowledge to select an algorithm design paradigm using multiple-choice questions. We present the construction of the \textit{algorithmic paradigm selection assessment} (APSA) and preliminary data demonstrating its effectiveness as an assessment. We discuss the key points we learned during this process to write multiple-choice questions for Algorithm Design Paradigms. We tested the internal consistency of our assessment using Cronbach's $\alpha$ and obtained a score of $0.73$, which is above the required threshold of $0.7$. APSA can be used across institutions as a standardized way to assess students' ability to select different algorithm design paradigms. APSA will assist researchers in evaluating whether a theory helps students improve their knowledge of different Algorithm Design Paradigms.

cs.CY↗

arXiv2026

The Effect of Transparency on Students' Perceptions of AI Graders

The development of effective autograders is key for scaling assessment and feedback. While NLP based autograding systems for open-ended response questions have been found to be beneficial for providing immediate feedback, autograders are not always liked, understood, or trusted by students. Our research tested the effect of transparency on students' attitudes towards autograders. Transparent autograders increased students' perceptions of autograder accuracy and willingness to discuss autograders in survey comments, but did not improve other related attitudes -- such as willingness to be graded by them on a test -- relative to the control without transparency. However, this lack of impact may be due to higher measured student trust towards autograders in this study than in prior work in the field. We briefly discuss possible reasons for this trend.

cs.HC↗

arXiv2025

Choose Your Own Solution: Supporting Optional Blocks in Block Ordering Problems

This paper extends the functionality of block ordering problems (such as Parsons problems and Proof Blocks) to include optional blocks. We detail the algorithms used to implement the optional block feature and present usage experiences from instructors who have integrated it into their curriculum. The optional blocks feature enables instructors to create more complex Parsons problems with multiple correct solutions utilizing omitted or optional blocks. This affords students a method to engage with questions that have several valid solutions composed of different answer components. Instructors can specify blocks with multiple mutually exclusive dependencies, which we represent using a multigraph structure. This multigraph is then collapsed into multiple directed acyclic graphs (DAGs), allowing us to reuse existing algorithms for grading block ordering problems represented as a DAG. We present potential use cases for this feature across various domains, including helping students learn Git workflows, shell command sequences, mathematical proofs, and Python programming concepts.

cs.HC↗

arXiv2025

Language Models are Few-Shot Graders

Providing evaluations to student work is a critical component of effective student learning, and automating its process can significantly reduce the workload on human graders. Automatic Short Answer Grading (ASAG) systems, enabled by advancements in Large Language Models (LLMs), offer a promising solution for assessing and providing instant feedback for open-ended student responses. In this paper, we present an ASAG pipeline leveraging state-of-the-art LLMs. Our new LLM-based ASAG pipeline achieves better performances than existing custom-built models on the same datasets. We also compare the grading performance of three OpenAI models: GPT-4, GPT-4o, and o1-preview. Our results demonstrate that GPT-4o achieves the best balance between accuracy and cost-effectiveness. On the other hand, o1-preview, despite higher accuracy, exhibits a larger variance in error that makes it less practical for classroom use. We investigate the effects of incorporating instructor-graded examples into prompts using no examples, random selection, and Retrieval-Augmented Generation (RAG)-based selection strategies. Our findings indicate that providing graded examples enhances grading accuracy, with RAG-based selection outperforming random selection. Additionally, integrating grading rubrics improves accuracy by offering a structured standard for evaluation.

cs.CL↗

arXiv2024

Construction and Preliminary Validation of a Dynamic Programming Concept Inventory

Concept inventories are standardized assessments that evaluate student understanding of key concepts within academic disciplines. While prevalent across STEM fields, their development lags for advanced computer science topics like dynamic programming (DP) -- an algorithmic technique that poses significant conceptual challenges for undergraduates. To fill this gap, we developed and validated a Dynamic Programming Concept Inventory (DPCI). We detail the iterative process used to formulate multiple-choice questions targeting known student misconceptions about DP concepts identified through prior research studies. We discuss key decisions, tradeoffs, and challenges faced in crafting probing questions to subtly reveal these conceptual misunderstandings. We conducted a preliminary psychometric validation by administering the DPCI to 172 undergraduate CS students finding our questions to be of appropriate difficulty and effectively discriminating between differing levels of student understanding. Taken together, our validated DPCI will enable instructors to accurately assess student mastery of DP. Moreover, our approach for devising a concept inventory for an advanced theoretical computer science concept can guide future efforts to create assessments for other under-evaluated areas currently lacking coverage.

cs.DS↗

arXiv2024

Mining Hierarchies with Conviction: Constructing the CS1 Skill Hierarchy with Pairwise Comparisons over Skill Distributions

Background and Context: Some skills taught in introductory programming courses are categorized into 1) explaining code, 2) arranging lines of code in correct sequence, 3) tracing through the execution of a program, and 4) writing code from scratch. Objective: Knowing if a programming skill is a prerequisite to another would benefit teachers in properly planning the course and structuring the order in which they present activities relating to new content. Prior attempts to establish a skill hierarchy have suffered from methodological issues. Method: In this study, we used the conviction measure from association rule mining to perform pair-wise comparisons of five skills: Write, Trace, Reverse trace, Sequence, and Explain code. We used the data from four exams with more than 600 participants where students solved programming assignments of different skills for several programming topics. Findings: Our findings matched the previous finding that tracing is a prerequisite for students to learn to write code. Contradicting the previous claims, our analysis showed that using the mean threshold writing code is a prerequisite to explaining code. However, there is no clear relationship when we change the threshold to the median. Unlike prior work, we did not find a clear prerequisite relationship between sequencing code and writing or explaining code. Implications: Our research can help instructors by systematically arranging the skills students exercise when encountering a new topic. The goal is to help instructors properly teach and assess programming in a fashion most effective for learning by leveraging the relationship between skills.

cs.HC↗

arXiv2024

Autograding Mathematical Induction Proofs with Natural Language Processing

In mathematical proof education, there remains a need for interventions that help students learn to write mathematical proofs. Research has shown that timely feedback can be very helpful to students learning new skills. While for many years natural language processing models have struggled to perform well on tasks related to mathematical texts, recent developments in natural language processing have created the opportunity to complete the task of giving students instant feedback on their mathematical proofs. In this paper, we present a set of training methods and models capable of autograding freeform mathematical proofs by leveraging existing large language models and other machine learning techniques. The models are trained using proof data collected from four different proof by induction problems. We use four different robust large language models to compare their performances, and all achieve satisfactory performances to various degrees. Additionally, we recruit human graders to grade the same proofs as the training data, and find that the best grading model is also more accurate than most human graders. With the development of these grading models, we create and deploy an autograder for proof by induction problems and perform a user study with students. Results from the study shows that students are able to make significant improvements to their proofs using the feedback from the autograder, but students still do not trust the AI autograders as much as they trust human graders. Future work can improve on the autograder feedback and figure out ways to help students trust AI autograders.

cs.AI↗

arXiv2024

Teaching Algorithm Design: A Literature Review

Algorithm design is a vital skill developed in most undergraduate Computer Science (CS) programs, but few research studies focus on pedagogy related to algorithms coursework. To understand the work that has been done in the area, we present a systematic survey and literature review of CS Education studies. We search for research that is both related to algorithm design and evaluated on undergraduate-level students. Across all papers in the ACM Digital Library prior to August 2023, we only find 94 such papers. We first classify these papers by topic, evaluation metric, evaluation methods, and intervention target. Through our classification, we find a broad sparsity of papers which indicates that many open questions remain about teaching algorithm design, with each algorithm topic only being discussed in between 0 and 10 papers. We also note the need for papers using rigorous research methods, as only 38 out of 88 papers presenting quantitative data use statistical tests, and only 15 out of 45 papers presenting qualitative data use a coding scheme. Only 17 papers report controlled trials. We then synthesize the results of the existing literature to give insights into what the corpus reveals about how we should teach algorithms. Much of the literature explores implementing well-established practices, such as active learning or automated assessment, in the algorithms classroom. However, there are algorithms-specific results as well: a number of papers find that students may under-utilize certain algorithmic design techniques, and studies describe a variety of ways to select algorithms problems that increase student engagement and learning. The results we present, along with the publicly available set of papers collected, provide a detailed representation of the current corpus of CS Education work related to algorithm design and can orient further research in the area.

cs.DS↗

arXiv2023

Measuring the Impact of Distractors on Student Learning Gains while Using Proof Blocks

Background: Proof Blocks is a software tool that enables students to construct proofs by assembling prewritten lines and gives them automated feedback. Prior work on learning gains from Proof Blocks has focused on comparing learning gains from Proof Blocks against other learning activities such as writing proofs or reading. Purpose: The study described in this paper aims to compare learning gains from different variations of Proof Blocks. Specifically, we attempt to quantify the difference in learning gains for students who complete Proof Blocks problems with and without distractors. Methods: We conducted a randomized controlled trial with three experimental groups: a control group that completed an off-topic Proof Blocks activity, one that completed a \tool{} activity without distractors, and one that completed a Proof Blocks activity with distractors. All three groups read a book chapter on proof by induction before completing their activity. Findings: The group that completed the Proof Blocks activity with distractors performed better on the posttest than the group that completed the Proof Blocks without distractors, who in turn performed better than the group that completed the off-topic Proof Blocks activity. However, none of these differences were statistically significant. While the results of this study are inconclusive, we hope that it can serve as a foundation for future work.

cs.HC↗

arXiv2023

Efficient Feedback and Partial Credit Grading for Proof Blocks Problems

Proof Blocks is a software tool that allows students to practice writing mathematical proofs by dragging and dropping lines instead of writing proofs from scratch. Proof Blocks offers the capability of assigning partial credit and providing solution quality feedback to students. This is done by computing the edit distance from a student's submission to some predefined set of solutions. In this work, we propose an algorithm for the edit distance problem that significantly outperforms the baseline procedure of exhaustively enumerating over the entire search space. Our algorithm relies on a reduction to the minimum vertex cover problem. We benchmark our algorithm on thousands of student submissions from multiple courses, showing that the baseline algorithm is intractable, and that our proposed algorithm is critical to enable classroom deployment. Our new algorithm has also been used for problems in many other domains where the solution space can be modeled as a DAG, including but not limited to Parsons Problems for writing code, helping students understand packet ordering in networking protocols, and helping students sketch solution steps for physics problems. Integrated into multiple learning management systems, the algorithm serves thousands of students each year.

cs.AI↗

arXiv2022

Efficiency of Learning from Proof Blocks Versus Writing Proofs

Proof Blocks is a software tool that provides students with a scaffolded proof-writing experience, allowing them to drag and drop prewritten proof lines into the correct order instead of starting from scratch. In this paper we describe a randomized controlled trial designed to measure the learning gains of using Proof Blocks for students learning proof by induction. The study participants were 332 students recruited after completing the first month of their discrete mathematics course. Students in the study took a pretest and read lecture notes on proof by induction, completed a brief (less than 1 hour) learning activity, and then returned one week later to complete the posttest. Depending on the experimental condition that each student was assigned to, they either completed only Proof Blocks problems, completed some Proof Blocks problems and some written proofs, or completed only written proofs for their learning activity. We find that students in the early phases of learning about proof by induction are able to learn just as much from reading lecture notes and using Proof Blocks as by reading lecture notes and writing proofs from scratch, but in far less time on task. This finding complements previous findings that Proof Blocks are useful exam questions and are viewed positively by students.

cs.CY↗

arXiv2022

Proof Blocks: Autogradable Scaffolding Activities for Learning to Write Proofs

Proof Blocks is a software tool which enables students to write proofs by dragging and dropping prewritten proof lines into the correct order. These proofs can be graded completely automatically, enabling students to receive rapid feedback on how they are doing with their proofs. When constructing a problem, the instructor specifies the dependency graph of the lines of the proof, so that any correct arrangement of the lines can receive full credit. This innovation can improve assessment tools by increasing the types of questions we can ask students about proofs, and can give greater access to proof knowledge by increasing the amount that students can learn on their own with the help of a computer.

cs.CY↗

arXiv2020

Experiences and Lessons Learned Creating and Validating Concept Inventories for Cybersecurity

We reflect on our ongoing journey in the educational Cybersecurity Assessment Tools (CATS) Project to create two concept inventories for cybersecurity. We identify key steps in this journey and important questions we faced. We explain the decisions we made and discuss the consequences of those decisions, highlighting what worked well and what might have gone better. The CATS Project is creating and validating two concept inventories---conceptual tests of understanding---that can be used to measure the effectiveness of various approaches to teaching and learning cybersecurity. The Cybersecurity Concept Inventory (CCI) is for students who have recently completed any first course in cybersecurity; the Cybersecurity Curriculum Assessment (CCA) is for students who have recently completed an undergraduate major or track in cybersecurity. Each assessment tool comprises 25 multiple-choice questions (MCQs) of various difficulties that target the same five core concepts, but the CCA assumes greater technical background. Key steps include defining project scope, identifying the core concepts, uncovering student misconceptions, creating scenarios, drafting question stems, developing distractor answer choices, generating educational materials, performing expert reviews, recruiting student subjects, organizing workshops, building community acceptance, forming a team and nurturing collaboration, adopting tools, and obtaining and using funding. Creating effective MCQs is difficult and time-consuming, and cybersecurity presents special challenges. Because cybersecurity issues are often subtle, where the adversarial model and details matter greatly, it is challenging to construct MCQs for which there is exactly one best but non-obvious answer. We hope that our experiences and lessons learned may help others create more effective concept inventories and assessments in STEM.

cs.CR↗

arXiv2017

Difference sets disjoint from a subgroup

We study finite groups $G$ having a subgroup $H$ and $D \subset G \setminus H$ such that the multiset $\{ xy^{-1}:x,y \in D\}$ has every non-identity element occur the same number of times (such a $D$ is called a {\it difference set}). We show that $H$ has to be normal, that $|G|=|H|^2$, and that $|D \cap Hg|=|H|/2$ for all $g \notin H$. We show that $H$ is contained in every normal subgroup of prime index, and other properties. We give a $2$-parameter family of examples of such groups. We show that such groups have Schur rings with four principal sets.

math.GR↗