@article{oliveira_gao_heckman_lynch_2024, title={Exploring Novice Programmer Testing Behavior: A First Step to Define Coding Struggle}, url={https://doi.org/10.1145/3626252.3630851}, DOI={10.1145/3626252.3630851}, abstractNote={To promote good coding practices, we need to understand what students do when they are on their own. In this research study, we explore students' testing behavior and response to persistent errors to better understand their coding patterns. We investigate how those patterns change when they struggle, and how help-seeking might influence their coding behaviors. We define struggle during coding as failing the same unit test case consecutively for more than four submission events, considering only unit test cases created by the instructors. To analyze the students' coding data, we use progress indicators, student test implementation indicators, and both student-generated and instructor-generated unit test results from each student submission event. In addition, we use office hours attendance records and amount of assignment-related posts created on the course forum. Results show that students tend not to follow test-driven development practices, even when explicitly directed to, and tend to create unit tests only to earn assignment credit rather than to guide their software development. Students also tend not to modify their own unit tests once they have earned the related credits, even when facing coding struggle; they tend to modify their unit tests only after they have been facing coding struggle for an extended number of submission events.}, journal={PROCEEDINGS OF THE 55TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, SIGCSE 2024, VOL. 1}, author={Oliveira, Gabriel Silva and Gao, Zhikai and Heckman, Sarah and Lynch, Collin}, year={2024}, pages={1251–1257} }
 @article{ma_celepkolu_boyer_wiebe_lynch_israel_2023, title={How Noisy is Too Noisy? The Impact of Data Noise on Multimodal Recognition of Confusion and Conflict During Collaborative Learning}, url={https://doi.org/10.1145/3577190.3614127}, DOI={10.1145/3577190.3614127}, abstractNote={Intelligent systems to support collaborative learning rely on real-time behavioral data, including language, audio, and video. However, noisy data, such as word errors in speech recognition, audio static or background noise, and facial mistracking in video, often limit the utility of multimodal data. It is an open question of how we can build reliable multimodal models in the face of substantial data noise. In this paper, we investigate the impact of data noise on the recognition of confusion and conflict moments during collaborative programming sessions by 25 dyads of elementary school learners. We measure language errors with word error rate (WER), audio noise with speech-to-noise ratio (SNR), and video errors with frame-by-frame facial tracking accuracy. The results showed that the model’s accuracy for detecting confusion and conflict in the language modality decreased drastically from 0.84 to 0.73 when the WER exceeded 20%. Similarly, in the audio modality, the model’s accuracy decreased sharply from 0.79 to 0.61 when the SNR dropped below 5 dB. Conversely, the model’s accuracy remained relatively constant in the video modality at a comparable level (> 0.70) so long as at least one learner’s face was successfully tracked. Moreover, we trained several multimodal models and found that integrating multimodal data could effectively offset the negative effect of noise in unimodal data, ultimately leading to improved accuracy in recognizing confusion and conflict. These findings have practical implications for the future deployment of intelligent systems that support collaborative learning in actual classroom settings.}, journal={PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, ICMI 2023}, author={Ma, Yingbo and Celepkolu, Mehmet and Boyer, Kristy Elizabeth and Wiebe, Eric and Lynch, Collin F. and Israel, Maya}, year={2023}, pages={326–335} }
 @article{vandenberg_lynch_boyer_wiebe_2022, title={"I remember how to do it": exploring upper elementary students' collaborative regulation while pair programming using epistemic network analysis}, volume={3}, ISSN={["1744-5175"]}, url={https://doi.org/10.1080/08993408.2022.2044672}, DOI={10.1080/08993408.2022.2044672}, abstractNote={ABSTRACT Background and Context Students’ self-efficacy toward computing affect their participation in related tasks and courses. Self-efficacy is likely influenced by students’ initial experiences and exposure to computer science (CS) activities. Moreover, student interest in a subject likely informs their ability to effectively regulate their learning in that domain. One way to enhance interest in CS is through using collaborative pair programming. Objective We wanted to explore upper elementary students’ self-efficacy for and conceptual understanding of CS as manifest in collaborative and regulated discourse during pair programming. Method We implemented a five-week CS intervention with 4th and 5th grade students and collected self-report data on students’ CS attitudes and conceptual understanding, as well as transcripts of dyads talking while problem solving on a pair programming task. Findings The students’ self-report data, organized by dyad, fell into three categories based on the dyad’s CS self-efficacy and conceptual understanding scores. Findings from within- and cross-case analyses revealed a range of ways the dyads’ self-efficacy and CS conceptual understanding affected their collaborative and regulated discourse. Implications Recommendations for practitioners and researchers are provided. We suggest that upper elementary students learn about productive disagreement and how to peer model. Additionally, our findings may help practitioners with varied ways to group their students.}, journal={COMPUTER SCIENCE EDUCATION}, publisher={Informa UK Limited}, author={Vandenberg, Jessica and Lynch, Collin and Boyer, Kristy Elizabeth and Wiebe, Eric}, year={2022}, month={Mar} }
 @article{erickson_heckman_lynch_2022, title={Characterizing Student Development Progress: Validating Student Adherence to Project Milestones}, DOI={10.1145/3478431.3499373}, abstractNote={As enrollment in CS programs have risen, it has become increasingly difficult for teaching staff to provide timely and detailed guidance on student projects. To address this, instructors use automated assessment tools to evaluate students' code and processes as they work. Even with automation, understanding students' progress, and more importantly, if students are making the 'right' progress toward the solution is challenging at scale. To help students manage their time and learn good software engineering processes, instructors may create intermediate deadlines, or milestones, to support progress. However, student's adherence to these processes is opaque and may hinder student success and instructional support. Better understanding of how students follow process guidance in practice is needed to identify the right assignment structures to support development of high-quality process skills. We use data collected from an automated assessment tool, to calculate a set of 15 progress indicators to investigate which types of progress are being made during four stages of two projects in a CS2 course. These stages are split up by milestones to help guide student activities. We show how looking at which progress indicators are triggered significantly more or less during each stage validates whether students are adhering to the goals of each milestone. We also find students trigger some progress indicators earlier on the second project suggesting improving processes over time.}, journal={PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1}, author={Erickson, Bradley and Heckman, Sarah and Lynch, Collin F.}, year={2022}, pages={15–21} }
 @article{gitinabard_heckman_barnes_lynch_2022, title={Designing a Dashboard for Student Teamwork Analysis}, DOI={10.1145/3478431.3499377}, abstractNote={Classroom dashboards are designed to help instructors effectively orchestrate classrooms by providing summary statistics, activity tracking, and other information. Existing dashboards are generally specific to an LMS or platform and they generally summarize individual work, not group behaviors. However, CS courses typically involve constellations of tools and mix on- and offline collaboration. Thus, cross-platform monitoring of individuals and teams is important to develop a full picture of the class. In this work, we describe our work on Concert, a data integration platform that collects data about student activities from several sources such as Piazza, My Digital Hand, and GitHub and uses it to support classroom monitoring through analysis and visualizations. We discuss team visualizations that we have developed to support effective group management and to help instructors identify teams in need of intervention.}, journal={PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1}, author={Gitinabard, Niki and Heckman, Sarah and Barnes, Tiffany and Lynch, Collin}, year={2022}, pages={446–452} }
 @article{gaweda_lynch_2022, title={Exploration of theWeek-by-Week ICAP Transitions by Students}, DOI={10.1145/3478432.3499068}, abstractNote={CS courses often use a variety of learning activities to assist students while learning concepts. These activities' levels of engagement can be categorized through the ICAP framework as Interactive, Constructive, Active, and Passive respectfully. For this work, we categorize learning activities from an online professional development course and analyzed the probabilities of transitioning between ICAP modalities on a week-by-week basis. This poster presents our analysis on which ICAP modes students visited during each week of the course. We found the majority of students would review Passive materials before Interactive activities, then repeating this process. The second most common transition followed the ICAP Framework, selecting activities with increasing levels of engagement. Contrary to our assumptions, students primarily worked on 'new' materials, rather than 'review' previous activities.}, journal={PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 2}, author={Gaweda, Adam M. and Lynch, Collin F.}, year={2022}, pages={1088–1088} }
 @article{ma_ruiz_brown_diaz_gaweda_celepkolu_boyer_lynch_wiebe_2022, title={It's Challenging but Doable: Lessons Learned from a Remote Collaborative Coding Camp for Elementary Students}, DOI={10.1145/3478431.3499327}, abstractNote={The COVID-19 pandemic shifted many U.S. schools from in-person to remote instruction. While collaborative CS activities had become increasingly common in classrooms prior to the pandemic, the sudden shift to remote learning presented challenges for both teachers and students in implementing and supporting collaborative learning. Though some research on remote collaborative CS learning has been conducted with adult learners, less has been done with younger learners such as elementary school students. This experience report describes lessons learned from a remote after-school camp with 24 elementary school students who participated in a series of individual and paired learning activities over three weeks. We describe the design of the learning activities, participant recruitment, group formation, and data collection process. We also provide practical implications for implementation such as how to guide facilitators, pair students, and calibrate task difficulty to foster collaboration. This experience report contributes to the understanding of remote CS learning practices, particularly for elementary school students, and we hope it will provoke methodological advancement in this important area.}, journal={PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1}, author={Ma, Yingbo and Ruiz, Julianna Martinez and Brown, Timothy D. and Diaz, Kiana-Alize and Gaweda, Adam M. and Celepkolu, Mehmet and Boyer, Kristy Elizabeth and Lynch, Collin F. and Wiebe, Eric}, year={2022}, pages={342–348} }
 @article{zakaria_vandenberg_tsan_boulden_lynch_boyer_wiebe_2022, title={Two-Computer Pair Programming: Exploring a Feedback Intervention to improve Collaborative Talk in Elementary Students.}, volume={32}, ISSN={["1744-5175"]}, url={https://doi.org/10.1080/08993408.2021.1877987}, DOI={10.1080/08993408.2021.1877987}, abstractNote={ABSTRACT Background and Context Researchers and practitioners have begun to incorporate collaboration in programming because of its reported instructional and professional benefits. However, younger students need guidance on how to collaborate in environments that require substantial interpersonal interaction and negotiation. Previous research indicates that feedback fosters students’ productive collaboration. Objective This study employs an intervention to explore the role instructor-directed feedback plays on elementary students’ dyadic collaboration during 2-computer pair programming. Method We used a multi-study design, collecting video data on students’ dyadic collaboration. Study 1 qualitatively explored dyadic collaboration by coding video transcripts of four dyads which guided the design of Study 2 that examined conversation of six dyads using MANOVA and non-parametric tests. Findings Result from Study 2 showed that students receiving feedback used productive conversation categories significantly higher than the control condition in the sample group considered. Results are discussed in terms of group differences in specific conversation categories. Implications Our study highlights ways to support students in pair programming contexts so that they can maximize the benefits afforded through these experiences.}, number={1}, journal={COMPUTER SCIENCE EDUCATION}, publisher={Informa UK Limited}, author={Zakaria, Zarifa and Vandenberg, Jessica and Tsan, Jennifer and Boulden, Danielle Cadieux and Lynch, Collin F. and Boyer, Kristy Elizabeth and Wiebe, Eric N.}, year={2022}, month={Jan}, pages={3–29} }
 @article{gao_heckman_lynch_2022, title={Who Uses Office Hours? A Comparison of In-Person and Virtual Office Hours Utilization}, DOI={10.1145/3478431.3499334}, abstractNote={In Computer Science (CS) education, instructors use office hours for one-on-one help-seeking. Prior work has shown that traditional in-person office hours may be underutilized. In response many instructors are adding or transitioning to virtual office hours. Our research focuses on comparing in-person and online office hours to investigate differences between performance, interaction time, and the characteristics of the students who utilize in-person and virtual office hours. We analyze a rich dataset covering two semesters of a CS2 course which used in-person office hours in Fall 2019 and virtual office hours in Fall 2020. Our data covers students' use of office hours, the nature of their questions, and the time spent receiving help as well as demographic and attitude data. Our results show no relationship between student's attendance in office hours and class performance. However we found that female students attended office hours more frequently, as did students with a fixed mindset in computing, and those with weaker skills in transferring theory to practice. We also found that students with low confidence in or low enjoyment toward CS were more active in virtual office hours. Finally, we observed a significant correlation between students attending virtual office hours and an increased interest in CS study; while students attending in-person office hours tend to show an increase in their growth mindset.}, journal={PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1}, author={Gao, Zhikai and Heckman, Sarah and Lynch, Collin}, year={2022}, pages={300–306} }
 @article{akintunde_limke_barnes_heckman_lynch_2021, title={PEDI - Piazza Explorer Dashboard for Intervention}, ISSN={["1943-6092"]}, DOI={10.1109/VL/HCC51201.2021.9576443}, abstractNote={Analytics about how students navigate online learning tools throughout the duration of an assignment is scarce. Knowledge about how students use online tools before a course's end could positively impact students' learning outcomes. We introduce PEDI (Piazza Explorer Dashboard for Intervention), a tool which analyzes and presents visualizations of forum activity on Piazza, a question and answer forum, to instructors. We outline the design principles and data-informed recommendations used to design PEDI. Our prior research revealed two critical periods in students' forum engagement over the duration of an assignment. Early engagement in the first half of an assignment duration positively correlates with class average performance. Whereas, extremely high engagement toward the deadline predicted lower class average performance. PEDI uses these findings to detect and flag troubling engagement levels and informs instructors through clear visualizations to promote data-informed interventions. By providing insights to instructors, PEDI may improve class performance and pave the way for a new generation of online tools.}, journal={2021 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING (VL/HCC 2021)}, author={Akintunde, Ruth Okoilu and Limke, Ally and Barnes, Tiffany and Heckman, Sarah and Lynch, Collin}, year={2021} }
 @article{vandenberg_zakaria_tsan_iwanski_lynch_boyer_wiebe_2021, title={Prompting collaborative and exploratory discourse: An epistemic network analysis study}, volume={8}, ISSN={["1556-1615"]}, DOI={10.1007/s11412-021-09349-3}, journal={INTERNATIONAL JOURNAL OF COMPUTER-SUPPORTED COLLABORATIVE LEARNING}, author={Vandenberg, Jessica and Zakaria, Zarifa and Tsan, Jennifer and Iwanski, Anna and Lynch, Collin and Boyer, Kristy Elizabeth and Wiebe, Eric}, year={2021}, month={Aug} }
 @article{ma_wiggins_celepkolu_boyer_lynch_wiebe_2021, title={The Challenge of Noisy Classrooms: Speaker Detection During Elementary Students' Collaborative Dialogue}, volume={12748}, ISBN={["978-3-030-78291-7"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78292-4_22}, abstractNote={Adaptive and intelligent collaborative learning support systems are effective for supporting learning and building strong collaborative skills. This potential has not yet been realized within noisy classroom environments, where automated speech recognition (ASR) is very difficult. A key challenge is to differentiate each learner’s speech from the background noise, which includes the teachers’ speech as well as other groups’ speech. In this paper, we explore a multimodal method to identify speakers by using visual and acoustic features from ten video recordings of children pairs collaborating in an elementary school classroom. The results indicate that the visual modality was better for identifying the speaker when in-group speech was detected, while the acoustic modality was better for differentiating in-group speech from background speech. Our analysis also revealed that recurrent neural network (RNN)-based models outperformed convolutional neural network (CNN)-based models with higher speaker detection F-1 scores. This work represents a critical step toward the classroom deployment of intelligent systems that support collaborative learning.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT I}, author={Ma, Yingbo and Wiggins, Joseph B. and Celepkolu, Mehmet and Boyer, Kristy Elizabeth and Lynch, Collin and Wiebe, Eric}, year={2021}, pages={268–281} }
 @article{vandenberg_tsan_boulden_zakaria_lynch_boyer_wiebe_2020, title={Elementary Students' Understanding of CS Terms}, volume={20}, ISSN={["1946-6226"]}, DOI={10.1145/3386364}, abstractNote={The language and concepts used by curriculum designers are not always interpreted by children as designers intended. This can be problematic when researchers use self-reported survey instruments in concert with curricula, which often rely on the implicit belief that students’ understanding aligns with their own. We report on our refinement of a validated survey to measure upper elementary students’ attitudes and perspectives about computer science (CS), using an iterative, design-based research approach informed by educational and psychological cognitive interview processes. We interviewed six groups of students over three iterations of the instrument on their understanding of CS concepts and attitudes toward coding. Our findings indicated that students could not explain the terms computer programs nor computer science as expected. Furthermore, they struggled to understand how coding may support their learning in other domains. These results may guide the development of appropriate CS-related survey instruments and curricular materials for K–6 students.}, number={3}, journal={ACM TRANSACTIONS ON COMPUTING EDUCATION}, author={Vandenberg, Jessica and Tsan, Jennifer and Boulden, Danielle and Zakaria, Zarifa and Lynch, Collin and Boyer, Kristy Elizabeth and Wiebe, Eric}, year={2020}, month={Sep} }
 @article{shabrina_akintunde_maniktala_barnes_lynch_rutherford_2020, title={Peeking through the Classroom Window : A Detailed Data-Driven Analysis on the Usage of a Curriculum Integrated Math Game in Authentic Classrooms}, DOI={10.1145/3375462.3375525}, abstractNote={We present a data-driven analysis that provides generalized insights of how a curriculum integrated educational math game gets used as a routinized classroom activity throughout the year in authentic primary school classrooms. Our study relates observations from a field study on Spatial Temporal Math (ST Math) to our findings mined from ST Math students' sequential game play data. We identified features that vary across game play sessions and modeled their relationship with session performance. We also derived data-informed suggestions that may provide teachers with insights into how to design classroom game play sessions to facilitate more effective learning.}, journal={LAK20: THE TENTH INTERNATIONAL CONFERENCE ON LEARNING ANALYTICS & KNOWLEDGE}, author={Shabrina, Preya and Akintunde, Ruth Okoilu and Maniktala, Mehak and Barnes, Tiffany and Lynch, Collin and Rutherford, Teomara}, year={2020}, pages={625–634} }
 @article{peddycord-liu_catete_vandenberg_barnes_lynch_rutherford_2019, title={A Field Study of Teachers Using a Curriculum-integrated Digital Game}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85067602037&partnerID=MN8TOARS}, DOI={10.1145/3290605.3300658}, abstractNote={We present a new framework describing how teachers use ST Math, a curriculum-integrated, year-long educational game, in 3rd-4th grade classrooms. We combined authentic classroom observations with teacher interviews to identify teacher needs and practices. Our findings extended and contrasted with prior work on teachers' behaviors around classroom games, identifying differences likely arising from a digital platform and year-long curricular integration. We suggest practical ways that curriculum-integrated games can be designed to help teachers support effective classroom culture and practice.}, journal={CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS}, publisher={ACM Press}, author={Peddycord-Liu, Zhongxiu and Catete, Veronica and Vandenberg, Jessica and Barnes, Tiffany and Lynch, Collin F. and Rutherford, Teomara}, year={2019} }
 @article{weldon_mueller_lynch_schuster_hedges_awe_li_barbeau_mattingly_2019, title={High-precision characterization of the neutron light output of stilbene along the directions of maximum and minimum response}, volume={927}, ISSN={["1872-9576"]}, DOI={10.1016/j.nima.2018.10.075}, abstractNote={The scintillation light output response of stilbene crystals has been measured for protons recoiling along the a, b, and c’ crystalline axes with energies between 1.3 and 10 MeV using neutrons produced with the tandem Van de Graaff accelerator at Triangle Universities Nuclear Laboratory. The proton recoil energy and direction were measured using the coincident detection of neutrons between a stilbene scintillator and an array of EJ-309 liquid scintillators spanning arranged neutron recoil angles. The maximum light output was found to coincide with proton recoils along the a-axis, in disagreement with other published measurements, which reported the b-axis as the direction of the maximum light output. Additional measurements were conducted using two different stilbene crystals to confirm these results: a second measurement using the coincident detection of neutrons; measurements of neutron full energy deposition events along the a and b axes; and measurements of the count rate for 252Cf neutrons traveling along the a and b axes directions. All measurements found that recoils along the a-axis produce the maximum light output.}, journal={NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT}, author={Weldon, R. A., Jr. and Mueller, J. M. and Lynch, C. and Schuster, P. and Hedges, S. and Awe, C. and Li, L. and Barbeau, P. and Mattingly, J.}, year={2019}, month={May}, pages={313–319} }
 @article{gitinabard_xu_heckman_barnes_lynch_2019, title={How Widely Can Prediction Models Be Generalized? Performance Prediction in Blended Courses}, volume={12}, ISSN={["1939-1382"]}, url={https://doi.org/10.1109/TLT.2019.2911832}, DOI={10.1109/TLT.2019.2911832}, abstractNote={Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students’ study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions, even for repeated offerings of the same course. In this paper, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models of student performance based on persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This paper tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.}, number={2}, journal={IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Gitinabard, Niki and Xu, Yiqiao and Heckman, Sarah and Barnes, Tiffany and Lynch, Collin F.}, year={2019}, pages={184–197} }
 @article{tsan_rodriguez_boyer_lynch_2018, title={"I Think We Should...": Analyzing Elementary Students' Collaborative Processes for Giving and Taking Suggestions}, DOI={10.1145/3159450.3159507}, abstractNote={Collaboration plays an essential role in computer science. While there is growing recognition that learners of all ages can benefit from collaborative learning, little is known about how elementary-age children engage in collaborative problem solving in computer science. This paper reports on the analysis of a dataset of elementary students collaborating on a programming project. We found that children tend to make several different types of suggestions. In turn, their partners address those suggestions in different ways such as by implementing them directly in code or by replying through dialogue. We observe that students regularly accept or reject suggestions without explanation or explicit acknowledgement and that it is often unclear whether they understand the substance of the suggestion. These behaviors may inhibit the development of a shared understanding between the partners and limit the value of the collaborative process. These results can inform instructional practice and the development of new adaptive tools that facilitate productive collaborative problem solving in computer science.}, journal={SIGCSE'18: PROCEEDINGS OF THE 49TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION}, author={Tsan, Jennifer and Rodriguez, Fernando J. and Boyer, Kristy Elizabeth and Lynch, Collin}, year={2018}, pages={622–627} }
 @inbook{shen_mostafavi_lynch_barnes_chi_2018, title={Empirically Evaluating the Effectiveness of POMDP vs. MDP Towards the Pedagogical Strategies Induction}, ISBN={9783319938455 9783319938462}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-93846-2_61}, DOI={10.1007/978-3-319-93846-2_61}, abstractNote={The effectiveness of Intelligent Tutoring Systems (ITSs) often depends upon their pedagogical strategies, the policies used to decide what action to take next in the face of alternatives. We induce policies based on two general Reinforcement Learning (RL) frameworks: POMDP&. MDP, given the limited feature space. We conduct an empirical study where the RL-induced policies are compared against a random yet reasonable policy. Results show that when the contents are controlled to be equal, the MDP-based policy can improve students’ learning significantly more than the random baseline while the POMDP-based policy cannot outperform the later. The possible reason is that the features selected for the MDP framework may not be the optimal feature space for POMDP.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Shen, Shitian and Mostafavi, Behrooz and Lynch, Collin and Barnes, Tiffany and Chi, Min}, year={2018}, pages={327–331} }
 @inbook{peddycord-liu_harred_karamarkovich_barnes_lynch_rutherford_2018, title={Learning Curve Analysis in a Large-Scale, Drill-and-Practice Serious Math Game: Where Is Learning Support Needed?}, ISBN={9783319938424 9783319938431}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-93843-1_32}, DOI={10.1007/978-3-319-93843-1_32}, abstractNote={This paper applies data-driven methods to understand learning and derives game design insights in a large-scale, drill-and-practice game: Spatial Temporal (ST) Math. In order for serious games to thrive we must develop efficient, scalable methods to evaluate games against their educational goals. Learning models have matured in recent years and have been applied across e-learning platforms but they have not been used widely in serious games. We applied empirical learning curve analyses to ST Math under different assumptions of how knowledge components are defined in the game and map to game contents. We derived actionable game design feedback and educational insights regarding fraction learning. Our results revealed cases where students failed to transfer knowledge between math skills, content, and problem representations. This work stresses the importance of designing games that support students’ comprehension of math concepts, rather than the learning of content- and situation-specific skills to pass games.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Peddycord-Liu, Zhongxiu and Harred, Rachel and Karamarkovich, Sarah and Barnes, Tiffany and Lynch, Collin and Rutherford, Teomara}, year={2018}, pages={436–449} }
 @inbook{crossley_sirbu_dascalu_barnes_lynch_mcnamara_2018, title={Modeling Math Success Using Cohesion Network Analysis}, ISBN={9783319938455 9783319938462}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-93846-2_12}, DOI={10.1007/978-3-319-93846-2_12}, abstractNote={This study examines math success within a blended undergraduate course using a Cohesion Network Analysis (CNA) approach while controlling for individual differences and click-stream variables that may also predict math success. Linear models indicated that math success was related to days spent on the forum and by students who more regularly posted in the online class forum and whose posts generally followed the semanticity of other students.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Crossley, Scott A. and Sirbu, Maria-Dorinela and Dascalu, Mihai and Barnes, Tiffany and Lynch, Collin F. and McNamara, Danielle S.}, year={2018}, pages={63–67} }
 @article{tsan_lynch_boyer_2018, title={“Alright, what do we need?”: A study of young coders’ collaborative dialogue}, volume={17}, ISSN={2212-8689}, url={http://dx.doi.org/10.1016/J.IJCCI.2018.03.001}, DOI={10.1016/J.IJCCI.2018.03.001}, abstractNote={Collaboration is a vital part of the discipline of computer science, yet very little is known about how young children collaborate to learn programming in the classroom. Consequently, we have much to understand about how we can most effectively support this learning experience. We have conducted a study of fifth grade students (ages 9–11) in the United States. Students in this study enrolled in an elective computer science course in which they completed a pair programming project spanning one week of class time (45 min per day). This article reports on a deep qualitative analysis of six collaborative student pairs. We examine the ways in which pair programming practices emerge organically within elementary school collaborations, including the ways in which students’ roles arise, equity of contributions to the dialogue, and how students manage their responsibilities during the collaborative process. Our results show that for some student pairs, making suggestions in the dialogue is a natural mechanism for swapping control, whereas for other students, the transition from “driver” to “navigator” requires substantial scaffolding. The findings provide insights into the ways in which we can scaffold the collaborative process to support young students’ computer science learning.}, journal={International Journal of Child-Computer Interaction}, publisher={Elsevier BV}, author={Tsan, Jennifer and Lynch, Collin F. and Boyer, Kristy Elizabeth}, year={2018}, month={Sep}, pages={61–71} }
 @inproceedings{tsan_rodriguez_boyer_lynch_2017, title={Let's work together: Improving block-based environments by supporting synchronous collaboration}, DOI={10.1109/blocks.2017.8120411}, abstractNote={Block-based programming environments are a good way to teach beginners how to code, in part because they eliminate syntax errors and provide visual feedback. However, many of the existing environments do not explicitly support synchronous collaboration. Collaboration is a critical component of computer science practice and CS education. We therefore argue that features to support collaboration could significantly enhance existing and new block-based programming environments. We review existing block-based programming environments, suggest design ideas for supporting synchronous collaboration, and evaluate environments that currently support some of these features.}, booktitle={2017 IEEE Blocks and Beyond Workshop (B&B)}, author={Tsan, J. and Rodriguez, F. J. and Boyer, K. E. and Lynch, C.}, year={2017}, pages={53–56} }
 @article{lynch_2017, title={Who prophets from big data in education? New insights and new challenges}, volume={15}, ISSN={["1741-3192"]}, DOI={10.1177/1477878517738448}, abstractNote={Big Data can radically transform education by enabling personalized learning, deep student modeling, and true longitudinal studies that compare changes across classrooms, regions, and years. With these promises, however, come risks to individual privacy and educational validity, along with deep policy and ethical issues. Education is largely a public service targeted primarily at minors. Participation is compulsory in most advanced societies, and in many ways, it is seen as a fundamental right. Academic success is necessary for advancement, but students often have little individual say in the process. For these reasons, it poses unique policy challenges that do not arise in other domains. Big data requires deep and constant monitoring of students, classes, and instructors. Who consents to such monitoring, and how will student or instructor privacy be preserved? Data also has immense commercial value. Who owns it? And who is permitted to profit from its use? In this article, I will discuss some of these unique issues, and I will outline some potential approaches that may be taken to address them.}, number={3}, journal={THEORY AND RESEARCH IN EDUCATION}, author={Lynch, Collin F.}, year={2017}, month={Nov}, pages={249–271} }
 @inproceedings{lynch_xue_chi_2016, title={Evolving augmented graph grammars for argument analysis}, DOI={10.1145/2908961.2908994}, abstractNote={Augmented Graph Grammars are a robust rule representation for rich graph data. In this paper we present our work on the automatic induction of graph grammars for argument diagrams via EC. We show that EC outperforms the existing grammar induction algorithms gSpan and Subdue on our dataset. We also show that it is possible to augment the standard EC process to harvest a set of diverse rules which can be filtered via a post-hoc Chi-Squared analysis.}, booktitle={Proceedings of the 2016 Genetic and Evolutionary Computation Conference (GECCO'16 Companion)}, author={Lynch, C. F. and Xue, L. T. and Chi, M.}, year={2016}, pages={65–66} }
 @inbook{mostafavi_zhou_lynch_chi_barnes_2015, title={Data-Driven Worked Examples Improve Retention and Completion in a Logic Tutor}, ISBN={9783319197722 9783319197739}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-19773-9_102}, DOI={10.1007/978-3-319-19773-9_102}, abstractNote={Research shows that expert-crafted worked examples can have a positive effect on student performance. To investigate the potential for data-driven worked examples to achieve similar results, we generated worked examples for the Deep Thought logic tutor, and conducted an experiment to assess their impact on performance. Students who received data-driven worked examples were much more likely to complete the tutor, and completed the tutor in less time. This study demonstrates that worked examples, automatically generated from student data, can be used to improve student learning in tutoring systems.}, booktitle={Lecture Notes in Computer Science}, publisher={Springer International Publishing}, author={Mostafavi, Behrooz and Zhou, Guojing and Lynch, Collin and Chi, Min and Barnes, Tiffany}, year={2015}, pages={726–729} }
 @inproceedings{mostafavi_zhou_lynch_chi_barnes_2015, title={Data-driven worked examples improve retention and completion in a logic tutor}, volume={9112}, booktitle={Artificial intelligence in education, aied 2015}, author={Mostafavi, B. and Zhou, G. J. and Lynch, C. and Chi, M. and Barnes, T.}, year={2015}, pages={726–729} }
 @inbook{lynch_ashley_chi_2014, title={Can Diagrams Predict Essay Grades?}, ISBN={9783319072203 9783319072210}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-07221-0_32}, DOI={10.1007/978-3-319-07221-0_32}, abstractNote={Diagrammatic models of argument have grown in prominence in recent years. While they have been applied in a number of tutoring contexts, it has not yet been shown that student-produced diagrams can be used to effectively grade students or predict their future performance. We show that manually-assigned diagram grades and automatic structural features of argument diagrams can be used to predict students’ future essay grades, thus supporting the use of argument diagrams for instruction. We also show that the automatic features are competitive with expert human grading despite the fact that semantic content was ignored in automatic processing.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer International Publishing}, author={Lynch, Collin F. and Ashley, Kevin D. and Chi, Min}, year={2014}, pages={260–265} }
 @inbook{ashley_lynch_pinkwart_aleven_2009, title={Toward Modeling and Teaching Legal Case-Based Adaptation with Expert Examples}, ISBN={9783642029974 9783642029981}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-642-02998-1_5}, DOI={10.1007/978-3-642-02998-1_5}, abstractNote={Studying examples of expert case-based adaptation could advance computational modeling but only if the examples can be succinctly represented and reliably interpreted. Supreme Court justices pose hypothetical cases, often adapting precedents, to evaluate if a proposed rule for deciding a problem needs to be adapted. This paper describes a diagrammatic representation of adaptive reasoning with hypothetical cases based on a process model. Since the diagrams are interpretations of argument texts, there is no one “correct” diagram, and reliability could be a challenge. An experiment assessed the reliability of expert grading of diagrams prepared by students reconstructing examples of hypothetical reasoning. Preliminary results indicate significant areas of agreement, including with respect to the ways tests are modified in response to hypotheticals, but slight agreement as to the role and import of hypotheticals. These results suggest that the diagrammatic representation will support studying and modeling the examples of case-based adaptation, but that the diagramming support needs to make certain features more explicit.}, booktitle={Case-Based Reasoning Research and Development}, publisher={Springer Berlin Heidelberg}, author={Ashley, Kevin and Lynch, Collin and Pinkwart, Niels and Aleven, Vincent}, year={2009}, pages={45–59} }
 @inbook{pinkwart_lynch_ashley_aleven_2008, title={Re-evaluating LARGO in the Classroom: Are Diagrams Better Than Text for Teaching Argumentation Skills?}, ISBN={9783540691303 9783540691327}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-69132-7_14}, DOI={10.1007/978-3-540-69132-7_14}, abstractNote={Diagrams appear to be a convenient vehicle for teaching argumentation skills in ill-defined domains, but can an ITS provide useful feedback on students’ argument diagrams without assuming a well-defined procedure for objectively evaluating argument? LARGO is an ITS for legal argumentation that supports students as they diagram transcripts of US Supreme Court oral argument. It provides on-demand advice by identifying small, interesting or incomplete patterns within students’ graphs. We conducted a study in which LARGO was used as mandatory part of a first-year law school class. In contrast to prior findings in lab studies with voluntary participants, the use of LARGO did not lead to superior learning as compared to a text-based note-taking tool. These results can be partially attributed to low use of the graphical tools and advice by the students as well as (and possibly due to) a different motivational focus. Some evidence was found that higher engagement with the system led to better learning, leaving open the tantalizing possibility of helping especially lower-aptitude students through use of LARGO.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Pinkwart, Niels and Lynch, Collin and Ashley, Kevin and Aleven, Vincent}, year={2008}, month={Aug}, pages={90–100} }
 @inbook{pinkwart_aleven_ashley_lynch_2006, title={Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques}, ISBN={9783540351597 9783540351603}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/11774303_23}, DOI={10.1007/11774303_23}, abstractNote={This paper presents an approach for intelligent tutoring in the field of legal argumentation. In this approach, students study transcripts of US Supreme Court oral argument and create a graphical representation of argument flow as tests offered by attorneys being challenged by hypotheticals posed by Justices. The proposed system, which is based on the collaborative modeling framework Cool Modes, is capable of detecting three types of weaknesses in arguments; when it does, it presents the student with a self explanation prompt. This kind of feedback seems more appropriate than the “strong connective feedback” typically offered by model-tracing or constraint-based tutors. Structural and context weaknesses in arguments are handled by graph grammars, and the critical problem of detecting and dealing with content weaknesses in student contributions is addressed through a collaborative filtering approach, thereby avoiding the critical problem of natural language processing in legal argumentation. An early version of the system was pilot tested with two students.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={Pinkwart, Niels and Aleven, Vincent and Ashley, Kevin and Lynch, Collin}, year={2006}, pages={227–236} }
 @inbook{vanlehn_bhembe_chi_lynch_schulze_shelby_taylor_treacy_weinstein_wintersgill_2004, title={Implicit Versus Explicit Learning of Strategies in a Non-procedural Cognitive Skill}, ISBN={9783540229483 9783540301394}, ISSN={0302-9743 1611-3349}, url={http://dx.doi.org/10.1007/978-3-540-30139-4_49}, DOI={10.1007/978-3-540-30139-4_49}, abstractNote={University physics is typical of many cognitive skills in that there is no standard procedure for solving problems, and yet a few students still master the skill. This suggests that their learning of problem solving strategies is implicit, and that an effective tutoring system need not teach problem solving strategies as explicitly as model-tracing tutors do. In order to compare implicit vs. explicit learning of problem solving strategies, we developed two physics tutoring systems, Andes and Pyrenees. Pyrenees is a model-tracing tutor that teaches a problem solving strategy explicitly, whereas Andes uses a novel pedagogy, developed over many years of use in the field, that provides virtually no explicit strategic instruction. Preliminary results from an experiment comparing the two systems are reported.}, booktitle={Intelligent Tutoring Systems}, publisher={Springer Berlin Heidelberg}, author={VanLehn, Kurt and Bhembe, Dumiszewe and Chi, Min and Lynch, Collin and Schulze, Kay and Shelby, Robert and Taylor, Linwood and Treacy, Don and Weinstein, Anders and Wintersgill, Mary}, year={2004}, pages={521–530} }