@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{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{tian_wiggins_fahid_emerson_bounajim_smith_boyer_wiebe_mott_lester_2021, title={Modeling Frustration Trajectories and Problem-Solving Behaviors in Adaptive Learning Environments for Introductory Computer Science}, volume={12749}, ISBN={["978-3-030-78269-6"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78270-2_63}, abstractNote={Modeling a learner’s frustration in adaptive environments can inform scaffolding. While much work has explored momentary frustration, there is limited research investigating the dynamics of frustration over time and its relationship with problem-solving behaviors. In this paper, we clustered 86 undergraduate students into four frustration trajectories as they worked with an adaptive learning environment for introductory computer science. The results indicate that students who initially report high levels of frustration but then reported lower levels later in their problem solving were more likely to have sought help. These findings provide insight into how frustration trajectory models can guide adaptivity during extended problem-solving episodes.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II}, author={Tian, Xiaoyi and Wiggins, Joseph B. and Fahid, Fahmid Morshed and Emerson, Andrew and Bounajim, Dolly and Smith, Andy and Boyer, Kristy Elizabeth and Wiebe, Eric and Mott, Bradford and Lester, James}, year={2021}, pages={355–360} }
 @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{min_park_wiggins_mott_wiebe_boyer_lester_2019, title={Predicting Dialogue Breakdown in Conversational Pedagogical Agents with Multimodal LSTMs}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85068335512&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-23207-8_37}, abstractNote={Recent years have seen a growing interest in conversational pedagogical agents. However, creating robust dialogue managers for conversational pedagogical agents poses significant challenges. Agents’ misunderstandings and inappropriate responses may cause breakdowns in conversational flow, lead to breaches of trust in agent-student relationships, and negatively impact student learning. Dialogue breakdown detection (DBD) is the task of predicting whether an agent’s utterance will cause a breakdown in an ongoing conversation. A robust DBD framework can support enhanced user experiences by choosing more appropriate responses, while also offering a method to conduct error analyses and improve dialogue managers. This paper presents a multimodal deep learning-based DBD framework to predict breakdowns in student-agent conversations. We investigate this framework with dialogues between middle school students and a conversational pedagogical agent in a game-based learning environment. Results from a study with 92 middle school students demonstrate that multimodal long short-term memory network (LSTM)-based dialogue breakdown detectors incorporating eye gaze features achieve high predictive accuracies and recall rates, suggesting that multimodal detectors can play an important role in designing conversational pedagogical agents that effectively engage students in dialogue.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Min, Wookhee and Park, Kyungjin and Wiggins, Joseph and Mott, Bradford and Wiebe, Eric and Boyer, Kristy Elizabeth and Lester, James}, year={2019}, pages={195–200} }
 @article{wiggins_kulkarni_min_boyer_mott_wiebe_lester_2019, title={Take the Initiative: Mixed Initiative Dialogue Policies for Pedagogical Agents in Game-Based Learning Environments}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85068350756&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-23207-8_58}, abstractNote={Pedagogical agents have been shown to be highly effective for supporting learning in a broad range of contexts, including game-based learning. However, there are key open questions around how to design dialogue policies for pedagogical agents that support students in game-based learning environments. This paper reports on a study to investigate two different agent dialogue policies with regard to conversational initiative, a core consideration in dialogue system design. In the User Initiative policy, only the student could initiate conversations with the agent, while in the Mixed Initiative policy, both the agent and the student could initiate conversations. In a study with 67 college students, results showed that the Mixed Initiative policy not only promoted more conversation, but also better supported the goals of the game-based learning environment by fostering exploration, yielding better performance on in-game assessments, and creating higher student engagement.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Wiggins, Joseph B. and Kulkarni, Mayank and Min, Wookhee and Boyer, Kristy Elizabeth and Mott, Bradford and Wiebe, Eric and Lester, James}, year={2019}, pages={314–318} }
 @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} }
 @article{catete_lytle_dong_boulden_akram_houchins_barnes_wiebe_lester_mott_et al._2018, title={Infusing Computational Thinking into Middle Grade Science Classrooms: Lessons Learned}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85056713650&partnerID=MN8TOARS}, DOI={10.1145/3265757.3265778}, abstractNote={There is a growing need to present all students with an opportunity to learn computer science and computational thinking (CT) skills during their primary and secondary education. Traditionally, these opportunities are available outside of the core curriculum as stand-alone courses often taken by those with preparatory privilege. Researchers have identified the need to integrate CT into core classes to provide equitable access to these critical skills. We have worked in a research-practice partnership with two magnet middle schools focused on digital sciences to develop and implement computational thinking into life sciences classes. In this report, we present initial lessons learned while conducting our design-based implementation research on integrating computational thinking into middle school science classes. These case studies suggest that several factors including teacher engagement, teacher attitudes, student prior experience with CS/CT, and curriculum design can all impact student engagement in integrated science-CT lessons.}, journal={WIPSCE'18: PROCEEDINGS OF THE 13TH WORKSHOP IN PRIMARY AND SECONDARY COMPUTING EDUCATION}, publisher={ACM Press}, author={Catete, Veronica and Lytle, Nicholas and Dong, Yihuan and Boulden, Danielle and Akram, Bita and Houchins, Jennifer and Barnes, Tiffany and Wiebe, Eric and Lester, James and Mott, Bradford and et al.}, year={2018}, pages={109–114} }
 @inproceedings{buffum_ying_zheng_boyer_wiebe_mott_blackburn_lester_2018, title={Introducing the Computer Science Concept of Variables in Middle School Science Classrooms}, ISBN={9781450351034}, url={http://dx.doi.org/10.1145/3159450.3159545}, DOI={10.1145/3159450.3159545}, abstractNote={The K-12 Computer Science Framework has established that students should be learning about the computer science concept of variables as early as middle school, although the field has not yet determined how this and other related concepts should be introduced. Secondary school computer science curricula such as Exploring CS and AP CS Principles often teach the concept of variables in the context of algebra, which most students have already encountered in their mathematics courses. However, when strategizing how to introduce the concept at the middle school level, we confront the reality that many middle schoolers have not yet learned algebra. With that challenge in mind, this position paper makes a case for introducing the concept of variables in the context of middle school science. In addition to an analysis of existing curricula, the paper includes discussion of a day-long pilot study and the consequent teacher feedback that further supports the approach. The CS For All initiative has increased interest in bringing computer science to middle school classrooms; this paper makes an argument for doing so in a way that can benefit students' learning of both computer science and core science content.}, booktitle={Proceedings of the 49th ACM Technical Symposium on Computer Science Education - SIGCSE '18}, publisher={ACM Press}, author={Buffum, Philip Sheridan and Ying, Kimberly Michelle and Zheng, Xiaoxi and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Mott, Bradford W. and Blackburn, David C. and Lester, James C.}, year={2018}, pages={906–911} }
 @article{barnes_boyer_hsiao_le_sosnovsky_2017, title={Preface for the Special Issue on AI-Supported Education in Computer Science}, volume={27}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-016-0123-y}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Barnes, Tiffany and Boyer, Kristy and Hsiao, Sharon I-Han and Le, Nguyen-Thinh and Sosnovsky, Sergey}, year={2017}, month={Mar}, pages={1–4} }
 @inproceedings{ezen-can_boyer_2015, title={A Tutorial dialogue system for real-time evaluation of unsupervised dialogue act classifiers: Exploring system outcomes}, volume={9112}, booktitle={Artificial intelligence in education, aied 2015}, author={Ezen-Can, A. and Boyer, K. E.}, year={2015}, pages={105–114} }
 @article{min_frankosky_mott_rowe_wiebe_boyer_lester_2015, title={DeepStealth: Leveraging Deep Learning Models for Stealth Assessment in Game-Based Learning Environments}, volume={9112}, ISBN={["978-3-319-19772-2"]}, ISSN={["1611-3349"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84949009361&partnerID=MN8TOARS}, DOI={10.1007/978-3-319-19773-9_28}, abstractNote={A distinctive feature of intelligent game-based learning environments is their capacity for enabling stealth assessment. Stealth assessments gather information about student competencies in a manner that is invisible, and enable drawing valid inferences about student knowledge. We present a framework for stealth assessment that leverages deep learning, a family of machine learning methods that utilize deep artificial neural networks, to infer student competencies in a game-based learning environment for middle grade computational thinking, Engage. Students’ interaction data, collected during a classroom study with Engage, as well as prior knowledge scores, are utilized to train deep networks for predicting students’ post-test performance. Results indicate deep networks that are pre-trained using stacked denoising autoencoders achieve high predictive accuracy, significantly outperforming standard classification techniques such as support vector machines and naïve Bayes. The findings suggest that deep learning shows considerable promise for automatically inducing stealth assessment models for intelligent game-based learning environments.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015}, author={Min, Wookhee and Frankosky, Megan H. and Mott, Bradford W. and Rowe, Jonathan P. and Wiebe, Eric and Boyer, Kristy Elizabeth and Lester, James C.}, year={2015}, pages={277–286} }
 @inproceedings{rodriguez_boyer_2015, title={Discovering individual and collaborative problem-solving modes with hidden Markov models}, volume={9112}, booktitle={Artificial intelligence in education, aied 2015}, author={Rodriguez, F. J. and Boyer, K. E.}, year={2015}, pages={408–418} }
 @inproceedings{buffum_frankosky_boyer_wiebe_mott_lester_2015, title={Leveraging collaboration to improve gender equity in a game-based learning environment for middle school computer science}, DOI={10.1109/respect.2015.7296496}, abstractNote={Game-based learning environments can deliver robust learning gains and also have a unique capacity to engage students. Yet they can unintentionally disadvantage students with less prior gaming experience. This is especially concerning in computer science education, as certain underrepresented groups (such as female students) may on average have less prior experience with games. This paper presents evidence that a collaborative gameplay approach can successfully address this problem at the middle school level. In an iterative, designed-based research study, we first used an experimental pilot study to investigate the nature of collaboration in the Engage game-based learning environment, and then deployed Engage in a full classroom study to measure its effectiveness at serving all students. In earlier phases of the intervention, male students outpaced their female peers in learning gains. However, female students caught up during a multi-week classroom implementation. These findings provide evidence that a collaborative gameplay approach may, over time, compensate for gender differences in experience and lead to equitable learning experiences within game-based learning environments for computer science education.}, booktitle={2015 Research in Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT)}, author={Buffum, P. S. and Frankosky, M. and Boyer, K. E. and Wiebe, Eric and Mott, B. and Lester, J.}, year={2015} }
 @article{buffum_boyer_wiebe_mott_lester_2015, title={Mind the Gap: Improving Gender Equity in Game-Based Learning Environments with Learning Companions}, volume={9112}, ISBN={["978-3-319-19772-2"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-19773-9_7}, abstractNote={Game-based learning environments hold great promise for engaging learners. Yet game mechanics can initially pose barriers for students with less prior gaming experience. This paper examines game-based learning for a population of middle school learners in the US, where female students tend to have less gaming experience than male students. In a pilot study with an early version of Engage, a game-based learning environment for middle school computer science education, female students reported higher initial frustration. To address this critical issue, we developed a prototype learning companion designed specifically to reduce frustration through the telling of autobiographical stories. In a pilot study of two 7th grade classrooms, female students responded especially positively to the learning companion, eliminating the gender gap in reported frustration. The results suggest that introducing learning companions can directly contribute to making the benefits of game-based learning equitable for all learners.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015}, author={Buffum, Philip Sheridan and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Mott, Bradford W. and Lester, James C.}, year={2015}, pages={64–73} }
 @inproceedings{tsan_boyer_2015, title={Supporting K-5 learners with dialogue systems}, volume={9112}, booktitle={Artificial intelligence in education, aied 2015}, author={Tsan, J. and Boyer, K. E.}, year={2015}, pages={873–876} }
 @inproceedings{vail_boyer_wiebe_lester_2015, title={The Mars and Venus effect: The influence of user gender on the effectiveness of adaptive task support}, volume={9146}, booktitle={User modeling, adaptation and personalization}, author={Vail, A. K. and Boyer, K. E. and Wiebe, E. N. and Lester, J. C.}, year={2015}, pages={265–276} }
 @inproceedings{vail_boyer_2014, title={Identifying effective moves in tutoring: On the refinement of dialogue act annotation schemes}, volume={8474}, booktitle={Intelligent tutoring systems, its 2014}, author={Vail, A. K. and Boyer, K. E.}, year={2014}, pages={199–209} }
 @article{grafsgaard_wiggins_boyer_wiebe_lester_2013, title={Automatically Recognizing Facial Indicators of Frustration: A Learning-Centric Analysis}, ISSN={["2156-8103"]}, DOI={10.1109/acii.2013.33}, abstractNote={Affective and cognitive processes form a rich substrate on which learning plays out. Affective states often influence progress on learning tasks, resulting in positive or negative cycles of affect that impact learning outcomes. Developing a detailed account of the occurrence and timing of cognitive-affective states during learning can inform the design of affective tutorial interventions. In order to advance understanding of learning-centered affect, this paper reports on a study to analyze a video corpus of computer-mediated human tutoring using an automated facial expression recognition tool that detects fine-grained facial movements. The results reveal three significant relationships between facial expression, frustration, and learning: (1) Action Unit 2 (outer brow raise) was negatively correlated with learning gain, (2) Action Unit 4 (brow lowering) was positively correlated with frustration, and (3) Action Unit 14 (mouth dimpling) was positively correlated with both frustration and learning gain. Additionally, early prediction models demonstrated that facial actions during the first five minutes were significantly predictive of frustration and learning at the end of the tutoring session. The results represent a step toward a deeper understanding of learning-centered affective states, which will form the foundation for data-driven design of affective tutoring systems.}, journal={2013 HUMAINE ASSOCIATION CONFERENCE ON AFFECTIVE COMPUTING AND INTELLIGENT INTERACTION (ACII)}, author={Grafsgaard, Joseph F. and Wiggins, Joseph B. and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Lester, James C.}, year={2013}, pages={159–165} }
 @inproceedings{grafsgaard_fulton_boyer_wiebe_lester_2012, title={Multimodal analysis of the implicit affective channel in computer-mediated textual communication}, DOI={10.1145/2388676.2388708}, abstractNote={Computer-mediated textual communication has become ubiquitous in recent years. Compared to face-to-face interactions, there is decreased bandwidth in affective information, yet studies show that interactions in this medium still produce rich and fulfilling affective outcomes. While overt communication (e.g., emoticons or explicit discussion of emotion) can explain some aspects of affect conveyed through textual dialogue, there may also be an underlying implicit affective channel through which participants perceive additional emotional information. To investigate this phenomenon, computer-mediated tutoring sessions were recorded with Kinect video and depth images and processed with novel tracking techniques for posture and hand-to-face gestures. Analyses demonstrated that tutors implicitly perceived students' focused attention, physical demand, and frustration. Additionally, bodily expressions of posture and gesture correlated with student cognitive-affective states that were perceived by tutors through the implicit affective channel. Finally, posture and gesture complement each other in multimodal predictive models of student cognitive-affective states, explaining greater variance than either modality alone. This approach of empirically studying the implicit affective channel may identify details of human behavior that can inform the design of future textual dialogue systems modeled on naturalistic interaction.}, booktitle={ICMI '12: Proceedings of the ACM International Conference on Multimodal Interaction}, author={Grafsgaard, J. F. and Fulton, R. M. and Boyer, K. E. and Wiebe, E. N. and Lester, J. C.}, year={2012}, pages={145–152} }
 @inproceedings{grafsgaard_lee_mott_boyer_lester, title={Modeling self-efficacy across age groups with automatically tracked facial expression}, volume={9112}, booktitle={Artificial intelligence in education, aied 2015}, author={Grafsgaard, J. F. and Lee, S. Y. and Mott, B. W. and Boyer, K. E. and Lester, J. C.}, pages={582–585} }