@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{wiggins_grafsgaard_boyer_wiebe_lester_2016, title={Do You Think You Can? The Influence of Student Self-Efficacy on the Effectiveness of Tutorial Dialogue for Computer Science}, volume={27}, ISSN={1560-4292 1560-4306}, url={http://dx.doi.org/10.1007/s40593-015-0091-7}, DOI={10.1007/s40593-015-0091-7}, number={1}, journal={International Journal of Artificial Intelligence in Education}, publisher={Springer Science and Business Media LLC}, author={Wiggins, Joseph B. and Grafsgaard, Joseph F. and Boyer, Kristy Elizabeth and Wiebe, Eric N. and Lester, James C.}, year={2016}, month={Feb}, pages={130–153} }
 @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} }