@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{emerson_henderson_min_rowe_minogue_lester_2021, title={Multimodal Trajectory Analysis of Visitor Engagement with Interactive Science Museum Exhibits}, volume={12749}, ISBN={["978-3-030-78269-6"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-78270-2_27}, abstractNote={Recent years have seen a growing interest in investigating visitor engagement in science museums with multimodal learning analytics. Visitor engagement is a multidimensional process that unfolds temporally over the course of a museum visit. In this paper, we introduce a multimodal trajectory analysis framework for modeling visitor engagement with an interactive science exhibit for environmental sustainability. We investigate trajectories of multimodal data captured during visitor interactions with the exhibit through slope-based time series analysis. Utilizing the slopes of the time series representations for each multimodal data channel, we conduct an ablation study to investigate how additional modalities lead to improved accuracy while modeling visitor engagement. We are able to enhance visitor engagement models by accounting for varying levels of visitors’ science fascination, a construct integrating science interest, curiosity, and mastery goals. The results suggest that trajectory-based representations of the multimodal visitor data can serve as the foundation for visitor engagement modeling to enhance museum learning experiences.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2021), PT II}, author={Emerson, Andrew and Henderson, Nathan and Min, Wookhee and Rowe, Jonathan and Minogue, James and Lester, James}, year={2021}, pages={151–155} }
 @article{emerson_cloude_azevedo_lester_2020, title={Multimodal learning analytics for game-based learning}, volume={51}, ISSN={["1467-8535"]}, DOI={10.1111/bjet.12992}, abstractNote={AbstractA distinctive feature of game‐based learning environments is their capacity to create learning experiences that are both effective and engaging. Recent advances in sensor‐based technologies such as facial expression analysis and gaze tracking have introduced the opportunity to leverage multimodal data streams for learning analytics. Learning analytics informed by multimodal data captured during students’ interactions with game‐based learning environments hold significant promise for developing a deeper understanding of game‐based learning, designing game‐based learning environments to detect maladaptive behaviors and informing adaptive scaffolding to support individualized learning. This paper introduces a multimodal learning analytics approach that incorporates student gameplay, eye tracking and facial expression data to predict student posttest performance and interest after interacting with a game‐based learning environment, Crystal Island. We investigated the degree to which separate and combined modalities (ie, gameplay, facial expressions of emotions and eye gaze) captured from students (n = 65) were predictive of student posttest performance and interest after interacting with Crystal Island. Results indicate that when predicting student posttest performance and interest, models utilizing multimodal data either perform equally well or outperform models utilizing unimodal data. We discuss the synergistic effects of combining modalities for predicting both student interest and posttest performance. The findings suggest that multimodal learning analytics can accurately predict students’ posttest performance and interest during game‐based learning and hold significant potential for guiding real‐time adaptive scaffolding.}, number={5}, journal={BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY}, author={Emerson, Andrew and Cloude, Elizabeth B. and Azevedo, Roger and Lester, James}, year={2020}, month={Sep}, pages={1505–1526} }
 @article{geden_emerson_carpenter_rowe_azevedo_lester_2021, title={Predictive Student Modeling in Game-Based Learning Environments with Word Embedding Representations of Reflection}, volume={31}, ISSN={["1560-4306"]}, DOI={10.1007/s40593-020-00220-4}, number={1}, journal={INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION}, author={Geden, Michael and Emerson, Andrew and Carpenter, Dan and Rowe, Jonathan and Azevedo, Roger and Lester, James}, year={2021}, month={Mar}, pages={1–23} }