@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={A 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{cloude_taub_lester_azevedo_2019, title={The Role of Achievement Goal Orientation on Metacognitive Process Use in Game-Based Learning}, volume={11626}, ISBN={["978-3-030-23206-1"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-030-23207-8_7}, abstractNote={To examine relations between achievement goal orientation—a construct of motivation, metacognition and learning, multiple data channels were collected from 58 students while problem solving in a game-based learning environment. Results suggest students with different goal orientations use metacognitive processes differently but found no differences in learning. Findings have implications for measuring motivation using multiple data channels to design adaptive game-based learning environments.}, journal={ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2019, PT II}, author={Cloude, Elizabeth B. and Taub, Michelle and Lester, James and Azevedo, Roger}, year={2019}, pages={36–40} }
 @article{rollins_cloude_2018, title={Development of mnemonic discrimination during childhood}, volume={25}, ISSN={["1549-5485"]}, DOI={10.1101/lm.047142.117}, abstractNote={The present study examined mnemonic discrimination in 5- and 6-yr-old children, 8- and 9-yr-old children, 11- and 12-yr-old children, and young adults. Participants incidentally encoded pictorial stimuli and subsequently judged whether targets (i.e., repeated stimuli), lures (i.e., mnemonically related stimuli), and foils (i.e., novel stimuli) were old, similar, or new. Compared to older age groups, younger children were more likely to (1) incorrectly identify lures as “old” (rather than “similar”) and (2) fail to recognize lures altogether, especially when lures were more mnemonically distinct from targets. These results suggest age-related improvements in pattern separation and pattern completion during childhood.}, number={6}, journal={LEARNING & MEMORY}, author={Rollins, Leslie and Cloude, Elizabeth B.}, year={2018}, month={Jun}, pages={294–297} }
 @article{cloude_taub_azevedo_2018, title={Investigating the Role of Goal Orientation: Metacognitive and Cognitive Strategy Use and Learning with Intelligent Tutoring Systems}, volume={10858}, ISBN={["978-3-319-91463-3"]}, ISSN={["1611-3349"]}, DOI={10.1007/978-3-319-91464-0_5}, abstractNote={Cognitive, affective, metacognitive, and motivational (CAMM) processes are critical components of self-regulated learning (SRL) essential for learning and problem solving. Currently, ITSs are designed to foster cognitive, affective, and metacognitive (CAM) strategies and processes, presenting major gaps in the research since motivation is a key component of SRL and influences the remaining CAM processes. In our study, students interacted with MetaTutor, a hypermedia-based ITS, to investigate how 190 undergraduate students’ proportional learning gain (PLG) related to sub-goals set, cognitive strategy use and metacognitive processes differed based on self-reported achievement goal orientation. Results indicated differences between approach, avoidance, and students who adopted both approach and avoidance goal orientations, but no differences between mastery, performance and students who adopted both mastery and performance goal orientations on PLG for content related to sub-goal 1. Conversely, no differences were found between goal orientation groups on PLG for sub-goal 2, revealing possible changes in goal orientation following sub-goal 1. Analyses indicated no differences between goal orientation groups on metacognitive processes and cognitive strategy use. Thus, we suggest turning away from self-report data, where future studies aim to incorporate multi-channel data over durations of tasks as students interact with ITSs to measure motivation and its tendency to fluctuate in real-time. Implications for using multiple data channels to measure motivation could contribute to adaptive ITS design based on all CAMM processes.}, journal={INTELLIGENT TUTORING SYSTEMS, ITS 2018}, author={Cloude, Elizabeth B. and Taub, Michelle and Azevedo, Roger}, year={2018}, pages={44–53} }