@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={Abstract}, 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} }