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Penelope J. Standen David J. Brown Mohammad Taheri Maria J. Galvez Trigo Helen Boulton Andrew Burton Madeline J. Hallewell James G. Lathe Nicholas Shopland Maria A. Blanco Gonzalez Gosia M. Kwiatkowska Elena Milli Stefano Cobello Annaleda Mazzucato Marco Traversi Enrique Hortal 《British journal of educational technology : journal of the Council for Educational Technology》2020,51(5):1748-1765
Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning. 相似文献
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Working with scientists has been suggested as an effective way for high-school students to learn authentic science. However, little research has involved students’ perceptions of science learning environments in a university internship. This study drew on the theoretical framework of community of practice with cogenerative dialogues to design an internship program that aims to build a constructivist internship for students. Students who learned science in the internship program developed stronger constructivist learning perceptions than those who learned science in school. Specifically, students perceived that they had more opportunities to think independently of the instructors and other students. Three effective principles for program design are: (a) high school students conduct open-inquiry projects with the support of scientists; (b) high school students and scientists conduct cogenerative dialogues regularly to address issues and share experiences; and (c) high school students present their project proposals and scientific findings at open house events. Implications of the results are discussed. 相似文献
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Penelope A. Yates-Mercer 《Information processing & management》1985,21(2):165-166
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