Monitoring learners\u2019 mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner\u2019s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners\u2019 current affective-emotional and cognitive states by measuring and decoding brain activity via a passive BCI. In two studies (N = 8 and N = 7), we investigate whether we can a) predict learners\u2019 affective and cognitive states during a learning or training session, b) provide continuous feedback of recognized states to the learner and, thereby, c) increase performance and intrinsic motivation. Oscillatory power measures in the alpha (8 \u2013 12 Hz) and theta (4 \u2013 7 Hz) frequency band served as features for the prediction and visualization. Our results reveal that machine learning algorithms can distinguish different states of cognitive workload and affect. The approach contributes to the development of closed-loop neuro-adaptive tutoring systems which allow to monitor learners\u2019 states, provide feedback, and adapt their parameters for an optimal learner-training fit and effective and positive learning experience.<\/p>\n\n","protected":false},"excerpt":{"rendered":"
Monitoring learners\u2019 mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner\u2019s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners\u2019 current affective-emotional and cognitive states by measuring and decoding brain activity via a passive BCI. In two studies (N = 8 and N = 7), we investigate whether we can a) predict learners\u2019 affective and cognitive states during a learning or training session, b) provide continuous feedback of recognized states to the learner and, thereby, c) increase performance and intrinsic motivation. Oscillatory power measures in the alpha (8 \u2013 12 Hz) and theta (4 \u2013 7 Hz) frequency band served as features for the prediction and visualization. Our results reveal that machine learning algorithms can distinguish different states of cognitive workload and affect. The approach contributes to the development of closed-loop neuro-adaptive tutoring systems which allow to monitor learners\u2019 states, provide feedback, and adapt their parameters for an optimal learner-training fit and effective and positive learning experience.<\/p>\n","protected":false},"featured_media":0,"template":"","categories":[],"tags":[],"product_cat":[],"topic":[],"technology":[],"knowhow":[],"industry":[],"writer":[84848,84850,84849],"glossary":[],"class_list":{"0":"post-111315","1":"book","2":"type-book","3":"status-publish","5":"writer-katharina-lingelbach","6":"writer-prof-dr-ing-prof-e-h-wilhelm-bauer","7":"writer-sabrina-gado","8":"product","9":"first","10":"instock","11":"downloadable","12":"virtual","13":"sold-individually","14":"taxable","15":"purchasable","16":"product-type-book"},"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"front-page-entry":false,"post-entry":false,"post-teaser":false,"post-teaser-mobile":false,"post-custom-size":false,"whitepaper-teaser":false,"card-big":false,"card-portrait":false,"card-big-company":false,"gp-listing":false,"1536x1536":false,"2048x2048":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"dgwt-wcas-product-suggestion":false},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Monitoring learners\u2019 mental states via a passive Brain-Computer Interface (BCI) allows to continuously estimate current abilities, available cognitive resources, and motivation. It bears the great potential to adapt educational contents, learning speed, and format to the learner\u2019s needs via an intelligent tutoring system. We present a neurophysiological-based approach to continuously monitor learners\u2019 current affective-emotional and…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/book\/111315","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/book"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/book"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=111315"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=111315"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=111315"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=111315"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=111315"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=111315"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=111315"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=111315"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=111315"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=111315"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}