{"id":19794,"date":"2024-04-01T11:59:28","date_gmt":"2024-04-01T03:59:28","guid":{"rendered":"https:\/\/cantor.math.ntnu.edu.tw\/?p=19794"},"modified":"2024-04-01T12:02:18","modified_gmt":"2024-04-01T04:02:18","slug":"talk20240410","status":"publish","type":"post","link":"https:\/\/cantor.math.ntnu.edu.tw\/index.php\/2024\/04\/01\/talk20240410\/","title":{"rendered":"<span style=\"color:#3566BD\">[\u5c08\u984c\u6f14\u8b1b] <\/span>\u30104\u670810\u65e5\u3011\u6797\u5fd7\u9d3b \/ Automated prediction of problem solving performance using eye movement: Can AI help?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"19794\" class=\"elementor elementor-19794\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5c5458f6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5c5458f6\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t\t<div class=\"elementor-background-overlay\"><\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-43b8ceff\" data-id=\"43b8ceff\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4115cdb6 elementor-widget elementor-widget-heading\" data-id=\"4115cdb6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Automated prediction of problem solving performance using eye movement: Can AI help?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-699139af elementor-widget elementor-widget-spacer\" data-id=\"699139af\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-619e1f6 elementor-widget elementor-widget-text-editor\" data-id=\"619e1f6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><strong><span style=\"color: #000080; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4; font-size: 18px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal;\">Time:<\/span><\/strong><span style=\"color: #000080; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4; font-size: 18px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: bold;\"> April 10 (Wed.) 14:00-15:00<br \/><\/span><span style=\"color: #000080; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4; font-size: 18px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: bold;\">Place: S101, Gongguan Campus, NTNU<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-11dc7f58 elementor-widget elementor-widget-image\" data-id=\"11dc7f58\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"254\" height=\"291\" src=\"https:\/\/cantor.math.ntnu.edu.tw\/wp-content\/uploads\/2024\/04\/0410\u6797\u5fd7\u9d3b.png\" class=\"attachment-full size-full wp-image-19796\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-33877fad elementor-widget elementor-widget-text-editor\" data-id=\"33877fad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div style=\"list-style: none; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; margin-top: 10px; font-size: 22px; line-height: 26px; text-align: center; color: #cc6633; font-weight: bold;\"><p>Asst. Prof. John Lin<br \/>\u6797\u5fd7\u9d3b \u52a9\u7406\u6559\u6388<\/p><\/div><div style=\"list-style: none; font-family: \u5fae\u8edf\u6b63\u9ed1\u9ad4; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; margin-top: 10px; font-size: 18px; line-height: 22px; text-align: center; color: #cc9966; font-weight: bold;\">\u5e2b\u5927 \u79d1\u5b78\u6559\u80b2\u7814\u7a76\u6240<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5de4ac7d elementor-widget elementor-widget-spacer\" data-id=\"5de4ac7d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cf4b31e elementor-widget elementor-widget-text-editor\" data-id=\"cf4b31e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"color: #000000;\">With the ability to predict learning behaviors in complex scenarios, artificial intelligence (AI) shows its potential in assessing the problem-solving performance. Given that AI, data-driven algorithms, could be helpful to address random signals (e.g., random fixations), using AI to predict learning performance based on eye movements seems promising. The aims to explorer the potential of an AI, designed based on the long-short-term memory neural structure, to predict whether mathematics problems could be solved in a digital problem-solving scenario using eye movements. Sixty-one students participated in this study. We examined whether types of eye movement features (AOI-based vs. fixation-based features) and information (Separated vs. integrated steps) during solving problems could impact the performance of AI models. In additional, the effects of two hyper-parameters, activations in hidden layers and number of neuros, were examined. The results suggested that fixation-based features outperform AOI-based features. Furthermore, information in separated steps could provide higher accuracy than that in the integrated steps. Regarding the impact of hyper-parameters on AI performance, the activation function \u2018tanh\u2019 and thirty neurons in each layer could be used to train AI models with higher accuracy. The inconsistent between human and AI-based assessment were discussed and visualized.<\/span><\/p><h2><span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">More information : <a style=\"color: #000000;\" href=\"https:\/\/johnlin.gise.ntnu.edu.tw\/index.php\/zh\/\" target=\"_blank\" rel=\"noopener\">https:\/\/johnlin.gise.ntnu.edu.tw\/index.php\/zh\/<\/a><\/span><\/strong><\/span><\/h2>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d369826 elementor-widget elementor-widget-image\" data-id=\"d369826\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1240\" height=\"758\" src=\"https:\/\/cantor.math.ntnu.edu.tw\/wp-content\/uploads\/2020\/08\/\u6f14\u8b1b\u6a19\u5c3e.jpg\" class=\"attachment-full size-full wp-image-7367\" alt=\"\" srcset=\"https:\/\/cantor.math.ntnu.edu.tw\/wp-content\/uploads\/2020\/08\/\u6f14\u8b1b\u6a19\u5c3e.jpg 1240w, https:\/\/cantor.math.ntnu.edu.tw\/wp-content\/uploads\/2020\/08\/\u6f14\u8b1b\u6a19\u5c3e-300x183.jpg 300w, https:\/\/cantor.math.ntnu.edu.tw\/wp-content\/uploads\/2020\/08\/\u6f14\u8b1b\u6a19\u5c3e-768x469.jpg 768w, https:\/\/cantor.math.ntnu.edu.tw\/wp-content\/uploads\/2020\/08\/\u6f14\u8b1b\u6a19\u5c3e-1024x626.jpg 1024w\" sizes=\"(max-width: 1240px) 100vw, 1240px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Automated prediction of problem solving performance usi 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