[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"ssr-repo-dspace000004552":3,"ssr-repo-divisions":72,"menu-footer-id":258,"menu-main-id":319},{"id":4,"title":5,"authors":6,"files":8,"advisors":35,"authors_detail":36,"advisors_detail":40,"publisher":41,"publish_place":42,"language":43,"type":44,"type_slug":45,"year":46,"department":47,"study_program":48,"keywords":49,"keywords_detail":56,"abstract":69,"abstract_html":70,"url":71},"repo-dspace000004552","Face Expression Recognition Using CNN to evaluate Student Stress Levels in learning comprehension within the VIAT-MAP Application",[7],"Chan Paul Amol Atem",[9,13,16,19,23,26,29,32],{"chapter":10,"requires_login":11,"label":12,"available":11},"abstrak",false,"Abstrak",{"chapter":14,"requires_login":11,"label":15,"available":11},"bab1","Bab I",{"chapter":17,"requires_login":11,"label":18,"available":11},"bab2","Bab II",{"chapter":20,"requires_login":21,"label":22,"available":11},"bab3",true,"Bab III",{"chapter":24,"requires_login":21,"label":25,"available":11},"bab4","Bab IV",{"chapter":27,"requires_login":21,"label":28,"available":11},"bab5","Bab V",{"chapter":30,"requires_login":21,"label":31,"available":11},"full","Dokumen Lengkap",{"chapter":33,"requires_login":11,"label":34,"available":21},"open","Akses Terbuka",[],[37],{"name":7,"nim":38,"slug":39},"2041720202","chan-paul-amol-atem",[],"Politeknik Negeri Malang","Malang","Inggris","Skripsi (D4)","skripsi-d4",2024,"D-IV TEKNIK INFORMATIKA",null,[50,51,52,53,54,55],"Student Stress","Convolutional Neural Networks","Online Face Expression System","VIAT-MAP","Correlation Analysis","Academic Performance",[57,59,61,63,65,67],{"name":50,"slug":58},"student-stress",{"name":51,"slug":60},"convolutional-neural-networks",{"name":52,"slug":62},"online-face-expression-system",{"name":53,"slug":64},"viat-map",{"name":54,"slug":66},"correlation-analysis",{"name":55,"slug":68},"academic-performance","Technology has significantly transformed education, shifting from traditional classroom instruction to interactive digital learning environments. This study investigates the correlation between student emotional states and comprehension in a digital learning setting using the VIAT-MAP application and an Online Face Expression Recognition System (OFES) implementing Convolutional Neural Networks (CNN) to assess facial expressions in real time. Results from Low-Performance Students indicate a weak negative correlation (R = -0.2302, p = 0.472054) between sad expressions and post-test scores, suggesting cognitive overload among lower-performing students. In contrast, High-Performance Students demonstrated a weak positive correlation (R = 0.052, p = 0.872491) between happy expressions and post-test scores, indicating a slight tendency for higher post-test scores to be associated with higher happiness levels. A moderate positive correlation was observed (R = 0.5758, p = 0.003237) between sad expressions and post-test scores, indicating active engagement. Similarly, a weak positive correlation (R = 0.4405, p = 0.031213) was found between happy expressions and post-test scores, suggesting that happiness levels also tend to increase as post-test scores increase. High-scoring students showed balanced emotional states, correlating with better comprehension. The study concludes that real-time emotional assessment can provide valuable insights into students' learning processes, with emotional expressions associated with academic performance, particularly happiness and a balanced emotional response. Recommendations include expanding the sample size, conducting In-depth Interviews, and implementing a real-time evaluation feature to enhance understanding of emotional impacts on learning.","\u003Cp>Technology has significantly transformed education, shifting from traditional classroom instruction to interactive digital learning environments. This study investigates the correlation between student emotional states and comprehension in a digital learning setting using the VIAT-MAP application and an Online Face Expression Recognition System (OFES) implementing Convolutional Neural Networks (CNN) to assess facial expressions in real time. Results from Low-Performance Students indicate a weak negative correlation (R = -0.2302, p = 0.472054) between sad expressions and post-test scores, suggesting cognitive overload among lower-performing students. In contrast, High-Performance Students demonstrated a weak positive correlation (R = 0.052, p = 0.872491) between happy expressions and post-test scores, indicating a slight tendency for higher post-test scores to be associated with higher happiness levels. A moderate positive correlation was observed (R = 0.5758, p = 0.003237) between sad expressions and post-test scores, indicating active engagement. 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