A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG
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| Language: | English |
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Kent State University / OhioLINK
2014
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| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=kent1416868268 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-kent1416868268 |
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oai_dc |
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NDLTD |
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English |
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Computer Science Cognitive modeling comprehension text multimedia EEG |
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Computer Science Cognitive modeling comprehension text multimedia EEG Al Madi, Naser S. A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG |
| author |
Al Madi, Naser S. |
| author_facet |
Al Madi, Naser S. |
| author_sort |
Al Madi, Naser S. |
| title |
A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG |
| title_short |
A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG |
| title_full |
A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG |
| title_fullStr |
A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG |
| title_full_unstemmed |
A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG |
| title_sort |
study of learning performance and cognitive activity during multimodal comprehension using segmentation-integration model and eeg |
| publisher |
Kent State University / OhioLINK |
| publishDate |
2014 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=kent1416868268 |
| work_keys_str_mv |
AT almadinasers astudyoflearningperformanceandcognitiveactivityduringmultimodalcomprehensionusingsegmentationintegrationmodelandeeg AT almadinasers studyoflearningperformanceandcognitiveactivityduringmultimodalcomprehensionusingsegmentationintegrationmodelandeeg |
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1719437166035075072 |
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ndltd-OhioLink-oai-etd.ohiolink.edu-kent14168682682021-08-03T06:28:08Z A STUDY OF LEARNING PERFORMANCE AND COGNITIVE ACTIVITY DURING MULTIMODAL COMPREHENSION USING SEGMENTATION-INTEGRATION MODEL AND EEG Al Madi, Naser S. Computer Science Cognitive modeling comprehension text multimedia EEG Though not exactly natural, reading is a dominant way of learning for modern humans. Textual content such as text books are widely used in education and teaching. However, with the advent of technology the same topic and content can also be presented via multimedia (audio-video multimedia) formats. Multimedia is also becoming popular as a substitute of textbooks. The central process that guides learning either by textual, visual or auditory means is comprehension. Comprehension involves many personal and environmental elements. However, in this study we focused on media type (text \& audio-video multimedia) and content material. Our work is focused on the semantic networks representation of text and multimedia comprehension, and the electroencephalography (EEG) signals during comprehension. EEG data gives us a direct indication to the emotions, mental state, and cognitive load experienced during comprehension. In this study we extend the existing model of text comprehension to include text and multimedia comprehension, this extension is based on the similarities between text and multimedia comprehension theories. This allows us to create a computational comparison between text comprehension and multimedia comprehension based on a computational modeling of comprehension. This computability afforded us to observe and quantify the advantages of each media type. Additionally, computational modeling gives us the ability to measure the individual elements involved in comprehension in a way that was not possible before. At the same time, we analyze EEG data to measure the emotional and mental states experienced during comprehension. The usage of EEG data can give us a unique view inside the human brain to understand comprehension and the mental states involved. As a result of this computational analysis of human study; we found that content material and media type affect comprehension. In addition, we found that textual media provides better comprehension when concepts are presented gradually, and audio-video multimedia is better when an overwhelming number of concepts is presented in a short time. Also, looking into the gender distribution of comprehension we can confirm that males and females share the same learning abilities. Additionally, we compared EEG band power in relation to electrode locations to find the differences between text and multimedia comprehension, and the results show that differences exist in Alpha and Beta band power of the Occipital lobe of the brain which is responsible for vision. Also, we presented our work in studying emotions during text and multimedia comprehension through EEG. This revealed that positive (happy) emotions are higher for video comprehension. At the same time, studying cognitive load through EEG revealed that text comprehension creates higher cognitive load than video comprehension. Additionally, we show a positive correlation between bands power and the number of acquired concepts and associations. Also, we show that both text and multimedia subjects start tasks with high attention and cognitive load. At the same time, subjects are able to perform video comprehension with less mental fatigue than text comprehension. This matches the previous result that text comprehension creates higher cognitive load than video comprehension. Finally, we combined EEG results on attention and cognitive load with concept networks analysis results on association and recognition thresholds, and we show that concept recognition depends on attention and engagement, and association recognition depends on cognitive and memory functions. Based on the results from this study, educators and content creators can design material in a way that enhances comprehension and utilizes the potentials of the media form that they use. The benefits of understanding the human abilities and limitations in comprehension and providing a measurement method for it are almost endless. Finally, studying a complex cognitive task such as comprehension using semantic networks growth and EEG is an important attempt to analyze and understand complex cognitive processes through computational methods. 2014-11-26 English text Kent State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=kent1416868268 http://rave.ohiolink.edu/etdc/view?acc_num=kent1416868268 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center. |