{"id":{"repo_id":"kennesaw","oai_identifier":"oai:digitalcommons.kennesaw.edu:mscs_etd-1026"},"canonical_url":"https://search.dev.ndltd.org/etd/kennesaw/oai:digitalcommons.kennesaw.edu:mscs_etd-1026","repository":{"repo_id":"kennesaw","name":"Kennesaw State University","base_url":"https://digitalcommons.kennesaw.edu/do/oai/"},"display":{"title":"USE OF POPE ENGAGEMENT INDEX TO MEASURE COGNITIVE LOAD OF PHYSICAL MODELING ACTIVITIES IN ORGANIC CHEMISTRY","abstract":"<p>Understanding how students learn and process information is critical to developing physical modeling activities that facilitate student learning by decreasing cognitive load in the working memory. Optimizing cognitive load during physical modeling activities in organic chemistry is the key to effective and efficient learning. Using EEG (electroencephalogram) and eye tracking technologies, researchers measured and recorded the cognitive processing of participants while they completed a chiral physical modeling activity. Analysis of the data using the Engagement Index developed by Pope <em>et al</em> provided information necessary to develop curriculum that does not undermine student learning due to excessive cognitive load. </p>","abstract_html":"&lt;p&gt;Understanding how students learn and process information is critical to developing physical modeling activities that facilitate student learning by decreasing cognitive load in the working memory. Optimizing cognitive load during physical modeling activities in organic chemistry is the key to effective and efficient learning. Using EEG (electroencephalogram) and eye tracking technologies, researchers measured and recorded the cognitive processing of participants while they completed a chiral physical modeling activity. Analysis of the data using the Engagement Index developed by Pope &lt;em&gt;et al&lt;/em&gt; provided information necessary to develop curriculum that does not undermine student learning due to excessive cognitive load. &lt;/p&gt;","abstract_has_math":false,"creators":["Calvert, Jenifer"],"institution":null,"degree_name":"Master of Science in Chemical Sciences (MSCB)","degree_level":"Thesis","degree_discipline":"Chemistry","degree_department":null,"school":null,"contributors":["Kimberly Cortes","Adriane Randolph","Thomas Leeper"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-11T07:00:00Z","date_published":"2019-07-11T07:00:00Z","updated_at":"2026-07-24T02:43:33Z","subjects":["cognitive load","EEG","electroencephalogram","organic chemistry","physical modeling","chirality","Chemistry","Science and Mathematics Education"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/mscs_etd/25","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kimberly Cortes","Adriane Randolph","Thomas Leeper"]},{"key":"dc:creator","label":"Author","values":["Calvert, Jenifer"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-07-25T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Chemistry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Chemical Sciences (MSCB)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["cognitive load","EEG","electroencephalogram","organic chemistry","physical modeling","chirality","Chemistry","Science and Mathematics Education"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/mscs_etd/25"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["<p>Understanding how students learn and process information is critical to developing physical modeling activities that facilitate student learning by decreasing cognitive load in the working memory. Optimizing cognitive load during physical modeling activities in organic chemistry is the key to effective and efficient learning. Using EEG (electroencephalogram) and eye tracking technologies, researchers measured and recorded the cognitive processing of participants while they completed a chiral physical modeling activity. Analysis of the data using the Engagement Index developed by Pope <em>et al</em> provided information necessary to develop curriculum that does not undermine student learning due to excessive cognitive load. </p>"]},{"key":"dc:title","label":"Title","values":["USE OF POPE ENGAGEMENT INDEX TO MEASURE COGNITIVE LOAD OF PHYSICAL MODELING ACTIVITIES IN ORGANIC CHEMISTRY"]}]}],"canonical_facts":{"dc:contributor":["Kimberly Cortes","Adriane Randolph","Thomas Leeper"],"dc:creator":["Calvert, Jenifer"],"dc:date.available":["2019-07-25T07:00:00Z"],"dc:description.abstract":["<p>Understanding how students learn and process information is critical to developing physical modeling activities that facilitate student learning by decreasing cognitive load in the working memory. Optimizing cognitive load during physical modeling activities in organic chemistry is the key to effective and efficient learning. Using EEG (electroencephalogram) and eye tracking technologies, researchers measured and recorded the cognitive processing of participants while they completed a chiral physical modeling activity. Analysis of the data using the Engagement Index developed by Pope <em>et al</em> provided information necessary to develop curriculum that does not undermine student learning due to excessive cognitive load. </p>"],"dc:identifier":["https://digitalcommons.kennesaw.edu/mscs_etd/25"],"dc:subject":["cognitive load","EEG","electroencephalogram","organic chemistry","physical modeling","chirality","Chemistry","Science and Mathematics Education"],"dc:title":["USE OF POPE ENGAGEMENT INDEX TO MEASURE COGNITIVE LOAD OF PHYSICAL MODELING ACTIVITIES IN ORGANIC CHEMISTRY"],"thesis:degree_discipline":["Chemistry"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Chemical Sciences (MSCB)"]},"updated_at":"2026-07-24T02:43:33Z"}