{"id":{"repo_id":"wustl","oai_identifier":"oai:openscholarship.wustl.edu:eng_etds-1569"},"canonical_url":"https://search.dev.ndltd.org/etd/wustl/oai:openscholarship.wustl.edu:eng_etds-1569","repository":{"repo_id":"wustl","name":"Washington University in St. Louis","base_url":"https://openscholarship.wustl.edu/do/oai/"},"display":{"title":"Exploring Usage of Web Resources Through a Model of API Learning","abstract":"Application programming interfaces (APIs) are essential to modern software development, and new APIs are frequently being produced. Consequently, software developers must regularly learn new APIs, which they typically do on the job from online resources rather than in a formal educational context. The Kelleher–Ichinco COIL model, an acronym for “Collection and Organization of Information for Learning,” was recently developed to model the entire API learning process, drawing from information foraging theory, cognitive load theory, and external memory research. We ran an exploratory empirical user study in which participants performed a programming task using the React API with the goal of validating and refining this model. Our results support the predictions made by the COIL model, especially the role of external memory in the API learning process. Participants extensively used browser tabs to store web resources in external memory, but their behavior suggests some inefficiencies that incur extraneous cognitive load.","abstract_html":"Application programming interfaces (APIs) are essential to modern software development, and new APIs are frequently being produced. Consequently, software developers must regularly learn new APIs, which they typically do on the job from online resources rather than in a formal educational context. The Kelleher–Ichinco COIL model, an acronym for “Collection and Organization of Information for Learning,” was recently developed to model the entire API learning process, drawing from information foraging theory, cognitive load theory, and external memory research. We ran an exploratory empirical user study in which participants performed a programming task using the React API with the goal of validating and refining this model. Our results support the predictions made by the COIL model, especially the role of external memory in the API learning process. Participants extensively used browser tabs to store web resources in external memory, but their behavior suggests some inefficiencies that incur extraneous cognitive load.","abstract_has_math":false,"creators":["Voichick, Finn"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computer Science & Engineering","degree_department":null,"school":null,"contributors":["Caitlin Kelleher","Caitlin Kelleher Alvitta Ottley Dennis Cosgrove"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-05-15T07:00:00Z","date_published":"2020-05-15T07:00:00Z","updated_at":"2026-07-24T06:13:40Z","subjects":["API learning","API usability","information foraging theory","cognitive load theory","external memory","Cognitive Psychology","Computer Sciences","Engineering","Graphics and Human Computer Interfaces","Physical Sciences and Mathematics","Software Engineering"],"languages":["English (en)"],"rights":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://openscholarship.wustl.edu/eng_etds/513"],"render_values":[{"text":"https://openscholarship.wustl.edu/eng_etds/513","href":"https://openscholarship.wustl.edu/eng_etds/513","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.7936/97h3-m963","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Caitlin Kelleher","Caitlin Kelleher Alvitta Ottley Dennis Cosgrove"]},{"key":"dc:creator","label":"Author","values":["Voichick, Finn"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-04-20T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Engineering","McKelvey School of Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["API learning","API usability","information foraging theory","cognitive load theory","external memory","Cognitive Psychology","Computer Sciences","Engineering","Graphics and Human Computer Interfaces","Physical Sciences and Mathematics","Software Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English (en)"]},{"key":"dc:rights","label":"Dc Rights","values":["I have not registered my thesis with the U.S. Copyright Office, and do not intend to."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.7936/97h3-m963","https://openscholarship.wustl.edu/eng_etds/513"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Permanent URL: https://doi.org/10.7936/97h3-m963"]},{"key":"dc:description.abstract","label":"Abstract","values":["Application programming interfaces (APIs) are essential to modern software development, and new APIs are frequently being produced. 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The Kelleher–Ichinco COIL model, an acronym for “Collection and Organization of Information for Learning,” was recently developed to model the entire API learning process, drawing from information foraging theory, cognitive load theory, and external memory research. We ran an exploratory empirical user study in which participants performed a programming task using the React API with the goal of validating and refining this model. Our results support the predictions made by the COIL model, especially the role of external memory in the API learning process. 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