{"id":30,"date":"2022-07-28T09:00:01","date_gmt":"2022-07-28T09:00:01","guid":{"rendered":"https:\/\/metric.qcri.org\/blog\/?p=30"},"modified":"2022-07-28T09:00:02","modified_gmt":"2022-07-28T09:00:02","slug":"using-the-taxonomy-of-cognitive-learning-to-model-online-searching","status":"publish","type":"post","link":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/using-the-taxonomy-of-cognitive-learning-to-model-online-searching\/","title":{"rendered":"Using the taxonomy of cognitive learning to model online searching"},"content":{"rendered":"\n<p>In this research, we investigated whether a learning process has unique information searching characteristics.<\/p>\n\n\n\n<p>The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in 426 searching tasks.<\/p>\n\n\n\n<p>We classified the searching tasks according to Anderson and Krathwohl\u2019s taxonomy of the cognitive learning domain. Research results indicate that applying and analyzing, the middle two of the six categories, generally take the most searching effort in terms of queries per session, topics searched per session, and total time searching. Interestingly, the lowest two learning categories, remembering and understanding, exhibit searching characteristics similar to the highest order learning categories of evaluating and creating.<\/p>\n\n\n\n<p>Our results suggest the view of Web searchers having simple information needs may be incorrect. Instead, we discovered that users applied simple searching expressions to support their higher-level information needs. It appears that searchers rely primarily on their internal knowledge for evaluating and creating information needs, using search primarily for fact checking and verification.<\/p>\n\n\n\n<p>Overall, results indicate that a learning theory may better describe the information searching process than more commonly used paradigms of decision making or problem solving. The learning style of the searcher does have some moderating effect on exhibited searching characteristics.<\/p>\n\n\n\n<p>The implication of this research is that rather than solely addressing a searcher\u2019s expressed information need, searching systems can also address the underlying learning need of the user.<\/p>\n\n\n\n<p>Jansen, B. J., Booth, D. and Smith, B. (2009)\u00a0<a rel=\"noreferrer noopener\" href=\"http:\/\/www.bernardjjansen.com\/uploads\/2\/4\/1\/8\/24188166\/jansen_using_the_taxonomy_of_cognitive_learning_to_model_online_searching.pdf\" target=\"_blank\">Using the taxonomy of cognitive learning to model online searching<\/a>. Information Processing &amp; Management. 45(6), 643-663.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this research, we investigated whether a learning process has unique information searching characteristics. The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in 426 searching tasks. We classified &#8230; <a title=\"Using the taxonomy of cognitive learning to model online searching\" class=\"read-more\" href=\"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/using-the-taxonomy-of-cognitive-learning-to-model-online-searching\/\" aria-label=\"More on Using the taxonomy of cognitive learning to model online searching\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-30","post","type-post","status-publish","format-standard","hentry","category-user-study"],"jetpack_featured_media_url":"","jetpack-related-posts":[{"id":52,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/confusion-prediction-from-eye-tracking-data-experiments-with-machine-learning\/","url_meta":{"origin":30,"position":0},"title":"Confusion Prediction from Eye-Tracking Data: Experiments with Machine Learning","date":"July 28, 2022","format":false,"excerpt":"Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user\u2019s confusion is correlated with primarily fixation-level features. We find that\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":231,"url":"https:\/\/metric.qcri.org\/blog\/2024\/06\/25\/benefits-of-metric-in-teaching-user-study-analytics\/","url_meta":{"origin":30,"position":1},"title":"Benefits of METRIC in Teaching User Study Analytics","date":"June 25, 2024","format":false,"excerpt":"Analytics Illustration [Source: ACUA] Various industries benefit from user analytic studies, both with and without knowledge. E-Commerce, logistics and transportation, hospitality, web development, real estate, healthcare, public services, and even education. Many industries need user feedback to improve their services, so implementing user analytic studies in academia benefits students. Here\u2026","rel":"","context":"In &quot;analytics&quot;","img":{"alt_text":"Benefits of METRIC in Teaching User Study Analytics","src":"https:\/\/i0.wp.com\/metric.qcri.org\/blog\/wp-content\/uploads\/2024\/06\/Screen-Shot-2024-06-13-at-5.59.38-PM.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":107,"url":"https:\/\/metric.qcri.org\/blog\/2023\/06\/11\/the-impact-of-ai-on-ux-design\/","url_meta":{"origin":30,"position":2},"title":"The Impact of AI on UX Design","date":"June 11, 2023","format":false,"excerpt":"Artificial intelligence (AI) can potentially transform several industries, such as the UX industry Different AI applications have the potential to handle routine, creative, analytical, and strategic processes, such as writing a script or summarising an article. Specific AI applications, such as ChatGPT, have gained widespread popularity for their intelligence and\u2026","rel":"","context":"In \"users\"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/metric.qcri.org\/blog\/wp-content\/uploads\/2023\/06\/free-photo-of-marketing-telefono-inteligente-internet-escritura.jpeg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":42,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/analyzing-demographic-bias-in-artificially-generated-facial-pictures\/","url_meta":{"origin":30,"position":3},"title":"Analyzing Demographic Bias in Artificially Generated Facial Pictures","date":"July 28, 2022","format":false,"excerpt":"Artificial generation of facial images is increasingly popular, with machine learning achieving photo-realistic results. Yet, there is a concern that the generated images might not fairly represent all demographic groups. We use a state-of-the-art method to generate 10,000 facial images and find that the generated images are skewed towards young\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":287,"url":"https:\/\/metric.qcri.org\/blog\/2024\/07\/05\/eye-tracking-beyond-conventional-uses\/","url_meta":{"origin":30,"position":4},"title":"Eye Tracking Beyond Conventional Uses","date":"July 5, 2024","format":false,"excerpt":"Eye tracking is a versatile technology with applications across various fields for its ability to provide detailed insights into visual attention and behavior. With more and more organizations gaining interest in this technology\u2019s abilities, there has been a rise in numerous offline and online eye-tracking tools such as METRIC. User\u2026","rel":"","context":"In &quot;analytics&quot;","img":{"alt_text":"eye-tracking technology for early screening and diagnosis of ASD","src":"https:\/\/i0.wp.com\/metric.qcri.org\/blog\/wp-content\/uploads\/2024\/07\/openart-image_notBvuei_1720099256913_raw.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":14,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/which-message-which-channel-which-customer-exploring-response-rates-in-multi-channel-marketing-using-short-form-advertising\/","url_meta":{"origin":30,"position":5},"title":"Which Message? Which Channel? Which Customer?: Exploring Response Rates in Multi-Channel Marketing Using Short Form Advertising","date":"July 28, 2022","format":false,"excerpt":"Formulating short form advertising messages with little ad content that work and choosing high-performing channels to disseminate them are persistent challenges in multichannel marketing. Drawing on the persuasive systems design model, we conducted an experiment with 33,848 actual customers of an international telecom company. In a real-life setting, we compared\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/30","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/comments?post=30"}],"version-history":[{"count":1,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/30\/revisions"}],"predecessor-version":[{"id":31,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/30\/revisions\/31"}],"wp:attachment":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/media?parent=30"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/categories?post=30"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/tags?post=30"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}