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{"work":{"id":19718889,"created_at":"2015-12-17T23:54:46.256-08:00","from_world_paper_id":145830753,"updated_at":"2025-01-31T16:13:40.167-08:00","_data":{"ai_title_tag":"Cognitive Presence Detection in Online Discussions","grobid_abstract":"In this paper we present the results of an exploratory study that examined the use of text mining and text classification for the automation of the content analysis of discussion transcripts within the context of distance education. We used Community of Inquiry (CoI) framework and focused on the content analysis of the cognitive presence construct given its central position within the CoI model. Our results demonstrate the potentials of proposed approach; The developed classifier achieved 58.4% accuracy and Cohen's Kappa of 0.41 for the 5-category classification task. In this paper we analyze different classification features and describe the main problems and lessons learned from the development of such a system. Furthermore, we analyzed the use of several novel classification features that are based on the specifics of cognitive presence construct and our results indicate that some of them significantly improve classification accuracy.","grobid_abstract_attachment_id":"42043871"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Automated cognitive presence detection in online discussion transcripts","broadcastable":true,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [4802399]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":42043871,"attachmentType":"pdf"}"><img alt="First page of “Automated cognitive presence detection in online discussion transcripts”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/42043871/mini_magick20190218-28646-4x8s76.png?1550497608" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">Automated cognitive presence detection in online discussion transcripts</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="4802399" href="https://independent.academia.edu/SreckoJoksimovic"><img alt="Profile image of Srecko Joksimovic" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Srecko Joksimovic</a></div><div class="ds-work-card--detail"><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">4 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 19718889; const worksViewsPath = "/v0/works/views?subdomain_param=api&work_ids%5B%5D=19718889"; const getWorkViews = async (workId) => { const response = await fetch(worksViewsPath); if (!response.ok) { throw new Error('Failed to load work views'); } const data = await response.json(); return data.views[workId]; }; // Get the view count for the work - we send this immediately rather than waiting for // the DOM to load, so it can be available as soon as possible (but without holding up // the backend or other resource requests, because it's a bit expensive and not critical). const viewCount = await getWorkViews(workId); const updateViewCount = (viewCount) => { try { const viewCountNumber = parseInt(viewCount, 10); if (viewCountNumber === 0) { // Remove the whole views element if there are zero views. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); return; } const commaizedViewCount = viewCountNumber.toLocaleString(); const viewCountBody = document.getElementById('work-metadata-view-count'); if (!viewCountBody) { throw new Error('Failed to find work views element'); } viewCountBody.textContent = `${commaizedViewCount} views`; } catch (error) { // Remove the whole views element if there was some issue parsing. document.getElementById('work-metadata-view-count')?.parentNode?.remove(); throw new Error(`Failed to parse view count: ${viewCount}`, error); } }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">In this paper we present the results of an exploratory study that examined the use of text mining and text classification for the automation of the content analysis of discussion transcripts within the context of distance education. We used Community of Inquiry (CoI) framework and focused on the content analysis of the cognitive presence construct given its central position within the CoI model. Our results demonstrate the potentials of proposed approach; The developed classifier achieved 58.4% accuracy and Cohen's Kappa of 0.41 for the 5-category classification task. In this paper we analyze different classification features and describe the main problems and lessons learned from the development of such a system. Furthermore, we analyzed the use of several novel classification features that are based on the specifics of cognitive presence construct and our results indicate that some of them significantly improve classification accuracy.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":42043871,"attachmentType":"pdf","workUrl":"https://www.academia.edu/19718889/Automated_cognitive_presence_detection_in_online_discussion_transcripts"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":42043871,"attachmentType":"pdf","workUrl":"https://www.academia.edu/19718889/Automated_cognitive_presence_detection_in_online_discussion_transcripts"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div><div class="ds-signup-banner-trigger-container"><div class="ds-signup-banner-trigger ds-signup-banner-trigger-control"></div></div><div class="ds-signup-banner ds-signup-banner-control"><div id="ds-signup-banner-close-button"><button class="ds2-5-button ds2-5-button--secondary ds2-5-button--inverse"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">close</span></button></div><div class="ds-signup-banner-ctas"><img src="//a.academia-assets.com/images/academia-logo-capital-white.svg" /><h4 class="ds2-5-heading-serif-sm">Sign up for access to the world's latest research</h4><button class="ds2-5-button ds2-5-button--inverse ds2-5-button--full-width js-swp-download-button" data-signup-modal="{"location":"signup-banner"}">Sign up for free<span class="material-symbols-outlined" style="font-size: 20px" translate="no">arrow_forward</span></button></div><div class="ds-signup-banner-divider"></div><div class="ds-signup-banner-reasons"><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Get notified about relevant papers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Save papers to use in your research</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Join the discussion with peers</span></div><div class="ds-signup-banner-reasons-item"><span class="material-symbols-outlined" style="font-size: 24px" translate="no">check</span><span>Track your impact</span></div></div></div><script>(() => { // Set up signup banner show/hide behavior: // 1. 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We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen’s kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Towards automated content analysis of discussion transcripts: A cognitive presence case","attachmentId":51813223,"attachmentType":"pdf","work_url":"https://www.academia.edu/17768300/Towards_automated_content_analysis_of_discussion_transcripts_A_cognitive_presence_case","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/17768300/Towards_automated_content_analysis_of_discussion_transcripts_A_cognitive_presence_case"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="37674040" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/37674040/Automated_analysis_of_cognitive_presence_in_online_discussions_written_in_Portuguese">Automated analysis of cognitive presence in online discussions written in Portuguese</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2936389" href="https://unisa-au.academia.edu/vkovanovic">Vitomir Kovanovic</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Lifelong Technology-Enhanced Learning, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper presents a method for automated content analysis of students’ messages in asynchronous discussions written in Portuguese. In particular, the paper looks at the problem of coding discussion transcripts for the levels of cognitive presence, a key construct in a widely used Community of Inquiry model of online learning. Although there are techniques to coding for cognitive presence in the English language, the literature is still poor in methods for others languages, such as Portuguese. The proposed method uses a set of 87 different features to create a random forest classifier to automatically extract the cognitive phases. The model developed reached Cohen’s κκ\kappa of .72, which represents a “substantial” agreement, and it is above the Cohen’s κκ\kappa threshold of .70, commonly used in the literature for determining a reliable quantitative content analysis. This paper also provides some theoretical insights into the nature of cognitive presence by looking at the classification features that were most relevant for distinguishing between the different phases of cognitive presence.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Automated analysis of cognitive presence in online discussions written in Portuguese","attachmentId":57663090,"attachmentType":"pdf","work_url":"https://www.academia.edu/37674040/Automated_analysis_of_cognitive_presence_in_online_discussions_written_in_Portuguese","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/37674040/Automated_analysis_of_cognitive_presence_in_online_discussions_written_in_Portuguese"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="71165780" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/71165780/Doc2Vec_and_Na%C3%AFve_Bayes_Learners_Cognitive_Presence_Assessment_through_Asynchronous_Online_Discussion_TQ_Transcripts">Doc2Vec &Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="56520962" href="https://independent.academia.edu/SamirBennani1">Samir Bennani</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Emerging Technologies in Learning (iJET)</p><p class="ds-related-work--abstract ds2-5-body-sm">Due to the lack of face to face interaction in online learning environment, this article aims essentially to give tutors the opportunity to understand and analyze learners’ cognitive behavior. In this perspective, we propose an automatic system to assess learners’ cognitive presence regarding their social interactions within synchronous online discussions. Combining Natural Language Preprocessing, Doc2Vec document embedding method and machine learning techniques; we first make some transformations and preprocessing to the given transcripts, then we apply Doc2Vec method to represent each message as a vector that will be concatenated with LIWC and context features. The vectors are input data of Naïve Bayes algorithm; a machine learning method; that aims to classify transcripts according to cognitive presence categories.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Doc2Vec \u0026Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts","attachmentId":80631410,"attachmentType":"pdf","work_url":"https://www.academia.edu/71165780/Doc2Vec_and_Na%C3%AFve_Bayes_Learners_Cognitive_Presence_Assessment_through_Asynchronous_Online_Discussion_TQ_Transcripts","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/71165780/Doc2Vec_and_Na%C3%AFve_Bayes_Learners_Cognitive_Presence_Assessment_through_Asynchronous_Online_Discussion_TQ_Transcripts"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="31335894" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/31335894/Cognitive_Presence_in_Web_Based_Learning_A_Content_Analysis_of_Students_Online_Discussions">Cognitive Presence in Web-Based Learning: A Content Analysis of Students' Online Discussions</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="26737913" href="https://independent.academia.edu/TomMcKlin">Tom McKlin</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This first phase of a content analysis of online, asynchronous, educational discussions is designed to generate a method for automatically categorizing messages into cognitive categories using neural network software. This phase of research answers two questions regarding the method of automatically analyzing discussion messages: Can a neural network reliably categorize messages under optimum circumstances, and how can the method be improved to generate great reliability? To determine whether neural network software can reliably categorize messages, two trials were conducted. The first, &quot;best fit&quot; trial, a proof of concept trial comprised only of messages which best fit the categorization model, generated strong reliability figures, and the second, systematic sample, a sample much more indicative of the messages generated in an online educational discussion, produced formative reliability figures from which the method of analysis may be optimized. This analysis also provid...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Cognitive Presence in Web-Based Learning: A Content Analysis of Students' Online Discussions","attachmentId":51721867,"attachmentType":"pdf","work_url":"https://www.academia.edu/31335894/Cognitive_Presence_in_Web_Based_Learning_A_Content_Analysis_of_Students_Online_Discussions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/31335894/Cognitive_Presence_in_Web_Based_Learning_A_Content_Analysis_of_Students_Online_Discussions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="17768277" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/17768277/Structure_matters_Adoption_of_structured_classification_approach_in_the_context_of_cognitive_presence_classification">Structure matters: Adoption of structured classification approach in the context of cognitive presence classification</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2936389" href="https://unisa-au.academia.edu/vkovanovic">Vitomir Kovanovic</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Information Retrieval Technology, Lecture Notes in Computer Science, 2015</p><p class="ds-related-work--abstract ds2-5-body-sm">Within online learning communities, receiving timely and meaningful insights into the quality of learning activities is an important part of an effective educational experience. Commonly adopted methods – such as the Community of Inquiry framework – rely on manual coding of online discussion transcripts, which is a costly and time consuming process. There are several efforts underway to enable the automated classification of online discussion messages using supervised ma- chine learning, which would enable the real-time analysis of interactions occurring within online learning communities. This paper investigates the importance of incorporating features that utilise the structure of on- line discussions for the classification of “cognitive presence” – the central dimension of the Community of Inquiry framework focusing on the quality of students’ critical thinking within online learning communities. We implemented a Conditional Random Field classification solution, which incorporates structural features that may be useful in increasing classification performance over other implementations. Our approach leads to an improvement in classification accuracy of 5.8% over current existing techniques when tested on the same dataset, with a precision and recall of 0.630 and 0.504 respectively.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Structure matters: Adoption of structured classification approach in the context of cognitive presence classification","attachmentId":39699145,"attachmentType":"pdf","work_url":"https://www.academia.edu/17768277/Structure_matters_Adoption_of_structured_classification_approach_in_the_context_of_cognitive_presence_classification","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/17768277/Structure_matters_Adoption_of_structured_classification_approach_in_the_context_of_cognitive_presence_classification"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="91397456" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/91397456/Content_Analysis_as_a_Method_to_Assess_Online_Discussions_for_Learning">Content Analysis as a Method to Assess Online Discussions for Learning</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="5894032" href="https://independent.academia.edu/MarkRodriguez3">Mark Rodriguez</a></div><p class="ds-related-work--metadata ds2-5-body-xs">SAGE Open, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">One of the challenges for instructors in online education is to create learning opportunities through online text-based discourse. The goal of this study was to examine the use of content analysis to better understand graduate students' learning in online discussions. The discussion transcripts of a hybrid graduate course were analyzed to determine levels and frequencies of learning or cognitive presence. The analysis of the online discussions was guided by social constructivist rationale, and includes descriptive statistics and inter-rater reliability measurements. Findings show that cognitive presence levels were concentrated in categories marked for exploration and integration. Non-cognitive messages resulted in the highest frequencies of discussion messages, demonstrating indications of social presence and teaching presence. Training was found to be a key factor in resolving coding inconsistencies to improve the reliability of the content analysis. The processes of content analysis applied in this study to evaluate learning in online discussions provided useful information for the development and study of online discussions for learning.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Content Analysis as a Method to Assess Online Discussions for Learning","attachmentId":94696539,"attachmentType":"pdf","work_url":"https://www.academia.edu/91397456/Content_Analysis_as_a_Method_to_Assess_Online_Discussions_for_Learning","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/91397456/Content_Analysis_as_a_Method_to_Assess_Online_Discussions_for_Learning"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="68312587" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/68312587/Analysing_discussion_forum_data_a_replication_study_avoiding_data_contamination">Analysing discussion forum data: a replication study avoiding data contamination</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="110725" href="https://edinburgh.academia.edu/ElaineFarrow">Elaine Farrow</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019</p><p class="ds-related-work--abstract ds2-5-body-sm">The widespread use of online discussion forums in educational settings provides a rich source of data for researchers interested in how collaboration and interaction can foster effective learning. Such online behaviour can be understood through the Community of Inquiry framework, and the cognitive presence construct in particular can be used to characterise the depth of a student&#39;s critical engagement with course material. Automated methods have been developed to support this task, but many studies used small data sets, and there have been few replication studies. In this work, we present findings related to the robustness and generalisability of automated classification methods for detecting cognitive presence in discussion forum transcripts. We closely examined one published state-of-the-art model, comparing different approaches to managing unbalanced classes in the data. By demonstrating how commonly-used data preprocessing practices can lead to over-optimistic results, we co...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Analysing discussion forum data: a replication study avoiding data contamination","attachmentId":78831600,"attachmentType":"pdf","work_url":"https://www.academia.edu/68312587/Analysing_discussion_forum_data_a_replication_study_avoiding_data_contamination","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/68312587/Analysing_discussion_forum_data_a_replication_study_avoiding_data_contamination"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="29692798" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/29692798/Finding_Cognitive_Presence_in_the_Online_Discussion_Forum_of_A_Hybrid_Course_docx">Finding Cognitive Presence in the Online Discussion Forum of A Hybrid Course.docx</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="56186399" href="https://unair.academia.edu/DavidSegoh">David Segoh</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This action research seeks to investigate the presence of cognitive processes in an online discussion forum of a hybrid graduate course on the Internet technologies. The research was carried out in the educational technology master's of education program at the University of Delaware, U.S.A. The data, in the form of the students' postings on the forum, were gathered during a summer session in 2010 that lasted for 5 weeks, or 9 face-to-face meetings. The analysis follows the content analysis model that was developed by that has been cited and used by many other studies. In this model, there are four higher-order thinking categories, namely triggering, exploration, integration, and resolution. And there is one extra category, named social/other, to contain postings that do not belong to one of the four categories. It is concluded that even though the instructor of the course only required the students to participate actively in the forum and discuss the project they were doing there, and did not specifically set the topics of the discussions nor started the discussion in every occasion evidence of higher-order thinking was indeed found. The results showed that although the majority of the postings fell into the social/or other category due to its highly student-centered orientation, the second and third most postings could be categorized as exploration and integration and if combined together the number is almost as high as that of the 'social/other' postings. If more specific prompts or questions that could intrigue the students' critical COGNITIVE PRESENCE IN AN ONLINE DISCUSSION FORUM 3 thinking are given, the percentage of postings that falls into the higher-order thinking categories will certainly increase significantly.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Finding Cognitive Presence in the Online Discussion Forum of A Hybrid Course.docx","attachmentId":50136617,"attachmentType":"docx","work_url":"https://www.academia.edu/29692798/Finding_Cognitive_Presence_in_the_Online_Discussion_Forum_of_A_Hybrid_Course_docx","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/29692798/Finding_Cognitive_Presence_in_the_Online_Discussion_Forum_of_A_Hybrid_Course_docx"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="37667970" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/37667970/Assessing_cognitive_presence_using_automated_learning_analytics_methods">Assessing cognitive presence using automated learning analytics methods</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2936389" href="https://unisa-au.academia.edu/vkovanovic">Vitomir Kovanovic</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2017</p><p class="ds-related-work--abstract ds2-5-body-sm">With the increasing pace of technological changes in the modern society, there has been a growing interest from educators, business leaders, and policymakers in teaching important higher-order skills which were identified as necessary for thriving in the present-day globalized economy. In this regard, one of the most widely discussed higher order skills is critical thinking, whose importance in shaping problem solving, decision making, and logical thinking has been recognized. Within the domain of distance and online education, the Community of Inquiry (CoI) model provides a pedagogical framework for understanding the critical dimensions of student learning and factors which impact the development of student critical thinking. The CoI model follows the social-constructivist perspective on learning in which learning is seen as happening in both individual minds of learners and through the discourse within the group of learners. Central to the CoI model is the construct of cognitive presence, which captures the student cognitive engagement and the development of critical thinking and deep thinking skills. However, the assessment of cognitive presence is challenging task, particularly given its latent nature and the inherent physical and time separation between students and instructors in distance education settings. One way to address this problem is to make use of the vast amounts of learning data being collected by learning systems. This thesis presents novel methods for understanding and assessing the levels of cognitive presence based on learning analytics techniques and the data collected by learning environments. We first outline a comprehensive model for cognitive presence assessment which builds on the well-established evidence-cantered design (ECD) assessment framework. The proposed assessment model provides a foundation of the thesis, showing how the developed analytical models and their components fit together and how they can be adjusted for new learning contexts. The thesis shows two distinct and complementary analytical methods for assessing students’ cognitive presence and its development. The first method is based on the automated classification of student discussion messages and captures learning as it is observed in the student dialogue. The second analytics method relies on the analysis of log data of students’ use of the learning platform and captures the individual dimension of the learning process. The developed analytics also extend current theoretical understanding of the cognitive presence construct through data-informed operationalization of cognitive presence with different quantitative measures extracted from the student use of online discussions. We also examine methodological challenges of assessing cognitive presence and other forms of cognitive engagement through the analysis of trace data. Finally, with the intent of enabling for the wider adoption of the CoI model for new online learning modalities, the last two chapters examine the use of developed analytics within the context of Massive Open Online Courses (MOOCs). Given the substantial differences between traditional online and MOOC contexts, we first evaluate the suitability of the CoI model for MOOC settings and then assess students’ cognitive presence using the data collected by the MOOC platform. We conclude the thesis with the discussion of practical application and impact of the present work and the directions for the future research.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Assessing cognitive presence using automated learning analytics methods","attachmentId":57656495,"attachmentType":"pdf","work_url":"https://www.academia.edu/37667970/Assessing_cognitive_presence_using_automated_learning_analytics_methods","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/37667970/Assessing_cognitive_presence_using_automated_learning_analytics_methods"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="11025313" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/11025313/Modeling_and_assessing_student_activities_in_on_line_discussions">Modeling and assessing student activities in on-line discussions</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="26680248" href="https://independent.academia.edu/ErinShaw2">Erin Shaw</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Proc. of the AAAI Workshop on Educational Data Mining, 2006</p><p class="ds-related-work--abstract ds2-5-body-sm">As web-enhanced courses become more successful, they put considerable burdens on instructors and teaching assistants. We present our work on developing software tools to support instructors by automatic assessment of pedagogical discussions. We are developing prototype measures of discussion quality that rely on the quantity of discussion contributions. We are also developing techniques for assessing discussion contributions automatically by mining discussion text. Using information retrieval and natural language processing techniques, our tools learn to detect the conversation focus of threaded discussions, classify topics of discussions, and estimate technical depth of contributions. The results from these assessment tools provide basis for the development of scaffolding and question answering techniques for pedagogical discourse.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Modeling and assessing student activities in on-line discussions","attachmentId":36744519,"attachmentType":"pdf","work_url":"https://www.academia.edu/11025313/Modeling_and_assessing_student_activities_in_on_line_discussions","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/11025313/Modeling_and_assessing_student_activities_in_on_line_discussions"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":42043871,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":42043871,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_42043871" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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