{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108346"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108346","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Explanation mining","abstract":"In this thesis, we propose the idea of computational analysis of explanations. Explanations are used to provide an understanding of a concept, procedure or reasoning to others. Although explanations are present online ubiquitously within textbooks, videos, blogposts, discussion forums, and many more, there is no way to mine them automatically. As a result, users in need of an explanation have to rely on search engines and potentially read through multiple documents in an attempt to find a suitable explanation. This process can be highly tedious for them and may not even be successful in some cases. On the other hand, there are many users such as educators, authors, who write explanations and can benefit from assistants that help enhance the quality of their explanations. The goal of computational analysis of explanations is to assist both these kinds of users. In this work, our focus is on Explanation Mining to assist users seeking explanations. For understanding some of the linguistic features of explanations across multiple domains, we first apply standard Learning-to-rank models to rank explanations collected from the Explain Like I'm Five (ELI5) reddit forum. Based on cross-domain experiments, we find that a model trained on a sufficiently large dataset achieves decent performance across all domains which suggests that there are some common markers of explanations. Next, to apply this knowledge to the practical problem of mining explanations of educational concepts, we propose a baseline approach based on the popular Language Modeling approach of information retrieval. We show that incorporating knowledge from a model trained on the ELI5 dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result it can be deployed for multiple online courses. Finally, we show a demo system that acts as an assistant to online learners while viewing slides. The system enables users to select any piece of text on the slide and find an explanation for it. We conclude with some interesting directions for future work in this field.","abstract_html":"In this thesis, we propose the idea of computational analysis of explanations. Explanations are used to provide an understanding of a concept, procedure or reasoning to others. Although explanations are present online ubiquitously within textbooks, videos, blogposts, discussion forums, and many more, there is no way to mine them automatically. As a result, users in need of an explanation have to rely on search engines and potentially read through multiple documents in an attempt to find a suitable explanation. This process can be highly tedious for them and may not even be successful in some cases. On the other hand, there are many users such as educators, authors, who write explanations and can benefit from assistants that help enhance the quality of their explanations. The goal of computational analysis of explanations is to assist both these kinds of users. In this work, our focus is on Explanation Mining to assist users seeking explanations. For understanding some of the linguistic features of explanations across multiple domains, we first apply standard Learning-to-rank models to rank explanations collected from the Explain Like I'm Five (ELI5) reddit forum. Based on cross-domain experiments, we find that a model trained on a sufficiently large dataset achieves decent performance across all domains which suggests that there are some common markers of explanations. Next, to apply this knowledge to the practical problem of mining explanations of educational concepts, we propose a baseline approach based on the popular Language Modeling approach of information retrieval. We show that incorporating knowledge from a model trained on the ELI5 dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result it can be deployed for multiple online courses. Finally, we show a demo system that acts as an assistant to online learners while viewing slides. The system enables users to select any piece of text on the slide and find an explanation for it. We conclude with some interesting directions for future work in this field.","abstract_has_math":false,"creators":["Bhavya"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-27T00:51:32Z","date_published":"2020-08-27T00:51:32Z","updated_at":"2026-07-22T22:24:48Z","subjects":["explanation mining","slide explainer"],"languages":["en"],"rights":["Copyright 2020 Bhavya"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108346","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Bhavya"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-27T00:51:32Z","2022-08-27T00:51:40Z","2020-05-13","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["explanation mining","slide explainer"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Bhavya"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108346"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this thesis, we propose the idea of computational analysis of explanations. Explanations are used to provide an understanding of a concept, procedure or reasoning to others. Although explanations are present online ubiquitously within textbooks, videos, blogposts, discussion forums, and many more, there is no way to mine them automatically. As a result, users in need of an explanation have to rely on search engines and potentially read through multiple documents in an attempt to find a suitable explanation. This process can be highly tedious for them and may not even be successful in some cases. On the other hand, there are many users such as educators, authors, who write explanations and can benefit from assistants that help enhance the quality of their explanations. The goal of computational analysis of explanations is to assist both these kinds of users. In this work, our focus is on Explanation Mining to assist users seeking explanations. For understanding some of the linguistic features of explanations across multiple domains, we first apply standard Learning-to-rank models to rank explanations collected from the Explain Like I'm Five (ELI5) reddit forum. Based on cross-domain experiments, we find that a model trained on a sufficiently large dataset achieves decent performance across all domains which suggests that there are some common markers of explanations. Next, to apply this knowledge to the practical problem of mining explanations of educational concepts, we propose a baseline approach based on the popular Language Modeling approach of information retrieval. We show that incorporating knowledge from a model trained on the ELI5 dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result it can be deployed for multiple online courses. Finally, we show a demo system that acts as an assistant to online learners while viewing slides. The system enables users to select any piece of text on the slide and find an explanation for it. We conclude with some interesting directions for future work in this field.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Bhavya, accepted the attached license on 2020-05-12 at 14:25.","The student, Bhavya, submitted this Thesis for approval on 2020-05-12 at 14:30.","This Thesis was approved for publication on 2020-05-13 at 08:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15341 on 2020-08-25 at 17:44:22","Made available in DSpace on 2020-08-27T00:51:32Z (GMT). No. of bitstreams: 2 BHAVYA-THESIS-2020.pdf: 2995449 bytes, checksum: 592a563d58fb0efb1d1536fa9bfc6df7 (MD5) LICENSE.txt: 4205 bytes, checksum: cbbf47ad3d04c99a5fe03047e26f11b0 (MD5) Previous issue date: 2020-05-13","Embargo set by: Seth Robbins for item 115961 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Explanation mining"]}]}],"canonical_facts":{"dc:contributor":["Zhai, ChengXiang"],"dc:creator":["Bhavya"],"dc:date":["2020-08-27T00:51:32Z","2022-08-27T00:51:40Z","2020-05-13","2020-05"],"dc:description":["In this thesis, we propose the idea of computational analysis of explanations. Explanations are used to provide an understanding of a concept, procedure or reasoning to others. Although explanations are present online ubiquitously within textbooks, videos, blogposts, discussion forums, and many more, there is no way to mine them automatically. As a result, users in need of an explanation have to rely on search engines and potentially read through multiple documents in an attempt to find a suitable explanation. This process can be highly tedious for them and may not even be successful in some cases. On the other hand, there are many users such as educators, authors, who write explanations and can benefit from assistants that help enhance the quality of their explanations. The goal of computational analysis of explanations is to assist both these kinds of users. In this work, our focus is on Explanation Mining to assist users seeking explanations. For understanding some of the linguistic features of explanations across multiple domains, we first apply standard Learning-to-rank models to rank explanations collected from the Explain Like I'm Five (ELI5) reddit forum. Based on cross-domain experiments, we find that a model trained on a sufficiently large dataset achieves decent performance across all domains which suggests that there are some common markers of explanations. Next, to apply this knowledge to the practical problem of mining explanations of educational concepts, we propose a baseline approach based on the popular Language Modeling approach of information retrieval. We show that incorporating knowledge from a model trained on the ELI5 dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result it can be deployed for multiple online courses. Finally, we show a demo system that acts as an assistant to online learners while viewing slides. The system enables users to select any piece of text on the slide and find an explanation for it. We conclude with some interesting directions for future work in this field.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Bhavya, accepted the attached license on 2020-05-12 at 14:25.","The student, Bhavya, submitted this Thesis for approval on 2020-05-12 at 14:30.","This Thesis was approved for publication on 2020-05-13 at 08:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15341 on 2020-08-25 at 17:44:22","Made available in DSpace on 2020-08-27T00:51:32Z (GMT). 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