際際滷shows by User: rbc4 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: rbc4 / Sat, 14 Oct 2023 19:10:50 GMT 際際滷Share feed for 際際滷shows by User: rbc4 How Anchoring Concepts Influence Essay Conceptual Structure And Test Performance /slideshow/how-anchoring-concepts-influence-essay-conceptual-structure-and-test-performance/262207330 celdapresentation-231014191050-47311e57
Presented October 21 at CELDA 2023 in Madeira Portugal, https://www.celda-conf.org/ Abstract: This quasi-experimental study seeks to improve the conceptual quality of summary essays by comparing two conditions, essay prompts with or without a list of 13 broad concepts, the concepts were selected across a continuum of the 100 most frequent words in the lesson materials. It is anticipated that only the most central concepts will be used as anchors when writing. Participants (n = 90) in an Architectural Engineering undergraduate course read the assigned lesson textbook chapter and attended lectures and labs, then in a final lab session were asked to write a 300-word summary of the lesson content. Data consists of the essays converted to networks and the end-of-unit multiple choice test. Compared to the expert network benchmark, the essay networks of those receiving the broad concepts in the writing prompt were not significantly different from those who did not receive these concepts. However those receiving the broad concepts were significantly more like peer essay networks (mental model convergence) and like the networks of the two PowerPoint lectures but neither were like the textbook chapter. Further, those receiving the broad concepts performed significantly better on the end-of-unit test than those not receiving the concepts. Term frequency analysis of the essays indicates as expected that the most network-central concepts had a greater frequency in essays, the other terms frequencies were remarkably the same for both the terms and no terms groups, suggesting a similar underlying conceptual mental model of this lesson content. To further explore the influence of anchoring concepts in summary writing prompts, essays were generated with the same two summary writing prompts using OpenAI (ChatGPT) and Google Bard, plus a new prompt that used the 13 most central concepts from the experts network. The quality of the essay networks for both AI systems were equivalent to the students' essay networks for the broad concepts and for the no concept treatments. However the AI essays derived with the 13 most central concepts were significantly better (more like the expert network) than the students and AI essays derived with broad concepts or no concepts treatments. In addition, Bard and OpenAI used several of the same concepts at a higher frequency than the students suggesting that the two AI systems have more similar knowledge graphs of this content. In sum, adding 13 broad conceptual terms to a summary writing prompt improved both structural and declarative knowledge outcomes, but adding 13 most central concepts may be even better. More research is needed to understand how including concepts and other terms in a writing prompt influences students essay conceptual structure and subsequent test performance.]]>

Presented October 21 at CELDA 2023 in Madeira Portugal, https://www.celda-conf.org/ Abstract: This quasi-experimental study seeks to improve the conceptual quality of summary essays by comparing two conditions, essay prompts with or without a list of 13 broad concepts, the concepts were selected across a continuum of the 100 most frequent words in the lesson materials. It is anticipated that only the most central concepts will be used as anchors when writing. Participants (n = 90) in an Architectural Engineering undergraduate course read the assigned lesson textbook chapter and attended lectures and labs, then in a final lab session were asked to write a 300-word summary of the lesson content. Data consists of the essays converted to networks and the end-of-unit multiple choice test. Compared to the expert network benchmark, the essay networks of those receiving the broad concepts in the writing prompt were not significantly different from those who did not receive these concepts. However those receiving the broad concepts were significantly more like peer essay networks (mental model convergence) and like the networks of the two PowerPoint lectures but neither were like the textbook chapter. Further, those receiving the broad concepts performed significantly better on the end-of-unit test than those not receiving the concepts. Term frequency analysis of the essays indicates as expected that the most network-central concepts had a greater frequency in essays, the other terms frequencies were remarkably the same for both the terms and no terms groups, suggesting a similar underlying conceptual mental model of this lesson content. To further explore the influence of anchoring concepts in summary writing prompts, essays were generated with the same two summary writing prompts using OpenAI (ChatGPT) and Google Bard, plus a new prompt that used the 13 most central concepts from the experts network. The quality of the essay networks for both AI systems were equivalent to the students' essay networks for the broad concepts and for the no concept treatments. However the AI essays derived with the 13 most central concepts were significantly better (more like the expert network) than the students and AI essays derived with broad concepts or no concepts treatments. In addition, Bard and OpenAI used several of the same concepts at a higher frequency than the students suggesting that the two AI systems have more similar knowledge graphs of this content. In sum, adding 13 broad conceptual terms to a summary writing prompt improved both structural and declarative knowledge outcomes, but adding 13 most central concepts may be even better. More research is needed to understand how including concepts and other terms in a writing prompt influences students essay conceptual structure and subsequent test performance.]]>
Sat, 14 Oct 2023 19:10:50 GMT /slideshow/how-anchoring-concepts-influence-essay-conceptual-structure-and-test-performance/262207330 rbc4@slideshare.net(rbc4) How Anchoring Concepts Influence Essay Conceptual Structure And Test Performance rbc4 Presented October 21 at CELDA 2023 in Madeira Portugal, https://www.celda-conf.org/ Abstract: This quasi-experimental study seeks to improve the conceptual quality of summary essays by comparing two conditions, essay prompts with or without a list of 13 broad concepts, the concepts were selected across a continuum of the 100 most frequent words in the lesson materials. It is anticipated that only the most central concepts will be used as anchors when writing. Participants (n = 90) in an Architectural Engineering undergraduate course read the assigned lesson textbook chapter and attended lectures and labs, then in a final lab session were asked to write a 300-word summary of the lesson content. Data consists of the essays converted to networks and the end-of-unit multiple choice test. Compared to the expert network benchmark, the essay networks of those receiving the broad concepts in the writing prompt were not significantly different from those who did not receive these concepts. However those receiving the broad concepts were significantly more like peer essay networks (mental model convergence) and like the networks of the two PowerPoint lectures but neither were like the textbook chapter. Further, those receiving the broad concepts performed significantly better on the end-of-unit test than those not receiving the concepts. Term frequency analysis of the essays indicates as expected that the most network-central concepts had a greater frequency in essays, the other terms frequencies were remarkably the same for both the terms and no terms groups, suggesting a similar underlying conceptual mental model of this lesson content. To further explore the influence of anchoring concepts in summary writing prompts, essays were generated with the same two summary writing prompts using OpenAI (ChatGPT) and Google Bard, plus a new prompt that used the 13 most central concepts from the experts network. The quality of the essay networks for both AI systems were equivalent to the students' essay networks for the broad concepts and for the no concept treatments. However the AI essays derived with the 13 most central concepts were significantly better (more like the expert network) than the students and AI essays derived with broad concepts or no concepts treatments. In addition, Bard and OpenAI used several of the same concepts at a higher frequency than the students suggesting that the two AI systems have more similar knowledge graphs of this content. In sum, adding 13 broad conceptual terms to a summary writing prompt improved both structural and declarative knowledge outcomes, but adding 13 most central concepts may be even better. More research is needed to understand how including concepts and other terms in a writing prompt influences students essay conceptual structure and subsequent test performance. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/celdapresentation-231014191050-47311e57-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented October 21 at CELDA 2023 in Madeira Portugal, https://www.celda-conf.org/ Abstract: This quasi-experimental study seeks to improve the conceptual quality of summary essays by comparing two conditions, essay prompts with or without a list of 13 broad concepts, the concepts were selected across a continuum of the 100 most frequent words in the lesson materials. It is anticipated that only the most central concepts will be used as anchors when writing. Participants (n = 90) in an Architectural Engineering undergraduate course read the assigned lesson textbook chapter and attended lectures and labs, then in a final lab session were asked to write a 300-word summary of the lesson content. Data consists of the essays converted to networks and the end-of-unit multiple choice test. Compared to the expert network benchmark, the essay networks of those receiving the broad concepts in the writing prompt were not significantly different from those who did not receive these concepts. However those receiving the broad concepts were significantly more like peer essay networks (mental model convergence) and like the networks of the two PowerPoint lectures but neither were like the textbook chapter. Further, those receiving the broad concepts performed significantly better on the end-of-unit test than those not receiving the concepts. Term frequency analysis of the essays indicates as expected that the most network-central concepts had a greater frequency in essays, the other terms frequencies were remarkably the same for both the terms and no terms groups, suggesting a similar underlying conceptual mental model of this lesson content. To further explore the influence of anchoring concepts in summary writing prompts, essays were generated with the same two summary writing prompts using OpenAI (ChatGPT) and Google Bard, plus a new prompt that used the 13 most central concepts from the experts network. The quality of the essay networks for both AI systems were equivalent to the students&#39; essay networks for the broad concepts and for the no concept treatments. However the AI essays derived with the 13 most central concepts were significantly better (more like the expert network) than the students and AI essays derived with broad concepts or no concepts treatments. In addition, Bard and OpenAI used several of the same concepts at a higher frequency than the students suggesting that the two AI systems have more similar knowledge graphs of this content. In sum, adding 13 broad conceptual terms to a summary writing prompt improved both structural and declarative knowledge outcomes, but adding 13 most central concepts may be even better. More research is needed to understand how including concepts and other terms in a writing prompt influences students essay conceptual structure and subsequent test performance.
How Anchoring Concepts Influence Essay Conceptual Structure And Test Performance from Roy Clariana
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Clariana AERA 2023 presentation.pptx /slideshow/clariana-aera-2023-presentationpptx/257348946 clarianaaera2023presentation-230412161439-a8d478c4
Presentation at AERA 2023 -- Investigation that considered the effect of adding key terms to an essay writing prompt. Funding from the Division of Undergraduate Education of the National Science Foundation (Award Abstract #2215807), Roy B. Clariana (PI).]]>

Presentation at AERA 2023 -- Investigation that considered the effect of adding key terms to an essay writing prompt. Funding from the Division of Undergraduate Education of the National Science Foundation (Award Abstract #2215807), Roy B. Clariana (PI).]]>
Wed, 12 Apr 2023 16:14:38 GMT /slideshow/clariana-aera-2023-presentationpptx/257348946 rbc4@slideshare.net(rbc4) Clariana AERA 2023 presentation.pptx rbc4 Presentation at AERA 2023 -- Investigation that considered the effect of adding key terms to an essay writing prompt. Funding from the Division of Undergraduate Education of the National Science Foundation (Award Abstract #2215807), Roy B. Clariana (PI). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/clarianaaera2023presentation-230412161439-a8d478c4-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presentation at AERA 2023 -- Investigation that considered the effect of adding key terms to an essay writing prompt. Funding from the Division of Undergraduate Education of the National Science Foundation (Award Abstract #2215807), Roy B. Clariana (PI).
Clariana AERA 2023 presentation.pptx from Roy Clariana
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GIKS NSF grant presentation Oct 14 2022.pptx /slideshow/giks-nsf-grant-presentation-oct-14-2022pptx/253554672 giksnsfgrantpresentationoct142022-221013174306-61c59ad0
Penn State University, 2nd Annual College of Education Research Conference Oct 14, 2022 ]]>

Penn State University, 2nd Annual College of Education Research Conference Oct 14, 2022 ]]>
Thu, 13 Oct 2022 17:43:06 GMT /slideshow/giks-nsf-grant-presentation-oct-14-2022pptx/253554672 rbc4@slideshare.net(rbc4) GIKS NSF grant presentation Oct 14 2022.pptx rbc4 Penn State University, 2nd Annual College of Education Research Conference Oct 14, 2022 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/giksnsfgrantpresentationoct142022-221013174306-61c59ad0-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Penn State University, 2nd Annual College of Education Research Conference Oct 14, 2022
GIKS NSF grant presentation Oct 14 2022.pptx from Roy Clariana
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Sentence versus Paragraph Processing: Linear and relational knowledge structure measures /slideshow/sentence-versus-paragraph-processing-linear-and-relational-knowledge-structure-measures/236717291 iwals2019rbc-200708142619
Clariana, R. B., Follmer, D. J., & Li, P. (2019). Sentence versus paragraph processing: Linear and relational knowledge structure measures. Presented at the 7th International Workshop on Advanced Learning Sciences (IWALS 2019), June 17-19, 2019, University of Jyv辰skyl辰, Finland]]>

Clariana, R. B., Follmer, D. J., & Li, P. (2019). Sentence versus paragraph processing: Linear and relational knowledge structure measures. Presented at the 7th International Workshop on Advanced Learning Sciences (IWALS 2019), June 17-19, 2019, University of Jyv辰skyl辰, Finland]]>
Wed, 08 Jul 2020 14:26:19 GMT /slideshow/sentence-versus-paragraph-processing-linear-and-relational-knowledge-structure-measures/236717291 rbc4@slideshare.net(rbc4) Sentence versus Paragraph Processing: Linear and relational knowledge structure measures rbc4 Clariana, R. B., Follmer, D. J., & Li, P. (2019). Sentence versus paragraph processing: Linear and relational knowledge structure measures. Presented at the 7th International Workshop on Advanced Learning Sciences (IWALS 2019), June 17-19, 2019, University of Jyv辰skyl辰, Finland <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iwals2019rbc-200708142619-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Clariana, R. B., Follmer, D. J., &amp; Li, P. (2019). Sentence versus paragraph processing: Linear and relational knowledge structure measures. Presented at the 7th International Workshop on Advanced Learning Sciences (IWALS 2019), June 17-19, 2019, University of Jyv辰skyl辰, Finland
Sentence versus Paragraph Processing: Linear and relational knowledge structure measures from Roy Clariana
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Directed versus undirected network analysis of student essays /slideshow/directed-versus-undirected-network-analysis-of-student-essays/103150843 iwalspresentationfinal-180626155713
IWALS2018 6th International Workshop on Advanced Learning Sciences Perspectives on the Learner: Cognition, Brain, and Education University of Pittsburgh, USA JUNE 6-8, 2018]]>

IWALS2018 6th International Workshop on Advanced Learning Sciences Perspectives on the Learner: Cognition, Brain, and Education University of Pittsburgh, USA JUNE 6-8, 2018]]>
Tue, 26 Jun 2018 15:57:13 GMT /slideshow/directed-versus-undirected-network-analysis-of-student-essays/103150843 rbc4@slideshare.net(rbc4) Directed versus undirected network analysis of student essays rbc4 IWALS2018 6th International Workshop on Advanced Learning Sciences Perspectives on the Learner: Cognition, Brain, and Education University of Pittsburgh, USA JUNE 6-8, 2018 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/iwalspresentationfinal-180626155713-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> IWALS2018 6th International Workshop on Advanced Learning Sciences Perspectives on the Learner: Cognition, Brain, and Education University of Pittsburgh, USA JUNE 6-8, 2018
Directed versus undirected network analysis of student essays from Roy Clariana
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Artificial Intelligence in E-learning (AI-Ed): Current and future applications /slideshow/artificial-intelligence-in-elearning-aied-current-and-future-applications/62616372 royeadl2016-160601131223
Presented May 2016 at the European Distance Learning Association EADL 2016, Nicosia, Cyprus]]>

Presented May 2016 at the European Distance Learning Association EADL 2016, Nicosia, Cyprus]]>
Wed, 01 Jun 2016 13:12:23 GMT /slideshow/artificial-intelligence-in-elearning-aied-current-and-future-applications/62616372 rbc4@slideshare.net(rbc4) Artificial Intelligence in E-learning (AI-Ed): Current and future applications rbc4 Presented May 2016 at the European Distance Learning Association EADL 2016, Nicosia, Cyprus <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/royeadl2016-160601131223-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Presented May 2016 at the European Distance Learning Association EADL 2016, Nicosia, Cyprus
Artificial Intelligence in E-learning (AI-Ed): Current and future applications from Roy Clariana
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Teaching & Learning with Technology TLT 2016 /slideshow/teaching-learning-with-technology-tlt-2016/61561971 tlt2016-160502021856
Pennsylvania State University]]>

Pennsylvania State University]]>
Mon, 02 May 2016 02:18:56 GMT /slideshow/teaching-learning-with-technology-tlt-2016/61561971 rbc4@slideshare.net(rbc4) Teaching & Learning with Technology TLT 2016 rbc4 Pennsylvania State University <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tlt2016-160502021856-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Pennsylvania State University
Teaching & Learning with Technology TLT 2016 from Roy Clariana
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All idiographic nomothetic /slideshow/all-idiographic-nomothetic/40167790 allidiographicnomothetic-141012105018-conversion-gate01
an asynchronous collaborative discussion using PowerPoint as a concept map tool, compare and contrast nomothetic and ideographic]]>

an asynchronous collaborative discussion using PowerPoint as a concept map tool, compare and contrast nomothetic and ideographic]]>
Sun, 12 Oct 2014 10:50:18 GMT /slideshow/all-idiographic-nomothetic/40167790 rbc4@slideshare.net(rbc4) All idiographic nomothetic rbc4 an asynchronous collaborative discussion using PowerPoint as a concept map tool, compare and contrast nomothetic and ideographic <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/allidiographicnomothetic-141012105018-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> an asynchronous collaborative discussion using PowerPoint as a concept map tool, compare and contrast nomothetic and ideographic
All idiographic nomothetic from Roy Clariana
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https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/celdapresentation-231014191050-47311e57-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/how-anchoring-concepts-influence-essay-conceptual-structure-and-test-performance/262207330 How Anchoring Concepts... https://cdn.slidesharecdn.com/ss_thumbnails/clarianaaera2023presentation-230412161439-a8d478c4-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/clariana-aera-2023-presentationpptx/257348946 Clariana AERA 2023 pre... https://cdn.slidesharecdn.com/ss_thumbnails/giksnsfgrantpresentationoct142022-221013174306-61c59ad0-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/giks-nsf-grant-presentation-oct-14-2022pptx/253554672 GIKS NSF grant present...