“Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook
Abstract Rapid developments in educational technology in higher education are intended to make learning more engaging and effective. At the same time, cognitive load theory stresses limitations of human cognitive architecture and urges educational developers to design learning tools that optimise le...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-03-01
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| Series: | International Journal of Educational Technology in Higher Education |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41239-021-00317-3 |
| _version_ | 1818915588571922432 |
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| author | Marta M. Koć-Januchta Konrad J. Schönborn Casey Roehrig Vinay K. Chaudhri Lena A. E. Tibell H. Craig Heller |
| author_facet | Marta M. Koć-Januchta Konrad J. Schönborn Casey Roehrig Vinay K. Chaudhri Lena A. E. Tibell H. Craig Heller |
| author_sort | Marta M. Koć-Januchta |
| collection | DOAJ |
| description | Abstract Rapid developments in educational technology in higher education are intended to make learning more engaging and effective. At the same time, cognitive load theory stresses limitations of human cognitive architecture and urges educational developers to design learning tools that optimise learners’ mental capacities. In a 2-month study we investigated university students’ learning with an AI-enriched digital biology textbook that integrates a 5000-concept knowledge base and algorithms offering the possibility to ask questions and receive answers. The study aimed to shed more light on differences between three sub-types (intrinsic, germane and extraneous) of cognitive load and their relationship with learning gain, self-regulated learning and usability perception while students interacted with the AI-enriched book during an introductory biology course. We found that students displayed a beneficial learning pattern with germane cognitive load significantly higher than both intrinsic and extraneous loads showing that they were engaged in meaningful learning throughout the study. A significant correlation between germane load and accessing linked suggested questions available in the AI-book indicates that the book may support deep learning. Additionally, results showed that perceived non-optimal design, which deflects cognitive resources away from meaningful processing accompanied lower learning gains. Nevertheless, students reported substantially more favourable than unfavourable opinions of the AI-book. The findings provide new approaches for investigating cognitive load types in relation to learning with emerging digital tools in higher education. The findings also highlight the importance of optimally aligning educational technologies and human cognitive architecture. |
| first_indexed | 2024-12-20T00:04:40Z |
| format | Article |
| id | doaj.art-bedb4da9b1e443c7bfe5d0c185ce84e5 |
| institution | Directory Open Access Journal |
| issn | 2365-9440 |
| language | English |
| last_indexed | 2024-12-20T00:04:40Z |
| publishDate | 2022-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | International Journal of Educational Technology in Higher Education |
| spelling | doaj.art-bedb4da9b1e443c7bfe5d0c185ce84e52022-12-21T20:00:40ZengSpringerOpenInternational Journal of Educational Technology in Higher Education2365-94402022-03-0119112210.1186/s41239-021-00317-3“Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbookMarta M. Koć-Januchta0Konrad J. Schönborn1Casey Roehrig2Vinay K. Chaudhri3Lena A. E. Tibell4H. Craig Heller5Department of Science and Technology (ITN), Linköping UniversityDepartment of Science and Technology (ITN), Linköping UniversityHarvard UniversityDepartment of Computer Science, Stanford UniversityDepartment of Science and Technology (ITN), Linköping UniversityBiology Department, Stanford UniversityAbstract Rapid developments in educational technology in higher education are intended to make learning more engaging and effective. At the same time, cognitive load theory stresses limitations of human cognitive architecture and urges educational developers to design learning tools that optimise learners’ mental capacities. In a 2-month study we investigated university students’ learning with an AI-enriched digital biology textbook that integrates a 5000-concept knowledge base and algorithms offering the possibility to ask questions and receive answers. The study aimed to shed more light on differences between three sub-types (intrinsic, germane and extraneous) of cognitive load and their relationship with learning gain, self-regulated learning and usability perception while students interacted with the AI-enriched book during an introductory biology course. We found that students displayed a beneficial learning pattern with germane cognitive load significantly higher than both intrinsic and extraneous loads showing that they were engaged in meaningful learning throughout the study. A significant correlation between germane load and accessing linked suggested questions available in the AI-book indicates that the book may support deep learning. Additionally, results showed that perceived non-optimal design, which deflects cognitive resources away from meaningful processing accompanied lower learning gains. Nevertheless, students reported substantially more favourable than unfavourable opinions of the AI-book. The findings provide new approaches for investigating cognitive load types in relation to learning with emerging digital tools in higher education. The findings also highlight the importance of optimally aligning educational technologies and human cognitive architecture.https://doi.org/10.1186/s41239-021-00317-3Artificial intelligenceCognitive loadUsabilityLearningEducational technologyAdaptive textbook |
| spellingShingle | Marta M. Koć-Januchta Konrad J. Schönborn Casey Roehrig Vinay K. Chaudhri Lena A. E. Tibell H. Craig Heller “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook International Journal of Educational Technology in Higher Education Artificial intelligence Cognitive load Usability Learning Educational technology Adaptive textbook |
| title | “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook |
| title_full | “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook |
| title_fullStr | “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook |
| title_full_unstemmed | “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook |
| title_short | “Connecting concepts helps put main ideas together”: cognitive load and usability in learning biology with an AI-enriched textbook |
| title_sort | connecting concepts helps put main ideas together cognitive load and usability in learning biology with an ai enriched textbook |
| topic | Artificial intelligence Cognitive load Usability Learning Educational technology Adaptive textbook |
| url | https://doi.org/10.1186/s41239-021-00317-3 |
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