Details
| Original language | English |
|---|---|
| Title of host publication | UMAP 2024 |
| Subtitle of host publication | Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization |
| Pages | 20-24 |
| Number of pages | 5 |
| ISBN (electronic) | 9798400704666 |
| Publication status | Published - 28 Jun 2024 |
| Event | 32nd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2024 - Cagliari, Italy Duration: 1 Jul 2024 → 4 Jul 2024 |
Abstract
Teachers report difficulties engaging in search in the context of lesson planning. Despite the availability of customised search solutions like Learning Object Repositories, most teachers report using general-purpose search engines like Google. Unlike students - to whom most research in educational search is directed - teachers are experts capable of using many types of resources for their tasks. In addition, since they teach multiple classes, they switch educational contexts frequently. These circumstances present unique challenges for user modelling. This thesis investigates how an adaptive browser-based search engine augmentation could support teachers in both exploratory and lookup search tasks. A preliminary user study was conducted to understand the actual search behaviour of teachers in their everyday environment and to investigate the inference of search tasks and context from search behaviour. Using this inferred information, personalized search aids could be delivered through the extension such as search engine result page (SERP) summarization for exploratory search and context-based search snippets. The relevance of existing search snippets was investigated and potential for improvement using teachers' search context was identified. With the rest of the PhD, I plan to expand the user study to get more concrete results about teacher search behaviour and implement and test this adaptive browser extension to see how it affects the search experience for teachers.
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UMAP 2024 : Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization. 2024. p. 20-24.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Adaptive Search Support for Teachers in Lesson Planning
AU - Sebastian, Ratan
N1 - Publisher Copyright: © 2024 Owner/Author.
PY - 2024/6/28
Y1 - 2024/6/28
N2 - Teachers report difficulties engaging in search in the context of lesson planning. Despite the availability of customised search solutions like Learning Object Repositories, most teachers report using general-purpose search engines like Google. Unlike students - to whom most research in educational search is directed - teachers are experts capable of using many types of resources for their tasks. In addition, since they teach multiple classes, they switch educational contexts frequently. These circumstances present unique challenges for user modelling. This thesis investigates how an adaptive browser-based search engine augmentation could support teachers in both exploratory and lookup search tasks. A preliminary user study was conducted to understand the actual search behaviour of teachers in their everyday environment and to investigate the inference of search tasks and context from search behaviour. Using this inferred information, personalized search aids could be delivered through the extension such as search engine result page (SERP) summarization for exploratory search and context-based search snippets. The relevance of existing search snippets was investigated and potential for improvement using teachers' search context was identified. With the rest of the PhD, I plan to expand the user study to get more concrete results about teacher search behaviour and implement and test this adaptive browser extension to see how it affects the search experience for teachers.
AB - Teachers report difficulties engaging in search in the context of lesson planning. Despite the availability of customised search solutions like Learning Object Repositories, most teachers report using general-purpose search engines like Google. Unlike students - to whom most research in educational search is directed - teachers are experts capable of using many types of resources for their tasks. In addition, since they teach multiple classes, they switch educational contexts frequently. These circumstances present unique challenges for user modelling. This thesis investigates how an adaptive browser-based search engine augmentation could support teachers in both exploratory and lookup search tasks. A preliminary user study was conducted to understand the actual search behaviour of teachers in their everyday environment and to investigate the inference of search tasks and context from search behaviour. Using this inferred information, personalized search aids could be delivered through the extension such as search engine result page (SERP) summarization for exploratory search and context-based search snippets. The relevance of existing search snippets was investigated and potential for improvement using teachers' search context was identified. With the rest of the PhD, I plan to expand the user study to get more concrete results about teacher search behaviour and implement and test this adaptive browser extension to see how it affects the search experience for teachers.
UR - http://www.scopus.com/inward/record.url?scp=85198920708&partnerID=8YFLogxK
U2 - 10.1145/3631700.3664921
DO - 10.1145/3631700.3664921
M3 - Conference contribution
AN - SCOPUS:85198920708
SP - 20
EP - 24
BT - UMAP 2024
T2 - 32nd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2024
Y2 - 1 July 2024 through 4 July 2024
ER -