Publicação
An explorative investigation into leveraging LLMs to predict university students' learning motivation
| Resumo: | Learning motivation is a key variable in learning. Therefore, its assessment has consistently been a popular research topic. While traditional methods like self-report still dominate, methods integrating passive mobile sensing have emerged, using smartphones to collect behavioral data and assess learning motivation via statistical and machine learning techniques. Recent advances in large language models (LLMs) offer new perspectives for psychological measurements, yet their application in learning motivation assessment remains underexplored. To bridge this gap, we propose a novel approach that integrates LLMs with passive mobile sensing to assess and predict students' learning motivation. We constructed our dataset using mobile sensing data and self-report measures, then designed zero-shot and few-shot tasks embedded in LLMs to evaluate the performance. Ultimately, our findings indicate the feasibility and highlight the potential of leveraging LLMs to predict learning motivation levels based on mobile sensing data. |
|---|---|
| Autores principais: | Feng, Yu |
| Outros Autores: | Zhao, Tongyu; Zu, Yatong; Tavares, Adriano; Gomes, Tiago Manuel Ribeiro; Xu, Hao |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1877774895461433344 |
|---|---|
| author | Feng, Yu |
| author2 | Zhao, Tongyu Zu, Yatong Tavares, Adriano Gomes, Tiago Manuel Ribeiro Xu, Hao |
| author2_role | author author author author author |
| author_facet | Feng, Yu Zhao, Tongyu Zu, Yatong Tavares, Adriano Gomes, Tiago Manuel Ribeiro Xu, Hao |
| author_role | author |
| contributor_name_str_mv | RepositóriUM - Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Feng, Yu\"},{\"Person.name\":\"Zhao, Tongyu\"},{\"Person.name\":\"Zu, Yatong\"},{\"Person.name\":\"Tavares, Adriano\"},{\"Person.name\":\"Gomes, Tiago Manuel Ribeiro\"},{\"Person.name\":\"Xu, Hao\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | RepositóriUM - Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Feng, Yu Zhao, Tongyu Zu, Yatong Tavares, Adriano Gomes, Tiago Manuel Ribeiro Xu, Hao |
| datacite.date.Accepted.fl_str_mv | 2025-01-01T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.titles.title.fl_str_mv | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| dc.contributor.none.fl_str_mv | RepositóriUM - Universidade do Minho |
| dc.creator.none.fl_str_mv | Feng, Yu Zhao, Tongyu Zu, Yatong Tavares, Adriano Gomes, Tiago Manuel Ribeiro Xu, Hao |
| dc.date.Accepted.fl_str_mv | 2025-01-01T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/101246 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.cclincense.fl_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.rights.rights.copyright.fl_str_mv | openAccess |
| dc.title.fl_str_mv | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | Learning motivation is a key variable in learning. Therefore, its assessment has consistently been a popular research topic. While traditional methods like self-report still dominate, methods integrating passive mobile sensing have emerged, using smartphones to collect behavioral data and assess learning motivation via statistical and machine learning techniques. Recent advances in large language models (LLMs) offer new perspectives for psychological measurements, yet their application in learning motivation assessment remains underexplored. To bridge this gap, we propose a novel approach that integrates LLMs with passive mobile sensing to assess and predict students' learning motivation. We constructed our dataset using mobile sensing data and self-report measures, then designed zero-shot and few-shot tasks embedded in LLMs to evaluate the performance. Ultimately, our findings indicate the feasibility and highlight the potential of leveraging LLMs to predict learning motivation levels based on mobile sensing data. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| id | rum_59f295a6846d844bc048a02f9db27b80 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/101246 |
| inst_facet_str | urn:organizationAcronym:repositorium{{{_:::_}}}Universidade do Minho |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/101246 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Feng, Yu Zhao, Tongyu Zu, Yatong Tavares, Adriano Gomes, Tiago Manuel Ribeiro Xu, Hao |
| publishDate | 2025 |
| repo_facet_str | urn:repositoryAcronym:rum{{{_:::_}}}RepositóriUM - Universidade do Minho |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engengLearning motivation is a key variable in learning. Therefore, its assessment has consistently been a popular research topic. While traditional methods like self-report still dominate, methods integrating passive mobile sensing have emerged, using smartphones to collect behavioral data and assess learning motivation via statistical and machine learning techniques. Recent advances in large language models (LLMs) offer new perspectives for psychological measurements, yet their application in learning motivation assessment remains underexplored. To bridge this gap, we propose a novel approach that integrates LLMs with passive mobile sensing to assess and predict students' learning motivation. We constructed our dataset using mobile sensing data and self-report measures, then designed zero-shot and few-shot tasks embedded in LLMs to evaluate the performance. Ultimately, our findings indicate the feasibility and highlight the potential of leveraging LLMs to predict learning motivation levels based on mobile sensing data.application/pdfengAn explorative investigation into leveraging LLMs to predict university students' learning motivationFeng, YuZhao, TongyuZu, YatongTavares, AdrianoGomes, Tiago Manuel RibeiroXu, HaoHostingInstitutionOrganizationalRepositóriUM - Universidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptCITATIONFeng, Y., Zhao, T., Zu, Y., Tavares, A., Gomes, T., & Xu, H. (2025). An Explorative Investigation into Leveraging LLMs to Predict University Students' Learning Motivation. Proceedings of the Annual Meeting of the Cognitive Science Society, 47. Retrieved from https://escholarship.org/uc/item/8xq2h626URLHasVersionhttps://escholarship.org/uc/item/8xq2h62620252025-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/101246http://purl.org/coar/access_right/c_abf2open access608556 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paper2025http://creativecommons.org/licenses/by/4.0/openAccesshttp://purl.org/coar/access_right/c_abf2application/pdfhttps://repositorium.uminho.pt/bitstreams/687017bc-dbe1-410d-bae8-066d1f1b6b3f/download |
| spellingShingle | An explorative investigation into leveraging LLMs to predict university students' learning motivation Feng, Yu |
| status | SINGLETON |
| title | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| title_full | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| title_fullStr | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| title_full_unstemmed | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| title_short | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| title_sort | An explorative investigation into leveraging LLMs to predict university students' learning motivation |
| url | https://hdl.handle.net/1822/101246 |
| visible | 1 |
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