Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models
This study investigates an Adaptive Feedback System (AFS) that integrates deep learning (a recurrent neural network trained with historical student data) and GPT-4 to provide personalized feedback in a Digital Art course. In a quasi-experimental design, the intervention group (<i>n</i> =...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Multimodal Technologies and Interaction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2414-4088/9/5/45 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126703407398912 |
|---|---|
| author | Óscar Cuéllar Manuel Contero Mauricio Hincapié |
| author_facet | Óscar Cuéllar Manuel Contero Mauricio Hincapié |
| author_sort | Óscar Cuéllar |
| collection | DOAJ |
| description | This study investigates an Adaptive Feedback System (AFS) that integrates deep learning (a recurrent neural network trained with historical student data) and GPT-4 to provide personalized feedback in a Digital Art course. In a quasi-experimental design, the intervention group (<i>n</i> = 42) received weekly feedback generated from model predictions, while the control group (<i>n</i> = 39) followed the same program without this intervention across four learning blocks or levels. The results revealed (1) a cumulative effect with a significant performance difference in the fourth learning block (+12.63 percentage points); (2) a reduction in performance disparities between students with varying levels of prior knowledge in the experimental group (−56.5%) versus an increase in the control group (+103.3%); (3) an “overcoming effect” where up to 42.9% of students surpassed negative performance predictions; and (4) a positive impact on active participation, especially in live class attendance (+30.21 points) and forum activity (+9.79 points). These findings demonstrate that integrating deep learning with LLMs can significantly improve learning outcomes in online educational environments, particularly for students with limited prior knowledge. |
| format | Article |
| id | doaj-art-b897262084464bb18ce147ae85cfcbac |
| institution | OA Journals |
| issn | 2414-4088 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Multimodal Technologies and Interaction |
| spelling | doaj-art-b897262084464bb18ce147ae85cfcbac2025-08-20T02:33:51ZengMDPI AGMultimodal Technologies and Interaction2414-40882025-05-01954510.3390/mti9050045Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language ModelsÓscar Cuéllar0Manuel Contero1Mauricio Hincapié2Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (Human-Tech), Universitat Politècnica de València, 46022 Valencia, SpainInstituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (Human-Tech), Universitat Politècnica de València, 46022 Valencia, SpainEscuela de Artes y Humanidades, Área Creación, Universidad EAFIT, Medellín 050022, ColombiaThis study investigates an Adaptive Feedback System (AFS) that integrates deep learning (a recurrent neural network trained with historical student data) and GPT-4 to provide personalized feedback in a Digital Art course. In a quasi-experimental design, the intervention group (<i>n</i> = 42) received weekly feedback generated from model predictions, while the control group (<i>n</i> = 39) followed the same program without this intervention across four learning blocks or levels. The results revealed (1) a cumulative effect with a significant performance difference in the fourth learning block (+12.63 percentage points); (2) a reduction in performance disparities between students with varying levels of prior knowledge in the experimental group (−56.5%) versus an increase in the control group (+103.3%); (3) an “overcoming effect” where up to 42.9% of students surpassed negative performance predictions; and (4) a positive impact on active participation, especially in live class attendance (+30.21 points) and forum activity (+9.79 points). These findings demonstrate that integrating deep learning with LLMs can significantly improve learning outcomes in online educational environments, particularly for students with limited prior knowledge.https://www.mdpi.com/2414-4088/9/5/45deep learninglarge language modelspersonalized feedbackassistive technologyeducational technologyperformance prediction |
| spellingShingle | Óscar Cuéllar Manuel Contero Mauricio Hincapié Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models Multimodal Technologies and Interaction deep learning large language models personalized feedback assistive technology educational technology performance prediction |
| title | Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models |
| title_full | Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models |
| title_fullStr | Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models |
| title_full_unstemmed | Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models |
| title_short | Personalized and Timely Feedback in Online Education: Enhancing Learning with Deep Learning and Large Language Models |
| title_sort | personalized and timely feedback in online education enhancing learning with deep learning and large language models |
| topic | deep learning large language models personalized feedback assistive technology educational technology performance prediction |
| url | https://www.mdpi.com/2414-4088/9/5/45 |
| work_keys_str_mv | AT oscarcuellar personalizedandtimelyfeedbackinonlineeducationenhancinglearningwithdeeplearningandlargelanguagemodels AT manuelcontero personalizedandtimelyfeedbackinonlineeducationenhancinglearningwithdeeplearningandlargelanguagemodels AT mauriciohincapie personalizedandtimelyfeedbackinonlineeducationenhancinglearningwithdeeplearningandlargelanguagemodels |