Tracing Knowledge States through Student Assessment in a Blended Learning Environment
Blended learning has recently acquired popularity in a variety of educational settings. This approach has the advantage of being able to autonomously monitor students' knowledge states using the collected learning data. Moodle is the most widely used learning management system in blended learni...
| Main Authors: | , |
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| Format: | Article |
| Language: | Indonesian |
| Published: |
Universitas Negeri Semarang
2024-02-01
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| Series: | Jurnal Teknik Elektro |
| Subjects: | |
| Online Access: | https://journal.unnes.ac.id/nju/jte/article/view/47861 |
| _version_ | 1797231274259120128 |
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| author | Indriana Hidayah Ebedia Hilda Am |
| author_facet | Indriana Hidayah Ebedia Hilda Am |
| author_sort | Indriana Hidayah |
| collection | DOAJ |
| description | Blended learning has recently acquired popularity in a variety of educational settings. This approach has the advantage of being able to autonomously monitor students' knowledge states using the collected learning data. Moodle is the most widely used learning management system in blended learning environments. Students can access Moodle to obtain supplementary materials, exercises, and assessments to complement their face-to-face meetings. However, its performance can be improved by more effectively tailoring students' skills and pace of learning. Several studies have been conducted on knowledge tracing; however, we have not discovered any studies that particularly investigate knowledge tracing in a blended learning setting with Moodle as a component. This study proposes a scheme for assessment using the features of the Moodle quiz platform. The assessment data is used to conduct knowledge tracing with the Bayesian Knowledge Tracing (BKT) model, which improves interpretability. The aforementioned data were collected from information engineering undergraduate students who completed 88 exercises that assessed 23 knowledge components within the course. We measure RMSE and MAE to evaluate the performance of the BKT model on our dataset. Furthermore, we compare the knowledge tracing performance to other well-known datasets. Our results show that the BKT model performed better with our dataset, with an RMSE of 0.314 and an MAE of 0.197. Moreover, the BKT model can be used to assess student performance and determine the level of mastery for each knowledge component. Thus, the outcomes can be applied to personalized learning in the future. |
| first_indexed | 2024-04-24T15:41:46Z |
| format | Article |
| id | doaj.art-7337b9e2cf5847188c3b78f314bb3e9b |
| institution | Directory Open Access Journal |
| issn | 1411-0059 2549-1571 |
| language | Indonesian |
| last_indexed | 2024-04-24T15:41:46Z |
| publishDate | 2024-02-01 |
| publisher | Universitas Negeri Semarang |
| record_format | Article |
| series | Jurnal Teknik Elektro |
| spelling | doaj.art-7337b9e2cf5847188c3b78f314bb3e9b2024-04-01T19:07:05ZindUniversitas Negeri SemarangJurnal Teknik Elektro1411-00592549-15712024-02-01152465210.15294/jte.v15i2.4786114777Tracing Knowledge States through Student Assessment in a Blended Learning EnvironmentIndriana Hidayah0Ebedia Hilda Am1Universitas Gadjah Mada, IndonesiaUniversitas Gadjah Mada, IndonesiaBlended learning has recently acquired popularity in a variety of educational settings. This approach has the advantage of being able to autonomously monitor students' knowledge states using the collected learning data. Moodle is the most widely used learning management system in blended learning environments. Students can access Moodle to obtain supplementary materials, exercises, and assessments to complement their face-to-face meetings. However, its performance can be improved by more effectively tailoring students' skills and pace of learning. Several studies have been conducted on knowledge tracing; however, we have not discovered any studies that particularly investigate knowledge tracing in a blended learning setting with Moodle as a component. This study proposes a scheme for assessment using the features of the Moodle quiz platform. The assessment data is used to conduct knowledge tracing with the Bayesian Knowledge Tracing (BKT) model, which improves interpretability. The aforementioned data were collected from information engineering undergraduate students who completed 88 exercises that assessed 23 knowledge components within the course. We measure RMSE and MAE to evaluate the performance of the BKT model on our dataset. Furthermore, we compare the knowledge tracing performance to other well-known datasets. Our results show that the BKT model performed better with our dataset, with an RMSE of 0.314 and an MAE of 0.197. Moreover, the BKT model can be used to assess student performance and determine the level of mastery for each knowledge component. Thus, the outcomes can be applied to personalized learning in the future.https://journal.unnes.ac.id/nju/jte/article/view/47861bayesian knowledge tracingblended learningknowledge statemoodle |
| spellingShingle | Indriana Hidayah Ebedia Hilda Am Tracing Knowledge States through Student Assessment in a Blended Learning Environment Jurnal Teknik Elektro bayesian knowledge tracing blended learning knowledge state moodle |
| title | Tracing Knowledge States through Student Assessment in a Blended Learning Environment |
| title_full | Tracing Knowledge States through Student Assessment in a Blended Learning Environment |
| title_fullStr | Tracing Knowledge States through Student Assessment in a Blended Learning Environment |
| title_full_unstemmed | Tracing Knowledge States through Student Assessment in a Blended Learning Environment |
| title_short | Tracing Knowledge States through Student Assessment in a Blended Learning Environment |
| title_sort | tracing knowledge states through student assessment in a blended learning environment |
| topic | bayesian knowledge tracing blended learning knowledge state moodle |
| url | https://journal.unnes.ac.id/nju/jte/article/view/47861 |
| work_keys_str_mv | AT indrianahidayah tracingknowledgestatesthroughstudentassessmentinablendedlearningenvironment AT ebediahildaam tracingknowledgestatesthroughstudentassessmentinablendedlearningenvironment |