Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63587239" target="_blank" >RIV/70883521:28140/24:63587239 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.emerald.com/insight/content/doi/10.1108/aci-12-2023-0207/full/html" target="_blank" >https://www.emerald.com/insight/content/doi/10.1108/aci-12-2023-0207/full/html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1108/ACI-12-2023-0207" target="_blank" >10.1108/ACI-12-2023-0207</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education
Popis výsledku v původním jazyce
PurposeThis study aims to assess subjective responses in computer science education to understand students' grasp of core concepts. Extracting key ideas from short answers remains challenging, necessitating an effective method to enhance learning outcomes.Design/methodology/approachThis study introduces KeydistilTF, a model to identify essential concepts from student and teacher responses. Using the University of North Texas dataset from Kaggle, consisting of 53 teachers and 1,705 student responses, the model's performance was evaluated using the F1 score for key concept detection.FindingsKeydistilTF outperformed baseline techniques with F1 scores improved by 8, 6 and 4% for student key concept detection and 10, 8 and 6% for teacher key concept detection. These results indicate the model's effectiveness in capturing crucial concepts and enhancing the understanding of key curriculum content.Originality/valueKeydistilTF shows promise in improving the assessment of subjective responses in education, offering insights that can inform teaching methods and learning strategies. Its superior performance over baseline methods underscores its potential as a valuable tool in educational settings.
Název v anglickém jazyce
Revealing essential notions: an algorithmic approach to distilling core concepts from student and teacher responses in computer science education
Popis výsledku anglicky
PurposeThis study aims to assess subjective responses in computer science education to understand students' grasp of core concepts. Extracting key ideas from short answers remains challenging, necessitating an effective method to enhance learning outcomes.Design/methodology/approachThis study introduces KeydistilTF, a model to identify essential concepts from student and teacher responses. Using the University of North Texas dataset from Kaggle, consisting of 53 teachers and 1,705 student responses, the model's performance was evaluated using the F1 score for key concept detection.FindingsKeydistilTF outperformed baseline techniques with F1 scores improved by 8, 6 and 4% for student key concept detection and 10, 8 and 6% for teacher key concept detection. These results indicate the model's effectiveness in capturing crucial concepts and enhancing the understanding of key curriculum content.Originality/valueKeydistilTF shows promise in improving the assessment of subjective responses in education, offering insights that can inform teaching methods and learning strategies. Its superior performance over baseline methods underscores its potential as a valuable tool in educational settings.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2024
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Applied Computing and Informatics
ISSN
2634-1964
e-ISSN
2210-8327
Svazek periodika
2024
Číslo periodika v rámci svazku
Neuveden
Stát vydavatele periodika
GB - Spojené království Velké Británie a Severního Irska
Počet stran výsledku
16
Strana od-do
1-16
Kód UT WoS článku
001365950000001
EID výsledku v databázi Scopus
2-s2.0-85210597009