Research Article
Saif Alneyadi, Yousef Wardat
CONT ED TECHNOLOGY, Volume 15, Issue 4, Article No: ep448
ABSTRACT
The study aimed to examine the influence of ChatGPT on the academic performance and learning perception of eleventh-grade students in a United Arab Emirates school in the field of electronic magnetism. The participants were randomly divided into two groups: an experimental group granted access to ChatGPT and a control group without access to ChatGPT. The research spanned four weeks, during which the experimental group was instructed to utilize ChatGPT whenever they required assistance with the course content. The study employed a mixed-methods design, collecting both quantitative and qualitative data to assess the impact of ChatGPT on student learning and perception. Quantitative data was gathered through pre-/post-test surveys, measuring participants’ course achievement and perception of learning with ChatGPT. Qualitative data was collected via open-ended survey questions, allowing participants to provide feedback and suggestions for improvement. The study revealed that ChatGPT had a positive influence on student achievement and perception of learning in the field of electronic magnetism. The experimental group displayed significant enhancements in their scores on the post-test measuring the impact of ChatGPT on student achievement, exhibiting higher mean scores across all subscales compared to the control group. Furthermore, both male and female participants acknowledged ChatGPT as a valuable learning tool, offering suggestions for enhancing its functionality. The study suggests that ChatGPT is an effective tool for enhancing student learning and achievement in specific academic domains. However, further research is needed to explore its generalizability to other contexts and disciplines, as well as to address potential challenges and limitations of ChatGPT use in education.
Keywords: ChatGPT, electronic magnetism, learning enhancement, natural language processing, student perception
Research Article
Jiseung Yoo, Min Kyeong Kim
CONT ED TECHNOLOGY, Volume 15, Issue 3, Article No: ep438
ABSTRACT
This study focuses on how teachers’ pedagogical content knowledge (PCK) of mathematics may differ depending on teacher interactions in an online teacher community of practice (CoP). The study utilizes data from 26,857 posts collected from the South Korean self-generated online teacher CoP, ‘Indischool’. This data was then analyzed using natural language processing techniques; specifically, text classification with word2vec, BERT, and machine learning classifiers was used. The results indicate that the texts of posts can predict the level of teacher interactions in the online CoP. BERT embedding and classifier exhibited the best performance, ultimately achieving an F1 score of .756. Moreover, topic modeling utilizing BERT embedding is used to uncover the specific PCK of teachers through high- and low-interaction posts. The results reveal that high-interaction posts with numerous likes and replies demonstrate more in-depth reflections on teaching mathematics and refined PCK. This study makes two significant contributions. First, it applies a data science framework that allows for the analysis of real data from an actual online teacher community. Secondly, it sheds light on the intricacies of knowledge management in an online teacher CoP, an area that has to this point received limited empirical attention.
Keywords: online community of practice, natural language processing, mathematics pedagogical content knowledge, knowledge management