TY - GEN
T1 - Mind the Gap
T2 - 19th European Conference on Technology Enhanced Learning, EC-TEL 2024
AU - Perach, Shai
AU - Alexandron, Giora
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/9/13
Y1 - 2024/9/13
N2 - The increasing demand for machine learning and deep learning (ML/DL) education spans K-12, college, and vocational training, highlighting the urgent need to prepare Computer Science (CS) teachers for these fields. This study aims to characterize the knowledge gaps and challenges experienced by CS teachers as they transition to teaching a rigorous ML curriculum. The paper attempts to achieve this in two distinct and self-contained ways: first, through a theoretical analysis conducted via a literature review examined through the lens of theoretical frameworks, and second, through an empirical qualitative analysis of a case study involving CS teachers transitioning to ML education. The empirical analysis echoes the findings of the theoretical analysis and sharpens them with additional insights. Our findings reveal significant difficulties, such as relearning mathematical foundations, adapting to new problem-solving paradigms, and developing ML-specific pedagogical content knowledge. Notably, the existing expertise of experienced CS teachers has limited relevance to ML/DL education, raising the question of why we focus mainly on CS teachers as the potential teaching workforce to train. The discussion integrates the theoretical and empirical findings to offer conclusions and recommendations for educational institutions, policymakers, and teacher training programs in enhancing ML/DL teaching capacities across various academic levels.
AB - The increasing demand for machine learning and deep learning (ML/DL) education spans K-12, college, and vocational training, highlighting the urgent need to prepare Computer Science (CS) teachers for these fields. This study aims to characterize the knowledge gaps and challenges experienced by CS teachers as they transition to teaching a rigorous ML curriculum. The paper attempts to achieve this in two distinct and self-contained ways: first, through a theoretical analysis conducted via a literature review examined through the lens of theoretical frameworks, and second, through an empirical qualitative analysis of a case study involving CS teachers transitioning to ML education. The empirical analysis echoes the findings of the theoretical analysis and sharpens them with additional insights. Our findings reveal significant difficulties, such as relearning mathematical foundations, adapting to new problem-solving paradigms, and developing ML-specific pedagogical content knowledge. Notably, the existing expertise of experienced CS teachers has limited relevance to ML/DL education, raising the question of why we focus mainly on CS teachers as the potential teaching workforce to train. The discussion integrates the theoretical and empirical findings to offer conclusions and recommendations for educational institutions, policymakers, and teacher training programs in enhancing ML/DL teaching capacities across various academic levels.
UR - http://www.scopus.com/inward/record.url?scp=85205120950&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-72315-5_24
DO - https://doi.org/10.1007/978-3-031-72315-5_24
M3 - منشور من مؤتمر
SN - 9783031723148
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 344
EP - 358
BT - Technology Enhanced Learning for Inclusive and Equitable Quality Education
A2 - Ferreira Mello, Rafael
A2 - Rummel, Nikol
A2 - Jivet, Ioana
A2 - Pishtari, Gerti
A2 - Ruipérez Valiente, José A.
PB - Springer Science and Business Media B.V.
Y2 - 16 September 2024 through 20 September 2024
ER -