Malallah, S., & Osiobe, E. U., & Marafie, Z., & Henriquez-Coronel, P., & Shamir, L., & Carlson, E. L., & Weese, J. L. (2024, June), Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science  Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47158

\bibitem{asee_peer_47158}
  Malallah, S., \& Osiobe, E. U., \& Marafie, Z., \& Henriquez-Coronel, P., \& Shamir, L., \& Carlson, E. L., \& Weese, J. L. (2024, June), \emph{Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science}
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  10.18260/1-2--47158

Safia Malallah, Ejiro U Osiobe, Zahraa Marafie, Patricia Henriquez-Coronel, Lior Shamir, Ella Lucille Carlson, and Joshua Levi Weese.  "Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/47158 Internet.  14 Dec, 2024

\bibitem{asee_peer_47158}
  Safia Malallah, Ejiro U Osiobe, Zahraa Marafie, Patricia Henriquez-Coronel, Lior Shamir, Ella Lucille Carlson, and Joshua Levi Weese.
  "Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/47158 Internet.  14 Dec, 2024

@INPROCEEDINGS{asee_peer_47158
author = "Safia Malallah, Ejiro U Osiobe, Zahraa Marafie, Patricia Henriquez-Coronel, Lior Shamir, Ella Lucille Carlson, and Joshua Levi Weese"
title = "Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science"
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/47158}
number = {10.18260/1-2--47158}
}

TY  - CPAPER
AB  - The field of data science education research faces a notable gap in assessment methodologies, leading to uncertainty and unexplored avenues for enhancing learning experiences. Effective assessment is crucial for educators to tailor teaching strategies and support student confidence in data science skills. We address this gap by developing a data science self-efficacy survey aimed to empower educators by identifying areas where students lack confidence, enabling the design of targeted plans to bolster data science education. Collaboration among experts from the fields of computer science, business, and statistics was instrumental in crafting a comprehensive survey that caters to the interdisciplinary nature of data science education. The survey evaluates 13 essential skills and knowledge areas, synthesized from literature reviews and industry demands, to provide a holistic assessment framework for educators in the field. Rigorous reliability and validity tests were conducted to ensure the survey’s robustness and efficacy in accurately assessing student proficiency.
AU  - Safia Malallah
AU  - Ejiro U Osiobe
AU  - Zahraa Marafie
AU  - Patricia Henriquez-Coronel
AU  - Lior Shamir
AU  - Ella Lucille Carlson
AU  - Joshua Levi Weese
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science
UR  - https://peer.asee.org/47158
DO  - 10.18260/1-2--47158
ER  -