2024 ASEE Annual Conference & Exposition

Developing an Instrument for Assessing Self-Efficacy Confidence in Data Science

Presented at DSA Technical Session 5

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.

Authors

Dr. Safia Malallah Kansas State University [biography]

Safia Malallah is a postdoc in the computer science department at Kansas State University working with Vision and Data science projects. She has ten years of experience as a computer analyst and graphic designer. Besides, she's passionate about developing curriculums for teaching coding, data science, AI, and engineering to young children by modeling playground environments. She tries to expand her experience by facilitating and volunteering for many STEM workshops.
Dr. Ejiro U Osiobe http://orcid.org/https://0000-0002-1107-6620 Baker University [biography]

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Zahraa Marafie http://orcid.org/0009-0005-0356-1537 Kuwait University
Patricia Henriquez-Coronel
Lior Shamir Kansas State University [biography]

Associate professor of computer science at Kansas State University.
Ella Lucille Carlson Kansas State University
Joshua Levi Weese Kansas State University [biography]

Dr. Josh Weese is a Teaching Assistant Professor at Kansas State University in the department of Computer Science. Dr. Weese joined K-State as faculty in the Fall of 2017. He has expertise in data science, software engineering, web technologies, computer science education research, and primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science education in Kansas including PK-12 model standards in 2019 with an implementation guide the following year. Work on CS teacher endorsement standards are also being developed. Dr. Weese has developed, organized and led activities for several outreach programs for K-12 impacting well more than 4,000 students.

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