TY - GEN
T1 - Assessing Student Learning Across Various Database Query Languages
AU - Li, Zepei
AU - Yang, Sophia
AU - Cunningham, Kathryn
AU - Alawini, Abdussalam
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Previous research has shown that students encounter difficulties when learning database systems and their corresponding languages. Researchers have categorized these challenges into syntax and semantic errors and have identified common error types and overall learning obstacles among students. However, most existing studies have primarily focused on quantitatively assessing students' overall performance in an aggregated manner' which may overlook valuable insights into individual-level knowledge transfer. In this study, we scrutinized over 250,000 submissions to query language programming assignments, their corresponding error messages, and the performance data of 702 students who took a database course in the Fall 2022 semester at the University of Illinois Urbana-Champaign to gain a comprehensive overview of each student's performance. We followed each student's progress in semantic and syntax errors across three query languages to determine their overall learning experience and whether knowledge transfer had occurred. Consequently, we discovered that many students may still encounter difficulties when transferring their knowledge from one language to another, despite having already learned and practiced the same abstract data operation concepts in one language. On the other hand, the majority of students were able to reduce syntax errors through practice in one language, but the rate of improvement varied among individuals. This study seeks to investigate two key aspects: the potential transfer of abstract data operation concepts among different database languages, and the possibility of a decrease in syntax errors through consistent practice within a single query language.
AB - Previous research has shown that students encounter difficulties when learning database systems and their corresponding languages. Researchers have categorized these challenges into syntax and semantic errors and have identified common error types and overall learning obstacles among students. However, most existing studies have primarily focused on quantitatively assessing students' overall performance in an aggregated manner' which may overlook valuable insights into individual-level knowledge transfer. In this study, we scrutinized over 250,000 submissions to query language programming assignments, their corresponding error messages, and the performance data of 702 students who took a database course in the Fall 2022 semester at the University of Illinois Urbana-Champaign to gain a comprehensive overview of each student's performance. We followed each student's progress in semantic and syntax errors across three query languages to determine their overall learning experience and whether knowledge transfer had occurred. Consequently, we discovered that many students may still encounter difficulties when transferring their knowledge from one language to another, despite having already learned and practiced the same abstract data operation concepts in one language. On the other hand, the majority of students were able to reduce syntax errors through practice in one language, but the rate of improvement varied among individuals. This study seeks to investigate two key aspects: the potential transfer of abstract data operation concepts among different database languages, and the possibility of a decrease in syntax errors through consistent practice within a single query language.
KW - MongoDB
KW - Neo4j
KW - Structured Query Language (SQL)
KW - knowledge transfer
KW - semantic errors
KW - syntax errors
UR - http://www.scopus.com/inward/record.url?scp=85183020501&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85183020501&partnerID=8YFLogxK
U2 - 10.1109/FIE58773.2023.10343409
DO - 10.1109/FIE58773.2023.10343409
M3 - Conference contribution
AN - SCOPUS:85183020501
T3 - Proceedings - Frontiers in Education Conference, FIE
BT - 2023 IEEE Frontiers in Education Conference, FIE 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023
Y2 - 18 October 2023 through 21 October 2023
ER -