TY - GEN
T1 - Conducting multi-institutional studies of Parsons problems
AU - Ericson, Barbara J.
AU - Pearce, Janice L.
AU - Rodger, Susan H.
AU - Csizmadia, Andrew
AU - Garcia, Rita
AU - Gutierrez, Francisco J.
AU - Liaskos, Konstantinos
AU - Padiyath, Aadarsh
AU - Scott, Michael James
AU - Smith, David H.
AU - Warriem, Jayakrishnan
AU - Bernuy, Angela Zavaleta
PY - 2023/6/29
Y1 - 2023/6/29
N2 - Many novice programmers struggle to write code from scratch and get frustrated when their code does not work. Parsons problems can reduce the difficulty of a coding problem by providing mixed-up blocks that the learner assembles in the correct order. Parsons problems can also include distractor blocks that are not needed in a correct solution, but which may help students learn to recognize and fix errors. Evidence indicates that students find Parsons problems engaging, easier than writing code from scratch, useful for learning patterns, and typically faster to solve than writing code from scratch with equivalent learning gains. This working group leverages the work of the 2022 ITiCSE working group which published an extensive literature review of Parsons problems and designed and piloted several studies based on the gaps identified by the literature review. The 2023 working group is revising, conducting, and creating new studies. We will analyze the data from these multi-institutional and multi-national studies and publish the results as well as recommendations for future working groups.
AB - Many novice programmers struggle to write code from scratch and get frustrated when their code does not work. Parsons problems can reduce the difficulty of a coding problem by providing mixed-up blocks that the learner assembles in the correct order. Parsons problems can also include distractor blocks that are not needed in a correct solution, but which may help students learn to recognize and fix errors. Evidence indicates that students find Parsons problems engaging, easier than writing code from scratch, useful for learning patterns, and typically faster to solve than writing code from scratch with equivalent learning gains. This working group leverages the work of the 2022 ITiCSE working group which published an extensive literature review of Parsons problems and designed and piloted several studies based on the gaps identified by the literature review. The 2023 working group is revising, conducting, and creating new studies. We will analyze the data from these multi-institutional and multi-national studies and publish the results as well as recommendations for future working groups.
U2 - 10.1145/3587103.3594211
DO - 10.1145/3587103.3594211
M3 - Conference contribution
SN - 9798400701399
SP - 571
EP - 572
BT - ITiCSE 2023 : Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 2
PB - Association for Computing Machinery
ER -