Program Comprehension: Identifying Learning Trajectories for Novice Programmers

Aggarwal, Ashish (author)
Cutts, Quintin (author)
Duran, Rodrigo (author)
Gutica, Mirela (Mirela Gutica (Mirela_Gutica)) (author)
Heinemann, Birte (author)
Izu, Cruz (author)
Kraemer, Eileen (author)
Lonati, Violetta (author)
Mirolo, Claudio (author)
Schulte, Carsten (author)
Weeda, Renske (author)
(author)
(editor)
(funder)
(translator)

© 2019 ACM

This working group asserts that Program Comprehension (PC) plays a critical part in the writing process. For example, this abstract is written from a basic draft that we have edited and revised until it clearly presents our idea. Similarly, a program is written in an incremental manner, with each step being tested, debugged and extended until the program achieves its goal. Novice programmers should develop their program comprehension as they learn to code, so that they are able to read and reason about code while they are writing it. To foster such competencies our group has identified two main goals: (1) to collect and define learning activities that explicitly cover key components of program comprehension and (2) to define possible learning trajectories that will guide teachers using those learning activities in their CS0/CS1 or K-12 courses. We plan to achieve these goals as follows: Step 1 Review the current state of research and development by analyzing literature on classroom activities that improve program comprehension. Step 2 Concurrently, survey lecturers at various institutions on their use of workshop activities to foster PC. Step 3 Use the outputs from both activities to define and conceptualize what is meant by PC in the context of novice programmers. Step 4 Catalog learning activities with regard to their prerequisites, intended learning outcomes and additional special characteristics. Step 5 Catalog learning activities with regard to their prerequisites, intended learning outcomes and additional special characteristics. Step 6 Develop a map of learning activities and thereby also models of probable learning trajectories.

2019-07-02

Not peer reviewed

https://dl.acm.org/citation.cfm?id=3325531