Pauses and spacing in learning to program

Conventional wisdom holds that time is an integral part of the learning process. Spacing out learning over multiple study sessions seems to be better for learning than having a single longer study session. Learners should also take pauses from the learning process to absorb, assimilate, and analyze what they have just learned. At the same time, pausing too often can be harmful for learning. Participants of two subsequent introductory programming courses completed programming tasks in an integrated development environment that saved detailed logs of their actions, including time stamps of all the participants' keypresses in said environment. Using this data with background variables and a self-regulation metric questionnaire, we study how the students space out their work, identify trends in between the kinds of pauses the participants took and the course outcomes, and their connection to background variables. Based on our research, students tend to space out their work, working on multiple days each week. In addition, a high relative amount of pauses of only a few seconds correlated positively with exam scores, while a high relative amount of pauses of a few minutes correlated negatively with exam scores. Student pausing behaviors are poorly explained by traditional self-regulation measures such as the Motivated Strategies for Learning Questionnaire and other background variables.Peer reviewe

Programming Process, Patterns and Behaviors: Insights from Keystroke Analysis of CS1 Students

With all the experiences and knowledge, I take programming as granted. But learning to program is still difficult for a lot of introductory programming students. This is also one of the major reasons for a high attrition rate in CS1 courses. If instructors were able to identify struggling students then effective interventions can be taken to help them. This thesis is a research done on programming process data that can be collected non-intrusively from CS1 students when they are programming. The data and their findings can be leveraged in understanding students’ thought process, detecting patterns and identifying behaviors that could possibly help instructors to identify struggling students, help them and design better courses