Recursive problem solving in the online learning environment CodingBat by computer science students 

The data package has been gathered within the scope of the dissertation of the data provider, Natalie Kiesler. It focuses on informative feedback, its design, and its implementation in basic programming education. It was used to answer the research question about the effects of informative feedback offered by (online) self-learning tools. Especially those coding tools with automatic assessment can lead to frustrations for learners, if they receive minimal feedback or if tasks are too complex. Therefore, it seemed reasonable to examine the effects of individual feedback types in greater detail in order to assess their impact in the context of programming education. Thus, the impact of informative feedback on learners was explored by means of thinking aloud experiments in order to obtain further clues for the design of feedback in basic programming education. Students’ steps during recursive problem solving of standard computer science problems (including the computation of the factorial of natural numbers, as well as the computation of the Fibonacci sequence) were recorded in a professional usability lab in form of both audio and video data (among them screencasts). A self-developed feedback prototype and the freely available online tool CodingBat were utilized for the investigation. The data on students’ steps includes their task processing time, interactions, feedback demands and use, so that the student steps in the problem solving process can be qualitatively traced. Thus, mental models, or student approaches towards recursive problem solving can become more transparent. The full transcripts of the thinking aloud experiments are too extensive, so they are not provided for reasons of data economy. Instead, the transcripts of the students’ screen activities in terms of coding are made available.