The goal of the present study was to use content analysis to gain insight into the process of problem-solving of novice and expert programmers. While classic work on programmers identifies goals / plans as key constructs needed to code, there is relatively little work using protocol analysis. We recruited 7 expert and 12 novice rogrammers who completed up to 3 brief programming problems while providing a talk-aloud of their inner problem solving process. Based on analysis of the transcriptions of this talk aloud data, we identified the goals and steps used, as well as the broad differences between experts and novices in their problem solving process. These differences were formalized into python ACT-R models, and model output was compared to programs written by human participants.

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

It is established that examples are beneficial for learning, but are certain types of examples more helpful than others?Erroneous examples include errors that students are asked to correct, something that can be helpful in addressing mis-conceptions. One domain that is vulnerable to misconceptions is fraction arithmetic. In the present study, undergraduatestudents solved fraction problems using a tutoring system we designed. Some participants worked with the Erroneous-Example tutor, which supplemented problems with erroneous examples, while other participants worked with a traditionalProblem-Solving tutor that did not include erroneous examples. To evaluate the impact of tutor type on learning andself-efficacy, we analyzed difference scores from pre-test to post-test. While overall participants significantly improvedtheir fraction knowledge and self-efficacy, there was no significant difference between the two groups. Bayesian analysesprovided evidence for the null model, i.e., that erroneous examples were not more beneficial than traditional problemsolving.

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

Researchers have started seriously considering the epistemic issue of whether and when we can claim an artificial intelli- gence (AI) has developed machine consciousness. Most cog- nitive theories of consciousness employ a functional character- ization of the property of consciousness. That is, they are com- mitted to an account of consciousness as a rule-governed pro- cess over mental states. Some cognitive scientists concerned with AI advocate an epistemically behaviorist approach to ma- chine consciousness; however, such approaches taken ontolog- ically, systematically fail to satisfy reasonable intuitions about in what consciousness ought to consist, and taken epistemi- cally, fail to provide sufficient evidence to individuate any in- ternal property, including consciousness, in non-human sub- jects. Therefore, in order to assess consciousness in ways that adequately account for reasonable intuitions as to its proper definition, such that we can reasonably assert the presence of machine consciousness in some AI, it is necessary to propose, test, and revise, functional theories of consciousness.