Markov analysis of students' professional skills in virtual internships
Abstract
In this paper, we conduct a Markov analysis of learners' professional skill development based on their conversations in virtual internships, an emerging category of learning systems characterized by the epistemic frame theory. This theory claims that professionals develop epistemic frames, or the network of skills, knowledge, identity, values, and epistemology (SKIVE) that are unique to that profession. Our goal here is to model individual students' development of epistemic frames as Markov processes and infer the stationary distribution of this process, i.e. of the SKIVE elements. Our analysis of a dataset from the engineering virtual internship Nephrotex showed that domain specific SKIVE elements have higher probability. Furthermore, while comparing the SKIVE stationary distributions of pairs of individual students and display the results as heat maps. we can identify students that play leadership or coordinator roles.
Publication Title
FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference
Recommended Citation
Rus, V., Gautam, D., Bowman, D., Graesser, A., & Shaeffer, D. (2017). Markov analysis of students' professional skills in virtual internships. FLAIRS 2017 - Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference, 116-121. Retrieved from https://digitalcommons.memphis.edu/facpubs/5085
