A Review of Cognitive Apprenticeship Methods in Computing Education Research

Cognitive Apprenticeship (CA) is an instructional model that outlines how experts can transfer their skills and knowledge to a learner for reasoning-based tasks, such as reading comprehension or mathematical problem solving. Specifically, CA includes 6 teaching methods---modeling, scaffolding, coaching, reflection, articulation, and exploration---that facilitate learners' observation, acquisition, and externalization of implicit processes and techniques for completing a task. In this paper, we present a systematic literature review of 143 conference papers across ACM and IEEE venues about CA in computer science education literature. Specifically, we aim to understand which teaching methods are typically referenced, the theory level (i.e., depth of CA theory discussion) present in the literature, and the key findings related to CA-based teaching approaches. Our review reveals that CA has been cited in computing education research as a guiding theory for various course designs, though there is a clear emphasis on papers related to modeling, scaffolding, and coaching whereas reflection, articulation, and exploration are under-explored. We found that CA methods have been effective in improving students' enthusiasm towards computing, improving pass-rates in courses, and improving instructors' capacity to accommodate more students by reducing instructor workload. However, a key challenge of CA approaches that emerged from our review is the difficulty in scaling the approach in settings with a high student to instructor ratio. Through this literature review, we aim to highlight effective CA approaches and how future initiatives can leverage CA to improve student learning.