Teachers’ Learning Profiles in Learning Programming: The Big Picture!

Abstract

Currently there is a need for studying learning strategies within Massive Open Online Courses | (MOOCs), especially in the context of in-service teachers. This study aims to bridge this gap and try to understand how in-service teachers approach and regulate their learning in MOOCs. In particular, it examines the strategies used by the in-service teachers as they study a course on how to teach programming. The study implemented a combination of unsupervised clustering and process mining in a large MOOC (n = 27,538 of which 8,547 completed). The results show similar trends compared to previous studies conducted within MOOCs, indicating that teachers are similar to other groups of students based on their learning strategies. The analysis identified three subgroups (i.e., clusters) with different strategies: (1) efficient (n = 3596, 42.1%), (2) clickers (n = 1785, 20.9%), and (3) moderates (n = 3,166, 37%). The efficient students finished the course in a short time, spent more time on each lesson, and moved forward between lessons. The clickers took longer to complete the course, repeated the lessons several times, and moved backwards to revise the lessons repeatedly. The moderates represented an intermediate approach between the two previous clusters. As such, our findings indicate that a significant fraction within teachers poorly regulate their learning, and therefore, teacher education should emphasize learning strategies and self-regulating learning skills so that teacher can better learn and transfer their skills to students.