An approach to measure coding competency evolution: Toward learning analytics

Abstract

There is a great deal of interest in the area of learning analytics and their use in assessing both student and content success in a digital learning environment. This chapter describes the results of a pilot study that used a combination of software tools and processes to collect the data generated from students taking an introductory C programming course, and their interactions related to specific study activities. This study helps determine whether it is possible to collect enough useful analytics to begin to identify constructive learning practices and strategies that determine competency evolution. We look at the types of learning traces collected through our system and then discuss how the data might be used to provide insight into student, class, or content actions. We then consider how additional analytics may be collected and used to supplement our initial results. The technologies used in this study address requirements of big data learning analytics where the data come from (a) the learner and his/her immediate surroundings; (b) the social network of the learner related to the learning tasks; and (c) the environment that inherently encases learning activities. While the data are used to measure competencies and their evolution over a period of time, the resultant profiles show the evolutionary processes students have adopted in developing specific competencies.