Informatics in Education

While research on Learning Analytics (LA) is plentiful, it often prioritises perspectives on LA systems over the practical ways instructors use data to analyse and refine the learning process per se. The present study addresses this inadequacy by investigating how student data is employed by educators in UK Higher Education Institutions (HEIs) and how it could be optimised. Specifically, a mixed-methods approach was employed combining survey data, mainly from one institution (N = 85) with insights gleaned from interviews with academics (N = 11). The findings reveal a real desire for better data capabilities and access, underscoring the need for HEIs to enhance data capture, better integrate systems and invest in professional development to enhance data literacy and foster a culture of data-driven decision-making. Importantly, a similar emphasis to that given to assessment and attendance needs to be given to data for the differentiation and personalisation of learning.

Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5.