Descriptive Analytics in an Undergraduate Mathematics Education MOOC Course at a University of Technology: A Review of the Algebra Component

Abstract

The study explores the learning of algebra in Mathematics 101 offered as Massive Open Online Courses (MOOCs) by using descriptive learning analytics. Delineated benefits of utilising learning analytics include improving course offerings, student outcomes, curriculum development and instructor effectiveness. Quantitative analysis was performed on overall mathematics scores for the population of 158 students. Qualitative analyses were performed on 40 randomly selected students’ examination responses to 11 algebra itemised questions to determine if deep, intermediate or surface learning had taken place. The results indicated 63 students passed the overall Mathematics 101 course but only 37 students passed the algebra section of the examination. The qualitative analysis exhibited four items of deep learning, one item of intermediate learning and six items of surface learning. The quantitative and qualitative analyses indicate that a review of the learning material and online pre-test and post-test data is necessary. Improvement of the discussion forum and tracking of students’ responses should be frequently monitored by online tutors. It is recommended that a community of inquiry model be established within the ODL context and in discussion forums so that student errors are timeously diagnosed.