Investigating variation in learning processes in a FutureLearn MOOC

Saman Rizvi,Jekaterina Rogaten,René F Kizilcec,Bart Rienties

doi:10.1007/s12528-019-09231-0

Abstract

Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals.

Highlights

Open-access learning environments such as Massive Open Online Courses (MOOCs) attract people with a wide range of interests and learning objectives, which is reflected in the degree and nature of engagement with the learning content (Milligan and Littlejohn 2017; Kizilcec and Schneider 2015)
The categorisation appeared to be unique within the relevant FutureLearn context, this categorisation is partially derived from, and partly based upon, similar categorisation used in previous MOOC engagement literature (Davis et al 2016; Ferguson and Clow 2015; Guo and Reinecke 2014)
The purpose of this exploratory study was to determine the nature and extent of differences in participatory behaviour and temporal learning paths of MOOC learners, in the light of learning activity type attributed from an established learning design model

Summary

Introduction

Open-access learning environments such as Massive Open Online Courses (MOOCs) attract people with a wide range of interests and learning objectives, which is reflected in the degree and nature of engagement with the learning content (Milligan and Littlejohn 2017; Kizilcec and Schneider 2015). Regardless of whether a learner completes a MOOC, academic success or failure may be partly hidden in their journey through the learning activities in the course (Rizvi et al 2018, 2019). Educational research on log-based behavioural modelling in Intelligent Tutoring System (ITS) and Learning Management Systems (LMS) has found that log-based analyses can provide deep insights into how learners engage and interact with different learning activities (Bogarín et al 2018; Sonnenberg and Bannert 2015). Despite increasing efforts to advance learning science research with log-based analyses in formal and blended learning environments, more research is needed to advance our understanding of learning processes in online learning environments (Bogarín et al 2018; Juhaňák et al 2017)

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