Abstract
Academic research in the past decade has indicated that using data and analyzing learning in curriculum design decisions can lead to improved student performance and student success. As learning in many instances has evolved into the flexible format online, anywhere at any time, learning analytics could potentially provide impactful insights into student engagement in massive open online courses (MOOCs). These may contribute to early identification of “at risk” participants and provide MOOC facilitators, educators, and learning designers with insights on how to provide effective interventions to ensure participants meet the course learning outcomes and encourage retention and completion of a MOOC. This chapter uses the essential human biology MOOC within the Australian AdelaideX initiative to implement learning analytics to investigate and compare demographics of participants, patterns of navigation including participation and engagement for passers and non-passers in two iterations of the MOOC, one instructor-led, and second self-paced.
TopIntroduction
Numerous studies in the research literature have indicated that using data and analyzing learning in curriculum design decisions can lead to improved student performance and success. While learning analytics are used in many Massive Open Online Courses (MOOCs) to report on student retention, behavior, participation, and performance, active strategies to diagnose and monitor achievement of learning outcomes are often lacking. Learning analytics has been defined in the literature as the measurement, collection, analysis, and reporting of data about participants and their contexts for purposes of understanding and optimizing learning and the environments in which it occurs. Learning analytics can also provide impactful insights into learner behavior and engagement with resources in online courses such as MOOCs, allowing facilitators to make real time modifications to improve the learner experience. These may contribute to the design of more effective educational interventions.
A common issue with courses, such as MOOCs, which are facilitated online is maintaining active engagement and retention of participants. Studies have shown that only half the number of participants that sign up for a MOOC follow through to take the course. With low startup numbers, even fewer participants have been shown to complete the course effecting levels of engagement. Engagement often dramatically drops off in the first few weeks, leading to a trend known as the participation funnel. Learning analytics provides information about participant interactions which can be regularly reviewed at pivotal points in the course investigating patterns of engagement, highlighting pedagogic approaches and learning designs that prove successful in retaining participants. Learning analytics is also a mechanism for teachers to evaluate student progress in an online course, by reviewing their interaction with online materials, tools, and content to gauge levels of learning, interactivity, and engagement. The number of views, clicks, and posts along with performance in assessment tasks can be used to reflect, refine and adjust the learning design by feeding forward what has been learned to new iterations. Course and instructional design, along with intervention planning, plays a vital role in fostering student learning in higher education. This critical, actionable intelligence will help educators devise agile interventions to increase participant engagement and performance in a MOOC.
This book examines “Fostering multiple levels of engagement in higher education environments”. The use of learning analytics as examined in this chapter is a prominent example of that, as it identifies levels of engagement through the lenses of MOOCs in a higher education environment. To date, there are few studies that explore learning design for MOOCs in an Australian context. As such, this chapter can be used to demonstrate best practice design approaches for a MOOC with an Australian demographic in STEM. This chapter also identifies the applicability of essential analytically-driven design features which can be replicated across any global MOOC context.
The objectives of this chapter were to investigate the use of learning analytics to gather information and compare demographics and patterns of navigation (including participation and engagement for passers and non-passers) in two iterations of the Essential Human Biology MOOC within the Australian AdelaideX initiative. The two iterations examine a) an instructor-led approach in which timelines and completion dates were shortened and followed a fixed timeframe more apparent to participants, b) a self-paced approach in which participants could engage and complete the course at a more flexible pace suited to their timeframe. Descriptive analytics were utilized to investigate three key engagement features in the two iterations, namely the age of the participants, navigational patterns and learning paths, and finally participation levels and engagement with the learning activities provided. The data acquired through the descriptive analytics along with an examination of the differentiation between these MOOC offerings provide meaningful insights about how to feed-forward information to enhance the future design aspects of these MOOCs and the applicability of these design principles to other MOOCs in general.
Two of Powell and MacNeills’ (2012) drivers for the application of learning analytics were used in the context of this chapter:
Key Terms in this Chapter
Certified: A participant in a course that passed the course content achieving an overall course grade > 50% and was supplied with official notification in the form of a certificate to acknowledge successful completion of the course.
Instructor-Led: Is the version of the MOOC that was facilitated by teaching staff with reminders (often by posts to the discussion board) to complete assessment tasks and flags for participants to have achieved certain check points in the course by a certain time.
Not-Passing: Are participants who did not complete the assessment tasks designed for the course and achieved an overall course grade of < 50%.
Participant: Is an individual (student) who enrolled in a MOOC and engaged with the course content in some way.
Self-Paced: Is the version of the MOOC that was not facilitated by teaching staff, participants were able to navigate and engage with the course materials in their own time at their own speed.
Passing: Are participants who completed the assessment tasks designed for the course and passed the course with an overall course grade of > 50%.
Learning Analytics: Is defined by The University of Adelaide as the practice of developing actionable insights through the collection, analysis and reporting of data about participants and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Engagement: In the context of this chapter, is defined as being active in the course, watching videos, posting comments of the forum and attempting problems.