Guanliang Chen

Retrieval Practice and Study Planning in MOOCs: Exploring Classroom-Based Self-regulated Learning Strategies at Scale

Massive Open Online Courses (MOOCs) are successful in delivering educational resources to the masses, however, the current retention rates—well below 10 %—indicate that they fall short in helping their audience become effective MOOC learners. In this paper, we report two MOOC studies we conducted in order to test the effectiveness of pedagogical strategies found to be beneficial in the traditional classroom setting: retrieval practice (i.e. strengthening course knowledge through actively recalling information) and study planning (elaborating on weekly study plans). In contrast to the classroom-based results, we do not confirm our hypothesis, that small changes to the standard MOOC design can teach MOOC learners valuable self-regulated learning strategies.

Beyond the MOOC platform: gaining insights about learners from the social web

Massive Open Online Courses (MOOCs) have enabled millions of learners across the globe to increase their levels of expertise in a wide variety of subjects. Research efforts surrounding MOOCs are typically focused on improving the learning experience, as the current retention rates (less than 7% of registered learners complete a MOOC) show a large gap between vision and reality in MOOC learning. Current data-driven approaches to MOOC adaptations rely on data traces learners generate within a MOOC platform such as edX or Coursera. As a MOOC typically lasts between five and eight weeks and with many MOOC learners being rather passive consumers of the learning material, this exclusive use of MOOC platform data traces limits the insights that can be gained from them. The Social Web potentially offers a rich source of data to supplement the MOOC platform data traces, as many learners are also likely to be active on one or more Social Web platforms. In this work, we present a first exploratory analysis of the Social Web platforms MOOC learners are active on --- we consider more than 320,000 learners that registered for 18 MOOCs on the edX platform and explore their user profiles and activities on StackExchange, GitHub, Twitter and LinkedIn.

Can Learners be Earners? Investigating a Design to Enable MOOC Learners to Apply their Skills and Earn Money in an Online Market Place

Massive Open Online Courses (MOOCs) aim to educate the world. More often than not, however, MOOCs fall short of this goal—a majority of learners are already highly educated (with a Bachelor’s degree or more) and come from specific parts of the (developed) world. Learners from developing countries without a higher degree are underrepresented, though desired, in MOOCs. One reason for those learners to drop out of a course can be found in their financial realities and the subsequent limited amount of time they can dedicate to a course besides earning a living. If we could pay learners to take a MOOC, this hurdle would largely disappear. With MOOCS, this leads to the following fundamental challenge: How can learners be paid at scale? Ultimately, we envision a recommendation engine that recommends tasks from online market places such as Upwork or witmart to learners, that are relevant to the course content of the MOOC. In this manner, the learners learn and earn money. To investigate the feasibility of this vision, in this paper, we explored to what extent (1) online market places contain tasks relevant to a specific MOOC, and (2) learners are able to solve real-world tasks correctly and with sufficient quality. Finally, based on our experimental design, we were also able to investigate the impact of real-world bonus tasks in a MOOC on the general learner population.

Concept Focus: Semantic Meta-Data for Describing MOOC Content.

MOOCs promised to herald a new age of open education. However, efficient access to MOOC content is still hard, thus unnecessarily complicating many use cases like efficient re-use of material, or tailored access for life-long learning scenarios. One of the reasons for this lack of accessibility is the shortage of meaningful semantic meta-data describing MOOC content and the resulting learning experience. In this paper, we explore Concept Focus, a new type of meta-data for describing a perceptual facet of modern video-based MOOCs, capturing how focused a learning resource is topic-wise, which is often an indicator of clarity and understandability. We provide the theoretical foundations of Concept Focus and outline a methodical workflow of how to automatically compute it for MOOC lectures. Furthermore, we show that the learners’ consumption behavior is correlated with a MOOC lecture’s Concept Focus, thus underlining that this type of meta-data is indeed relevant for user-centric querying, personalizing or even designing the MOOC experience. For showing this, we performed an extensive study with real-life MOOCs and 12,849 learners over the duration of three months.

Buying time: enabling learners to become earners with a real-world paid task recommender system

Massive Open Online Courses (MOOCs) aim to educate the world, especially learners from developing countries. While MOOCs are certainly available to the masses, they are not yet fully accessible. Although all course content is just clicks away, deeply engaging with a MOOC requires a substantial time commitment, which frequently becomes a barrier to success. To mitigate the time required to learn from a MOOC, we here introduce a design that enables learners to earn money by applying what they learn in the course to real-world marketplace tasks. We present a Paid Task Recommender System (Rec-

Workshop on integrated learning analytics of MOOC post-course development

ys), which automatically recommends course-relevant tasks to learners as drawn from online freelance platforms. Rec-

Follow the successful crowd: raising MOOC completion rates through social comparison at scale

ys has been deployed into a data analysis MOOC and is currently under evaluation.

Gauging MOOC Learners' Adherence to the Designed Learning Path.

MOOC research is typically limited to evaluations of learner behavior in the context of the learning environment. However, some research has begun to recognize that the impact of MOOCs may extend beyond the confines of the course platform or conclusion of the course time limit. This workshop aims to encourage our community of learning analytics researchers to examine the relationship between performance and engagement within the course and learner behavior and development beyond the course. This workshop intends to build awareness in the community regarding the importance of research measuring multi-platform activity and long-term success after taking a MOOC. We hope to build the communitys understanding of what it takes to operationalize MOOC learner success in a novel context by employing data traces across the social web.