Posts tagged dan davis

Enabling Real-Time Adaptivity in MOOCs with a Personalized Next-Step Recommendation Framework

This paper is presented at the Fourth (2017) ACM Conference on Learning @ Scale in Cambridge (MA), USA, April 20–21, 2017.

Abstract

In this paper, we demonstrate a first-of-its-kind adaptive intervention in a MOOC utilizing real-time clickstream data and a novel machine learned model of behavior. We detail how we
augmented the edX platform with the capabilities necessary to support this type of intervention which required both tracking learners’ behaviors in real-time and dynamically adapting content based on each learner’s individual clickstream history. Our chosen pilot intervention was in the category of adaptive pathways and courseware and took the form of a navigational suggestion appearing at the bottom of every non-forum content page in the course. We designed our pilot intervention to help students more efficiently navigate their way through a MOOC by predicting the next page they were likely to spend significant time on and allowing them to jump directly to that page. While interventions which attempt to optimize for learner achievement are candidates for this adaptive framework, behavior prediction has the benefit of not requiring causal assumptions to be made in its suggestions. We present a novel extension of a behavioral model that takes into account students’ time spent on pages and forecasts the same. Several approaches to representing time using Recurrent Neural Networks are evaluated and compared to baselines without time, including a basic n-gram model. Finally, we discuss design considerations and handling of edge cases for real-time deployment, including considerations for training a machine learned model on a previous offering of a course for use in a subsequent offering where courseware may have changed. This work opens the door to broad experimentation with adaptivity and serves as a first example of delivering a data-driven personalized learning experience in a MOOC.

Keywords

Adaptivity; Personalization; Real-time intervention; MOOC; RNN; Behavioral modeling; Navigational efficiency; edX

Reference

Zachary A. Pardos, Steven Tang, Daniel Davis, and Christopher Vu Le. 2017. Enabling Real-Time Adaptivity in MOOCs with a Personalized Next-Step Recommendation Framework. In Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (L@S ’17). ACM, New York, NY, USA, 23-32. DOI: https://doi.org/10.1145/3051457.3051471

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

This article is published in the journal IEEE Transactions on Learning Technologies.

Abstract

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 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.

Reference

Guanliang Chen, Dan Davis, Markus Krause, Efthimia Aivaloglou, Claudia Hauff, Geert-Jan Houben, “Can Learners be Earners? Investigating a Design to Enable MOOC Learners to Apply their Skills and Earn Money in an Online Market Place”, IEEE Transactions on Learning Technologies, vol. , no. , pp. 1, 5555, doi:10.1109/TLT.2016.2614302

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