@conference {1282871,
	title = {Adaptive Assessment Experiment in a HarvardX MOOC},
	booktitle = {Proceedings of the 10th International Conference on Educational Data Mining},
	year = {2017},
	pages = {466-471},
	address = {International Conference on Educational Data Mining},
	abstract = {We report an experimental implementation of adaptive learning functionality in a self-paced HarvardX MOOC (massive open online course). In MOOCs there is need for evidence-based instructional designs that create the optimal conditions for learners, who come to the course with widely differing prior knowledge, skills and motivations. But users in such a course are free to explore the course materials in any order they deem fit and may drop out any time, and this makes it hard to predict the practical challenges of implementing adaptivity, as well as its effect, without experimentation. This study explored the technological feasibility and implications of adaptive functionality to course (re)design in the edX platform. Additionally, it aimed to establish the foundation for future study of adaptive functionality in MOOCs on learning outcomes, engagement and drop-out rates. Our preliminary findings suggest that the adaptivity of the kind we used leads to a higher efficiency of learning (without an adverse effect on learning outcomes, learners go through the course faster and attempt fewer problems, since the problems are served to them in a targeted way). Further research is needed to confirm these findings and explore additional possible effects.},
	url = {http://educationaldatamining.org/EDM2017/proc_files/papers/paper_167.pdf},
	author = {Ilia Rushkin and Yigal Rosen and Andrew Ang and Colin Fredericks and Tingley, Dustin and Mary Jean Blink and Lopez, Glenn}
}
