Posted in 2017

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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Teaching Software Engineering Principles to K-12 Students: A MOOC on Scratch

This paper is accepted for Software Engineering Education and Training @ 39th International Conference on Software Engineering (SEET-ICSE 2017) in Buenos Aires in May 2017.

Abstract

In the last few years, many books, online puzzles, apps and games have been created to teach young children programming. However, most of these do not introduce children to broader concepts from software engineering, such as debugging and code quality issues like smells, duplication, refactoring and naming. To address this, we designed and ran an online introductory Scratch programming course in which we teach elementary programming concepts and software engineering concepts simultaneously. In total 2,220 children actively participated in our course in June and July 2016, most of which (73%) between the ages of 7 and 11. In this paper we describe our course design and analyze the resulting data. More specifically, we investigate whether 1) students find programming concepts more difficult than software engineering concepts, 2) there are age-related differences in their performance and 3) we can predict successful course completion. Our results show that there is no difference in students’ scores between the programming concepts and the software engineering concepts, suggesting that it is indeed possible to teach these concepts to this age group. We also find that students over 12 years of age perform significantly better in
questions related to operators and procedures. Finally, we identify the factors from the students’ profile and their behaviour in the first week of the course that can be used to predict its successful completion.

Keywords

Programming education, MOOC, Scratch, code, smells, dropout prediction

Reference

Felienne Hermans, Efthimia Aivaloglou (2017) Teaching Software Engineering Principles to K-12 Students: A MOOC on Scratch. TUD-SERG-2017-003. ISSN 1872-5392

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