Semi-Markov model for simulating MOOC students

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use Semi-Markov Chains for modeling student's activities and Expectation-Maximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students

Sequence Modelling For Analysing Student Interaction with Educational Systems

The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses previously unseen log data from Edulab, the largest provider of digital learning for mathematics in Denmark, to analyse the sessions of its users, where 1.08 million student sessions are extracted from a subset of their data. We propose to model students as a distribution of different underlying student behaviours, where the sequence of actions from each session belongs to an underlying student behaviour. We model student behaviour as Markov chains, such that a student is modelled as a distribution of Markov chains, which are estimated using a modified k-means clustering algorithm. The resulting Markov chains are readily interpretable, and in a qualitative analysis around 125,000 student sessions are identified as exhibiting unproductive student behaviour. Based on our results this student representation is promising, especially for educational systems offering many different learning usages, and offers an alternative to common approaches like modelling student behaviour as a single Markov chain often done in the literature.Comment: The 10th International Conference on Educational Data Mining 201

Towards high quality, scalable education: Techniques in automated assessment and probabilistic user behavior modeling

There are two primary challenges for instructors in offering a high-quality course at large scale. The first is scaling educational experiences to such a large audience. The second major challenge encountered is that of enabling adaptivity of the educational experience. This thesis addresses both major challenges in the way of high-quality scalable education by developing new techniques for large-scale automated assessment (for addressing scalability) and developing new models for interpretable user behavior analysis in educational environments for improving the quality of interaction via personalized education. Specifically, I perform a study of automated assessment of complex assignments where I explore the effectiveness of different types of features in a feasibility study. I argue for re-framing automated assessment techniques in these more complex contexts as a ranking problem, and provide a systematic approach for integrating expert, peer, and automated assessment techniques via an active-learning-to-rank formulation that outperforms a traditional randomized training solution. I also present the design and implementation of CLaDS---a Cloud-based Lab for Data Science---to enable students to engage with real-world data science problems at-scale with minimal cost ($7.40/student). I discuss our experience with deploying seven major text data assignments for students in both on-campus and online courses and show that the general infrastructure of CLaDS can be used to efficiently deliver a wide range of hands-on data science assignments. Understanding student behavior is necessary for improving the quality of scalable education through adaptivity. To this end, I present two general user behavior models for analyzing student interaction log data to understand student behavior. The first focuses on the discovery and analysis of action-based roles in community question answering (CQA) platforms using a generative model called the MDMM behavior model. I show interesting distinctions within CQA communities in question-asking behavior (where two distinct types of askers can be identified) and answering behavior (where two distinct roles surrounding answers emerge). Second, I find that where there are statistically significant differences in health metrics across topical groups on StackExchange, there are also statistically significant differences in behavior compositions, suggesting a relationship between behavior composition and health. Third, I show that the MDMM behavior model can be used to demonstrate similar but distinct evolutionary patterns between topical groups. The second model focuses on discovering temporal action patterns of learners in Coursera MOOCs. I present a two-layer hidden Markov model (2L-HMM) to extract a multi-resolution summary of user behavior patterns and their evolution, and show that these patterns can be used to extract latent features that correlate with educational outcomes. Finally, I develop the Piazza Educational Role Mining (PERM) system to close the gap between theory and practice by providing an easy-to-use web-based interface for leveraging probabilistic user behavior models on Piazza CQA interaction data. PERM allows instructors to easily crawl their courses and run subsequent MDMM behavior analyses on them. Analyses provide instructors with insight into the common user behavior patterns (roles) uncovered by plotting their action distributions in a browser. PERM enables instructors to perform deep-dives into an individual role by viewing the concrete sessions that have been assigned a specific role by the model, along with each session's individual actions and associated content. This allows instructors to flexibly combine data-driven statistical inference (through the MDMM behavior model) with a qualitative understanding of the behavior within a role. Finally, PERM develops a model of individual users as mixtures over the discovered roles, which instructors can also deep-dive into to explore exactly what individual users were doing on the platform

The student-produced electronic portfolio in craft education

The authors studied primary school students’ experiences of using an electronic portfolio in their craft education over four years. A stimulated recall interview was applied to collect user experiences and qualitative content analysis to analyse the collected data. The results indicate that the electronic portfolio was experienced as a multipurpose tool to support learning. It makes the learning process visible and in that way helps focus on and improves the quality of learning. © ISLS.Peer reviewe