Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Logistic Knowledge Tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. The models presented here avoid student-level fixed parameters to increase generalizability. We also introduce features to stand in for these intercepts. We argue that to be maximally applicable, a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability

Are you going to the party: depends, who else is coming? [Learning hidden group dynamics via conditional latent tree models]

Scalable probabilistic modeling and prediction in high dimensional multivariate time-series is a challenging problem, particularly for systems with hidden sources of dependence and/or homogeneity. Examples of such problems include dynamic social networks with co-evolving nodes and edges and dynamic student learning in online courses. Here, we address these problems through the discovery of hierarchical latent groups. We introduce a family of Conditional Latent Tree Models (CLTM), in which tree-structured latent variables incorporate the unknown groups. The latent tree itself is conditioned on observed covariates such as seasonality, historical activity, and node attributes. We propose a statistically efficient framework for learning both the hierarchical tree structure and the parameters of the CLTM. We demonstrate competitive performance in multiple real world datasets from different domains. These include a dataset on students' attempts at answering questions in a psychology MOOC, Twitter users participating in an emergency management discussion and interacting with one another, and windsurfers interacting on a beach in Southern California. In addition, our modeling framework provides valuable and interpretable information about the hidden group structures and their effect on the evolution of the time series

Student Modeling From Different Aspects

With the wide usage of online tutoring systems, researchers become interested in mining data from logged files of these systems, so as to get better understanding of students. Varieties of aspects of students’ learning have become focus of studies, such as modeling students’ mastery status and affects. On the other hand, Randomized Controlled Trial (RCT), which is an unbiased method for getting insights of education, finds its way in Intelligent Tutoring System. Firstly, people are curious about what kind of settings would work better. Secondly, such a tutoring system, with lots of students and teachers using it, provides an opportunity for building a RCT infrastructure underlying the system. With the increasing interest in Data mining and RCTs, the thesis focuses on these two aspects. In the first part, we focus on analyzing and mining data from ASSISTments, an online tutoring system run by a team in Worcester Polytechnic Institute. Through the data, we try to answer several questions from different aspects of students learning. The first question we try to answer is what matters more to student modeling, skill information or student information. The second question is whether it is necessary to model students’ learning at different opportunity count. The third question is about the benefits of using partial credit, rather than binary credit as measurement of students’ learning in RCTs. The fourth question focuses on the amount that students spent Wheel Spinning in the tutoring system. The fifth questions studies the tradeoff between the mastery threshold and the time spent in the tutoring system. By answering the five questions, we both propose machine learning methodology that can be applied in educational data mining, and present findings from analyzing and mining the data. In the second part, we focused on RCTs within ASSISTments. Firstly, we looked at a pilot study of reassessment and relearning, which suggested a better system setting to improve students’ robust learning. Secondly, we proposed the idea to build an infrastructure of learning within ASSISTments, which provides the opportunities to improve the whole educational environment

The MOOC and learning analytics innovation cycle (MOLAC): a reflective summary of ongoing research and its challenges.

This article describes the MOOCS and Learning Analytics framework (MOLAC). Based on a brief review of ongoing challenges in the field, the article develops a vision for the future use of MOOCs and Learning Analytics to foster educational innovation

Expanding evidence approaches for learning in a digital world

Executive Summary: Relatively low-cost digital technology is ubiquitous in daily life and work. The Web is a vast source of information, communication, and connection opportunities available to anyone with Internet access. Most professionals and many students have a mobile device in their pocket with more computing power than early supercomputers. These technological advances hold great potential for improving educational outcomes, but by themselves hardware and networks will not improve learning. Decades of research show that high-quality learning resources and sound implementations are needed as well.The learning sciences have found that today’s technologies offer powerful capabilities for creating high-quality learning resources, such as capabilities for visualization, simulation, games, interactivity, intelligent tutoring, collaboration, assessment, and feedback. Further, digital learning resources enable rapid cycles of iterative improvement, and improvements to resources can be instantly distributed over the Internet. In addition, digital technologies are attracting exciting new talent, both from other industries and from the teacher workforce itself, into the production of digital learning resources. Yet even with so many reasons to expect dramatic progress, something more—better use of evidence— is needed to support the creation, implementation, and continuous enhancement of high-quality learning resources in ways that improve student outcomes