Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc, or the forgetting effect), and (iii) estimates the content organization and intrinsic difficulty of the assessment questions. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and a summary of the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instance. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, as well as analyzing the quality and content organization of learning resources, the question-concept associations, and the question intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses than existing collaborative filtering and knowledge tracing approaches for personalized education

A group learning management method for intelligent tutoring systems

In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

Team-Based Learning in Law

Used for over thirty years in a wide variety of fields, Team-Based Learning is a powerful teaching strategy that improves student learning. Used effectively, it enables students to actively engage in applying legal concepts in every class -- without sacrificing coverage. Because this teaching strategy has been used in classes with over 200 students, it also provides an efficient and affordable way to provide significant learning. Based on the principles of instructional design, Team-Based Learning has built-in student accountability, promotes independent student preparation, and fosters professional skills. This article provides an overview of Team-Based Learning, reasons to adopt this teaching strategy in light of Best Practices for Legal Education and the Carnegie and MacCrate reports, concrete methods to use Team-Based Learning in Law School, and ways to address challenges to this teaching strategy. Co-authors Sophie M. Sparrow and Margaret Sova McCabe provide examples from their years of teaching a variety of courses using Team-Based Learning