8,389 research outputs found

    Time-varying Learning and Content Analytics via Sparse Factor Analysis

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    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

    Forward-Thinking: HR\u27s Path To Improving Its Worth

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    [Excerpt] Human Resources has only just begun to move past its transactional roots into a truly strategic and value-adding role. Popular television characters like Toby from NBC’s The Office have all but glamorized HR, leaving individuals with a somewhat negative perception of the Human Resources function. When you add to this the personal interactions that individuals have had in the past with small and strictly transactional HR departments, it is understandable that these negative perceptions exist. For critics and skeptics of HR, these observations constitute reality. However, this doesn’t have to be the case. This essay will explore how focusing on a more results-driven analytical approach, adapting to an evolving workforce, and increasing visibility within the organization will help HR better convey its value and continue to evolve at all levels of the company

    RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

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    Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin
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