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Creators/Authors contains: "Bralin, Amir"

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  1. ; ; ; ; ; (, American Association of Physics Teachers)
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  2. ; ; ; (, American Association of Physics Teachers)
    We analyzed the essays that were written on various topics in an introductory physics course using two unsupervised machine learning algorithms. One of them was Latent Dirichlet Allocation (LDA). This algorithm is used for extracting abstract topics from a collection of text documents. The other algorithm was Non-negative Matrix Factorization (NMF). It is used for similar purposes but also in other domains such as image recognition. We applied these two algorithms to the dataset that consisted of N=683 student essays. Although there were some built-in, important differences between LDA and NMF, they both found similar topics in our data by large. This offers instructors a promising and productive way of accessing useful information about their students' written work, especially in large-enrollment classes. 
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  3. ; ; ; ; ; (, Proceedings of the Eighth Annual STEM Education Conference)
    By integrating physics laboratories with engineering design and computer science, students apply physics principles to ill-structured and complex problems, engage in knowledge transfer, and gain interest in STEM. The introductory physics labs at Purdue have been updated to include engineering design and computer science principles that ground physics in authentic problems. Integrated labs have been evaluated using student perception post-surveys, student course performance, interviews, and case-study observations. Preliminary results indicate that integrated physics labs promote transfer, enhanced metacognitive skills, student interest, and motivation. 
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