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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. 

Recently there have been calls to integrate engineering design experiences to support students’ scientific understanding. There is a need for instructional strategies in which learners are encouraged to identify and reflect on ways scientific principles can be applied to inform their designs and evaluate alternative designs. Studies show that the inclusion of contrasting cases can improve students’ conceptual understanding and reasoning. Yet, such tasks depend on how they are scaffolded. In this study, pre-service elementary teachers in a conceptual physics course analyzed contrasting solutions to a design problem. Two forms of scaffolds were embedded to facilitate case evaluation: 1) identify similarities and differences and 2) evaluate and produce an argument for a “good” design solution. We investigated the scientific ideas that the participants used as they contrasted multiple design solutions and the impact of the two approaches in students’ understanding of heat transfer. We found no significant differences in students’ conceptual understanding, but the argumentation condition had a significantly larger number of scientific ideas ‘cited’, ‘explained’ or ‘applied’ in their solutions,. The results suggest that contrasting designs with argumentation may be a promising intervention to facilitate students to use science concepts in engineering design. Future work is needed in order to investigate better scaffolds that can help students’ increase in conceptual learning. 

Recently there have been calls to integrate engineering design experiences to support students’ scientific understanding. There is a need for instructional strategies in which learners are encouraged to identify and reflect on ways scientific principles can be applied to inform their designs and evaluate alternative designs. Studies show that the inclusion of contrasting cases can improve students’ conceptual understanding and reasoning. Yet, such tasks depend on how they are scaffolded. In this study, pre-service elementary teachers in a conceptual physics course analyzed contrasting solutions to a design problem. Two forms of scaffolds were embedded to facilitate case evaluation: 1) identify similarities and differences and 2) evaluate and produce an argument for a “good” design solution. We investigated the scientific ideas that the participants used as they contrasted multiple design solutions and the impact of the two approaches in students’ understanding of heat transfer. We found no significant differences in students’ conceptual understanding, but the argumentation condition had a significantly larger number of scientific ideas ‘cited’, ‘explained’ or ‘applied’ in their solutions,. The results suggest that contrasting designs with argumentation may be a promising intervention to facilitate students to use science concepts in engineering design. Future work is needed in order to investigate better scaffolds that can help students’ increase in conceptual learning.