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Register a new userStudent engagement and learning with Quantum Composer
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Added by
Heather Lewandowski
Publication date
2021
fields
Physics
and research's language is
English
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Knowledge of quantum mechanical systems is becoming more important for many science and engineering students who are looking to join the emerging quantum workforce. To better prepare a wide range of students for these careers, we must seek to develop new tools to enhance our education in quantum topics. We present initial studies on the use of one of these such tools, Quantum Composer, a 1D quantum simulation and visualization tool developed for education and research purposes. In particular, we conducted five think-aloud interviews with students who worked through an exercise using Quantum Composer that focused on the statics and dynamics of quantum states in single- and double-harmonic well systems. Our results show that Quantum Composer helps students to obtain the correct answers to the questions posed, but additional support is needed to facilitate the development of student reasoning behind these answers. In addition, we find that students explore familiar and unfamiliar problems in similar ways, indicating that Quantum Composer is a useful tool for exploring systems that students have not seen before.
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Encouraging student engagement is a key aim in any educational setting. Allowing students the freedom to pursue their own methods of solving problems through independent experimentation has been shown to markedly improve this. In many contexts, however, allowing students this flexibility in their learning is hampered by constraints of the material itself, such as in the electronics laboratory, where expensive equipment confines the learning environment to the laboratory room. To address this, we present the development of a low-cost, portable electronics learning platform, the WinterLab board and software interface, designed for use in an introductory science undergraduate electronics course. The platform is comparable or lower in cost than a typical textbook, fits in the palm of the hand, connects to the users computer via USB, and incorporates all equipment used in a typical undergraduate electronics laboratory. The WinterLab platform was given to a subset of students in the 2019 edition of an electronics and signal processing second year undergraduate course, who used it to complete the courses laboratory curriculum. Students reception of the board was positive, and several requested to keep the board beyond the end of the semester for use in personal projects. Equipping students with a low-cost test and measurement platform, such as the WinterLab board, that can be used at home and kept after the end of the course represents an accessible avenue for improving engagement in electronics learning at the science undergraduate level.
Immersive virtual reality (VR) has enormous potential for education, but classroom resources are limited. Thus, it is important to identify whether and when VR provides sufficient advantages over other modes of learning to justify its deployment. In a between-subjects experiment, we compared three methods of teaching Moon phases (a hands-on activity, VR, and a desktop simulation) and measured student improvement on existing learning and attitudinal measures. While a substantial majority of students preferred the VR experience, we found no significant differences in learning between conditions. However, we found differences between conditions based on gender, which was highly correlated with experience with video games. These differences may indicate certain groups have an advantage in the VR setting.
Engagement is a key indicator of the quality of learning experience, and one that plays a major role in developing intelligent educational interfaces. Any such interface requires the ability to recognise the level of engagement in order to respond appropriately; however, there is very little existing data to learn from, and new data is expensive and difficult to acquire. This paper presents a deep learning model to improve engagement recognition from images that overcomes the data sparsity challenge by pre-training on readily available basic facial expression data, before training on specialised engagement data. In the first of two steps, a facial expression recognition model is trained to provide a rich face representation using deep learning. In the second step, we use the models weights to initialize our deep learning based model to recognize engagement; we term this the engagement model. We train the model on our new engagement recognition dataset with 4627 engaged and disengaged samples. We find that the engagement model outperforms effective deep learning architectures that we apply for the first time to engagement recognition, as well as approaches using histogram of oriented gradients and support vector machines.
This study investigates how faculty, student, and course features are linked to student outcomes in Learning Assistant (LA) supported courses. Over 4,500 students and 17 instructors from 13 LA Alliance member institutions participated in the study. Each participating student completed an online concept inventory at the start (pre) and end (post) of their term. The physics concept inventories included Force and Motion Concept Evaluation (FMCE) and the Brief Electricity and Magnetism Assessment (BEMA). Concepts inventories from the fields of biology and chemistry were also included. Our analyses utilize hierarchical linear models that nest student level data (e.g. pre/post scores and gender) within course level data (e.g. discipline and course enrollment) to build models that examine student outcomes across institutions and disciplines. We report findings on the connections between students outcomes and their gender, race, and time spent working with LAs as well as instructors experiences with LAs.
Common research tasks ask students to identify a correct answer and justify their answer choice. We propose expanding the array of research tasks to access different knowledge that students might have. By asking students to discuss answers they may not have chosen naturally, we can investigate students abilities to explain something that is already established or to disprove an incorrect response. The results of these research tasks also provide us with information about how students responses vary across the different tasks. We discuss three underused question types, their possible benefits and some preliminary results from an electric circuits pretest utilizing these new question types. We find that the answer students most commonly choose as correct is the same choice most commonly eliminated as incorrect. Also, students given the correct answer can provide valuable reasoning to explain it, but they do not spontaneously identify it as the correct answer.
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