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The teaching of Mathematics – in particular, Geometry, through conventional methods has been a challenging task for tutors. Augmented Reality (AR) based applications available in commercial space, have not followed any structured pedagogical approach in the designing process, and also do not ensure that the learning time of students is spent prolifically. In this paper, we explore the use of AR in mathematics for geometry education, to aid visualization of multidimensional objects and long-term retention of concepts by the learners. For designing an appropriate AR application it is necessary to identify some principles which support better memory retention of the students. The application has been specifically designed on the basis of identified principles affecting memory retention. We further explain the development of an AR-based Geometry Learning Assistant (AR-GLA), using a structural approach to pedagogical-design for teaching 3-dimensional geometry to higher school students through improved visualization and enhance their memory retention for related concepts. A sample of 54 K-12 students and 2 teachers with expertise in mathematics were part of the experiment. The students were divided into two different groups; one of the groups was taught with AR-based content whereas the other group was given Interactive Simulation (IS) based learning. The results illustrated that AR-based learning provides better retention of memory as compared to IS-based learning were tested over a period of two months. [ABSTRACT FROM AUTHOR]

Much has been written about instructor attitudes towards lecture capture, particularly concerning political issues such as opt-out policies and the use of recordings by management. Additionally, the pedagogical concerns of lecturers have been extensively described and focus on the belief that recording lectures will impact on attendance and will reduce interactivity and active learning activities in lectures. However, little work has looked at the relationship between attitudes towards lecture capture and broader conceptions of learning and teaching. In this pre-registered study, we administered the Conceptions of Learning and Teaching scale and a novel lecture capture attitude scale to 159 higher education teachers. We found that appreciation of active learning predicted more positive attitudes towards lecture recordings as an educational support tool, whilst higher teacher-centred scores predicted greater concern about the negative educational impact of recordings. The effects observed were small; however, they are strong evidence against the view that it is instructors who value participatory and active learning that are opposed to lecture capture. Exploratory analyses also suggested that those who did not view recordings as an essential educational resource record fewer of their lectures, highlighting the real-world impact that attitudes can have, and further strengthening the need for staff to be provided with evidence-based guidance upon which to base their teaching practice. Data, analysis code, and the pre-registration are available athttps://osf.io/uzs3t/. [ABSTRACT FROM AUTHOR]

Classification is one of the foremost machine learning tasks in this modern era. Neural Network (NN) is one of the powerful classification techniques. NN can achieve high classification accuracy on highly imbalanced and complex datasets, but lacks in explanation of its reasoning process which limits its applicability in various domains which require transparent decision along with good accuracy. There are some techniques which extract rules from NN and make it transparent; however, attribute pruning, rule pruning and class overlap algorithms are not sufficiently effective. Therefore, this paper proposes a rule extraction algorithm, called Transparent Rule Extraction using Neural Network (TRENN) to convert NN into white box with greater emphasis on attribute pruning and rule pruning. The proposed TRENN is a pedagogical approach and an extension of one of the existing algorithms named Rule Extraction from Neural Network using Classified and Misclassified data (RxNCM). The proposed TRENN extends the RxNCM with sequential floating backward search for feature and rule selection to improve the comprehensibility of the generated rules. Besides, the proposed TRENN uses probabilistic approach for the treatment of class overlapping problem in the rule updating phase instead of reclassification used in RxNCM where the overlap may persist. Experiments are conducted with eight real datasets collected from the UCI repository. Performance of the TRENN is measured with Precision, Recall, FP-Rate, F-measure, and local and global comprehensibility. It is observed from the experimental results that TRENN performs better than Re-RX, RxNCM and RxREN. [ABSTRACT FROM AUTHOR]

The pedagogical practices of mathematics education for young children in English and Korean pre-schools were compared through analyses of interviews with practitioners, observations of classroom activities and an examination of documents related to the planning and implementation of mathematics education. Practitioners in both countries used integrated activities to teach mathematics, although they used such activities more frequently in nurseries than in reception classes in England and more frequently in state settings than in independent settings in Korea. Furthermore, mathematics education was more structured, more likely to be led by an adult and less holistic in reception classes than in nurseries in England, whereas it was more structured and didactic in independent than in state settings in Korea. However, mathematics education in England was more systematic, used a more individualised approach and incorporated a variety of hands-on materials and rigorous outdoor activities, whereas it was more group-oriented and utilised limited materials and fewer outdoor activities in Korea. [ABSTRACT FROM AUTHOR]