MATE: Masked Autoencoders are Online 3D Test-Time Learners

We propose MATE, the first Test-Time-Training (TTT) method designed for 3D data. It makes deep networks trained in point cloud classification robust to distribution shifts occurring in test data, which could not be anticipated during training. Like existing TTT methods, which focused on classifying 2D images in the presence of distribution shifts at test-time, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: Each test point cloud has a large portion of its points removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. Further, we show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, which reduces its memory footprint and makes it lightweight. We also highlight that MATE achieves competitive performance by adapting sparingly on the test data, which further reduces its computational overhead, making it ideal for real-time applications.Comment: Minor fix in citation

Modelling Authenticity in Science Education

The terms ‘authenticity’ and ‘authentic’ have been used increasingly frequently in educational contexts over the past decades. In science education, authenticity is claimed to be a crucial concept, inter alia, for students’ motivation and interest in science. However, both terms are used, defined and conceptualised in various and ambiguous ways. So far, however, a model to integrate and structure the various conceptualisations, definitions and findings with their implementation in a teaching context is lacking. In this contribution, we introduce such a model, coherently integrating a broad range of work done by previous authors. Meanwhile, the model is flexible enough for future extensions and refinements. As many authors have shown, the concept of authenticity is multidimensional. In the present contribution, we therefore introduce a multidimensional model, explaining each dimension with reference to previous work on authenticity before integrating them as the complete model. We will outline a tool for practitioners and researchers which is based on the introduced model

An instrument to measure students'perception of the authenticity of an out-of-school learning Place

Progress in Science Education, Vol. 4, No. 1, S. 66-74,Background: One of the big opportunities offered by out-of-school learning places at research institutions is their authenticity, as they can provide insight into authentic research and work of scientists. Purpose: To what extent the students perceive this “authenticity of place” may however be individually different. In order to measure whether students indeed perceive a given out-of-school learning offer as an authentic learning place from their individual perspective an instrument is needed. Sample/setting: The Paul Scherrer Institute (PSI) is a genuine research environment for natural and engineering sciences and can therefore be considered as an authentic out-of-school learning place. Students from 3 different cantons in Switzerland participated in a field trip to the PSI, including a guided tour to one of its research facilities (on renewable energies) and a hands-on workshop in its science outreach lab (iLab) related to that topic. Data about test characteristics were collected in a pilot and in a main study (n = 80, March 2018 and n = 94, May to September 2018). All the classes were taught by the same teacher to learn about the basics of the research being done in that particular field of research at PSI. The guided tour was done by the same scientist from PSI for all classes. Design and methods: The questionnaire consists of a 6 point Likert scale with 9 items. An item analysis was carried out, as well as a factor analysis testing for the dimensionality of the questionnaire. Results: In terms of content the items for authenticity of place can be divided into one group with a cognitive focus and another group with an emotional focus. The item analysis of the total instrument yields good to very good characteristics (Cronbach’s Alpha as estimate of internal consistency ?C = .91, average item-test-correlation rit = .71), similarly for the sub-tests with cognitive and emotional focus (?C = .80, rit = .63 and ?C = .89, rit = .77) A performed confirmatory factor analysis proved compatible with a two-factor and a one-factor model (CFI = 0.98 and 0.97, respectively). The fact that the correlation between the two factors “cognitive” and “emotional” is very high (.94) argues in favour of the one factor model (McDonald’s omega as estimate of internal consistency adapted to factor analysis: ? = 0.92). Conclusions/Implications for practice and future research: The instrument presented here can be used as a one factor scale with good to very good test characteristics, if an overall measure of perceived authenticity of place is needed. The two subscales with cognitive and emotional focus could also be used separately, as their test characteristics are also satisfactory to good. Due to its short format and administration time (around 2 minutes) the instrument can be well integrated in the evaluation of out-of-school learning places. The scale was developed specifically for a research institute and has to be adapted for other out-of-school learning places such as museums, science centres or field trips. For future research it will be interesting to include other dimensions of perceived authenticity (such as authenticity of a person, e.g. the scientist at a research institute) and to study their combined effects on educational outcomes. Work along these lines within the framework of a larger research project on out-of-school science learning is in progress