This study examines to what extent elementary students use feedback loop reasoning, a key component of systems thinking, to reason about interactions among organisms in ecosystems. We conducted clinical interviews with 44 elementary students (1st through 4th grades). We asked students to explain how populations change in two contexts: a sustainable ecosystem and an ecosystem that is missing predators. We used an iterative process to develop a learning progression for feedback loop reasoning, and used the learning progression to code interview episodes. The study produces three findings. First, very few students recognised the cyclical relationships among populations in a sustainable ecosystem (Level 7). Second, very few students identified both reproduction and food as the factors affecting population in a context missing predators (Level 4). Finally, students' reasoning was inconsistent across the two contexts. We also discuss the implication of these findings for teaching and learning of food webs at elementary school.

Scene text image super-resolution (STISR) aims to improve the resolution and visual quality of lowresolution (LR) scene text images, and consequently boost the performance of text recognition. However, most of existing STISR methods regard text images as natural scene images, ignoring the categorical information of text. In this paper, we make an inspiring attempt to embed categorical text prior into STISR model training. Specifically, we adopt the character probability sequence as the text prior, which can be obtained conveniently from a text recognition model. The text prior provides categorical guidance to recover high-resolution (HR) text images. On the other hand, the reconstructed HR image can refine the text prior in return. Finally, we present a multi-stage text prior guided super-resolution (TPGSR) framework for STISR. Our experiments on the benchmark TextZoom dataset show that TPGSR can not only effectively improve the visual quality of scene text images, but also significantly improve the text recognition accuracy over existing STISR methods. Our model trained on TextZoom also demonstrates certain generalization capability to the LR images in other datasets.

The flourishing blossom of deep learning has witnessed the rapid development of text recognition in recent years. However, the existing text recognition methods are mainly for English texts, whereas ignoring the pivotal role of Chinese texts. As another widely-spoken language, Chinese text recognition in all ways has extensive application markets. Based on our observations, we attribute the scarce attention on Chinese text recognition to the lack of reasonable dataset construction standards, unified evaluation methods, and results of the existing baselines. To fill this gap, we manually collect Chinese text datasets from publicly available competitions, projects, and papers, then divide them into four categories including scene, web, document, and handwriting datasets. Furthermore, we evaluate a series of representative text recognition methods on these datasets with unified evaluation methods to provide experimental results. By analyzing the experimental results, we surprisingly observe that state-of-the-art baselines for recognizing English texts cannot perform well on Chinese scenarios. We consider that there still remain numerous challenges under exploration due to the characteristics of Chinese texts, which are quite different from English texts. The code and datasets are made publicly available at https://github.com/ FudanVI/benchmarking-chinese-text-recognition. Figure 1. Three reasons for the scarce attention of Chinese text recognition. (a) People may use different ways to crop text regions, which leads to unfair comparison. (b) It is necessary to specify the equivalence between lowercase and uppercase, half-width and full-width, simplified and traditional characters. (c) The existing methods are mainly evaluated with English datasets rather than Chinese datasets.

The paper starts from the analysis of the interpreting process, analyzes the problems and factors that may effects the quality of interpreting in different procedures, then focuses on the discussion of four categories of interpreting skills, namely, listening comprehension skills, decoding skills, recording skills, and re-expressing skills to overcome those problems and factors.

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