Rich environments for active learning: a definition

Rich Environments for Active Learning, or REALs, are comprehensive instructional systems that evolve from and are consistent with constructivist philosophies and theories. To embody a constructivist view of learning, REALs: promote study and investigation within authentic contexts; encourage the growth of student responsibility, initiative, decision making, and intentional learning; cultivate collaboration among students and teachers; utilize dynamic, interdisciplinary, generative learning activities that promote higher-order thinking processes to help students develop rich and complex knowledge structures; and assess student progress in content and learning-to-learn within authentic contexts using realistic tasks and performances. REALs provide learning activities that engage students in a continuous collaborative process of building and reshaping understanding as a natural consequence of their experiences and interactions within learning environments that authentically reflect the world around them. In this way, REALs are a response to educational practices that promote the development of inert knowledge, such as conventional teacher-to-student knowledge-transfer activities. In this article, we describe and organize the shared elements of REALs, including the theoretical foundations and instructional strategies to provide a common ground for discussion. We compare existing assumptions underlying education with new assumptions that promote problem-solving and higher-level thinking. Next, we examine the theoretical foundation that supports these new assumptions. Finally, we describe how REALs promote these new assumptions within a constructivist framework, defining each REAL attribute and providing supporting examples of REAL strategies in action

Learning by Seeing by Doing: Arithmetic Word Problems

Learning by doing in pursuit of real-world goals has received much attention from education researchers but has been unevenly supported by mathematics education software at the elementary level, particularly as it involves arithmetic word problems. In this article, we give examples of doing-oriented tools that might promote children\u27s ability to see significant abstract structures in mathematical situations. The reflection necessary for such seeing is motivated by activities and contexts that emphasize affective and social aspects. Natural language, as a representation already familiar to children, is key in these activities, both as a means of mathematical expression and as a link between situations and various abstract representations. These tools support children\u27s ownership of a mathematical problem and its expression; remote sharing of problems and data; software interpretation of children\u27s own word problems; play with dynamically linked representations with attention to children\u27s prior connections; and systematic problem variation based on empirically determined level of difficulty

eWOM: the effects of online consumer reviews on purchasing decision of electronic goods

Internet has become the primary source of information for a large number of consumers and it has dramatically changed the consumer behaviour. One of the main changes in modern consumer behaviour has been the transition from a passive to an active and informed consumer. Internet enables customers to share their opinions on, and experiences with, goods and services with a multitude of other consumers. Online consumer reviews are used by prospective buyers of related products who are interested in obtaining more information from people who have purchased and used a product of interest. Word-of-mouth (WOM) is one of the most important information sources when a consumer is making a purchase decision. The arrival and expansion of the Internet has extended consumers' options for gathering product information by including other consumers' comments, posted on the Internet, and has provided consumers opportunities to offer their own consumption-related advice by engaging in electronic word-of-mouth (eWOM). eWOM can be defined as all informal communications directed at consumers through Internet-based technology related to the usage or characteristics of particular goods and services, or their sellers. The aim of this study is to assess the impact of, one type of electronic word-of-mouth (eWOM), the online consumer review, on purchasing decision of electronic products. This empirical study also focuses on the relationship between reviews and purchasing behaviour. An instrument was prepared to measure the proposed constructs, with questionnaire items taken from prior studies but adapted to fit the context of e-commerce. The survey was applied to academicians in Turkey through internet. The data was analyzed using the SPSS package. The results show that consumer reviews have a causal impact on consumer purchasing behaviour and they have an effect on choosing the products by consumer. Finally, the results and their implications are discussed

Auto-Encoding Scene Graphs for Image Captioning

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation `person on bike', it is natural to replace `on' with `ride' and infer `person riding bike on a road' even the `road' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph ($\mathcal{G}$) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image ($\mathcal{I}$) and sentence ($\mathcal{S}$). In the textual domain, we use SGAE to learn a dictionary ($\mathcal{D}$) that helps to reconstruct sentences in the $\mathcal{S}\rightarrow \mathcal{G} \rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline, where $\mathcal{D}$ encodes the desired language prior; in the vision-language domain, we use the shared $\mathcal{D}$ to guide the encoder-decoder in the $\mathcal{I}\rightarrow \mathcal{G}\rightarrow \mathcal{D} \rightarrow \mathcal{S}$ pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art $127.8$ CIDEr-D on the Karpathy split, and a competitive $125.5$ CIDEr-D (c40) on the official server even compared to other ensemble models