This study tested a hypothesis that focused on whether or not teachers' pedagogical content knowledge (PCK) is a necessary body of knowledge for reformed science teaching. This study utilized a quantitative research method to investigate the correlation between a teacher's PCK level as measured by the PCK rubric (Park et al. 2008) and the degree to which his/her classroom is reform-oriented as measured by RTOP (Sawada et al. 2002). Data included 33 instructional sessions of photosynthesis and heredity videotaped with 7 high school biology teachers. Each session was given a score on both the PCK rubric and RTOP by two independent raters. Results indicate that PCK score is significantly related to RTOP score in terms of both total score (r=.831, p<.01) and subcomponent scores (ranging from r=.616 to .805, p<.01). Implications for science teacher education and future research are discussed.

Background: It has been widely accepted that reflective teaching is a powerful component of effective teaching. Accordingly, educating reflective practitioners has been a major goal of teacher education. Reflective teaching refers to the act of thinking such as analyzing or assessing educational meanings, intentions, beliefs, decisions, actions, or products yielded through those thinking processes. Reflective practitioners are those who contemplate and negotiate the complexities of teaching to enhance their decision-making power and autonomy in classrooms. However, despite the enormous proliferation of literature on reflection, little is known about the nature of reflection which will provide significant insights into teacher education that aims to prepare reflective practitioners. Purpose: The purpose of this study is to investigate the nature of three exceptional physical education (PE) teachers' reflection in terms of its focus and roles. Research questions that guided this study were: a) what is the focus of reflection-in/on-action of exceptional PE teachers? and b) in what ways does the reflection influence the teachers' practice? Methods: This study was grounded in a social constructivist framework and employed a multiple case study design. Major data sources included semi-structured interviews, non-participant observations, teachers' written reflections, students' work samples, relevant archival data, and researcher's field notes. The data were analyzed through the constant comparative method using Atlas.ti as an aid. To enhance the trustworthiness of the study, triangulation of multiple data sources and member checking were conducted. Results: Data analysis indicated that the teachers' reflection focused on four main topics: (a) the students, (b) instruction, (c) context, and (d) critical incidents. Reflection impacted the teachers' practice playing four key roles such as (a) making sense of unforeseen events, (b) developing knowledge-in-action, (c) making on-the-spot decisions, and (d) reconstructing teachers' belief systems.

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We investigated the in situ applicability of the electrokinetic process with a hexagonal electrode configuration in order to remediate arsenic (As)-, copper (Cu)-, and lead (Pb)-contaminated paddy rice field soil at a field scale (width 17 m, length 12.2 m, and depth 1.6 m). An iron electrode was used in order to prevent the severe acidification of the soil near the anode. We selected ethylenediaminetetraacetic acid (EDTA) as a pursing electrolyte to enhance the extraction of Cu and Pb. The system removed 44.4% of the As, 40.3% of the Cu, and 46.6% of the Pb after 24 weeks of operation. Fractionation analysis showed that the As bound to amorphous ion (Fe) and aluminum (Al) oxyhydroxides was changed into a form of As specifically bound. In the case of Cu and Pb, the fraction bound to Fe-Mn oxyhydroxide primarily decreased. The EDTA formed negatively charged complexes with Cu and Pb, and those complexes were transported toward the anode. The energy consumption was very low compared to that on a small scale because there was less energy consumption due to Joule heating. These results show that the in situ electrokinetic process could be applied in order to remediate paddy rice fields contaminated with multiple metals.

Given a large graph, how can we calculate the relevance between nodes fast and accurately? Random walk with restart (RWR) provides a good measure for this purpose and has been applied to diverse data mining applications including ranking, community detection, link prediction, and anomaly detection. Since calculating RWR from scratch takes a long time, various preprocessing methods, most of which are related to inverting adjacency matrices, have been proposed to speed up the calculation. However, these methods do not scale to large graphs because they usually produce large dense matrices that do not fit into memory. In addition, the existing methods are inappropriate when graphs dynamically change because the expensive preprocessing task needs to be computed repeatedly. In this article, we propose B ear , a fast, scalable, and accurate method for computing RWR on large graphs. B ear has two versions: a preprocessing method B ear S for static graphs and an incremental update method B ear D for dynamic graphs. B ear S consists of the preprocessing step and the query step. In the preprocessing step, B ear S reorders the adjacency matrix of a given graph so that it contains a large and easy-to-invert submatrix, and precomputes several matrices including the Schur complement of the submatrix. In the query step, B ear S quickly computes the RWR scores for a given query node using a block elimination approach with the matrices computed in the preprocessing step. For dynamic graphs, B ear D efficiently updates the changed parts in the preprocessed matrices of B ear S based on the observation that only small parts of the preprocessed matrices change when few edges are inserted or deleted. Through extensive experiments, we show that B ear S significantly outperforms other state-of-the-art methods in terms of preprocessing and query speed, space efficiency, and accuracy. We also show that B ear D quickly updates the preprocessed matrices and immediately computes queries when the graph changes.

Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional information help recommendation? Recommender systems play an important role in many ecommerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. Matrix factorization and random walk with restart are the most representative collaborative filtering methods. However, it is still unclear which method provides better recommendation performance despite their extensive utility.In this paper, we provide a comparative study of matrix factorization and RWR in recommender systems. We exactly formulate each correspondence of the two methods according to various tasks in recommendation. Especially, we newly devise an RWR method using global bias term which corresponds to a matrix factorization method using biases. We describe details of the two methods in various aspects of recommendation quality such as how those methods handle cold-start problem which typically happens in collaborative filtering. We extensively perform experiments over real-world datasets to evaluate the performance of each method in terms of various measures. We observe that matrix factorization performs better with explicit feedback ratings while RWR is better with implicit ones. We also observe that exploiting global popularities of items is advantageous in the performance and that side information produces positive synergy with explicit feedback but gives negative effects with implicit one.Index Terms-matrix factorization; random walk with restart; recommender systems arXiv:1708.09088v2 [cs.IR]

Given a large graph, how can we determine similarity between nodes in a fast and accurate way? Random walk with restart (RWR) is a popular measure for this purpose and has been exploited in numerous data mining applications including ranking, anomaly detection, link prediction, and community detection. However, previous methods for computing exact RWR require prohibitive storage sizes and computational costs, and alternative methods which avoid such costs by computing approximate RWR have limited accuracy.In this paper, we propose TPA, a fast, scalable, and highly accurate method for computing approximate RWR on large graphs. TPA exploits two important properties in RWR: 1) nodes close to a seed node are likely to be revisited in following steps due to block-wise structure of many real-world graphs, and 2) RWR scores of nodes which reside far from the seed node are proportional to their PageRank scores. Based on these two properties, TPA divides approximate RWR problem into two subproblems called neighbor approximation and stranger approximation. In the neighbor approximation, TPA estimates RWR scores of nodes close to the seed based on scores of few early steps from the seed. In the stranger approximation, TPA estimates RWR scores for nodes far from the seed using their PageRank. The stranger and neighbor approximations are conducted in the preprocessing phase and the online phase, respectively. Through extensive experiments, we show that TPA requires up to 3.5× less time with up to 40× less memory space than other state-of-the-art methods for the preprocessing phase. In the online phase, TPA computes approximate RWR up to 30× faster than existing methods while maintaining high accuracy.