CoRe: A way to build pedagogical content knowledge for beginning teachers

Research has shown that one of the factors which enables teachers to be effective is their rich pedagogical content knowledge (PCK). Beginning teachers need support to develop this PCK and recent research in the field has proposed a conceptual tool known as “content representations”, or CoRes, as a model for doing this. The study reported here brought together science and technology experts in content and pedagogy, early career secondary teachers, and researchers to design a CoRe to assist development of teacher PCK. The study then researched the early career teachers’ use of the CoRe in their planning and delivery of a unit in their classrooms to examine the effect of the CoRe on teaching and learning, and on the development of the teachers’ PCK

KEMAMPUAN GURU DALAM MENGINTEGRASIKAN PENALARAN DAN WELLBEING SISWA PADA PEDAGOGICAL CONTENT KNOWLEDGE MELALUI PELATIHAN

Penelitian ini bertujuan untuk memberikan gambaran kemampuan guru dalam mengintegrasikan penalaran dan wellbeing siswa ke dalam PCK pada materi ekosistem dan pemanasan global sebelum dan setelah pelatihan. Metode yang digunakan adalah weak experimental design. Subyek pada penelitian ini adalah tiga orang guru mata pelajaran IPA kelas VII dari MGMP Lembang di Kabupaten Bandung Barat, Jawa Barat yang dilibatkan ke dalam kegiatan pelatihan penalaran, wellbeing, PCK, dan integrasinya ke dalam PCK. Kemampuan guru dalam mengintegrasikan penalaran dan wellbeing siswa pada PCK diungkap menggunakan instrumen CoRe untuk sebelum pelatihan dan setelah pelatihan dengan menggunakan instrumen CoRe dan PaP-eRs. Hasil penelitian menunjukkan bahwa sebelum pelatihan, kemampuan guru dalam mengintegrasikan penalaran dan wellbeing siswa pada PCK masih rendah. Setelah pelatihan, kemampuan guru dalam mengintegrasikan penalaran dan wellbeing siswa pada PCK mengalami peningkatan. Hal ini dikarenakan melalui pelatihan, guru memperoleh pembekalan informasi mengenai penalaran, wellbeing, dan PCK. Guru juga diberikan informasi dan masukan mengenai pertimbangan pedagogis yang sebaiknya dilakukan dalam melaksanakan kegiatan pembelajaran. Peningkatan ini dianalisis dari jawaban pertanyaan instrumen CoRe sebelum dan setelah pelatihan serta didukung dari jawaban instrumen PaP-eRs yang disusun oleh guru setelah kegiatan pembelajaran berlangsung. Kemampuan PCK seorang guru juga dipengaruhi oleh karakteristik konten yang diajarkan. Ini ditunjukkan dari kategori PCK guru sebelum dan setelah pelatihan. Sebelum pelatihan, PCK guru berada pada level growing dan setelah pelatihan berada pada level growing dan maturing untuk materi ekosistem. Kategori PCK guru pada materi pemanasan global sebelum pelatihan berada pada level pra, growing, serta maturing dan setelah pelatihan berada pada level growing dan maturing. ;--- This study aimed to describe the teachers’ ability to integrate reasoning and students’ wellbeing into PCK in ecosystem and global warming materials before and after the training. Method in this study was weak experimental design. Subject in this study were three VII grade science teachers from MGMP Lembang in Bandung Barat that followed in the training activities of reasoning, students’ wellbeing, PCK, and its integration. Teachers’ ability to integrate reasoning and students’ wellbeing in PCK revealed by using CoRe instrument for before and for after the training revealed by using CoRe and PaP-eRs instruments. The results showed that before the training, teachers’ ability to integrate reasoning and students’ wellbeing in the teacher’s PCK were still low. After the training, teachers’ ability to integrate students' reasoning and wellbeing in PCK increased. This is because through the training, teachers got information of reasoning, students’ wellbeing, and PCK. Teachers were also given information and input on pedagogical considerations that should be done in the learning activities. These improvement can be seen from the answers of CoRe before and after the training which is supported by the answers of PaP-eRs instrument. Teachers’ PCK ability was also influenced by the characteristics of the content. This is shown by the category of teachers’ PCK before and after the training. Before the training, teachers’ PCK was on growing level and after the training was on growing and maturing PCK for ecosystem materials. Teachers’ PCK for global warming material before the training was on pra, growing, and maturing PCK and after the training was on growing and maturing PCK

Performance of Graduate Students at Identifying Introductory Physics Students' Difficulties Related to Kinematics Graphs

The Test of Understanding Graphs in Kinematics (TUG-K) is a multiple choice test developed by Beichner to assess students' understanding of kinematics graphs. Many of the items on the TUG-K have strong distractor choices which correspond to introductory students' common difficulties with kinematics graphs. Instruction is unlikely to be effective if instructors do not know these common difficulties and take them into account in their instructional design. We evaluate the performance of first year physics graduate students at identifying introductory students' common difficulties related to kinematics graphs. In particular, for each item on the TUG-K, the graduate students were asked to identify which incorrect answer choice they thought would be most commonly selected by introductory physics students if they did not know the correct answer after instruction in relevant concepts. We used the introductory student data from Beichner's original paper to assess graduate students' knowledge of introductory students' difficulties. Furthermore, we selected the four questions on the TUG-K on which the graduate student performance was the poorest for a more detailed analysis which included think-aloud interviews. We present results which can partly account for the poor graduate student performance on these questions and also inform instruction in professional development courses.Comment: Peer reviewed conference proceedings 4 page

Investigating content representations (CoRes) as pedagogical tools for science teacher education

In this article Anne Hume discusses how use of scholarship and action research led me to introduce an intervention into my science education programmes called Content Representations (CoRes). My initial findings strongly indicate CoRes could be very useful tools for helping student teachers develop the professional knowledge base they need for teaching

El Conocimiento Didáctico del Contenido en ciencias: estado de la cuestión

This paper gives a descriptive overview of the literature related to Pedagogical Content Knowledge - PCK - in the sciences. It is expected that this review can contribute to a better understanding of PCK, pointing out what has been investigated about this concept. Specifically, we analyze: a) how PCK is defined, what are its main features and how it has been appropriated by teachers; b) the relationship between PCK, knowledge of the contents to be taught and students learning; c) how PCK was actually used in teachers' training and teachers' evaluation; and, d) the scientific areas in which PCK has been studied. It concludes that PCK is an essential tool for improving the quality of teacher training

Signal Shaping for BICM at Low SNR

The mutual information of bit-interleaved coded modulation (BICM) systems, sometimes called the BICM capacity, is investigated at low signal-to-noise ratio (SNR), i.e., in the wideband regime. A new linear transform that depends on bits' probabilities is introduced. This transform is used to prove the asymptotical equivalence between certain BICM systems with uniform and nonuniform input distributions. Using known results for BICM systems with a uniform input distribution, we completely characterize the combinations of input alphabet, input distribution, and binary labeling that achieve the Shannon limit -1.59 dB. The main conclusion is that a BICM system achieves the Shannon limit at low SNR if and only if it can be represented as a zero-mean linear projection of a hypercube, which is the same condition as for uniform input distributions. Hence, probabilistic shaping offers no extra degrees of freedom to optimize the low-SNR mutual information of BICM systems, in addition to what is provided by geometrical shaping. These analytical conclusions are confirmed by numerical results, which also show that for a fixed input alphabet, probabilistic shaping of BICM can improve the mutual information in the low and medium SNR range over any coded modulation system with a uniform input distribution

Brother, Can You Paradigm? Toward a Theory of Pedagogical Content Knowledge in Social Studies

Although research on pedagogical content knowledge (PCK) has accelerated in recent years, social studies educators have not generally been part of the conversation. This article explores why a theory of PCK for social studies has been so difficult to elaborate, focusing on the field’s inability to come to consensus on its aims and purposes and on a pervasive distrust of traditional academic disciplines and scholarship they produce. These factors have helped make the effective preparation of social studies teachers, something researchers studying PCK hope to improve, exceptionally difficult. This article proposes that if the field can resolve its relationship to the disciplines, a more coherent conceptualization of teacher education in social studies could come into focus. Such a reconceptualization could help position social studies teacher educators to contribute to the knowledge base on PCK, particularly with regard to the transformation of disciplinary content into school curriculum

On (strong) proper vertex-connection of graphs

A path in a vertex-colored graph is a {\it vertex-proper path} if any two internal adjacent vertices differ in color. A vertex-colored graph is {\it proper vertex $k$-connected} if any two vertices of the graph are connected by $k$ disjoint vertex-proper paths of the graph. For a $k$-connected graph $G$, the {\it proper vertex $k$-connection number} of $G$, denoted by $pvc_{k}(G)$, is defined as the smallest number of colors required to make $G$ proper vertex $k$-connected. A vertex-colored graph is {\it strong proper vertex-connected}, if for any two vertices $u,v$ of the graph, there exists a vertex-proper $u$-$v$ geodesic. For a connected graph $G$, the {\it strong proper vertex-connection number} of $G$, denoted by $spvc(G)$, is the smallest number of colors required to make $G$ strong proper vertex-connected. These concepts are inspired by the concepts of rainbow vertex $k$-connection number $rvc_k(G)$, strong rainbow vertex-connection number $srvc(G)$, and proper $k$-connection number $pc_k(G)$ of a $k$-connected graph $G$. Firstly, we determine the value of $pvc(G)$ for general graphs and $pvc_k(G)$ for some specific graphs. We also compare the values of $pvc_k(G)$ and $pc_k(G)$. Then, sharp bounds of $spvc(G)$ are given for a connected graph $G$ of order $n$, that is, $0\leq spvc(G)\leq n-2$. Moreover, we characterize the graphs of order $n$ such that $spvc(G)=n-2,n-3$, respectively. Finally, we study the relationship among the three vertex-coloring parameters, namely, $spvc(G), \ srvc(G)$and the chromatic number$\chi(G)$of a connected graph$G$.Comment: 12 page

SFNet: Learning Object-aware Semantic Correspondence

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the regression of a single global geometric transformation between images may be sensitive to image-specific details such as background clutter. We propose a new CNN architecture, dubbed SFNet, which implements this idea. It leverages a new and differentiable version of the argmax function for end-to-end training, with a loss that combines mask and flow consistency with smoothness terms. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms the state of the art on standard benchmarks.Comment: cvpr 2019 oral pape