Learning Large-Scale Bayesian Networks with the sparsebn Package

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure

Peningkatan Aktivitas Peserta Didik Pembelajaran IPS dengan Menggunakan Media Gambar di Kelas IV Sdn 17 Pontianak Kota

The background of this research is learning IPS in SDN 17 Pontianak City, especially the fourth grade social studies lesson was carried out by conventional methods, such as lectures and question. As a result of activities and learning outcomes of students is low . For that reason, this study will use a class action method using demonstration to improve the learners in the learning activity. A common problem of this study is " Does the demonstration method can improve the learning process in the Social Sciences fourth grade SDN 17 Pontianak City?" The purpose of the study to describe the improvement of the learning process of learners in the learning of Social Sciences at the demonstration using grade IV SDN 17 Pontianak City. The method used is descriptive method. Collaborative nature. Research subjects who teaches social studies teachers and learners fourth grade SDN 17 Pontianak City. Setting research in the classroom setting. The technique used is the technique of direct observation and measurement techniques. While the data collection tool is a matter of observation sheets and instruments. The data collected is data about the learning process. The results of this study, it can be concluded that in fact the demonstration method proven to improve the learning process of students on average 56.57% in first cycle to 84.57 % in second cycle, an increase to 28%. Demonstrations have proven methods to improve learning outcomes of learners IPS average value of 67.42% in first cycle to an average of 84.57% in srcond cycle, an increase to 39.35%. Based on these conclusions, it can be suggested that the demonstration can be used as alternative methods in an effort to improve the learning activity

Designs for Research, Teaching and Learning

This bookoffers a coherent theoretical and multimodal perspective on research, teaching and learning in different non-formal, semi-formal, and formal learning environments. Drawing on examples across a range of different settings, the book provides a conceptual framework for research on learning in different environments. It provides conceptual models around learning design which act as a framework for how to think about contemporary learning, a guideline for how to do research on learning in different sites, and a tool for innovative, collaborative design with other professionals. The book highlights concepts like multimodal knowledge representations; framing and setting; transformation, transduction, and re-design; signs of learning and cultures of recognition in different social contexts. The book supports innovative thinking on how we understand learning, and will appeal to academics, scholars and postgraduate students in the fields of education research and theory, learning sciences, and multimodal and social semiotics. It will also be of interest to school leaders, university provosts and professionals working in education

A model of the pre-assessment learning effects of summative assessment in medical education

It has become axiomatic that assessment impacts powerfully on student learning. However, surprisingly little research has been published emanating from authentic higher education settings about the nature and mechanism of the pre-assessment learning effects of summative assessment. Less still emanates from health sciences education settings. This study explored the pre-assessment learning effects of summative assessment in theoretical modules by exploring the variables at play in a multifaceted assessment system and the relationships between them. Using a grounded theory strategy, in-depth interviews were conducted with individual medical students and analyzed qualitatively. Respondents’ learning was influenced by task demands and system design. Assessment impacted on respondents’ cognitive processing activities and metacognitive regulation activities. Individually, our findings confirm findings from other studies in disparate non-medical settings and identify some new factors at play in this setting. Taken together, findings from this study provide, for the first time, some insight into how a whole assessment system influences student learning over time in a medical education setting. The findings from this authentic and complex setting paint a nuanced picture of how intricate and multifaceted interactions between various factors in an assessment system interact to influence student learning. A model linking the sources, mechanism and consequences of the pre-assessment learning effects of summative assessment is proposed that could help enhance the use of summative assessment as a tool to augment learning

Faster quantum mixing for slowly evolving sequences of Markov chains

Markov chain methods are remarkably successful in computational physics, machine learning, and combinatorial optimization. The cost of such methods often reduces to the mixing time, i.e., the time required to reach the steady state of the Markov chain, which scales as $\delta^{-1}$, the inverse of the spectral gap. It has long been conjectured that quantum computers offer nearly generic quadratic improvements for mixing problems. However, except in special cases, quantum algorithms achieve a run-time of $\mathcal{O}(\sqrt{\delta^{-1}} \sqrt{N})$, which introduces a costly dependence on the Markov chain size $N,$ not present in the classical case. Here, we re-address the problem of mixing of Markov chains when these form a slowly evolving sequence. This setting is akin to the simulated annealing setting and is commonly encountered in physics, material sciences and machine learning. We provide a quantum memory-efficient algorithm with a run-time of $\mathcal{O}(\sqrt{\delta^{-1}} \sqrt[4]{N})$, neglecting logarithmic terms, which is an important improvement for large state spaces. Moreover, our algorithms output quantum encodings of distributions, which has advantages over classical outputs. Finally, we discuss the run-time bounds of mixing algorithms and show that, under certain assumptions, our algorithms are optimal.Comment: 20 pages, 2 figure

School Goes Online With Avatars: Extended Learning in a Secondary School

Proceeding du Colloque "International Society of the Learning Sciences" (CSCL), ENS, Lyon (France), 17-21 June 2019International audienceAbstract: This paper focuses on the initial implications of students’ extended activity between virtual and in-presence learning. The study is part of an ongoing project founded in 2018 in a CSCL setting titled “e-PIm” (Incubator of Immersive Pedagogy for Virtual Reality) taking place in a secondary school in France labelled as pilot in 2016. For this study, some data are selected and qualitatively analysed. The implication of the implementation of the Multi-user Virtual Environment emerges in the field of didactics, student-teacher interactions, and students’ corporal and socio-cognitive behaviours ; the uses of the MUVE are revealed to be an ongoing transformative learning experience through an extended learning space and institutional change

Designs for Research, Teaching and Learning

This bookoffers a coherent theoretical and multimodal perspective on research, teaching and learning in different non-formal, semi-formal, and formal learning environments. Drawing on examples across a range of different settings, the book provides a conceptual framework for research on learning in different environments. It provides conceptual models around learning design which act as a framework for how to think about contemporary learning, a guideline for how to do research on learning in different sites, and a tool for innovative, collaborative design with other professionals. The book highlights concepts like multimodal knowledge representations; framing and setting; transformation, transduction, and re-design; signs of learning and cultures of recognition in different social contexts. The book supports innovative thinking on how we understand learning, and will appeal to academics, scholars and postgraduate students in the fields of education research and theory, learning sciences, and multimodal and social semiotics. It will also be of interest to school leaders, university provosts and professionals working in education

The Construction of Cosmopolitan Glocalities in Secondary Classrooms through Content and Language Integrated Learning (CLIL) in the Social Sciences

Our article argues for content and language integrated learning (CLIL) in the social sciences, as part of a new literacy towards 21st century challenges at school. At first, we will show how multilingualism is closely juxtaposed with global discourses in a worldwide network of glocalities. Thereafter, for the conceptual framework of the suggested pedagogy, we explain why cosmopolitanism must constitute an integral part thereof, accompanying the genesis of classroom glocalities. The heart of our competence model for CLIL in the social sciences fosters the promotion of global discourse competence with adolescent students. In short, this learning aim is a hybrid of subject and language learning, incorporating the merits of language didactics as well as “21st century skills”. Finally, in the last step, we will present #climonomics, a simulation of a multilingual EU parliamentary debate about climate change and climate action for secondary students. This example intends to demonstrate how multilingualism through CLIL amplifies the magnitude of global discourses during a simulation yet realistic setting. It should provide ‘food for thought’ for similar initiatives in research and teaching, to encourage the facilitation of cosmopolitan visions in classroom glocalities