Learning and Discovery

We formulate a dynamic framework for an individual decision-maker within which discovery of previously unconsidered propositions is possible. Using a standard game-theoretic representation of the state space as a tree structure generated by the actions of agents (including acts of nature), we show how unawareness of propositions can be represented by a coarsening of the state space. Furthermore we develop a semantics rich enough to describe the individual's awareness that currently undiscovered propositions may be discovered in the future. Introducing probability concepts, we derive a representation of ambiguity in terms of multiple priors, reflecting implicit beliefs about undiscovered proposition, and derive conditions for the special case in which standard Bayesian learning may be applied to a subset of unambiguous propositions. Finally, we consider exploration strategies appropriate to the context of discovery, comparing and contrasting them with learning strategies appropriate to the context of justification, and sketch applications to scientific research and entrepreneurship.

Inductive queries for a drug designing robot scientist

It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments

Active learning for feasible region discovery

Often in the design process of an engineer, the design specifications of the system are not completely known initially. However, usually there are some physical constraints which are already known, corresponding to a region of interest in the design space that is called feasible. These constraints often have no analytical form but need to be characterised based on expensive simulations or measurements. Therefore, it is important that the feasible region can be modeled sufficiently accurate using only a limited amount of samples. This can be solved by using active learning techniques that minimize the amount of samples w.r.t. what we try to model. Most active learning strategies focus on classification models or regression models with classification accuracy and regression accuracy in mind respectively. In this work, regression models of the constraints are used, but only the (in) feasibility is of interest. To tackle this problem, an information-theoretic sampling strategy is constructed to discover these regions. The proposed method is then tested on two synthetic examples and one engineering example and proves to outperform the current state-of-the-art

Penerapan Model Discovery Learning untuk Meningkatkan Hasil Belajar Matematika Siswa Kelas XI Mia 1 SMA Negeri 8 Pekanbaru

This research is a class action research that aims to improve the learning process and to increase the student's mathematics learning outcomes by applying Discovery Learning model. The subjects of this research are the student of class XI MIA 1 SMAN 8 Pekanbaru at second semester of academic years 2014/2015, which amounts to 35 students. This research consists of two cycles, each cycle consists of four stages: planning, implementation, observation, and reflection. Data collected through observation and learning outcomes test. Data analysis is done by observation data analysis and student's mathematics learning outcomes data analysis. The action is successful if teacher's activities and student's activities increase in every meeting and the number of students who reach Minimum Mastery Criteria increases in every cycle. Observation data analysis showed that learning process in class XI MIA 1 SMAN 8 Pekanbaru has improved at each meeting. Student's mathematics learning outcomes data analysis show that student's mathematics learning increase before action and after action, that aims: (1) Amount students that reach behavior Minimum Mastery Criteria of confidence in base score is 25 students increase to 32 students at the first cycle and 35 students at the second cycle. For discipline, in base score is 29 students increase to 32 students at the first cycle and 34 students at the second cycle. For responsibility, in base score is 31 students at the first cycle and at the second cycle; (2) Amount students that reach knowledge Minimum Mastery Criteria in base score is 17 students increase to 22 students at the first cycle and 29 students at the second cycle; and (3) Amount students that reach skill Minimum Mastery Criteria in base score is 12 students increase to 16 students at the first cycle and 18 students at the second cycle. The result of this research showed that cooperative learning Discovery Learning model improve the learning process and increase the mathematics learning outcomes the students of class XI MIA 1 SMAN 8 Pekanbaru at second semester of academic years 2014/2015

Teaching the YouTube generation: exploring the benefits of an interactive teaching approach in sustainable product design

This paper presents findings from a doctoral study, which investigated effective methods for teaching social sustainability within product design courses in British and Irish universities. Specifically exploring, how to foster a holistic understanding of the social aspects of sustainable product design amongst undergraduate and postgraduate students, through design thinking. Perceived relevance is considered as a fundamental aspect in enabling students to engage deeply with sustainability [1]. Authors [2;3;4] note that 'Net Generation' learners have specific learning preferences that can be targeted in order to improve the students learning experience. Through the careful design of materials which build upon the students tendency towards visual learning and seeking increase relevance and motivation, by offering opportunities for collaborative learning and learning through discovery. Three 'Rethinking Design' workshops were designed and developed as part of a doctoral study to introduce students to the wider social aspects of sustainability and these were conducted in five universities in Britain and Ireland. The workshops featured visually rich audio visual introductions followed by collaborative group based mind mapping activities, which were successful in fostering deep learning by facilitating learning through discovery, critical reflection, peer learning and creativity leading to an exploration of design thinking solutions

Machine learning for crystal identification and discovery

As computers get faster, researchers -- not hardware or algorithms -- become the bottleneck in scientific discovery. Computational study of colloidal self-assembly is one area that is keenly affected: even after computers generate massive amounts of raw data, performing an exhaustive search to determine what (if any) ordered structures occur in a large parameter space of many simulations can be excruciating. We demonstrate how machine learning can be applied to discover interesting areas of parameter space in colloidal self assembly. We create numerical fingerprints -- inspired by bond orientational order diagrams -- of structures found in self-assembly studies and use these descriptors to both find interesting regions in a phase diagram and identify characteristic local environments in simulations in an automated manner for simple and complex crystal structures. Utilizing these methods allows analysis methods to keep up with the data generation ability of modern high-throughput computing environments.Comment: Fixed typo, added missing acknowledgment, added supplementary informatio

Penerapan Discovery Learning untuk Meningkatkan Hasil Belajar Matematika Siswa Kelas Viiib SMP Beer Seba Pekanbaru

This research based on the students\u27 math achievement on grade VIIIB of SMP Beer Seba Pekanbaru which under the minimum completeness criteria with percentage 32.14% for math test with linear equations systems of two variables topic and 28.57% for Pythagoras\u27s Theorem topic. The research is classroom action research. The research aims to improve the math learning process and improve math learning result at grade VIIIB of SMP Beer Seba Pekanbaru with applying the discovery learning. The research subjects were students of class VIIIB SMP Beer Seba Pekanbaru which consists of 15 boys and 14 girls in the even semester of 2015/2016 academic years. The instruments of data collection were observation sheets and students\u27 math achievement tests. The observation sheets were analyzed in desciptive narative and the students\u27 math achievement tests were analyzed in statistic desciptive. The result of descriptive narative had showed an improvement of learning process prior to the action on the first cycle and from the first cycle to the second cycle. The result of statistic descriptive had showed an increasing number of students\u27 math achievement from the basic score to the first math test with percentage 22.78% and from the first math test to the second math test with percentage 3.58%. So, the result of this research showed that the implementation of discovery learning can improve the learning process and improve the students\u27 math achievement for grade VIIIB SMP Beer Seba Pekanbaru in the even semester of 2015/2016 academic years

Pengaruh Discovery Learning Berbantuan Paket Program Simulasi Phet terhadap Prestasi Belajar Fisika

Discovery learning dirancang dengan tujuan agar siswa dapat menemukan sendiri konsep yang dipelajari dan bekerja secara efektif dalam kelompok. Tujuan penelitian ini adalah untuk mengetahui pengaruh discovery learning berbantuan program simulasi PhET terhadap prestasi belajar dibandingkan dengan discovery learning. Penelitian menggunakan quasi experiments dengan desain faktorial 2 x 2. Teknik pengumpulan data kemampuan awal dengan melakukan tes kemampuan awal pada awal penelitian dan pada akhir penelitian diberikan tes prestasi belajar fisika. Teknik analsis data menggunakan uji anava dua jalur. Hasil uji Anava dua jalur menunjukkan bahwa prestasi belajar fisika kelompok siswa yang belajar melalui discovery learning berbantuan paket program simulasi PhET lebih tinggi daripada kelompok siswa yang belajar melalui discovery learning. Dengan demikian, discovery learning berbantuan program simulasi PhET mempengaruhi secara positif prestasi belajar siswa