Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well to- wards unseen tasks with increasing lengths, variable topologies, and changing objectives.Comment: ICRA 201

A way forward to managing the transition to professional practice for beginning teachers

The high attrition rate of beginning teachers in Australia and overseas is well-documented. This trend is easily understood as many beginning teachers enter the profession with little support or mentoring (Department of Education, Science and Training (DEST), 2002; Herrington & Herrington, 2004; Ramsey, 2000). Continual calls for more comprehensive approaches to teacher induction in which universities and employing bodies share the responsibilities of the transition to professional practice (House of Representatives Standing Committee on Education and Vocational Training, 2007) have, to date, largely been ignored. This paper reports on a trial project conducted at a university in south-east Queensland, Australia that addresses these shortfalls. The aim of the project is to facilitate and support the development of high quality teachers and teaching through an extended model of teacher preparation. The model comprises a 1+2 program of formal teacher preparation: a one-year teacher education course (the Graduate Diploma in Education), followed by a comprehensive two year program of workplace induction and ongoing professional learning tailored to meet graduate and employer needs. This paper reports on graduating students’ perceptions of their preparedness to teach as they transition from the Graduate Diploma in Education program to professional practice. The study concludes that innovative programs, including university-linked, ongoing professional learning support for teacher education graduates, may provide the way forward for enhancing the transition to practice for beginning teachers

Embracing New Realities: Professional Growth for New Principals and Mentors

This paper highlights one state model providing mentoring and induction for new school leaders in the U.S.A. The importance of mentoring and induction as a continuation of leadership preparation is highlighted in program components and participant perceptions in The Kansas Educational Leadership Institute’s (KELI) mentoring and induction program and professional learning seminars. Experienced and trained mentors provide critical support for new principals serving schools and communities in their first year of practice. A program description, initial operational processes, program requirements, and mentor training are shared along with information about KELI’s second year program, evaluation results, and next steps

The Difficulties of Learning Logic Programs with Cut

As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages.Comment: See http://www.jair.org/ for any accompanying file

Neural networks learn to detect and emulate sorting algorithms from images of their execution traces

Context: recent advancements in the applicability of neural networks across a variety of fields, such as computer vision, natural language processing and others, have re-sparked an interest in program induction methods. Problem: when performing a program induction task, it is not feasible to search across all possible programs that map an input to an output because the number of possible combinations or sequences of instructions is too high: at least an exponential growth based on the generated program length. Moreover, there does not exist a general framework to formulate such program induction tasks and current computational limitations do not allow a very wide range of machine learning applications in the field of computer programs generation. Objective: in this study, we analyze the effectiveness of execution traces as learning representations for neural network models in a program induction set-up. Our goal is to generate visualizations of program execution dynamics, specifically of sorting algorithms, and to apply machine learning techniques on them to capture their semantics and emulate their behavior using neural networks. Method: we begin by classifying images of execution traces for algorithms working on a finite array of numbers, such as various sorting and data structures algorithms. Next we experiment with detecting sub-program patterns inside the trace sequence of a larger program. The last step is to predict future steps in the execution of various sorting algorithms. More specifically, we try to emulate their behavior by observing their execution traces. We also discuss generalizations to other classes of programs, such as 1-D cellular automata. Results: our experiments show that neural networks are capable of modeling the mechanisms underlying simple algorithms if enough execution traces are provided as data. We compare the performance of our program induction model with other similar experimental results from Graves et al. [4] and Vinyals et al. [5]. We were also able to demonstrate that sorting algorithms can be treated both as images displaying spatial patterns, as well as sequential instructions in a domain specific language, such as swapping two elements. We tested our approach on three types of increasingly harder tasks: detection, recognition and emulation. Conclusions: we demonstrate that simple algorithms can be modelled using neural networks and provide a method for representing specific classes of programs as either images or sequences of instructions in a domain-specific language, such that a neural network can learn their behavior. We consider the complexity of various set-ups to arrive at some improvements based on the data representation type. The insights from our experiments can be applied for designing better models of program induction

A Case Study of the Retention-Supporting Needs of Beginning Teachers in a West Central Georgia School System

Even though the State of Georgia has issued suggested guidance for new teacher induction programs, not all school systems follow that guidance and varying induction practices have been implemented. Because replacing exiting teachers in the first 5 years of their career has become costly to school systems—both financially and academically regarding student achievement—it is in all stakeholders’ best interest to support new teachers to increase retention rates. The purpose of this case study was to describe 1st-year teachers’ experiences in a West Central Georgia school system induction program and to identify the retention-supporting needs these new teachers reported as part of a successful induction program. This case study included a document analysis review of the school system’s Induction Program Handbook and interviews with six teachers (two elementary, two middle, and two high school) at two points of time in the academic year. Coding the interviews for themes, I used a conceptual framework based on research-proven practices that are strong components for induction programs. This study provides an understanding of what these 1st-year teachers experienced in the induction program and what supports they identified as being most useful to them as they completed their 1st year of employment in a public PreK-12 school system. The results support existing research that outlines induction program needs to increase new teacher intention rates and describes how these supports can be structured to meet all stakeholders’ needs. Purposeful mentoring from a trained mentor, collaboration with multiple professionals, and individualized professional learning activities tailored to the unique needs of each 1st-year teacher were identified as strong retention supporting induction program component

Manfaat Sertifikat Induksi Bagi Guru Pemula untuk Kenaikan Pangkat

The teacher plays a central role in every learning process. Apart from being a teacher, teachers are also required to be able to be innovators, facilitators, and motivators. The ability of teachers to present learning so that it becomes exciting and fun will determine students' success. Therefore, the Beginner Teacher Induction Program is essential to implement as a means to improve teacher competence. The implementation of the induction PIGP (Program Induksi Guru Pemula) aims to guide novice teachers to adapt to the work climate and culture of the school/madrasah. Thus, after the activity, it is hoped that every teacher can have good pedagogical, professional, social, and personal competence

Few-Shot Bayesian Imitation Learning with Logical Program Policies

Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies from very few examples. We represent policies as logical combinations of programs drawn from a domain-specific language (DSL), define a prior over policies with a probabilistic grammar, and derive an approximate Bayesian inference algorithm to learn policies from demonstrations. In experiments, we study five strategy games played on a 2D grid with one shared DSL. After a few demonstrations of each game, the inferred policies generalize to new game instances that differ substantially from the demonstrations. Our policy learning is 20--1,000x more data efficient than convolutional and fully convolutional policy learning and many orders of magnitude more computationally efficient than vanilla program induction. We argue that the proposed method is an apt choice for tasks that have scarce training data and feature significant, structured variation between task instances.Comment: AAAI 202

INNOVATION: A CASE STUDY OF AN ENGLISH TEACHERS’ INDUCTION

The induction of new English teachers is not often made the focus of language programs. In many institutions, the orientation experience receives little attention, resulting in work-related stress at the beginning of an instructor’s teaching contract. Consequently, not only the quality of teaching is affected but also the teachers’ motivation and perception of the program. This research article analyses the results of a case study of an innovation to a new teacher induction in a language program in the city of Cuenca, Ecuador. For this, the case study was based on two-way communication between the administration and the teaching staff through direct feedback, the consideration of language program management principles, as well as the application of a teacher survey after implementation. As a result, the innovation to the induction of new teachers seemed to reduce teachers’ job-related stress during the first week of classes, thus helping to create a learning environment where the program, its teachers, and its students benefit as a whole