Active Learning Strategies for Technology Assisted Sensitivity Review

Government documents must be reviewed to identify and protect any sensitive information, such as personal information, before the documents can be released to the public. However, in the era of digital government documents, such as e-mail, traditional sensitivity review procedures are no longer practical, for example due to the volume of documents to be reviewed. Therefore, there is a need for new technology assisted review protocols to integrate automatic sensitivity classification into the sensitivity review process. Moreover, to effectively assist sensitivity review, such assistive technologies must incorporate reviewer feedback to enable sensitivity classifiers to quickly learn and adapt to the sensitivities within a collection, when the types of sensitivity are not known a priori. In this work, we present a thorough evaluation of active learning strategies for sensitivity review. Moreover, we present an active learning strategy that integrates reviewer feedback, from sensitive text annotations, to identify features of sensitivity that enable us to learn an effective sensitivity classifier (0.7 Balanced Accuracy) using significantly less reviewer effort, according to the sign test (p < 0.01 ). Moreover, this approach results in a 51% reduction in the number of documents required to be reviewed to achieve the same level of classification accuracy, compared to when the approach is deployed without annotation features

Active strategies in adult learning

У цьому розділі ми хочемо ознайомити вас з деякими методами активного навчання, серед яких представлено 13. За жартівливими назвами груп методів приховується надзвичайно важливе значення. По суті, дотепна назва окреслює головні цілі кожної з цих груп методів, адже кожна з них допомагає досягти певної мети, спрямована на реалізацію конкретних розвиваючих завдань. Але при цьому вони тісно переплетені, пов'язані між собою, залежать одна від одної. Гарних результатів в процесі навчання можна досягти вмілим підбором активних методів до кожної частини заняття. Зазначимо, що планування заняття складна і копітка робота, вона вимагає ознайомлення із загальною характеристикою усіх методів та вибору найдоцільніших у кожному конкретному випадку.В этом разделе мы хотим ознакомить вас с некоторыми методами активного обучения, среди которых представлены 13. За шутливыми изображениями методов скрывается чрезвычайно важное значение. По сути, остроумная название определяет главные цели каждой из этих групп методов, ведь каждая из них помогает достичь определенной цели, направлена на реализацию конкретных развивающих задач. Но при этом они тесно переплетены, связаны между собой, зависят друг от друга. Хороших результатов в процессе обучения можно достичь умелым подбором активных методов в каждой части занятия. Отметим, что планирование занятия сложная и кропотливая работа, она требует ознакомления с общей характеристикой всех методов и выбора наиболее целесообразных в каждом конкретном случае.In this section we want to introduce you to some active learning methods, among which represented 13. humorous names of groups protected methods essential. In fact, witty name identifies the main objectives of each of these groups of methods, because each of them helps to achieve their goals, aimed at the realization of specific developmental tasks. But they are intertwined, interrelated, dependent on each other. Good results in learning can be achieved by a skilful selection of active methods for each of the classes. Note that lesson planning is complicated and hard work, it requires the introduction of a common characteristic of all methods and choosing the most appropriate in each case

Effective active learning strategies I have used in University class room

In this paper I summarize the various activities used in class room and laboratory teaching of first and second year engineering. These activities can be grouped under ‘active learning’. I describe the activities and the various attributes associated with each activity along with the advantages of using the mentioned activity model instead of simply a single ended lecturing model. Although most of these have erupted from an urge to increase students learning while making the topics increasingly interesting for them, most of these strategies have been researched out globally as effective teaching practices. Traditionally lecturers may think that they are doing active learning when questions are asked and a few students always answer or discussions amongst the same group of people take place from time to time. Although this includes student participation, it is engaging only a small fraction of a big class which is not optimum in terms of benefit to the class as whole and individuals of the class. Active learning is taking place in your class when you ask a question, pose a problem, or throw some type of challenge at them; ask your students to work individually or in pairs or small groups to come up with a response; give them some set time to do it; stop them, and invite one or more individuals or groups to share their responses with the class. The teacher as an expert can further comment on the answer if required. This paper concludes with a number of proven methods of including active learning strategies in first and second year Physics/electronics engineering class. Reference to global research about these strategies is included

Flow Navigation by Smart Microswimmers via Reinforcement Learning

Smart active particles can acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction. Their goal is to learn the best way to navigate by exploiting the underlying flow whenever possible. As an example, we focus our attention on smart gravitactic swimmers. These are active particles whose task is to reach the highest altitude within some time horizon, given the constraints enforced by fluid mechanics. By means of numerical experiments, we show that swimmers indeed learn nearly optimal strategies just by experience. A reinforcement learning algorithm allows particles to learn effective strategies even in difficult situations when, in the absence of control, they would end up being trapped by flow structures. These strategies are highly nontrivial and cannot be easily guessed in advance. This Letter illustrates the potential of reinforcement learning algorithms to model adaptive behavior in complex flows and paves the way towards the engineering of smart microswimmers that solve difficult navigation problems.Comment: Published on Physical Review Letters (April 12, 2017