Практика здобувачів ступеня магістр

Автори позиціонують навчальний посібник як системний, теоретичний і прикладний інструмент остаточної систематизації накопичених студентами знань, умінь та інформаційних ресурсів за темою магістерської дисертації, як дорожню карту виконання завдань такої систематизації, як один із прикладів результатів систематизації у вигляді структурних елементів і окремих частин магістерської дисертації. Навчальний посібник розроблений для студентів-магістрантів першого і другого року навчання, які навчаються за спеціальністю 113 «Прикладна математика» за освітньою програмою «Наука про дані та математичне моделювання» факультету прикладної математики КПІ ім. Ігоря Сікорського, як науковий і методичний інструмент проходження практики.The authors position the study guide as a systematic, theoretical and practical tool for the final systematization of the knowledge, skills and information resources accumulated by students on the topic of the master's thesis, as a road map for the implementation of the tasks of such systematization, as one of the examples of the results of systematization in the form of structural elements and separate parts of the master's thesis. The study guide was developed for first- and second-year master's students who study in specialty 113 "Applied Mathematics" under the educational program "Data Science and Mathematical Modeling" of the Faculty of Applied Mathematics of KPI named after Igor Sikorsky, as a scientific and methodical tool for practice

An adaptive domain-independent agents-based tutor for Web-based supplemental learning environments

Thesis (Ph. D .)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.Includes bibliographical references (p. 170-185).The Physics Interactive Video Tutor (PIVoT) is a Web-based multimedia resource for college-level Newtonian mechanics. The Personal Tutor (PT) is an intelligent tutoring system (ITS) integrated into PIVoT, assisting students and teachers in navigating through, understanding, and assessing PIVoT's educational media. PT is adaptive in that it personalizes its functionality to the preferences of its user. The combined PIVoT / PT system was designed to be domain-independent with respect to the style of pedagogy, models of user learning, and instructional algorithms. Thus, this design is easily adapted for use beyond the tested domain of introductory college physics. PT is designed in the object-oriented paradigm, building upon the recent work in multi-agent systems (MAS). This agents-based approach, along with innovations in negotiating student-agent control and communication, allow current and future competing pedagogical strategies and cognitive theories to coexist harmoniously. New efficient, domain-independent techniques for discovering, updating, and presenting students' contextual interests improve information retrieval and site navigation. Unlike other computer-based instruction systems used as a tool for primary learning and assessment, PIVoT is used as a supplementary resource focusing on providing formative assessment to both student and educator alike. The PIVoT / PT system leverages reusability and system independence, two often-overlooked strengths of agent-based approaches to intelligent tutoring systems. Combined, PIVoT and the Personal Tutor provide an effective proving ground for innovations in intelligent tutoring system design that also reduces the cost of making such software.by Steven Niemczyk.Ph.D

Building a Stochastic Dynamic Model of Application Use

Many intelligent user interfaces employ application and user models to determine the user's preferences, goals and likely future actions. Such models require application analysis, adaptation and expansion. Building and maintaining such models adds a substantial amount of time and labour to the application development cycle. We present a system that observes the interface of an unmodified application and records users' interactions with the application. From a history of such observations we build a coarse state space of observed interface states and actions between them. To refine the space, we hypothesize substates based upon the histories that led users to a given state. We evaluate the information gain of possible state splits, varying the length of the histories considered in such splits. In this way, we automatically produce a stochastic dynamic model of the application and of how it is used. To evaluate our approach, we present models derived from real-world a..