The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013. However, this growth is not limited to higher education. New York City, San Francisco and Oakland public schools will soon be offering computer science to all students at all schools from preschool to 12th grade, although it will be an elective for high school students. This unprecedented demand means that CS departments are likely to teach not only more students in the coming decades, but more diverse students, with more varied backgrounds, motivations, preparations, and abilities. This growth is an unparalleled opportunity to expand the reach of computing education. However, this growth is also a unique research challenge, as we know very little about how best to teach our current students, let alone the students soon to arrive. The burgeoning field of Computing Education Research (CER) is positioned to address this challenge by answering research questions such as, how should we teach computer science, from programming to advanced principles, to a broader and more diverse audience? We argue that computer science departments should lead the way in establishing CER as a foundational research area of computer science, discovering the best ways to teach CS, and inventing the best technologies with which to teach it. This white paper provides a snapshot of the current state of CER and makes actionable recommendations for academic leaders to grow CER as a successful research area in their departments.Comment: A Computing Community Consortium (CCC) white paper, 12 page

The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013

The Importance of Computing Education Research

Interest in computer science is growing. As a result, computer science (CS) and related departments are experiencing an explosive increase in undergraduate enrollments and unprecedented demand from other disciplines for learning computing. According to the 2014 CRA Taulbee Survey, the number of undergraduates declaring a computing major at Ph.D. granting departments in the US has increased 60% from 2011-2014 and the number of degrees granted has increased by 34% from 2008-2013

Новый подход к процессу автоматизации обучения на основе данных о поведении пользователей в цифровых библиотеках

The author introduces the mathematical model of recurrent neural network with external memory. It is intended for predicting efficient education trajectory in digital information environments, e. g. digital libraries. The goal of computer-aided learning based on neural networks is to personalize user trajectories. In the study, user behavior is modeled for the more precise personalization in various aspects using recurrent neural networks. The method is designed for two types of recurrent neural networks, i. e. the classic one with sigmoidal activation function and that with LSTM (Long Short-Term Memory). The experiments demonstrated serious advantages of recurrent neural networks over analogous methods in predicting education trajectory. Thus, the proposed model is the more efficient in predictive accuracy (by 15–20% higher than analogous methods). Its prime application area is prediction of optimum user education trajectory in the digital information environment, and digital library, in particulПредставлена математическая модель применения рекуррентной сети с внешней памятью. Она предназначена для предсказания оптимальной образовательной траектории пользователя в цифровых информационных средах, к которым могут быть отнесены цифровые библиотеки. Основная задача, решаемая с помо щью метода машинного обучения, основанного на применении нейронных сетей, – индивидуализация образовательных траекторий пользователя. Цель работы – моделирование различных аспектов деятельности обучающегося с использованием рекуррентных нейронных сетей для более точной индивидуализации образовательной траектории. В основе метода лежат две разновидности рекуррентных нейронных сетей: классическая с сигмоидальной функцией активации и сеть с долгой краткосрочной памятью LSTM (Long Short-Term Memory). Результаты проведённых экспериментов показали существенные преимущества применения рекуррентных нейронных сетей для предсказания шагов образовательной траектории по сравнению с аналогичными методами. Таким образом, разработанная модель имеет более высокую точность предсказания (на 15–20% выше относительно аналогов). Основная область её применения – предсказание оптимальной образовательной траектории пользователя в цифровой информационной среде, в частности – цифровой библиотеке

Fuzz Testing Projects in Massive Courses

ABSTRACT Scaffolded projects with automated feedback are core instructional components of many massive courses. In subjects that include programming, feedback is typically provided by test cases constructed manually by the instructor. This paper explores the effectiveness of fuzz testing, a randomized technique for verifying the behavior of programs. In particular, we apply fuzz testing to identify when a student's solution differs in behavior from a reference implementation by randomly exploring the space of legal inputs to a program. Fuzz testing serves as a useful complement to manually constructed tests. Instructors can concentrate on designing targeted tests that focus attention on specific issues while using fuzz testing for comprehensive error checking. In the first project of a 1,400-student introductory computer science course, fuzz testing caught errors that were missed by a suite of targeted test cases for more than 48% of students. As a result, the students dedicated substantially more effort to mastering the nuances of the assignment