Engaging Students through Video: Integrating Assessment and Instrumentation

CS50 is Harvard’s introductory course for majors and non-majors alike. For years, we have posted videos of the course’s lectures and sections online for the sake of review and distance education alike. But students’ experience with these videos has been historically passive. Students have been able to watch the course’s content on demand, rewinding and fast-forwarding at will, but they have not had means to engage interactively with the content or to check their understanding of material while watching videos. Furthermore, while we collected basic usage data (e.g., how many times a video was viewed), we lacked detailed analytics describing, for example, which portions of a video were commonly skipped or watched multiple times by students. To make videos more immersive and engaging for students, we developed CS50 Video, an open-source video player for desktop and mobile devices. CS50 Video allows instructors to integrate assessment questions to be answered by students at their own pace or at specific points in time directly into a video player. CS50 Video also allows students to search over video transcripts to find content easily as well as view videos at variable playback speeds (in order to make videos more accessible for ESL learners). Finally, CS50 Video integrates with third-party analytics solutions to allow instructors to view detailed usage statistics describing how students are interacting with videos (e.g., which videos or portions of videos are commonly watched or skipped over). We have deployed CS50 Video to students taking CS50 online and have obtained preliminary results. Because CS50 Video stores responses to questions server-side, we have been able to track students’ performance on in-video assessments. Thus far, we have observed that only 28% of students who watch online videos have engaged with assessment questions. Students who answer an assessment question incorrectly on their first attempt will often try again until reaching a correct answer, with 84.5% of correct answers reached in at most three attempts. We next plan to analyze the effects of in-video assessments on students’ mastery of material and introduce A/B-testing functionality for questions. We also plan to use students’ performance on assessments to understand the topics with which students struggle.Engineering and Applied Science

Interactive Imitation Learning in Robotics: A Survey

Interactive Imitation Learning (IIL) is a branch of Imitation Learning (IL) where human feedback is provided intermittently during robot execution allowing an online improvement of the robot's behavior. In recent years, IIL has increasingly started to carve out its own space as a promising data-driven alternative for solving complex robotic tasks. The advantages of IIL are its data-efficient, as the human feedback guides the robot directly towards an improved behavior, and its robustness, as the distribution mismatch between the teacher and learner trajectories is minimized by providing feedback directly over the learner's trajectories. Nevertheless, despite the opportunities that IIL presents, its terminology, structure, and applicability are not clear nor unified in the literature, slowing down its development and, therefore, the research of innovative formulations and discoveries. In this article, we attempt to facilitate research in IIL and lower entry barriers for new practitioners by providing a survey of the field that unifies and structures it. In addition, we aim to raise awareness of its potential, what has been accomplished and what are still open research questions. We organize the most relevant works in IIL in terms of human-robot interaction (i.e., types of feedback), interfaces (i.e., means of providing feedback), learning (i.e., models learned from feedback and function approximators), user experience (i.e., human perception about the learning process), applications, and benchmarks. Furthermore, we analyze similarities and differences between IIL and RL, providing a discussion on how the concepts offline, online, off-policy and on-policy learning should be transferred to IIL from the RL literature. We particularly focus on robotic applications in the real world and discuss their implications, limitations, and promising future areas of research

Effect of Virtual Reality on Motivation and Achievement of Middle-School Students

The introduction of low-cost hand-held devices has provided K-12 educators with the opportunity to teach using virtual reality (VR). However, the efficacy of VR in K-12 classrooms for teaching and learning has not been established. Therefore, the purpose of this quasi-experimental study was to examine the influence of virtual reality field trips on middle-school students social studies academic achievement and motivation. The district chosen for the study is in a rural, economically depressed county, where generational poverty persists. However, the district has a history of being an early adopter of technology. Participants included 76 seventh-grade students at two middle schools, who participated in social studies instruction using either the traditional lecture method or a virtual reality system. The virtual reality system used in this study was the Google Expeditions Virtual Reality System, which uses smartphone technology and iBlue Google VR 3-D Glasses. Before and after the instruction was provided, participants were assessed using the Instructional Materials Motivation Survey (IMMS) and teacher designed social studies test. The results of the two one-way ANCOVAs, demonstrated that students using virtual reality scored significantly higher than students participating in traditional instruction on both their academic achievement and motivation. These findings provide support for the use of virtual reality in middle-school social studies classrooms

Deep learning macroeconomics

Limited datasets and complex nonlinear relationships are among the challenges that may emerge when applying econometrics to macroeconomic problems. This research proposes deep learning as an approach to transfer learning in the former case and to map relationships between variables in the latter case. Several machine learning techniques are incorporated into the econometric framework, but deep learning remains focused on time-series forecasting. Firstly, transfer learning is proposed as an additional strategy for empirical macroeconomics. Although macroeconomists already apply transfer learning when assuming a given a priori distribution in a Bayesian context, estimating a structural VAR with signal restriction and calibrating parameters based on results observed in other models, to name a few examples, advance in a more systematic transfer learning strategy in applied macroeconomics is the innovation we are introducing. When developing economics modeling strategies, the lack of data may be an issue that transfer learning can fix. We start presenting theoretical concepts related to transfer learning and proposed a connection with a typology related to macroeconomic models. Next, we explore the proposed strategy empirically, showing that data from different but related domains, a type of transfer learning, helps identify the business cycle phases when there is no business cycle dating committee and to quick estimate an economic-based output gap. In both cases, the strategy also helps to improve the learning when data is limited. The approach integrates the idea of storing knowledge gained from one region’s economic experts and applying it to other geographic areas. The first is captured with a supervised deep neural network model, and the second by applying it to another dataset, a domain adaptation procedure. Overall, there is an improvement in the classification with transfer learning compared to baseline models. To the best of our knowledge, the combined deep and transfer learning approach is underused for application to macroeconomic problems, indicating that there is plenty of room for research development. Secondly, since deep learning methods are a way of learning representations, those that are formed by the composition of multiple non-linear transformations, to yield more abstract representations, we apply deep learning for mapping low-frequency from high-frequency variables. There are situations where we know, sometimes by construction, that there is a relationship be-tween input and output variables, but this relationship is difficult to map, a challenge in which deep learning models have shown excellent performance. The results obtained show the suitability of deep learning models applied to macroeconomic problems. Additionally, deep learning proved adequate for mapping low-frequency variables from high-frequency data to interpolate, distribute, and extrapolate time series by related series. The application of this technique to Brazilian data proved to be compatible with benchmarks based on other techniques.Conjuntos de dados limitados e complexas relações não-lineares estão entre os desafios que podem surgir ao se aplicar econometria a problemas macroeconômicos. Esta pesquisa propõe aprendizagem profunda como uma abordagem para transferir aprendizagem no primeiro caso e para mapear relações entre variáveis no último caso. Várias técnicas de aprendizado de máquina estão incorporadas à estrutura econométrica, mas o aprendizado profundo continua focado na previsão de séries temporais. Primeiramente, aprendizagem por transferência é proposta como uma estratégia adicional para a macroeconomia empírica. Embora os macroeconomistas já apliquem aprendizagem por transferência ao assumir uma dada distribuição a priori em um contexto Bayesiano, estimar um VAR estrutural com restrição de sinal e calibrar parâmetros com base em resultados observados em outros modelos, para citar alguns exemplos, avançar em uma estratégia mais sistemática de transferência de aprendizagem em macroeconomia aplicada é a inovação que estamos introduzindo. Ao desenvolver estratégias de modelagem econômica, a falta de dados pode ser um problema que aprendizagem por transferência pode corrigir. Começamos por apresentar conceitos teóricos relacionados à transferência de aprendizagem e propomos uma conexão com uma tipologia relacionada a modelos macroeconômicos. Em seguida, exploramos a estratégia proposta empiricamente, mostrando que os dados de domínios diferentes, mas relacionados, um tipo de aprendizagem por transferência, ajudam a identificar as fases do ciclo de negócios quando não há comitê de datação do ciclo de negócios e a estimar rapidamente um hiato do produto de base econômica. Em ambos os casos, a estratégia também ajuda a melhorar o aprendizado quando os dados são limitados. A abordagem integra a ideia de armazenar conhecimento obtido de especialistas em economia de uma região e aplicá-lo a outras áreas geográficas. O primeiro é capturado com um modelo de rede neural profunda supervisionado e o segundo aplicando-o a outro conjunto de dados, um procedimento de adaptação de domínio. No geral, há uma melhora na classificação com a aprendizagem por transferência em comparação com os modelos de base. Até onde sabemos, a abordagem combinada de aprendizagem profunda e transferência é subutilizada para aplicação a problemas macroeconômicos, indicando que há muito espaço para o desenvolvimento de pesquisas. Em segundo lugar, uma vez que os métodos de aprendizagem profunda são uma forma de aprender representações, aquelas que são formadas pela composição de várias transformações não lineares, para produzir representações mais abstratas, aplicamos aprendizagem profunda para mapear variáveis de baixa frequência a partir de variáveis de alta frequência. Há situações em que sabemos, às vezes por construção, que existe uma relação entre as variáveis de entrada e saída, mas essa relação é difícil de mapear, um desafio no qual os modelos de aprendizagem profunda têm apresentado excelente desempenho. Os resultados obtidos mostram a adequação de modelos de aprendizagem profunda aplicados a problemas macroeconômicos. Além disso, o aprendizado profundo se mostrou adequado para mapear variáveis de baixa frequência a partir de dados de alta frequência para interpolar, distribuir e extrapolar séries temporais por séries relacionadas. A aplicação dessa técnica em dados brasileiros mostrou-se compatível com benchmarks baseados em outras técnicas

Reinforcement Learning from Demonstration

Off-the-shelf Reinforcement Learning (RL) algorithms suffer from slow learning performance, partly because they are expected to learn a task from scratch merely through an agent\u27s own experience. In this thesis, we show that learning from scratch is a limiting factor for the learning performance, and that when prior knowledge is available RL agents can learn a task faster. We evaluate relevant previous work and our own algorithms in various experiments. Our first contribution is the first implementation and evaluation of an existing interactive RL algorithm in a real-world domain with a humanoid robot. Interactive RL was evaluated in a simulated domain which motivated us for evaluating its practicality on a robot. Our evaluation shows that guidance reduces learning time, and that its positive effects increase with state space size. A natural follow up question after our first evaluation was, how do some other previous works compare to interactive RL. Our second contribution is an analysis of a user study, where na ive human teachers demonstrated a real-world object catching with a humanoid robot. We present the first comparison of several previous works in a common real-world domain with a user study. One conclusion of the user study was the high potential of RL despite poor usability due to slow learning rate. As an effort to improve the learning efficiency of RL learners, our third contribution is a novel human-agent knowledge transfer algorithm. Using demonstrations from three teachers with varying expertise in a simulated domain, we show that regardless of the skill level, human demonstrations can improve the asymptotic performance of an RL agent. As an alternative approach for encoding human knowledge in RL, we investigated the use of reward shaping. Our final contributions are Static Inverse Reinforcement Learning Shaping and Dynamic Inverse Reinforcement Learning Shaping algorithms that use human demonstrations for recovering a shaping reward function. Our experiments in simulated domains show that our approach outperforms the state-of-the-art in cumulative reward, learning rate and asymptotic performance. Overall we show that human demonstrators with varying skills can help RL agents to learn tasks more efficiently

Multimedia in Communication Education: A Survey of Communication Educators

This project was an attempt to determine if and how educators are adapting their curriculum to reflect changes in technology. The survey revealed that the respondents, for the most part, are incorporating multimedia technologies into their curriculum. Results also showed an interesting pattern to that integration. This pattern involved two concepts of multimedia: 1) multimedia as a new form of communication (HTML, CD-ROMs, etc.), and 2) multimedia as a tool to enhance traditional forms of communication (broadcast news, print journalism, etc.). It was found that educators considered multimedia applications to enhance traditional forms of communication (i.e. image creation software, desktop/non-linear editing applications) as most important