MineObserver 2.0 is an AI framework that uses Computer Vision and Natural Language Processing for assessing the accuracy of learner-generated descriptions of Minecraft images that include some scientifically relevant content. The system automatically assesses the accuracy of participant observations, written in natural language, made during science learning activities that take place in Minecraft. We demonstrate our system working in real-time and describe a teacher dashboard to showcase observations, both of which advance our previous work. We present the results of a study showing that MineObserver 2.0 improves over its predecessor both in perceived accuracy of the system's generated descriptions as well as in usefulness of the system's feedback. In future work, we intend improve system generated descriptions to give more teacher control and shift the system to perform continuous learning to more rapidly respond to novel observations made by learners.

Diversity has been a prevalent issue in the American STEM workforce for a number of years. Efforts to increase diversification have resulted in alternate learning spaces such as makerspaces, after school programs, and technology integrated curriculums. Our study, hosted at a non-profit organization serving underrepresented youth, leveraged the video game Minecraft (MC) as a way to engage summer campers in scientific concepts and inquiries over one week. Reoccurring themes from interviews include familial rules on technology use at home, engaging with STEM in a novel way, and a love for building and creating within MC. We discuss our insight into the discoveries and challenges of these types of STEM-oriented program that takes place in informal settings.

The Virtual Museum Guides are two virtual humans set in an exhibit at the Museum of Science, Boston, designed to promote interest in Science, Technology, Engineering and Mathematics (STEM). The primary audience is children between ages 7 to 14, in particular females and other groups under-represented in STEM.The Guides are based on and extend the approach used in the SGT Star character and the Gunslinger project. To interact with the characters, an operator presses a push-totalk button and speaks into a microphone. An audio acquisition client then sends audio to the automatic speech recognizer (ASR), which creates hypotheses of the words that were said, and then sends this text to the Language Understanding (LU) module.

The role of culture in learning is an underexplored area of research. Learners come with a variety of cultural backgrounds, belief systems, and perspectives. Research in education has shown that teaching methodologies and instructional design cannot always be universally applied as their impact can greatly vary from one culture to another. In other words, pedagogical strategies that are effective for one cultural group may not be effective with a different one. Researchers in the Artificial Intelligence in Education (AIED) community are beginning to address these issues by incorporating models of culture into their systems with goal of either adapting to learners' varying cultural backgrounds or by culturally-appropriate underlying teaching methodologies into their systems. Developing AIED systems with cultural discernment capabilities therefore – among other possibilities – may reduce misunderstanding and confusion that is derived from learner behaviour as well as allow for customized learning according to cultural needs. A greater cultural focus can also increase the flexibility of the systems we build and promote their acceptance and wider spread use.

The role of explicit feedback in learning has been studied from a variety of perspectives and in many contexts. In this paper, we examine the impact of the specificity of feedback delivered by an intelligent tutoring system in a game-based environment for cultural learning. We compared two versions: one that provided only “bottom-out” hints and feedback versus one that provided only conceptual messages. We measured during-training performance, in-game transfer, and long-term retention. Consistent with our hypotheses, specific feedback utterances produced inferior learning on the in-game transfer task when compared to conceptual utterances. No differences were found on a web-based post-test. We discuss possible explanations for these findings, particularly as they relate to the learning of loosely defined skills and serious games.

Traditional methods of evaluating student programs are not always appropriate for assessment of different instructional interventions. They tend to focus on the final product rather than on the process that led to it. This paper presents intention-based scoring (IBS), an approach to measuring programming ability that looks at intermediate programs produced over the course of an implementation rather than just the one at the end. The intent is to assess a student's ability to produce algorithmically correct code on the first attempt at achieving each program goal. In other words, the goal is to answer question "How close was the student to being initially correct?" but not to speak to a student's debugging skills or ability to ultimately produce a working program. To produce an IBS, it is necessary to inspect a student's online protocol, which is simply the collection of all programs submitted to a compiler. IBS involves a three-phase process of (1) identification of the subset of all programs in a protocol that represent the initial attempts at achieving programming goals, (2) bug identification, and (3) rubric-based scoring. We conclude with an example application of IBS in the evaluation of a tutoring system for beginning programmers and also show how an IBS can be broken down by the underlying bug categories to reveal more subtle differences.

Coached program planning is a dialogue-based style of tutoring aimed at helping novices during the early stages of program writing. The intent is to help novices understand and solve problems in their own words through the construction of natural-language style pseudocode as the first step in solving a programming problem. We have designed an environment supporting coached program planning and have used it in a human-to-human, computer-mediated evaluation of 16 novice programmers enrolled in a pre-CS1 programming course at the University of Pittsburgh. The results show that students who underwent coached program planning, compared to those who did not, were more prolific with comments in their programs, committed fewer structural mistakes, and exhibited less erratic programming behavior during their implementation. The dialogues collected from this experiment followed a clear 4-step pattern. Starting with this observation, we are developing a dialogue-based intelligent tutoring system called the Pseudocode Tutor to support coached program planning.