The benefits of personalized social robots must be evaluated in real-world educational contexts over periods of time longer than a single session to understand their full potential to impact learning outcomes. In this work, we describe a personalization system designed for longer-term personalization that orders curriculum based on an adaptive Hidden Markov Model (HMM) that evaluates students’ skill proficiencies. We present a study investigating the effectiveness of this system in a five-session interaction with a robot tutor, taking place over the course of 2 weeks. Our system is evaluated in the context of native Spanish-speaking first-graders interacting with a social robot tutor while completing an English Language Learning educational task. Participants either received lessons: (1) ordered by our adaptive HMM personalization system which selects a lesson based on a skill that the individual participant needs more practice with (“personalized condition”) or (2) ordered randomly from among the lessons the participant had not yet seen (“non-personalized condition”). We found that participants who received personalized lessons from the robot tutor outperformed participants who received non-personalized lessons on a post-test by 2.0 standard deviations on average, corresponding to a mean learning gain in the 98th percentile.

Where would we be without them? Teaching assistants (TAs) make it possible for us to deliver high-quality large-scale computer science courses with relatively few faculty. Though their responsibilities vary by institution, TAs often play a crucial role in student learning. The use of teaching assistants in computer science courses is a common and longstanding practice and, yet, little has been published about how to choose the best TAs among those interested in the job. This paper describes the development of an interview rubric in use by faculty teaching a large introductory computer science course to score applicant responses in a formal in-person 30-minute interview. We describe the motivation behind developing such a rubric, the initial development process, its refinement based on feedback provided by students about their TAs, and the preliminary results of implementing this hiring system.

Two-thirds of all computer science teachers in the United States do not have a degree in computer science. As demand for K-12 computer science education continues to grow, and as industry continues to lure computer science graduates away from careers in education, school administrators are increasingly forced to ask teachers with little formal training in computer science to teach computer science courses. This is such a common phenomenon that there are now many competing summer professional development programs aimed at training teachers of other subjects to teach a first course in computer science. However, once these teachers become comfortable teaching the introductory material, there is little support available for them to gain more content knowledge to be able to support their more advanced students or design additional computer science courses. In this paper, we describe our approach to providing more advanced computer science content knowledge to computer science teachers in a week-long summer professional development program.

In education research, there is a widely-cited result called "Bloom's two sigma" that characterizes the differences in learning outcomes between students who receive one-on-one tutoring and those who receive traditional classroom instruction. Tutored students scored in the 95th percentile, or two sigmas above the mean, on average, compared to students who received traditional classroom instruction. In human-robot interaction research, however, there is relatively little work exploring the potential benefits of personalizing a robot's actions to an individual's strengths and weaknesses. In this study, participants solved grid-based logic puzzles with the help of a personalized or non-personalized robot tutor. Participants' puzzle solving times were compared between two non-personalized control conditions and two personalized conditions (n=80). Although the robot's personalizations were less sophisticated than what a human tutor can do, we still witnessed a "one-sigma" improvement (68th percentile) in post-tests between treatment and control groups. We present these results as evidence that even relatively simple personalizations can yield significant benefits in educational or assistive human-robot interactions.

Does the emotional content of a robot's speech affect how people teach it? In this experiment, participants were asked to demonstrate several "dances" for a robot to learn. Participants moved their bodies in response to instructions displayed on a screen behind the robot. Meanwhile, the robot faced the participant and appeared to emulate the participant's movements. After each demonstration, the robot received an accuracy score and the participant chose whether or not to demonstrate that dance again. Regardless of the participant's input, however, the robot's dancing and the scores it received were arranged in advance and constant across all participants. The only variation between groups in this study was what the robot said in response to its scores. Participants saw one of three conditions: appropriate emotional responses, often-inappropriate emotional responses, or apathetic responses. Participants that taught the robot with appropriate emotional responses demonstrated the dances, on average, significantly more frequently and significantly more accurately than participants in the other two conditions.

We examine affective vocalizations provided by human teachers to robotic learners. In unscripted one-on-one interactions, participants provided vocal input to a robotic dinosaur as the robot selected toy buildings to knock down. We find that (1) people vary their vocal input depending on the learner's performance history, (2) people do not wait until a robotic learner completes an action before they provide input and (3) people naively and spontaneously use intensely affective prosody. Our findings suggest modifications may be needed to traditional machine learning models to better fit observed human tendencies. Our observations of human behavior contradict the popular assumptions made by machine learning algorithms (in particular, reinforcement learning) that the reward function is stationary and path-independent for social learning interactions.