Family Size Decreases Conversation Orientation and Increases Conformity Orientation

The family is a critical context for the development and maintenance of communication patterns and relationships. Family’s communication patterns are derived from two orientations: conversation and conformity. Family members also use relational maintenance strategies to sustain their relationships. Previous research has established the association between communication orientations and relational maintenance strategies, but has not explored how family size (i.e., number of siblings) may impact these variables. This study reports on results from an online survey of N = 784 participants. Our results indicate that number of siblings negatively predicted conversation orientation, but positively predicted conformity orientation. In addition, conversation orientation positively predicted the use of all relational maintenance strategies; conformity orientation positively predicted all the relational maintenance strategies except positivity and conflict resolution. These results demonstrate that family size impacts family communication orientations and suggest that future research on family communication should measure family size alongside other demographic variables that impact family dynamics

Robotic Table Tennis: A Case Study into a High Speed Learning System

We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0.Comment: Published and presented at Robotics: Science and Systems (RSS2023

An Analysis of Student Learning Gains After Interacting with AutoTutor

Abstract: The pedagogical effectiveness of two versions of AutoTutor was assessed in two student learning outcome studies. Sixty students enrolled in a computer literacy course received tutoring or reread material on two of the following topics: Hardware, Operating Systems, and the Internet. All participants then received a comprehension test on all three topics. A within-subjects design enabled the following conditions to be compared: AutoTutor versus a reread condition versus a control condition. Results from these two studies suggest that AutoTutor was an effective pedagogical tool. Both versions of AutoTutor provided an effect size increment of approximately.5 standard deviations units when compared to the reread and control condition. A Description of AutoTutor AutoTutor is an animated pedagogical agent that engages in a conversation with the learner while simulating the dialog moves of human tutors. AutoTutor is currently designed to help college students learn about topics that are typically covered in an introductory computer literacy course (e.g., hardware, operating systems, the Internet). AutoTutor’s architecture is comprise