Episode 110: Leveraging real-world learning for students and companies

2021 ◽  
Author(s):  
Andrew Geary ◽  
Mohamed Ahmed
Keyword(s):  
Real World ◽  
Real World Learning

scholarly journals How to train your robot with deep reinforcement learning: lessons we have learned

2021 ◽  
pp. 027836492098785
Author(s):  
Julian Ibarz ◽  
Jie Tan ◽  
Chelsea Finn ◽  
Mrinal Kalakrishnan ◽  
Peter Pastor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Case Studies ◽  
Real World ◽  
Review Article ◽  
The Real ◽  
Complex Skills ◽  
Real World Learning ◽  
Level Sensor ◽  
Embodied Agent

Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulated control, which does not connect with the constraints of learning in real environments, deep RL has also demonstrated promise in enabling physical robots to learn complex skills in the real world. At the same time, real-world robotics provides an appealing domain for evaluating such algorithms, as it connects directly to how humans learn: as an embodied agent in the real world. Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains. In this review article, we present a number of case studies involving robotic deep RL. Building off of these case studies, we discuss commonly perceived challenges in deep RL and how they have been addressed in these works. We also provide an overview of other outstanding challenges, many of which are unique to the real-world robotics setting and are not often the focus of mainstream RL research. Our goal is to provide a resource both for roboticists and machine learning researchers who are interested in furthering the progress of deep RL in the real world.

scholarly journals The Validation of Digital Analysis Tool-Assisted Real-World Inquiry (Digita-RI) as a Modification of the Inquiry-Based Learning Model in the Digital Age

2021 ◽  
Vol 10 (3) ◽  
pp. 387-399
Author(s):  
S. Nurohman ◽  
W. Sunarno ◽  
S. Sarwanto ◽  
S. Yamtinah
Keyword(s):  
Real World ◽  
Group Discussion ◽  
Scientific Work ◽  
Learning Model ◽  
Analysis Tool ◽  
Digital Analysis ◽  
Inquiry Based Learning ◽  
Real World Learning ◽  
Feasibility Test ◽  
Scientific Facts

Inquiry-based learning has been tested to improve conceptual understanding, reduce misconceptions, and provide students with experiences in scientific work. However, in its implementation, inquiry-based learning is often faced with scientific facts from the real world with data which hard to analyze using traditional methods. Therefore, a breakthrough is needed to overcome the weaknesses of inquiry-based learning by integrating digital analysis tools and the concept of real-world learning. This integration produces a new learning model, the Digital Analysis Tool-Assisted Real-World Inquiry (Digita-RI). This study aims to test the feasibility and practicality of the Digita-RI learning model. This Research and Development (R&D) use the steps proposed by Barg and Gall. The feasibility test of the Digita-RI model was carried out through the Focus Group Discussion (FGD) method and the assessment of the Digita-RI model book involving seven experts. The practicality test was carried out through the Think Aloud Protocol (TAP), and the assessment of the Digita-RI model guidebook involved five practitioner lecturers and six students. The results of expert, practitioner, and user assessments were analyzed using the Aiken coefficient (Aiken’s V). The results showed that Digita-RI is a feasible and practical learning model. Therefore, it can be concluded that Digita-RI has the feasibility and practicality to be used in science learning in the classroom.

scholarly journals Undergraduates Writing Publishable Cases: The Trials, The Tribulations, And The Real-World Learning

2011 ◽  
Vol 4 (12) ◽  
Author(s):  
Jason Smith ◽  
Josh Edwards ◽  
Patricia C. Kelley
Keyword(s):  
Case Studies ◽  
Real World ◽  
The Real ◽  
Real World Learning ◽  
Case Writing

If given the chance, undergraduates have the ability to write excellent case studies worthy of being published.  This essay describes the benefits, challenges, and process of undergraduate case writing. 

scholarly journals Brain-to-Brain Synchrony and Learning Outcomes Vary by Student–Teacher Dynamics: Evidence from a Real-world Classroom Electroencephalography Study

2019 ◽  
Vol 31 (3) ◽  
pp. 401-411 ◽  
Author(s):  
Dana Bevilacqua ◽  
Ido Davidesco ◽  
Lu Wan ◽  
Kim Chaloner ◽  
Jess Rowland ◽  
...  
Keyword(s):  
High School Students ◽  
Student Teacher ◽  
Real World ◽  
Cognitive Outcomes ◽  
School Students ◽  
Group Settings ◽  
Social Closeness ◽  
Neuroscience Research ◽  
Real World Learning ◽  
Neurophysiological Data

How does the human brain support real-world learning? We used wireless electroencephalography to collect neurophysiological data from a group of 12 senior high school students and their teacher during regular biology lessons. Six scheduled classes over the course of the semester were organized such that class materials were presented using different teaching styles (videos and lectures), and students completed a multiple-choice quiz after each class to measure their retention of that lesson's content. Both students' brain-to-brain synchrony and their content retention were higher for videos than lectures across the six classes. Brain-to-brain synchrony between the teacher and students varied as a function of student engagement as well as teacher likeability: Students who reported greater social closeness to the teacher showed higher brain-to-brain synchrony with the teacher, but this was only the case for lectures—that is, when the teacher is an integral part of the content presentation. Furthermore, students' retention of the class content correlated with student–teacher closeness, but not with brain-to-brain synchrony. These findings expand on existing social neuroscience research by showing that social factors such as perceived closeness are reflected in brain-to-brain synchrony in real-world group settings and can predict cognitive outcomes such as students' academic performance.

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