Development of assessment indicators for measuring the student learning effects of artificial intelligence‐based robot design

2019 ◽  
Vol 27 (4) ◽  
pp. 863-868 ◽  
Author(s):  
Wen‐Jye Shyr ◽  
Fu‐Chun Yang ◽  
Po‐Wen Liu ◽  
Ying‐Ming Hsieh ◽  
Ci‐Syong You ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Student Learning ◽  
Robot Design ◽  
Learning Effects

Educational Responsibility in the Deepfake Era

2021 ◽  
pp. 1-23
Author(s):  
Rebecca J. Blankenship
Keyword(s):  
Artificial Intelligence ◽  
Instructional Design ◽  
Student Learning ◽  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Web Based ◽  
Student Work ◽  
Integrated Classrooms ◽  
The Right ◽  
Design Challenges

Choosing the right technologies to match student learning outcomes in today's technology-integrated classrooms presents educators with multiple instructional design challenges including selecting appropriate technologies to match desired student learning outcomes. As students continue to have broad access to information from a variety of web-based platforms, teachers are increasingly tasked with ensuring the information used to complete key assignments is authentic and from a verifiable resource. As such, the era of deep fakes in images, audio, videos, and digital texts is more prevalent than ever as numerous programs using artificial intelligence (AI) can significantly alter original content to fundamentally change the intent of original content. A discussion of educational and pedagogic responsibility in the era of deep fakes will serve as the primer for reform of the TPACK construct with recommendations for remediating student work in which deep fake resources were utilized.

scholarly journals Assessing Student Social Studies Learning: Effects of Learning Environment, Inquiry, and Student Learning Interest

2019 ◽  
Vol 6 (1) ◽  
pp. 46-57
Author(s):  
Okta Rosfiani ◽  
Ma’ruf Akbar ◽  
Amos Neolaka
Keyword(s):  
Data Collection ◽  
Social Studies ◽  
Learning Environment ◽  
Student Learning ◽  
Primary Schools ◽  
Learning Assessment ◽  
Learning Effects ◽  
Muslim Society ◽  
Learning Score ◽  
Learning Interest

AbstractThis study aims to examine the effect of the learning environment, inquiry, and learning interest on student social studies learning assessment. The participants involved in this study are 130 students from public primary schools in South Jakarta. Data collection consists of social studies learning score, learning environment scale, inquiry scale, and learning interest scale. The results of the study show that the learning environment, inquiry, and learning interest directly influenced student social studies learning assessment in which inquiry and learning interest have a significant effect on student social studies learning assessment.AbstrakPenelitian ini bertujuan untuk menguji pengaruh lingkungan belajar, inkuiri, dan minat belajar terhadap penilaian belajar Ilmu Pendidikan Sosial (IPS) siswa. Peserta yang terlibat adalah 130 siswa dari sekolah dasar negeri di Jakarta Selatan. Pengumpulan data terdiri dari skor pembelajaran IPS, skala lingkungan belajar, skala inkuiri, dan skala minat belajar. Hasil penelitian menunjukkan bahwa lingkungan belajar, inkuiri, dan minat belajar secara langsung mempengaruhi penilaian belajar IPS siswa. Dimana inkuiri dan minat belajar memiliki pengaruh yang signifikan terhadap penilaian pembelajaran IPS.How to Cite : Rosfiani, O., Akbar, M., Neolaka, A. (2019).  Assessing Student Social Studies Learning: Effects of Learning Environment, Inquiry, and Student Learning Interest. TARBIYA: Journal of Education in Muslim Society, 6(1), 46-57. doi:10.15408/tjems.v6i1.11593.

scholarly journals Hearing Artificial Intelligence: Sonification Guidelines & Results From a Case-study in Melanoma Diagnosis

2019 ◽  
Author(s):  
R. Michael Winters ◽  
Ankur Kalra ◽  
Bruce N. Walker
Keyword(s):  
Artificial Intelligence ◽  
Data Driven ◽  
Learning Effects ◽  
General Technique ◽  
Melanoma Diagnosis ◽  
The Mean ◽  
Sensitivity Specificity ◽  
Improved Accuracy ◽  
Full Classification

The applications of artificial intelligence are becoming more and more prevalent in everyday life. Although many AI systems can operate autonomously, their goal is often assisting humans. Knowledge from the AI system must somehow be perceptualized. Towards this goal, we present a case-study in the application of data-driven non-speech audio for melanoma diagnosis. A physician photographs a suspicious skin lesion, triggering a sonification of the system’s penultimate classification layer. We iterated on sonification strategies and coalesced around designs representing three general approaches. We tested each in a group of novice listeners (n=7) for mean sensitivity, specificity, and learning effects. The mean accuracy was greatest for a simple model, but a trained dermatologist preferred a perceptually compressed model of the full classification layer. We discovered that training the AI on sonifications from this model improved accuracy further. We argue for perceptual compression as a general technique and for a comprehensible number of simultaneous streams.

scholarly journals How can we make junior business students understand the importance of learning organizational behaviour and management?

Seminar.net ◽  
2011 ◽  
Vol 7 (2) ◽  
Author(s):  
Cecile Asting ◽  
Anne B. Swanberg
Keyword(s):  
Student Learning ◽  
Teaching And Learning ◽  
Business Students ◽  
Learning Activities ◽  
Learning Effects ◽  
Large Classes ◽  
Organizational Behaviour ◽  
Case Comparison ◽  
Further Development ◽  
Enhance Student Learning

Teaching behavioural subjects to business students is a challenge, increasingly so with growing class sizes. In this paper we focus on these special challenges, particularly drawing attention to how feedback can enhance student learning and understanding. One-to-one feedback is not possible in large classes, but students can receive feedback on their progress through well-planned teaching and learning activities. We implemented a range of different feedback activities in our course to support student learning. Measuring learning effects is difficult and, in this case, comparison of grades was not possible. Our experience, however, led to a somewhat better understanding of what can be done and what needs further development to provide valuable feedback for students in their learning process.

scholarly journals Model AI Assignments 2019

2019 ◽  
Vol 33 ◽  
pp. 9751-9753 ◽  
Author(s):  
Todd W. Neller ◽  
Raja Sooriamurthi ◽  
Michael Guerzhoy ◽  
Lisa Zhang ◽  
Paul Talaga ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Student Learning ◽  
Learning Experience ◽  
The Core ◽  
Education Community

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.

scholarly journals Electromagnetic Weaponed Anti Terrorism Robot

2021 ◽  
pp. 24-30
Author(s):  
Mert Demir
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Target Tracking ◽  
Control Algorithm ◽  
Robot Design ◽  
Literature Study ◽  
Range Restriction ◽  
Dynamic Constraints ◽  
Artificial Intelligence Techniques ◽  
New Literature

This article proposes the integration of electromagnetic weapons on a robot, design and construction of an electromagnetic armed robot, autonomous targeting of possible targets with the electromagnetic weapon, and the features a electromagnetic armed safety robot should have. Unlike traditional user-targeted field security robot approaches, the robot mentioned in this study detects potential threats in the task area with image processing and artificial intelligence techniques, so the user can accurately identify and autonomously target targets without the need for controlled targeting. Unlike today’s armed robots, an electromagnetic armed robot, which will be a new literature study, has been developed to create a reference path. An electromagnetic weapon that can be carried by a robot is produced and integrated into the robot and a new armed robot approach with electromagnetic weapon is introduced. Various methods are proposed considering the range restriction of electromagnetic weapons and possible targeting errors of the robot user. A control algorithm has been developed to have the most appropriate targeting under the dynamic constraints of the robot and user for target tracking. Prototyping and experiments show the ability of an autonomous security robot with an autonomous targeting system to troubleshoot user problems and targeting problems. In addition, various methods and recommendations are provided for the features that a electromagnetic armed security robot working in the field should have.

scholarly journals Model AI Assignments 2020

2020 ◽  
Vol 34 (09) ◽  
pp. 13509-13511
Author(s):  
Todd W. Neller ◽  
Stephen Keeley ◽  
Michael Guerzhoy ◽  
Wolfgang Hoenig ◽  
Jiaoyang Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Student Learning ◽  
Learning Experience ◽  
The Core ◽  
Education Community

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of nine AI assignments from the 2020 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

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