MINT-Robo: Empowering Gifted High School Students with Robotics

Kandlhofer, Martin; Steinbauer, Gerald; Menzinger, Manuel; Halatschek, Richard; Kemény, Ferenc; Landerl, Karin

doi:10.1109/fie43999.2019.9028478

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…This process of pattern extraction is supported by a domain general learning mechanism: statistical learning, which is the ability of identifying structure and patterns based on distributional environmental information (Saffran et al, 1996;Frost et al, 2015). Statistical learning has been associated with a wide variety of cognitive skills and mechanisms, including reading (Arciuli and Simpson, 2012;Schmalz et al, 2019), language development (Weiss et al, 2010;Hsu and Bishop, 2014), language processing (Conway et al, 2010), computational thinking (Kandlhofer et al, 2019), music cognition (Pearce, 2018) and numerical cognition (Levy et al, 2020). The current study focuses on the role of statistical learning in lexical development, and tests how statistical learning and verbal short term memory skills contribute to vocabulary knowledge in typically developing children (Experiment 1) and children with developmental language disorder (Experiment 2).…”

Section: Introductionmentioning

confidence: 99%

The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

Kemény

Lukács

2021

Front. Commun.

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Purpose: Studies on the interface between statistical learning and language are dominated by its role in word segmentation and association with grammar skills, while research on its role in lexical development is scarce. The current study is aimed at exploring whether and how statistical learning and verbal short-term memory are associated with lexical skills in typically developing German-speaker primary school children (Experiment 1) and Hungarian-speaking children with developmental language disorder (DLD, Experiment 2).Methods: We used the language-relevant Peabody Picture Vocabulary Tests to measure individual differences in vocabulary. Statistical learning skills were assessed with the Weather Prediction task, in which participants learn probabilistic cue-outcome associations based on item-based feedback. Verbal short-term memory span was assessed with the Forward digit span task.Results: Hierarchical linear regression modelling was used to test the contribution of different functions to vocabulary size. In TD children, statistical learning skills had an independent contribution to vocabulary size over and above age, receptive grammatical abilities and short-term memory, whereas working memory did not have an independent contribution. The pattern was reverse in SLI: Vocabulary size was predicted by short-term memory skills over and above age, receptive grammar and statistical learning, whereas statistical learning had no independent contribution.Conclusion: Our results suggest that lexical development rely on different underlying memory processes in typical development and in developmental language disorder to different degrees. This qualitative difference is discussed in the light of different stages of lexical development, as well as the contribution of the different human memory systems to vocabulary acquisition.

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Section: Introductionmentioning

confidence: 99%

The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

Kemény

Lukács

2021

Front. Commun.

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“…Further implementations are rolled-out continuously. The entire approach is based on a number of prior projects which focused on teaching fundamental concepts of AI and Robotics to teachers and young people of different age groups, ranging from kindergarten kids to high school and undergraduate students [15,25,40,42,44]. Educational concepts to teach basic Robotics and AI principles to young school students and teachers were developed and tested in [25,40,44], whereas concepts to teach more advanced Robotics and AI principles to older school students were developed and evaluated in [15,42].…”

Section: Introductionmentioning

confidence: 99%

EDLRIS: A European Driving License for Robots and Intelligent Systems

Kandlhofer¹,

Steinbauer

Lassnig

et al. 2021

Künstl Intell

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This article presents a novel educational project aiming at the development and implementation of a professional, standardized, internationally accepted system for training and certifying teachers, school students and young people in Artificial Intelligence (AI) and Robotics. In recent years, AI and Robotics have become major topics with a huge impact not only on our everyday life but also on the working environment. Hence, sound knowledge about principles and concepts of AI and Robotics are key skills for this century. Nonetheless, hardly any systematic approaches exist that focus on teaching principles of intelligent systems at K-12 level, addressing students as well as teachers who act as multipliers. In order to meet this challenge, the European Driving License for Robots and Intelligent Systems—EDLRIS was developed. It is based on a number of previously implemented and evaluated projects and comprises teaching curricula and training modules for AI and Robotics, following a competency-based, blended learning approach. Additionally, a certification system proves peoples’ acquired competencies. After developing the training and certification system, the first 32 trainer and trainee courses with a total of 445 participants have been implemented and evaluated. By applying this innovative approach—a standardized and widely recognized training and certification system for AI and Robotics at K-12 level for both high school teachers and students—we envision to foster AI/Robotics literacy on a broad basis.

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“…More importantly, ROS adopts a framework configuration that is widely accepted by robot development communities [10] [11] [12]. Existing ROS-based robotics curriculum shows positive feedbacks from their learners [13] [14] [15].…”

Section: Introductionmentioning

confidence: 99%

A project-based platform for students’ Robot Operation System (ROS) programming experience

Chang,

Wang,

Zhang

2022 ASEE Annual Conference &Amp; Exposition Proceedings

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Robot Operating System (ROS) is an open-source software framework for robot automation. ROS includes a protocol of robot commanding and integrates with a collection of software libraries. ROS aims to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms. Currently, ROS is the major robotics software framework for researchers in graduate schools.One of the obstacles in exposing ROS to students is lacking appropriate projects for starters. ROS is not learner-friendly for its steep learning curve and comprehensive programming background needed. In this article, a robotics platform is designed by the author's institution, based on an iRobot Create robot, is introduced. The robotics platform that includes a mobile base, a webcam, and a Lidar, can fully support ROS-based programming for autonomous navigation. To help students start ROS programming practicing, a ROS-based student autonomous navigation project, including components of path-planning, image recognition, and simultaneous localization and mapping (SLAM) is implemented on this robotics platform. A pilot study shows that the platform is favored by students and inspiring for students to learn ROS. However, the curriculum needs improvements to ensure the students' success in completing the tasks.

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MINT-Robo: Empowering Gifted High School Students with Robotics

Cited by 3 publications

References 32 publications

The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

EDLRIS: A European Driving License for Robots and Intelligent Systems

A project-based platform for students’ Robot Operation System (ROS) programming experience

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