Advancing Human Services Education: Exploratory Study of International Service Learning and Digital Pedagogy

This study focuses on advancing the understanding of human services education in international service learning focused study abroad programs. There is a gap in the literature pertaining to service-learning education for human services students. This study explores the integration of service-learning, reflection, and technology in human services education. Case study methodology and document review analysis are used to examine the use of ePortfolio as a reflective tool in addition to the skill development of upper-level undergraduate human services students through service learning. Seven themes arose from the findings, which suggest that international service-learning assists students in human services skill development and ePortfolio are effective as a reflective tool in human services education

Dynamic reconfiguration of human brain networks during learning

Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes -- flexibility and selection -- must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we explore the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4 figures, 3 table

Fostering Human Learning in Sequential Decision-Making: Understanding the Role of Evaluative Feedback

Cognitive rehabilitation, STEM skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. We also explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes

Incremental Learning for Robot Perception through HRI

Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. However, there is still a gap between their performance on image datasets and real-world robotics scenarios. We present a novel paradigm for incrementally improving a robot's visual perception through active human interaction. In this paradigm, the user introduces novel objects to the robot by means of pointing and voice commands. Given this information, the robot visually explores the object and adds images from it to re-train the perception module. Our base perception module is based on recent development in object detection and recognition using deep learning. Our method leverages state of the art CNNs from off-line batch learning, human guidance, robot exploration and incremental on-line learning

Investigation of sequence processing: A cognitive and computational neuroscience perspective

Serial order processing or sequence processing underlies many human activities such as speech, language, skill learning, planning, problem-solving, etc. Investigating the neural bases of sequence processing enables us to understand serial order in cognition and also helps in building intelligent devices. In this article, we review various cognitive issues related to sequence processing with examples. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, a theoretical approach based on statistical models and reinforcement learning paradigm is presented. These theoretical ideas are useful for studying sequence learning in a principled way. This article also suggests a two-way process diagram integrating experimentation (cognitive neuroscience) and theory/ computational modelling (computational neuroscience). This integrated framework is useful not only in the present study of serial order, but also for understanding many cognitive processes

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table

Kurikulum Humanistik Dan Pendidikan Karakter: Sebuah Gagasan Pengembangan Kurikulum Masa Depan

Humanistic curriculum gives rise to the coverage of need of self-actualization of the students so as to develop and to build integrity. The social reconstructionists suggests that social needs are available in any individual interest. The obligation of a curriculum is to generate the better future through a social reformation. Social values and their application should be accommodated in critical thinking process framed by a curriculum; in the way that curriculum has become the best vehicle for the students to develop their skill in grasping with the learning materials, understanding the personal and local problems. Personality, social values, thinking skill, problem solving skill can be all developed altogether through a curriculum – which in return can bring about the better future of human nature

Perspectives on skill: a study with a group of state training providers, manufacturing managers, and production workers in Oklahoma

There is a recognised and recurring projected skill shortage in Oklahoma’s manufacturing industries. This research investigated how skill is perceived by a sample of people from three key groups in the manufacturing industry: state training providers, manufacturing managers, and production workers. The study adopted a qualitative approach, utilising focus groups and interviews with members of the key groups to explore their understandings of skill and how they assign responsibility for skill development in the current and future contexts of the manufacturing industry in Oklahoma. The study also explored how human capital theory, the concept of lifelong learning and a skill ecosystem approach provide different frameworks for understanding skill in manufacturing, with particular reference to Oklahoma. The findings suggest that opportunities for learning and understanding skill and skill development are crucial for individuals and industry, from the perspectives of the participants. Managers can have a key role in nurturing workers to develop a desire for the development of skills. In addition, employability skills, focused on showing initiative to learn and grounded in the application of theoretical knowledge in manufacturing contexts, are imperative for students who want to enter manufacturing. The findings indicate that the best avenue that was perceived by these participants for developing employability skills is work-based learning that allows for the application of theory to develop skill. In terms of who has ‘responsibility for skill’, the notion of ‘opportunity’ emerged as key for skill development. The participants thought that manufacturers have opportunities to initiate skill development in partnerships with educational institutions and state workforce agencies but that management needed also to communicate how workers can acquire skill development and ‘seize’ those opportunities to learn the skills that are necessary. Analysis of the participants’ understandings of how Industry 4.0 (including automation and smart technology) will impact manufacturing’s future skills revealed that the managers recognised that they had to think strategically about skill. Despite this, they and some production line workers focused on it being the individual worker’s responsibility for ‘seizing’ opportunities to learn a new skill although this approach had enjoyed limited success. There were, however, participants in each of the study’s three groups who indicated interest in pursuing a dialogue between higher educational institutions and industry, and expressed support for better strategic thinking and funding options. The findings from the study suggest that for industry in Oklahoma to better understand how to create opportunities for skill development and to better make opportunity for skill development a reality, it needs to form a strategic partnership with career and technology education and higher education. In addition, to increase successful adoption of skill development, managers need to dialogue with educators in order to have input into skill development, both in the design and the delivery process. This changed focus, the study concludes, requires a move away from human capital and individualised lifelong learning approaches to a skills ecosystem approach if industry in Oklahoma is to provide better access to skill development. Additionally, managers in partnership with state training providers need to provide clear skill and career progression for students and the current workforce that will align with the skills needed to adapt to Industry 4.0 and its associated technologies. It was concluded that managers, in partnership with state training providers, need to provide clear skill and career progression for students and the current workforce that will align with the skills needed to adapt to Industry 4.0 and its associated technologies