The Practices of Play and Informal Learning in the miniGEMS STEAM Camp

Science, Technology, Engineering, and Mathematics (STEM) play an important role in the educational reform and global economy. However, STEM education lacks the hands-on laboratory in the formal middle school and high school curricula. The widespread gender gap in multiple STEM disciplines causes middle-school aged girls have lower positive attitudes and interests towards STEM fields than male students. In recent years, Science, Technology, Engineering, Arts, and Mathematics (STEAM) education has been viewed as other approaches to increase students’ interests and improve study accesses to STEM fields in the United States. The addition of the arts in STAEM education provides more learning opportunities and real-world contexts which meet more students’ interests. miniGEMS 2017 was a free two-week summer STEAM and programming camp for middle school girls in grades six to eight hosted by the Autonomous Vehicle Systems (AVS) Research and Education Laboratory at the University of the Incarnate Word (UIW). miniGEMS was the first free camp with a special focus on engineering and programming in San Antonio. The camp utilized project-based learning curriculum and provided multiple hands-on experiments, field trips, and significant interactions with guest speakers, all of which were designed to increase the middle school girls’ interests in STEM-related fields. This paper provides an overview of miniGEMS STEAM camp, motivation for miniGEMS camp, and details on practicing project-based play activities in an informal learning environment.Cockrell School of Engineerin

Organization of STEAM lessons in the innovative classroom

У статті окреслено проблему підготовки майбутніх вчителів до впровадження STEAM-освіти за допомогою інноваційних моделей навчання та нових підходів до організації освітнього процесу. Автори здійснили аналіз особливостей використання інноваційного класу для організації STEAM-навчання майбутніх учителів початкової школи на основі застосування інноваційних педагогічних технологій та інноваційних форм організації навчальної діяльності студентів, що базуються на ротаційній моделі. Зокрема, визначено об’єкти екосистеми STEAM-навчання, місце та роль навчальних середовищ на прикладі інноваційного класу (ICR) в них. В статті представлено результат дослідження визначених студентами Київського університету імені Бориса Грінченка освітніх трендів, інноваційних педагогічних технологій та методів для використання в інноваційному класі, який буде задовольняти потреби учасників освітнього процесу. Описано характеристики інноваційного класу, його структура та організація залежно від моделі ротації та кількості ротаційних станцій, їх переваги та недоліки. Подано опис інноваційного класу в Університеті Грінченка, який спроектовано як інноваційний освітній центр для підготовки майбутніх учителів початкової школи, навчання вчителів інноваційним технологіям, зокрема з метою реалізації завдань STEAM-освіти. Наведено сценарій тренінгу проведення STEAM-заняття з використанням вказаних інноваційних технологій та моделей. Учасники тренінгу виконували навчальний проєкт, застосовуючи дослідницько-пізнавальний метод, кожен етап якого здійснювався на окремій ротаційній станції інноваційного класу. Дослідження, результати якого викладені в статті, проведено в рамках проекту «Модернізація педагогічної вищої освіти з використання інноваційних інструментів викладання» (MoPED) програми ЄС Еразмус + КА2 – Розвиток потенціалу вищої освіти, № 586098-EPP-1-2017-1-UA-EPPKA2-CBHE-JP.The article outlines the problem of future teachers preparation for implementation of STEAM education with the help of innovative learning models and new approaches to organization of educational process. The authors carried out the analysis of innovative classroom utilization peculiarities for organization of STEAM training of future teachers on the basis of innovative pedagogical technologies utilization and innovative forms of students’ learning activities organization that are based on the rotation model. In particular, objects of STEAM learning ecosystem were defined as well as the place and the role of learning environments there exemplified by innovative classroom (ICR). The article represents the results of the research on educational trends, innovative pedagogical technologies and methods of innovative classroom organization which will satisfy the needs of the learning process participants. These results are based on the survey where 198 students of Borys Grinchenko Kyiv University took part. Characteristics of innovative classroom, approaches to organization of ICR depending on the number of rotation stations, rotation models, their advantages and disadvantages are described. There is a description of innovative classroom in Borys Grinchenko Kyiv University which is designed as an educational centre for preparation of future primary school teachers, training teachers in the sphere of innovative technologies, in particular, to implement tasks of STEAM education. The practical part of the research is in description of STEAM lesson organization in the innovative classroom using the example of the training carried out using innovative classroom equipment, mentioned innovative technologies and models. The participants of the training had to perform a learning project using inquiry-based method where each stage of the project had to be fulfilled on a definite rotation station of the innovative classroom. The research leading to these results received, within the framework of the Modernization of Pedagogical Higher Education by Innovative Teaching Instruments. MoPED – KA2 CBHE – 586098-EPP-1-2017-1-UA-EPPKA2-CBHE-JP

Toward future 'mixed reality' learning spaces for STEAM education

Digital technology is becoming more integrated and part of modern society. As this begins to happen, technologies including augmented reality, virtual reality, 3d printing and user supplied mobile devices (collectively referred to as mixed reality) are often being touted as likely to become more a part of the classroom and learning environment. In the discipline areas of STEAM education, experts are expected to be at the forefront of technology and how it might fit into their classroom. This is especially important because increasingly, educators are finding themselves surrounded by new learners that expect to be engaged with participatory, interactive, sensory-rich, experimental activities with greater opportunities for student input and creativity. This paper will explore learner and academic perspectives on mixed reality case studies in 3d spatial design (multimedia and architecture), paramedic science and information technology, through the use of existing data as well as additional one-on-one interviews around the use of mixed reality in the classroom. Results show that mixed reality can provide engagement, critical thinking and problem solving benefits for students in line with this new generation of learners, but also demonstrates that more work needs to be done to refine mixed reality solutions for the classroom

Artificial intelligence in steam cracking modeling : a deep learning algorithm for detailed effluent prediction

Chemical processes can benefit tremendously from fast and accurate effluent composition prediction for plant design, control, and optimization. The Industry 4.0 revolution claims that by introducing machine learning into these fields, substantial economic and environmental gains can be achieved. The bottleneck for high-frequency optimization and process control is often the time necessary to perform the required detailed analyses of, for example, feed and product. To resolve these issues, a framework of four deep learning artificial neural networks (DL ANNs) has been developed for the largest chemicals production process-steam cracking. The proposed methodology allows both a detailed characterization of a naphtha feedstock and a detailed composition of the steam cracker effluent to be determined, based on a limited number of commercial naphtha indices and rapidly accessible process characteristics. The detailed characterization of a naphtha is predicted from three points on the boiling curve and paraffins, iso-paraffins, olefins, naphthenes, and aronatics (PIONA) characterization. If unavailable, the boiling points are also estimated. Even with estimated boiling points, the developed DL ANN outperforms several established methods such as maximization of Shannon entropy and traditional ANNs. For feedstock reconstruction, a mean absolute error (MAE) of 0.3 wt% is achieved on the test set, while the MAE of the effluent prediction is 0.1 wt%. When combining all networks-using the output of the previous as input to the next-the effluent MAE increases to 0.19 wt%. In addition to the high accuracy of the networks, a major benefit is the negligible computational cost required to obtain the predictions. On a standard Intel i7 processor, predictions are made in the order of milliseconds. Commercial software such as COILSIM1D performs slightly better in terms of accuracy, but the required central processing unit time per reaction is in the order of seconds. This tremendous speed-up and minimal accuracy loss make the presented framework highly suitable for the continuous monitoring of difficult-to-access process parameters and for the envisioned, high-frequency real-time optimization (RTO) strategy or process control. Nevertheless, the lack of a fundamental basis implies that fundamental understanding is almost completely lost, which is not always well-accepted by the engineering community. In addition, the performance of the developed networks drops significantly for naphthas that are highly dissimilar to those in the training set. (C) 2019 THE AUTHORS. Published by Elsevier LTD on behalf of Chinese Academy of Engineering and Higher Education Press Limited Company

Technology Transfer Versus Transformation

Research defines technology transfer from the viewpoint of business processes and personnel skills (Rogers, Takegami & Yin, 2001). The focus is on action to adapt and embrace an existing technology to gain efficiency (Gilsing et al., 2011). We examine this phenomenon as innovation based on the ability to transfer existing needs, desires, behaviors, and expectations to new technology. We find technology is adopted when transfer opportunities become manifest and each transfer builds upon its predecessor to create transformation in the long term. This relationship between transfer and transformation gradually builds technology adoption across chasms of the S-curve technology innovation curve

Diocese of Providence Partners with SCS to Integrate STEAM in Catholic Schools

Professional Education Center program will help Catholic school teachers tie arts into instruction on STEM subjects