scholarly journals The Data Sensor Hub (DaSH): A Physical Computing System to Support Middle School Inquiry Science Instruction

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6243
Author(s):  
Alexandra Gendreau Chakarov ◽  
Quentin Biddy ◽  
Colin Hennessy Elliott ◽  
Mimi Recker
Keyword(s):  
Data Streams ◽  
Teacher Practices ◽  
Computing System ◽  
Sensor Data ◽  
Programming Environment ◽  
Physical Computing ◽  
Science Practices ◽  
Instructional Units ◽  
Scientific Phenomena ◽  
Science Classes

This article describes a sensor-based physical computing system, called the Data Sensor Hub (DaSH), which enables students to process, analyze, and display data streams collected using a variety of sensors. The system is built around the portable and affordable BBC micro:bit microcontroller (expanded with the gator:bit), which students program using a visual, cloud-based programming environment intended for novices. Students connect a variety of sensors (measuring temperature, humidity, carbon dioxide, sound, acceleration, magnetism, etc.) and write programs to analyze and visualize the collected sensor data streams. The article also describes two instructional units intended for middle grade science classes that use this sensor-based system. These inquiry-oriented units engage students in designing the system to collect data from the world around them to investigate scientific phenomena of interest. The units are designed to help students develop the ability to meaningfully integrate computing as they engage in place-based learning activities while using tools that more closely approximate the practices of contemporary scientists as well as other STEM workers. Finally, the article articulates how the DaSH and units have elicited different kinds of teacher practices using student drawn modeling activities, facilitating debugging practices, and developing place-based science practices.

scholarly journals Developing an On-Demand Cloud-Based Sensing-as-a-Service System for Internet of Things

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Mihui Kim ◽  
Mihir Asthana ◽  
Siddhartha Bhargava ◽  
Kartik Krishnan Iyyer ◽  
Rohan Tangadpalliwar ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Internet Of Things ◽  
Service System ◽  
Value Added ◽  
Computing System ◽  
Sensor Data ◽  
Dynamic Provisioning ◽  
On Demand ◽  
Cloud Computing System ◽  
Iot Devices

The increasing number of Internet of Things (IoT) devices with various sensors has resulted in a focus on Cloud-based sensing-as-a-service (CSaaS) as a new value-added service, for example, providing temperature-sensing data via a cloud computing system. However, the industry encounters various challenges in the dynamic provisioning of on-demand CSaaS on diverse sensor networks. We require a system that will provide users with standardized access to various sensor networks and a level of abstraction that hides the underlying complexity. In this study, we aim to develop a cloud-based solution to address the challenges mentioned earlier. Our solution, SenseCloud, includes asensor virtualizationmechanism that interfaces with diverse sensor networks, amultitenancymechanism that grants multiple users access to virtualized sensor networks while sharing the same underlying infrastructure, and adynamic provisioningmechanism to allow the users to leverage the vast pool of resources on demand and on a pay-per-use basis. We implement a prototype of SenseCloud by using real sensors and verify the feasibility of our system and its performance. SenseCloud bridges the gap between sensor providers and sensor data consumers who wish to utilize sensor data.

scholarly journals Testing Creativity and Personality to Explore Creative Potentials in the Science Classroom

2021 ◽  
Author(s):  
Tamara Roth ◽  
Cathérine Conradty ◽  
Franz X. Bogner
Keyword(s):  
Science Classroom ◽  
Cognitive Learning ◽  
Personal Characteristics ◽  
Male Students ◽  
School Students ◽  
Fostering Creativity ◽  
Extreme Groups ◽  
Scientific Phenomena ◽  
Science Classes ◽  
Educational Modules

AbstractIntegrating creativity into science classes may pave the way to tapping complex scientific phenomena. Although not yet conclusively defined nor assessed using standardized measures, creativity is understood to support cognitive learning in formal and informal settings. However, the successful integration of creativity in educational modules depends on many factors. As our knowledge of how to identify these factors is still limited, teachers may have difficulties effectively monitoring and fostering creativity. Consequently, a valid means to measure creativity would help teachers to identify creativity and its influencing factors within the limited scope of science lessons. In the present study, we collected data from 538 Bavarian secondary school students (M ± SD = 16.96 ± 2.99; 65.4%, female) focussing on personality and creativity measures. Comparable to previous studies, two subscales for creativity were applied: act, comprising conscious and adaptable cognitive processes, and flow, describing a creative mental state of full immersion. Since personality is understood to be linked to creativity, we used the Big Five scale with its shortened item battery to assess personality. We found that personal characteristics such as conscientiousness and flow, openness and agreeableness, and extraversion and neuroticism were significantly correlated. Anticipated gender and age differences were only evident when extreme groups were compared: age influenced act in younger male students and flow in older female students. Drawing on the literature and our results, we suggest pedagogical approaches to provide opportunities for creativity in science classrooms.

scholarly journals Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881130 ◽  
Author(s):  
Jaanus Kaugerand ◽  
Johannes Ehala ◽  
Leo Mõtus ◽  
Jürgo-Sören Preden
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Fusion ◽  
Data Streams ◽  
Ad Hoc ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Temporal Consistency ◽  
Variable Delays

This article introduces a time-selective strategy for enhancing temporal consistency of input data for multi-sensor data fusion for in-network data processing in ad hoc wireless sensor networks. Detecting and handling complex time-variable (real-time) situations require methodical consideration of temporal aspects, especially in ad hoc wireless sensor network with distributed asynchronous and autonomous nodes. For example, assigning processing intervals of network nodes, defining validity and simultaneity requirements for data items, determining the size of memory required for buffering the data streams produced by ad hoc nodes and other relevant aspects. The data streams produced periodically and sometimes intermittently by sensor nodes arrive to the fusion nodes with variable delays, which results in sporadic temporal order of inputs. Using data from individual nodes in the order of arrival (i.e. freshest data first) does not, in all cases, yield the optimal results in terms of data temporal consistency and fusion accuracy. We propose time-selective data fusion strategy, which combines temporal alignment, temporal constraints and a method for computing delay of sensor readings, to allow fusion node to select the temporally compatible data from received streams. A real-world experiment (moving vehicles in urban environment) for validation of the strategy demonstrates significant improvement of the accuracy of fusion results.

Sensor Data Streams

2014 ◽  
pp. 291-321 ◽  
Author(s):  
Stephen Voida ◽  
Donald J. Patterson ◽  
Shwetak N. Patel
Keyword(s):  
Data Streams ◽  
Sensor Data
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