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Integrating Sensor Technologies in Educational Settings

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 13455

Special Issue Editors

Department of Instructional Technology and Learning Sciences, Utah State University, 2830 Old Main Hill, Logan, UT 84322, USA
Interests: educational data mining; teacher professional development; digital libraries; online education; computational thinking; computer science education
Department of Instructional Technology and Learning Sciences, Utah State University, 2830 Old Main Hill, Logan, UT 84322, USA
Interests: materiality of science learning communities; place-based computational thinking; epistemic agency; informal STEM learning; sociopolitical perspectives and educational equity
Institute of Cognitive Science, University of Colorado Boulder, 1777 Exposition Dr, Boulder, CO 80301, USA
Interests: effective science learning and teaching; phenomena-driven learning; NGSS-aligned 3D learning and formative assessment; computational thinking; pedagogical content knowledge; teacher professional learning

Special Issue Information

Dear Colleagues,  

Just as sensor technologies are deeply impacting engineering and scientific practices, so too are they increasingly becoming integrated in educational settings to support innovative learning activities. The growing availability of low-cost, portable sensors can be deployed to collect real-time measurements of properties from the surrounding world, for example light, temperature, magnetism, acceleration, sound, and CO2. When coupled with microcontrollers (such as the micro:bit), sensor data streams can be easily programmed to control the behaviors of actuators and data displays, thereby making the invisible visible, inspectable, and actionable.

This Special Issue will provide a forum for describing innovative approaches to using programmable sensor technologies in a range of educational settings.

Papers are solicited on the general theme of this issue. Topics of interest include, but are not limited to:

  • Reviewing the use of sensor technology in education settings;
  • Describing the development of science and data science instructional materials that integrate sensor technology;
  • Investigating how students engage in authentic scientific practices through exploring phenomena using sensor technology;
  • Describing the development of real-time data displays of sensor data streams to support learning;
  • Supporting the development of students’ computational thinking in sensor-based instructional materials; and
  • Describing the development of physical computing learning environments integrating sensor technology.

Prof. Dr. Mimi Recker
Dr. Colin Hennessy Elliott
Dr. Quentin Biddy
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensors
  • learning
  • instruction
  • science inquiry
  • computational thinking
  • science and data science education

Published Papers (5 papers)

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Research

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18 pages, 5241 KiB  
Article
Augmenting Social Science Research with Multimodal Data Collection: The EZ-MMLA Toolkit
by Bertrand Schneider, Javaria Hassan and Gahyun Sung
Sensors 2022, 22(2), 568; https://doi.org/10.3390/s22020568 - 12 Jan 2022
Cited by 5 | Viewed by 1992
Abstract
While the majority of social scientists still rely on traditional research instruments (e.g., surveys, self-reports, qualitative observations), multimodal sensing is becoming an emerging methodology for capturing human behaviors. Sensing technology has the potential to complement and enrich traditional measures by providing high frequency [...] Read more.
While the majority of social scientists still rely on traditional research instruments (e.g., surveys, self-reports, qualitative observations), multimodal sensing is becoming an emerging methodology for capturing human behaviors. Sensing technology has the potential to complement and enrich traditional measures by providing high frequency data on people’s behavior, cognition and affects. However, there is currently no easy-to-use toolkit for recording multimodal data streams. Existing methodologies rely on the use of physical sensors and custom-written code for accessing sensor data. In this paper, we present the EZ-MMLA toolkit. This toolkit was implemented as a website and provides easy access to multimodal data collection algorithms. One can collect a variety of data modalities: data on users’ attention (eye-tracking), physiological states (heart rate), body posture (skeletal data), gestures (from hand motion), emotions (from facial expressions and speech) and lower-level computer vision algorithms (e.g., fiducial/color tracking). This toolkit can run from any browser and does not require dedicated hardware or programming experience. We compare this toolkit with traditional methods and describe a case study where the EZ-MMLA toolkit was used by aspiring educational researchers in a classroom context. We conclude by discussing future work and other applications of this toolkit, potential limitations and implications. Full article
(This article belongs to the Special Issue Integrating Sensor Technologies in Educational Settings)
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28 pages, 3685 KiB  
Article
Smart Sensors for Augmented Electrical Experiments
by Sebastian Kapp, Frederik Lauer, Fabian Beil, Carl C. Rheinländer, Norbert Wehn and Jochen Kuhn
Sensors 2022, 22(1), 256; https://doi.org/10.3390/s22010256 - 30 Dec 2021
Cited by 6 | Viewed by 2227
Abstract
With the recent increase in the use of augmented reality (AR) in educational laboratory settings, there is a need for new intelligent sensor systems capturing all aspects of the real environment. We present a smart sensor system meeting these requirements for STEM (science, [...] Read more.
With the recent increase in the use of augmented reality (AR) in educational laboratory settings, there is a need for new intelligent sensor systems capturing all aspects of the real environment. We present a smart sensor system meeting these requirements for STEM (science, technology, engineering, and mathematics) experiments in electrical circuits. The system consists of custom experiment boxes and cables combined with an application for the Microsoft HoloLens 2, which creates an AR experiment environment. The boxes combine sensors for measuring the electrical voltage and current at the integrated electrical components as well as a reconstruction of the currently constructed electrical circuit and the position of the sensor box on a table. Combing these data, the AR application visualizes the measurement data spatially and temporally coherent to the real experiment boxes, thus fulfilling demands derived from traditional multimedia learning theory. Following an evaluation of the accuracy and precision of the presented sensors, the usability of the system was evaluated with n=20 pupils in a German high school. In this evaluation, the usability of the system was rated with a system usability score of 94 out of 100. Full article
(This article belongs to the Special Issue Integrating Sensor Technologies in Educational Settings)
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16 pages, 5183 KiB  
Article
The Data Sensor Hub (DaSH): A Physical Computing System to Support Middle School Inquiry Science Instruction
by Alexandra Gendreau Chakarov, Quentin Biddy, Colin Hennessy Elliott and Mimi Recker
Sensors 2021, 21(18), 6243; https://doi.org/10.3390/s21186243 - 17 Sep 2021
Cited by 10 | Viewed by 2211
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Integrating Sensor Technologies in Educational Settings)
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Review

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32 pages, 2269 KiB  
Review
How Can High-Frequency Sensors Capture Collaboration? A Review of the Empirical Links between Multimodal Metrics and Collaborative Constructs
by Bertrand Schneider, Gahyun Sung, Edwin Chng and Stephanie Yang
Sensors 2021, 21(24), 8185; https://doi.org/10.3390/s21248185 - 08 Dec 2021
Cited by 13 | Viewed by 3486
Abstract
This paper reviews 74 empirical publications that used high-frequency data collection tools to capture facets of small collaborative groups—i.e., papers that conduct Multimodal Collaboration Analytics (MMCA) research. We selected papers published from 2010 to 2020 and extracted their key contributions. For the scope [...] Read more.
This paper reviews 74 empirical publications that used high-frequency data collection tools to capture facets of small collaborative groups—i.e., papers that conduct Multimodal Collaboration Analytics (MMCA) research. We selected papers published from 2010 to 2020 and extracted their key contributions. For the scope of this paper, we focus on: (1) the sensor-based metrics computed from multimodal data sources (e.g., speech, gaze, face, body, physiological, log data); (2) outcome measures, or operationalizations of collaborative constructs (e.g., group performance, conditions for effective collaboration); (3) the connections found by researchers between sensor-based metrics and outcomes; and (4) how theory was used to inform these connections. An added contribution is an interactive online visualization where researchers can explore collaborative sensor-based metrics, collaborative constructs, and how the two are connected. Based on our review, we highlight gaps in the literature and discuss opportunities for the field of MMCA, concluding with future work for this project. Full article
(This article belongs to the Special Issue Integrating Sensor Technologies in Educational Settings)
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Other

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11 pages, 217 KiB  
Project Report
Sensors, Students, and Self: Exploring Knowledge, Self-Efficacy, and Interest Impact of Ocean Sensor Learning on High School Marine Science Students
by Travis W. Windleharth and Colin Katagiri
Sensors 2022, 22(4), 1534; https://doi.org/10.3390/s22041534 - 16 Feb 2022
Cited by 1 | Viewed by 1912
Abstract
This study examined the effect of online technical lessons of how ocean sensors function on student interest in ocean science technology, as well as knowledge gain outcomes. Additionally, the study contributes novel findings to sensor-based learning literature by measuring changes to self-efficacy and [...] Read more.
This study examined the effect of online technical lessons of how ocean sensors function on student interest in ocean science technology, as well as knowledge gain outcomes. Additionally, the study contributes novel findings to sensor-based learning literature by measuring changes to self-efficacy and confidence gains stemming from sensor-based learning, as well as changes in interest in ocean careers. The area of educational focus was also novel—focusing on how the sensors themselves function, not just what they do. Precipitated by COVID-19 pandemic constraints, the team used a remote learning approach to provide lessons on sensors at a distance, providing an additional opportunity to contrast this approach with previously studied hands-on learning modes. A sample of students from four high school marine science classes completed two assessments both before and after a series of lessons on ocean sensors. This included a self-reported survey (N = 48), and an open-ended knowledge assessment (N = 40). Results showed modest gains in knowledge assessments, and students experienced statistically significant gains in confidence in their ability to explain what sensors are, confidence in their ability to use sensors and understand resulting data, and confidence in accuracy of sensor data (p < 0.05). No changes were observed for several measures of interest in ocean technology, nor were there changes in an already high belief that understanding these sensors is important to marine science careers. Notably, these findings measure a positive shift in several measures of self-efficacy and confidence, which is a new finding for sensor-based learning. The findings also contrast with prior related work that included hands-on activities with sensors, which reported an increase in interest after working with sensors, whereas this intervention did not. This suggests a hands-on component is key to increasing interest in ocean technology. Full article
(This article belongs to the Special Issue Integrating Sensor Technologies in Educational Settings)
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