Sensor-based Learning Research Articles

Sensors, Students, and Self: Exploring Knowledge, Self-Efficacy, and Interest Impact of Ocean Sensor Learning on High School Marine Science Students.

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.

Gesture recognition of sign language alphabet with a convolutional neural network using a magnetic positioning system

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;Gesture recognition is a fundamental step to enable efficient communication for the deaf through the automated translation of sign language. This work proposes the usage of a high-precision magnetic positioning system for 3D positioning and orientation tracking of the fingers and hands palm. The gesture is reconstructed by the MagIK (magnetic and inverse kinematics) method and then processed by a deep learning gesture classification model trained to recognize the gestures associated with the sign language alphabet. Results confirm the limits of vision-based systems and show that the proposed method based on hand skeleton reconstruction has good generalization properties. The proposed system, which combines sensor-based gesture acquisition and deep learning techniques for gesture recognition, provides a 100% classification accuracy, signer independent, after a few hours of training using transfer learning technique on well-known ResNet CNN architecture. The proposed classification model training method can be applied to other sensor-based gesture tracking systems and other applications, regardless of the specific data acquisition technology.&lt;/span&gt;&lt;/p&gt;

Sensor-Based Machine Learning Approach for Indoor Air Quality Monitoring in an Automobile Manufacturing

The alternative control concept using emission from the machine has the potential to reduce energy consumption in HVAC systems. This paper reports on a study of alternative inputs for a control system of HVAC using machine learning algorithms, based on data that are gathered in a welding area of an automotive factory. A data set of CO2, fine dust, temperatures and air velocity was logged using continuous and gravimetric measurements during two typical production weeks. The HVAC system was reduced gradually each day to trigger fluctuations of emission. The data were used to train and test various machine learning models using different statistical indices, consequently to choose a best fit model. Different models were tested and the Long Short-Term Memory model showed the best result, with 0.821 discrepancy on R2. The gravimetric samples proved that the reduction of air exchange rate does not correlate to escalation of fine dust linearly, which means one cannot rely on just gravimetric samples for HVAC system optimization. Furthermore, by using machine learning algorithms, this study shows that by using commonly available low cost sensors in a production hall, it is possible to correlate fine dust data cost effectively and reduce electricity consumption of the HVAC.

Task Analysis on Yoga Poses Toward a Wearable Sensor-based Learning System for Users with Visual Impairment

People with visual impairments may experience difficulties in learning new physical exercises due to a lack of visual feedback. Learning and practicing yoga is especially challenging for this population as yoga requires imitation-oriented learning. A typical yoga class requires students to observe and copy poses and movements as the instructor presents them, while maintaining postural balance during the practice. Without additional, nonvisual feedback, it can be difficult for students with visual impairments to understand whether they have accurately copied a pose – and if they have not, how to fix an inaccurate pose. Therefore, there is a need for an intelligent learning system that can capture a person’s physical posture and provide additional, nonvisual feedback to guide them into a correct pose. This study is a preliminary step toward the development of a wearable inertial sensor-based virtual learning system for people who are blind or have low vision. Using hierarchical task analysis, we developed a step-by-step conceptual model of yoga poses, which can be used in constructing an effective nonvisual feedback system. We also ranked sensor locations according to their importance by analyzing postural deviations in each pose compared to the reference starting pose.

Collecting and Using Students’ Digital Well-Being Data in Multidisciplinary Teaching

This article examines how students (N=198; aged 13 to 17) experienced the new methods for sensor-based learning in multidisciplinary teaching in lower and upper secondary education that combine the use of new sensor technology and learning from self-produced well-being data. The aim was to explore how students perceived new methods from the point of view of their learning and did the teaching methods provide new information that could promote their own well-being. We also aimed to find out how to collect digital well-being data from a large number of students and how the collected big data set can be utilized to predict school success from the students’ well-being data by using machine learning methods (lasso regression and multilayer perceptron). Results showed that sensor-based learning can promote students’ learning and well-being. All upper secondary school (n=37) and 87% of lower secondary school pupils (n=161) argued that when data are produced by their bodies, learning is more interesting, and they mostly found that well-being analysis was useful (upper secondary 97%; lower secondary 78%) and can improve personal well-being (upper secondary 78%; lower secondary 67%). The predictive powers with lasso regression and multilayer perceptron (MLP) were quite weak (correlation: −0.14 and 0.34, respectively).

GPS sensor-based mobile learning for English: an exploratory study on self-efficacy, self-regulation and student achievement.

This study investigated the effects of self-efficacy and self-regulation on student achievement in a context-aware learning environment. Particularly, an innovative global positioning system (GPS) sensor-based mobile learning system was used to facilitate English learning of different plants on campus. A total of 41 university students participated in the study, divided into high and low groups of self-efficacy and self-regulation. The findings showed that both self-regulation and self-efficacy were significant predictors of learning achievement in the mobile context-aware learning (MCL) context. Moreover, while the GPS-based MCL learning session had positive effects on learning achievement, no significant increase in self-regulation or self-efficacy was found in either the high or the low group, supposedly due to the short duration of the activity. The participants found the system easy to use and useful, but they also raised critical concerns that can inform future improvements. We hope this exploratory study can serve as a starting point from which more interactive, user-friendly GPS sensor-based learning systems will be generated and more areas of application will be further explored to foster self-regulated, self-motivated ubiquitous learning of mobile learners.

Augmenting the senses: a review on sensor-based learning support.

In recent years sensor components have been extending classical computer-based support systems in a variety of applications domains (sports, health, etc.). In this article we review the use of sensors for the application domain of learning. For that we analyzed 82 sensor-based prototypes exploring their learning support. To study this learning support we classified the prototypes according to the Bloom's taxonomy of learning domains and explored how they can be used to assist on the implementation of formative assessment, paying special attention to their use as feedback tools. The analysis leads to current research foci and gaps in the development of sensor-based learning support systems and concludes with a research agenda based on the findings.

Developing a sensor-based learning concentration detection system

Purpose – This study aims to use sensing technology to observe the learning status of learners in a teaching and learning environment. In a general instruction environment, teachers often encounter some teaching problems. These are frequently related to the fact that the teacher cannot clearly know the learning status of students, such as their degree of learning concentration and capacity to absorb knowledge. In order to deal with this situation, this study uses a learning concentration detection system (LCDS), combining sensor technology and an artificial intelligence method, to better understand the learning concentration of students in a learning environment. Design/methodology/approach – The proposed system uses sensing technology to collect information about the learning behavior of the students, analyzes their concentration levels, and applies an artificial intelligence method to combine this information for use by the teacher. This system includes a pressure detection sensor and facial detection sensor to detect facial expressions, eye activities and body movements. The system utilizes an artificial bee colony (ABC) algorithm to optimize the system performance to help teachers immediately understand the degree of concentration and learning status of their students. Based on this, instructors can give appropriate guidance to several unfocused students at the same time. Findings – The fitness value and computation time were used to evaluate the LCDS. Comparing the results of the proposed ABC algorithm with those from the random search method, the algorithm was found to obtain better solutions. The experimental results demonstrate that the ABC algorithm can quickly obtain near optimal solutions within a reasonable time. Originality/value – A learning concentration detection method of integrating context-aware technologies and an ABC algorithm is presented in this paper. Using this learning concentration detection method, teachers can keep abreast of their students' learning status in a teaching environment and thus provide more appropriate instruction.

A Sensor-Based Learning Algorithm for the Self-Organization of Robot Behavior

Ideally, sensory information forms the only source of information to a robot. We consider an algorithm for the self-organization of a controller. At short time scales the controller is merely reactive but the parameter dynamics and the acquisition of knowledge by an internal model lead to seemingly purposeful behavior on longer time scales. As a paradigmatic example, we study the simulation of an underactuated snake-like robot. By interacting with the real physical system formed by the robotic hardware and the environment, the controller achieves a sensitive and body-specific actuation of the robot.

Sensor-based learning for practical planning of fine motions in robotics

This paper presents an implemented approach to part-mating of three-dimensional non-cylindrical parts with a 6 DOF manipulator, considering uncertainties in modeling, sensing and control. The core of the proposed solution is a reinforcement learning algorithm for selecting the actions that achieve the goal in the minimum number of steps. Position and force sensor values are encoded in the state of the system by means of a neural network. Experimental results are presented for the insertion of different parts – circular, quadrangular and triangular prisms – in three dimensions. The system exhibits good generalization capabilities for different shapes and location of the assembled parts. These results significantlyextend most of the previous achievements in fine motion tasks, which frequentlymodel the robot as a polygon translating in the plane in a polygonal environment or do not present actual implemented prototypes. 2002 Elsevier Science Inc. All rights reserved.