Mobile Sensing with Smart Wearables of the Physical Context of Distance Learning Students to Consider Its Effects on Learning

Abstract

Research shows that various contextual factors can have an impact on learning. Some of these factors can originate from the physical learning environment (PLE) in this regard. When learning from home, learners have to organize their PLE by themselves. This paper is concerned with identifying, measuring, and collecting factors from the PLE that may affect learning using mobile sensing. More specifically, this paper first investigates which factors from the PLE can affect distance learning. The results identify nine types of factors from the PLE associated with cognitive, physiological, and affective effects on learning. Subsequently, this paper examines which instruments can be used to measure the investigated factors. The results highlight several methods involving smart wearables (SWs) to measure these factors from PLEs successfully. Third, this paper explores how software infrastructure can be designed to measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. The design and implementation of the Edutex software infrastructure described in this paper will enable learning analytics stakeholders to use data from and about the learners’ physical contexts. Edutex achieves this by utilizing sensor data from smartphones and smartwatches, in addition to response data from experience samples and questionnaires from learners’ smartwatches. Finally, this paper evaluates to what extent the developed infrastructure can provide relevant information about the learning context in a field study with 10 participants. The evaluation demonstrates how the software infrastructure can contextualize multimodal sensor data, such as lighting, ambient noise, and location, with user responses in a reliable, efficient, and protected manner.

Responsive Dashboard as a Component of Learning Analytics System for Evaluation in Emergency Remote Teaching Situations

“…The result of the present study can be compared with 10 of these relevant papers [ 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ]. While other researchers have analyzed the effects of ERT on high school teachers [ 68 ], state universities [ 69 ], and the challenges faced by educational institutions [ 70 ], for the proposed model, the developed EvalMathI system was tested to be able to answer questions Q1–Q4, questions that support the development of the model for the evaluation system (LAEM), and also validate the software instrument called EvalMathI.…”

“…The authors answered Q1—How useful is EvalMathI in evaluating courses in an ERT situation?—by introducing and integrating the dashboard in a responsive panel to facilitate and streamline the evaluation process. In addition, other researchers have previously analyzed students’ performance in an ERT situation [ 71 ], the challenges faced by math teachers in an ERT situation [ 72 ], the level of emotions in the learning process [ 75 ], the factors influencing home learning [ 78 ], and students’ emotions and the perception of teachers in ERT [ 76 , 77 ]. In this context, the present study analyzed the methodology of evaluation in ERT conditions and proposed a tool called EvalMathI, which was tested in a case study of six courses conducted in ERT at our university.…”

From Sensor Data to Educational Insights

“…For example, Mat Sanusi et al [ 24 ] designed and implemented the Table Tennis Tutor (T3), a multi-sensor system consisting of a smartphone device with built-in sensors for collecting motion data and a Microsoft Kinect for tracking body position that could be used to perform live coaching and feedback of the table tennis forehand strokes of the trainee. Then, the work of [ 25 ] explored the factors from the physical learning environment (PLE) that can affect distance learning and built a software infrastructure that can measure, collect, and process the identified multimodal data from and about the PLE by utilizing mobile sensing. Finally, they conducted an evaluation with 10 participants regarding what extent the software can provide relevant information about the learning context.…”

Smart home service terminal design for elderly families integrating the KANO model and perceptual engineering

“…First of all, smart home products help us to save resources. Intelligent home lighting systems can realize automatic adjustment of the brightness of lamps and lanterns, which can ensure the brightness of the room while minimizing energy consumption [5]. In addition, the intelligent lighting system can achieve the light on when people come and go, giving us very much convenience, on the other hand, it can also prevent forgetting to turn off the lights and cause power waste [6,7].…”