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

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6649
Author(s):  
George-Petru Ciordas-Hertel ◽  
Sebastian Rödling ◽  
Jan Schneider ◽  
Daniele Di Mitri ◽  
Joshua Weidlich ◽  
...  
Keyword(s):  
Distance Learning ◽  
Relevant Information ◽  
Mobile Sensing ◽  
Sensor Data ◽  
Learning Context ◽  
Software Infrastructure ◽  
Multimodal Data ◽  
Response Data ◽  
Physical Context ◽  
Physical Learning Environment

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.

Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients (Preprint)

2021 ◽  
Author(s):  
Joanne Zhou ◽  
Bishal Lamichhane ◽  
Dror Ben-Zeev ◽  
Andrew Campbell ◽  
Akane Sano
Keyword(s):  
Prediction Models ◽  
Gaussian Mixture ◽  
Mobile Sensing ◽  
Cluster Models ◽  
Sensor Data ◽  
Sensing Data ◽  
Model Based ◽  
Clustering Model ◽  
Relapse Prediction ◽  
Lower Variability

BACKGROUND Behavioral representations obtained from mobile sensing data could be helpful for the prediction of an oncoming psychotic relapse in schizophrenia patients and delivery of timely interventions to mitigate such relapse. OBJECTIVE In this work, we aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to daily living of patients as well as atypical behavioral trends associated with impending relapse. METHODS We used the mobile sensing data obtained in the CrossCheck project for our analysis. Continuous data from six different mobile sensing-based modalities (e.g. ambient light, sound/conversation, acceleration etc.) obtained from a total of 63 schizophrenia patients, each monitored for up to a year, were used for the clustering models and relapse prediction evaluation. Two clustering models, Gaussian Mixture Model (GMM) and Partition Around Medoids (PAM), were used to obtain behavioral representations from the mobile sensing data. These models have different notions of similarity between behaviors as represented by the mobile sensing data and thus provide differing behavioral characterizations. The features obtained from the clustering models were used to train and evaluate a personalized relapse prediction model using Balanced Random Forest. The personalization was done by identifying optimal features for a given patient based on a personalization subset consisting of other patients who are of similar age. RESULTS The clusters identified using the GMM and PAM models were found to represent different behavioral patterns (such as clusters representing sedentary days, active but with low communications days, etc.). While GMM based models better characterized routine behaviors by discovering dense clusters with low cluster spread, some other identified clusters had a larger cluster spread likely indicating heterogeneous behavioral characterizations. PAM model based clusters on the other hand had lower variability of cluster spread, indicating more homogeneous behavioral characterization in the obtained clusters. Significant changes near the relapse periods were seen in the obtained behavioral representation features from the clustering models. The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0.24 for the relapse prediction task in a leave-one-patient-out evaluation setting. This obtained F2 score is significantly higher than a random classification baseline with an average F2 score of 0.042. CONCLUSIONS Mobile sensing can capture behavioral trends using different sensing modalities. Clustering of the daily mobile sensing data may help discover routine as well as atypical behavioral trends. In this work, we used GMM and PAM-based cluster models to obtain behavioral trends in schizophrenia patients. The features derived from the cluster models were found to be predictive for detecting an oncoming psychotic relapse. Such relapse prediction models can be helpful to enable timely interventions.

scholarly journals Student access to and skills in using technology in an open and distance learning context

2012 ◽  
Vol 13 (4) ◽  
pp. 250 ◽  
Author(s):  
Hanlie Liebenberg ◽  
Yuraisha Chetty ◽  
Paul Prinsloo
Keyword(s):  
Higher Education ◽  
Distance Learning ◽  
Digital Divide ◽  
South African ◽  
Learning Context ◽  
Access To Technology ◽  
Critical Issues ◽  
Information And Communication ◽  
Definition Of ◽  
South African Higher Education

<p>Amidst the different challenges facing higher education, and particularly distance education (DE) and open distance learning (ODL), access to information and communication technology (ICT) and students’ abilities to use ICTs are highly contested issues in the South African higher education landscape. While there are various opinions about the scope and definition of the digital divide, increasing empirical evidence questions the uncritical use of the notion of the digital divide in South African and international higher education discourses.</p><p>In the context of the University of South Africa (Unisa) as a mega ODL institution, students’ access to technology and their functional competence are some of the critical issues to consider as Unisa prepares our graduates for an increasingly digital and networked world.</p><p>This paper discusses a descriptive study that investigated students’ access to technology and their capabilities in using technology, within the broader discourse of the “digital divide.” Results support literature that challenges a simplistic understanding of the notion of the “digital divide” and reveal that the nature of access is varied.</p>

scholarly journals Physical Context Detection using Wearable Wireless Sensor Networks

2008 ◽  
Vol 4 (3) ◽  
pp. 191 ◽  
Author(s):  
Muhannad Quwaider ◽  
Subir Biswas
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Human Subjects ◽  
Real Life ◽  
Sensor Data ◽  
Physical Context ◽  
Significant Extension ◽  
The Individual ◽  
Stochastic Identification

This paper presents the architecture of a wearable sensor network and a Hidden Markov Model (HMM) processingframework for stochastic identification of body postures andphysical contexts. The key idea is to collect multi-modal sensor data from strategically placed wireless sensors over a human subject’s body segments, and to process that using HMM in order to identify the subject’s instantaneous physical context. The key contribution of the proposed multi-modal approach is a significant extension of traditional uni-modal accelerometry in which only the individual body segment movements, without their relative proximities and orientation modalities, is used for physical context identification. Through real-life experiments with body mounted sensors it is demonstrated that while the unimodal accelerometry can be used for differentiating activityintensive postures such as walking and running, they are not effective for identification and differentiation between lowactivity postures such as sitting, standing, lying down, etc. In the proposed system, three sensor modalities namely acceleration, relative proximity and orientation are used for context identification through Hidden Markov Model (HMM) based stochastic processing. Controlled experiments using human subjects are carried out for evaluating the accuracy of the HMMidentified postures compared to a naïve threshold based mechanism over different human subjects.

scholarly journals Realizing a Mobile Multimodal Platform for Serious Games Analytics

2019 ◽  
Vol 6 (4) ◽  
pp. 19-48
Author(s):  
Laila Shoukry ◽  
Stefan Göbel
Keyword(s):  
Game Design ◽  
Serious Games ◽  
Sensor Data ◽  
Educational Game ◽  
Evaluation Procedure ◽  
Multimodal Data ◽  
Design Elements ◽  
Implementation Challenges ◽  
Cognitive States ◽  
Game Developers

This paper presents the design and development of "StoryPlay Multimodal", a mobile multimodal analytics platform for the evaluation of Serious Games. It is intended to serve researchers, teachers and educational game developers as a means to assess their Serious Game Design. This is done by capturing, pre-processing, synchronizing and visualizing multimodal serious games analytics and mobile sensor data from playtesting sessions. By linking log data with multimodal data, it is possible to uncover relations between design elements, gameplay interactions, context parameters and affective and cognitive states. This is crucial for gaining full insight into the session, even if not present with the player at the same location. After discussing design requirements, the architecture of the software, the different modules, additional features, implementation challenges and solutions are presented. The testing settings, participants and results are also discussed to demonstrate how the evaluation procedure can be applied to deliver valuable outcomes for Serious Games Research.

An Attention-Based Framework for Context Identification in Autonomous Robots

2017 ◽  
Author(s):  
Maria Alessandra Montironi ◽  
Harry H. Cheng
Keyword(s):  
Information Content ◽  
Computational Models ◽  
Markov Models ◽  
Relevant Information ◽  
Recursive Estimation ◽  
Sensor Data ◽  
Infrared Range ◽  
Contextual Knowledge ◽  
Visual Sensor

Being able to correctly assess the context it is currently acting in is a very important ability for every autonomous robot performing a task in a real world scenario such as navigating, manipulating an object or interacting with a user. Sensors are the primary interface with the external world and the means through which contextual knowledge is generated. Humans and animals use cognitive processes such as attention to selectively process perceived task-relevant information and to recognize the context they are currently acting in. Biologically inspired computational models of attention have been developed in recent years to be used as interpretation keys of mainly visual sensor data. This paper presents a new framework for situation assessment that expands existing computational models of attention by providing a unified methodology to interpret and combine data from different sources. The method utilizes probabilistic state estimation techniques such as Bayesian recursive estimation, Kalman filter, and hidden Markov models to interpret features extracted from sensor data and formulate hypotheses about different aspects of the task the robot is performing or of the environment it is currently acting in. The concept of Bayesian surprise is also used to mark the information content of each new hypothesis. A weight that takes into account the confidence in the estimate that generated the hypothesis, its information content, and the quality of the data is then calculated. The methodology presented in this paper is general and allows to consistently apply the framework to data from different types of sensors and to then combine their hypotheses. Once formulated, hypotheses can then be used for context-based reasoning and plan adaptation. The framework was implemented on a small two-wheel differential drive robot equipped with a camera, an ultrasonic and two infrared range sensors. Three different sets of results that evaluate the performance of different features of the framework are presented. First, the method has been applied to detect a target object and to distinguish it from similar objects. Second, the hypotheses strength calculation method has been characterized by isolating the effect of belief, surprise, and of the quality of the data. Third, the combination of hypotheses from different modules has been evaluated in the context of environment classification.

E-Pedagogical Support Strategies to Teaching Methodology of Economics Students at an Open Distance Learning University

2020 ◽  
Vol 10 (3) ◽  
pp. 1-18
Author(s):  
Micheal M. van Wyk
Keyword(s):  
Teacher Education ◽  
Distance Learning ◽  
Student Learning ◽  
Online Teaching ◽  
Online Survey ◽  
Teaching Methodology ◽  
Learning Context ◽  
Online Questionnaire ◽  
Pedagogical Strategies ◽  
Support Strategies

A systematic review of the literature of e-pedagogical support strategies for an open distance learning context was done to explore the knowledge “gap” on existing scholarly works. This paper investigates the use of pedagogical support strategies employed to support student learning in an online Teaching Methodology of Economics course. The research followed a pragmatic approach—an explanatory mixed-methods design—to conduct the research. An online questionnaire and eDiscussion forum entries were employed to collect data. Convenient and purposive sampling of postgraduate students (n=179) in Teaching Methodology of Economics were selected. Students voluntarily completed the online survey. Findings and practical implications were formulated to advance online pedagogical strategies to support student learning and thus promote essential competencies for the course in the college of education at an open distance learning university. The current study has only examined a small sampling of student views regarding pedagogical strategies employed in a teacher education online course. More research is needed to establish whether a larger sample, comparing similar courses in the teacher education programme, will yield different results.

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