Augmented Reality applications have become the newest technology used in the Cultural Heritage domain. These applications can be used in education and tourism. Various methods and software tools provide the means of designing such applications. This study provides an overview of the most recent Augmented Reality projects in Cultural Heritage sites in urban environments, comparing tracking methods, devices, themes, and settings used in each project. The most frequently used tracking method is camera-based, with handheld devices being almost entirely preferred in such projects. There is an even distribution of themes, while outdoor scenarios are the preferred setting.

This paper aims to provide the reader with a comprehensive background for understanding current knowledge on the use of non-intrusive Mobile Sensing methodologies for emotion recognition in Smartphone devices. We examined the literature on experimental case studies conducted in the domain during the past six years (2015–2020). Search terms identified 95 candidate articles, but inclusion criteria limited the key studies to 30. We analyzed the research objectives (in terms of targeted emotions), the methodology (in terms of input modalities and prediction models) and the findings (in terms of model performance) of these published papers and categorized them accordingly. We used qualitative methods to evaluate and interpret the findings of the collected studies. The results reveal the main research trends and gaps in the field. The study also discusses the research challenges and considers some practical implications for the design of emotion-aware systems within the context of Distance Education.

Emotion recognition is essential for assessing human emotional states and predicting user behavior to provide appropriate and personalized feedback. The wide range of Smartphones with accelerometers, microphones, GPSs, gyroscopes, and more motivate researchers to explore the automatic emotion detection through Smartphone sensors. To this end, mobile sensing can facilitate the data retrieval process in a non-intrusive way without disturbing the user's experience. This study seeks to contribute to the field of non-intrusive mobile sensing for emotion recognition by detecting user emotions via accelerometer and gyroscope sensors in Smartphones. A prototype gaming app was designed and a sensor log app for Android OS was used to monitor the users’ sensor data while interacting with the game. The recorded data from 40 users was processed and used to train different classifiers for two emotions: a positive (enjoyment) and a negative (frustration) one. The validation study demonstrates a high prediction of 87.90% for enjoyment and 89.45% for frustration. Our findings indicate that by analyzing accelerometer and gyroscope data, it is possible to make efficient predictions of a user's emotional state. The proposed model and its empirical development and validation are described in this paper.

Although there is a great interest on MOOCs, it is not clear what a MOOC should provide to learners enabling them to achieve their objectives. This paper proposes the MOOC Affordances Model (MOOC-AM) for characterizing a MOOC. The MOOC-AM consists of eight dimensions of affordances: 1) Massiveness, 2) Openness, 3) Interaction, Communication, Cooperation & Collaboration, 4) Personalization & Adaptation, 5) Autonomy, Choice & Control, 6) Support, Scaffolding, Help, Facilitation, Assistance, Feedback & Recommendations, 7) Mobility & Ubiquity, and 8) Accreditation, Certification & Assessment. In order to illustrate the application of the MOOC-AM, MOOCs on Programming in Python were examined regarding the affordances they provide. The findings reveal that none MOOC excels in all eight affordances. Learners, educators, designers, developers, and policy makers could consider this MOOC-AM to make appropriate decisions.

One of the major priorities of education systems nowadays is to promote Inquiry-Based Science Learning (IBSL). Research has shown that mobile learning can support and enhance inquiry-based science learning promoting learning achievement and motivation. However, engagement, as a consequence of motivation, in the context of mobile-assisted inquiry-based science learning, has not been adequately investigated. The current study implements a collaborative mobile-assisted inquiry-based science learning intervention in the context of secondary school science. The study is aiming at explaining and predicting student engagement in terms of the three motivational concepts of the Self-Determination Theory (SDT) of motivation: autonomy, competence and relatedness. Data collected for 80 secondary school students and analyzed with structural equation modeling. The proposed model explains about 63% of the variance in students' engagement in mobile-assisted inquiry-based science learning. Perceived autonomy was found to be the strongest predictor of engagement, followed by perceived relatedness. Based on the research findings, implications for practice and suggestions for future studies are also discussed.

In collaborative learning contexts, it is necessary to recommend educational resources to groups of students instead of individuals. However, this task is not trivial, because students in a group may not be fulfilled by the same items, yet wish to meet their own expectations. Existing approaches either merge individual profiles and recommend items accordingly, or fuse the lists of individual recommendations. Both perspectives achieve low quality performance and goodness of recommendation for majority of students in heterogeneous groups. This paper follows a game-theoretic approach for solving conflict of interest among students and recommending resources to both homogeneous and heterogeneous groups in collaborative learning contexts. The group members are the players, the resources comprise the set of possible actions, and selecting those items that will maximize all students' satisfaction - both individually and as a whole - is a problem of finding the Nash Equilibrium. During the empirical evaluation of the suggested approach compared to other state-of-the-art methods in a real dataset, the relevance of each item to its corresponding students was explored from two perspectives: the group's (as a whole) and the individual student's (within the group). Results indicate a statistically significant improvement in accuracy of predicted group and individual satisfaction, as well as in the goodness of the ranked list of recommendations.

Students' satisfaction from educational resources is a subjective perception of how well these resources meet students' expectations for learning. Recommending educational resources to groups of students, targeting at optimizing all students' satisfaction, is a complicated task due to the lack of joint group profiles. Instead of merging individual profiles or fusing individual recommendations, this paper follows a game-theoretic perspective for solving conflict of interest among students and recommending resources to groups in online collaborative learning contexts: the group members are the players, the resources comprise the set of possible actions, and maximizing each individual member's satisfaction from the selected resources is a problem of finding the Nash Equilibrium. In case the Nash Equilibrium is Pareto efficient, none of the players can get more payoff (satisfaction) without decreasing the payoff of any other player, indicating an optimal benefit for the group as a whole. The comparative evaluation of the suggested approach to other state-of-the-art methods provided statistically significant results regarding the error in predicted group satisfaction from the recommendation and the goodness of the ranked list of recommendations.

This study focuses on compiling students' response-time allocated to answer correctly or wrongly, their self-regulation, as well as their satisfaction from content, in order to explain high or medium/low learning performance. To this end, it proposes a conceptual model in conjunction with research propositions. For the evaluation of the approach, an empirical study with 452 students was conducted. The fuzzy set qualitative comparative analysis (fsQCA) revealed five configurations driven by the admitted factors that explain students' high performance, as well as five additional patterns, interpreting students' medium/low performance. These findings advance our understanding of the relations between actual usage and latent behavioral factors, as well as their combined effect on students' test score. Limitations and potential implications of these findings are also discussed.

The special track “Motivating students with Mobiles, Ubiquitous applications and the Internet of Things for STEM (MUMI4STEM)”, within the “EDUCON20I7 IEEE Global Engineering Education Conference”, integrates two main areas of interest in STEM education: I) motivating students with mobile devices and 2) exploiting ubiquitous computing and the Internet of Things. Following the growing interest of the educational and research community towards fostering STEM education, this special session aims at promoting the discussion about the motivational aspects of mobile learning and the benefits of ubiquitous applications and the Internet of Things (IoT), with special focus on supporting STEM education.

Instant emotions affect our daily activities including learning procedures. This study examines which emotions influence learner's behavioral intention to use a Computer Based Assessment (CBA). We used Facereader during a self - assessment test to measure learner's instant emotions (Happy, Surprise, Angry, Fear, Disgust, Sad and Neutral) through facial expressions. In addition we used the Computer Based Assessment Acceptance Model (CBAAM) to evaluate the constructs that influence learners' behavioral intention to use a CBA. More specifically, this research aims at investigating the moderating effect of instant emotions on Behavioral Intention to Use a CBA system and the direct paths of the most important determinants such as Perceived Playfulness, Perceived Usefulness, Perceived Ease of Use, Perceived Content and Perceived Importance. An appropriate survey questionnaire was completed by 117 students. Results demonstrate that Emotions moderate the most important direct effects on Behavioral Intention to Use a CBA system and they play an important role in CBA's acceptance. Important implications of these results are discussed.

Mobile-based Assessment (MBA) is a relatively new delivery mode of assessment. MBA not only offers an alternative to web-based tests and quizzes that can be answered anytime and anywhere but it also introduces a new assessment paradigm offering adaptive, personalized, context-aware and ubiquitous assessment activities embedded in learning flow. However, for an effective use of MBA, instructional designers and educators need to be aware of its underpinning motivational dimensions and concepts. The current study proposes MBAMF, a Mobile-Based Assessment Motivational Framework based on the Self-Determination Theory (SDT) of Motivation. The framework aims to connect the basic SDT constructs with features offered by mobile-based assessment. For a preliminary evaluation of the model, a pilot study with 47 medical students in a near-patients clinical training environment was conducted. The study provides empirical evidence that fits into the proposed framework. Mobile-assisted assessment can effectively support the three basic psychological needs of SDT, namely perceived autonomy, competence and relatedness. The current work provides a foundation for further elaboration towards a more comprehensive motivational framework for mobile-based assessment. Implications are discussed.

In order to personalize the assessment services, the assessment systems need to build suitable student models for heterogeneous student populations. The present study focuses on efficiently modeling students according to their time-varying behavior during web-based self-assessment, enriching the models with a notion of dynamics. The suggested approach forms and revises the student models on-the-fly, using three popular stream mining classification techniques. All methods use specific time-based features as predictors, and the students' self-assessment achievement levels as target values. The obtained results demonstrate that level of certainty, effort and time-spent on answering correctly/wrongly could contribute to pursuing fine-grained and robust student models during self-assessment.

This paper introduces a novel, analytic framework for modeling security attacks in Internet of Things (IoT) infrastructures. The devised model is quite generic, and as such, it could flexibly be adapted to various IoT architectures. Its flexibility lies in the underlying theory; it is based on a dynamic G-network, where the positive arrivals denote the data streams that originated from the various data collection networks (e.g., sensor networks), while the negative arrivals denote the security attacks that result in data losses (e.g., jamming attacks). In addition, we take into account the intensity of an attack by considering both light and heavy attacks. The light attack implies simple losses of traffic data, while the heavy attack causes massive data loss. The introduced model is solved subject to the arrival and departure rates in terms of: 1) average number of data packets in the application domain and 2) attack impact (loss rate). A comprehensive verification discussion accompanied by numerous numerical results verify the accuracy of the proposed model. Moreover, the assessment of the presented model highlights notable operation characteristics of the underlying IoT system under light and heavy attacks.

A typical cyber-physical system (CPS) refers to a system that features a tight integration of computation, networking, and physical elements for interactions between cyber and physical spaces. The Internet of Things (IoT) is considered to be the networking infrastructure of CPS. Applications of CPS cover numerous smart-world research areas that our daily life depends upon, including smart transportation, smart electrical power grid, smart cities, smart medical systems, smart manufacturing systems, and others. While major research on improving the efficiency and reliability of CPS by using advanced information and communication technologies has been conducted, the risks of cyberspace security and privacy breaches in CPS need to be seriously investigated before a massive deployment of CPS technologies can or should be realized.

Differences in learners' behavior have a deep impact on their educational performance. Consequently, there is a need to detect and identify these differences and build suitable learner models accordingly. In this paper, we report on the results from an alternative approach for dynamic student behavioral modeling based on the analysis of time-based student-generated trace data. The goal was to unobtrusively classify students according to their time-spent behavior. We applied 5 different supervised learning classification algorithms on these data, using as target values (class labels) the students' performance score classes during a Computer-Based Assessment (CBA) process, and compared the obtained results. The proposed approach has been explored in a study with 259 undergraduate university participant students. The analysis of the findings revealed that a) the low misclassification rates are indicative of the accuracy of the applied method and b) the ensemble learning (treeBagger) method provides better classification results compared to the others. These preliminary results are encouraging, indicating that a time-spent driven description of the students' behavior could have an added value towards dynamically reshaping the respective models.

Wireless Sensor Networks (WSNs) have been established as a valuable tool in a wide variety of applications, systems and paradigms. Many application, such as surveillance of a military region, entail unattended operation, where sensor nodes are randomly deployed in an area, known as sensor area. Such a sensor network may be vulnerable to several harmful threats such as wormhole, blackhole, selective forwarding, hello flood, and Sybil attack. One of the most complicated threat is the Sybil attack, where one or more malicious nodes illegitimately declare multiple identities. Additionally, the attack could be even more arduous, if the malicious node(s) declare that the Sybil nodes are directly connected to them. The so-called indirect Sybil attack is the main focus of this study. A performance analysis is devised, where the expected potential number of indirect Sybil nodes in randomly deployed WSNs is computed. Moreover, the probability of an (indirect) Sybil-free sensor network is calculated subject to the number of sensor nodes and the sensor area intensity. The analysis is thoroughly validated by simulation results.

Considering the declining enrolling in computing fields and the increasing demand in STEM disciplines, innovative methods should be employed to attract students in computing disciplines. MIT Scratch and App Inventor for Android visual programming environments are two such approaches. This is a comparative study to investigate any differences in the transition of students' motivation to learn programming using Scratch and App Inventor for Android in K-12 educational settings. Intrinsic goal orientation, task value, control of learning beliefs and self efficacy were found to be increased using these two entry-level learning programming environments from the beginning to the middle of the course. No effect on extrinsic motivation was found. Evaluating the transition in motivation throughout the whole course period for both environments (work in progress) will have an impact on educators to retain students' interest in programming and improve their attitudes towards computing.

Mobile-based assessment offers a promising (complementary to paper-based and computer-based) assessment delivery mode. However, its successful implementation depends on users' acceptance. The present study is the first towards the investigation of the factors that influence the acceptance of mobile-based assessment. It combines two theoretical frameworks: Technology Acceptance Model and Self-Determination Theory of Motivation. Partial Least Squares were used to test the proposed structural model. Perceived Autonomy, Perceived Relatedness and Perceived Competency, along with Perceived Usefulness and Perceived Ease of Use, influence Attitudes Towards Use and Behavior Intention to use Mobile-Based Assessment. The study confirms Technology Acceptance Model and showed that Self Determination Theory can be useful in predicting students' acceptance in the context of mobile-based assessment.

The present study reports the experiences from the Hour of Code implementation in the context of two introductory Informatics courses, one in a high school level and one in a first-year University level. It explores learning motivation among 47 high school students and 51 first-year University level students, during their participation in the Hour of Code activity. The theoretical framework is Self-Determination Theory of Motivation. The theory distinguishes mainly four types of motivation, namely Intrinsic Motivation, Identified Regulation, External Regulation and Amotivation. Students were found to exhibit more self-determined types of motivation during the Hour of Code activities. Both groups found the activities to be attractive and useful. The results may be helpful to practitioners in order to design more intrinsically motivated educational scenarios when modeled appropriately.

Provision of adaptive and personalized Computer Based Assessment (CBA) services to learners is a multidimensional research field. In this paper we investigate the effect of extraversion and conscientiousness with temporal learning analytics on students' performance during computer based testing. For this purpose, we used the LAERS assessment environment to track the temporal activity - time spent behavior - of 96 students and the Big Five Instrument (BFI) questionnaire to record their personality traits. Partial Least Squares (PLS) was used to find fundamental relationships between the collected data. Preliminary results indicate a positive effect of conscientiousness on (un-)certainty and a positive effect of extraversion on goal expectancy. Further implications of these results are also discussed.

Predicting student's performance is a challenging, yet complicated task for institutions, instructors and learners. Accurate predictions of performance could lead to improved learning outcomes and increased goal achievement. In this paper we explore the predictive capabilities of student's time-spent on answering (in-)correctly each question of a multiple-choice assessment quiz, along with student's final quiz-score, in the context of computer-based testing. We also explore the correlation between the time-spent factor (as defined here) and goal-expectancy. We present a case study and investigate the value of using this parameter as a learning analytics factor for improving prediction of performance during computer-based testing. Our initial results are encouraging and indicate that the temporal dimension of learning analytics should be further explored.

Due to the limited transmit power and other constraints, the transmission of data between two nodes in a Wireless Sensor Network (WSN) may have to be accomplished with the help of one or multiple relay nodes. Game theory has been proven very useful for accurately modeling the underlying strategic interactions between the sensor nodes and for providing the relays with incentives to forward the other nodes' data. In the present paper, we study a class of such games played at the network layer among a set of relay nodes and the source-destination pair. We formulate the source routing problem using a Stackelberg game framework. The Stackelberg equilibrium strategies derived from our game define the expected performance of the network under different traffic conditions, and motivate an efficient routing protocol design for this type of wireless networks. Different network configurations are studied to illustrate the effectiveness of our game theoretic approach.

Wireless sensor networks (WSNs) are gaining more and more interest in the research community due to their unique characteristics. Besides energy consumption considerations, security has emerged as an equally important aspect in their network design. This is because WSNs are vulnerable to various types of attacks and to node compromises, and as such, they require security mechanisms to defend against them. An intrusion detection system (IDS) is one such solution to the problem. While several signature-based and anomaly-based detection algorithms have been proposed to date for WSNs, none of them is specifically designed for the ultra-wideband (UWB) radio technology. UWB is a key solution for wireless connectivity among inexpensive devices characterized by ultra-low power consumption and high precision ranging. Based on these principles, in this paper, we propose a novel anomaly-based detection and location-attribution algorithm for cluster-based UWB WSNs. The proposed algorithm, abbreviated as ADLU, has dedicated procedures for secure cluster formation, periodic re-clustering, and efficient cluster member monitoring. The performance of ADLU in identifying and localizing intrusions using a rule-based anomaly detection scheme is studied via simulations.

Wireless sensor networks (WSNs) are gaining more and more interest in the research community due to their unique characteristics. Besides energy consumption considerations, security has emerged as an equally important aspect in their network design. WSNs are vulnerable to various types of attacks, and as such, they require mechanisms to defend against them. Several anomaly detection algorithms have been proposed to date as a solution to the problem. However, none of them is specifically designed for the ultra-wideband (UWB) technology. UWB is a key solution for wireless connectivity characterized by ultra low power consumption, and high precision ranging. Based on these principles, we propose a novel anomaly detection and localization algorithm for cluster-based UWB wireless sensor networks. Towards securing the cluster formation protocol, we also define a novel, trust-aware leader election metric. The performance of the proposed algorithm in identifying intrusions using a rule-based detection technique is studied via simulations.

Security concerns are a major deterrent in many applications wireless sensor networks are envisaged to support. To date, various security mechanisms have been proposed for these networks dealing with either Medium Access Control (MAC) layer or network layer security issues, or key management problems. Security visualization is the latest weapon that has been added in the arsenal of a security officer who is tasked with detecting network anomalies by analyzing large amounts of audit data. This paper proposes a novel security visualization system for analyzing and detecting complex patterns of sensor network attacks, called SRNET. Both selective forwarding and jamming attacks are identified through visualizing and analyzing network traffic data on multiple coordinated views, namely the multidimensional crossed view, the crossed view perspective, and the track area view. Through simulations, we demonstrate that SRNET is able to help detect and further identify the root cause of the aforementioned sensor network attacks.

Empathetic behavior has been suggested to be one effective way for Embodied Conversational Agents (ECAs) to provide feedback to learners' emotions. An issue that has been raised is the effective integration of parallel and reactive empathy. The aim of this study is to examine the impact of ECAs' emotional facial and tone of voice expressions combined with empathetic verbal behavior when displayed as feedback to students' fear, sad, and happy emotions in the context of a self-assessment test. Three identical female agents were used for this experiment: 1) an ECA performing parallel empathy combined with neutral emotional expressions, 2) an ECA performing parallel empathy displaying emotional expressions that were relevant to the emotional state of the student, and 3) an ECA performing parallel empathy by displaying relevant emotional expressions followed by emotional expressions of reactive empathy with the goal of altering the student's emotional state. Results indicate that an agent performing parallel empathy displaying emotional expressions relevant to the emotional state of the student may cause this emotion to persist. Moreover, the agent performing parallel and then reactive empathy appeared to be effective in altering an emotional state of fear to a neutral one.

Almost everyone has experienced fear at least once in their life because of a test. Fear can positively mobilise students, when it is under control. However, when fear becomes excessive, it can completely destroy students' performance. Moreover, when dealing with a computerised test, fear can have an even more intense influence on students. The objective of this paper is to analyze about these issues and to propose an adaptive self-assessment system for reducing fear and supporting students' learning during the preparation for exams.

We describe an approach that refines assessment results through user knowledge exploration, incorporating probabilities. We argue that the proposed approach may lead to a better mapping of the assessment results to user knowledge in terms of its adaptivity to the response style of each individual learner.

We present the STack ARchitecture (STAR) automaton. It is a fixed structure, multiaction, reward-penalty learning automaton, characterized by a star-shaped state transition diagram. Each branch of the star contains D states associated with a particular action. The branches are connected to a central "neutral" state. The most general version of STAR involves probabilistic state transitions in response to reward and/or penalty, but deterministic transitions can also be used. The learning behavior of STAR results from the stack-like operation of the branches; the learning parameter is D. By mathematical analysis, it is shown that STAR with deterministic reward/probabilistic penalty and a sufficiently large D can be rendered /spl epsi/-optimal in every stationary environment. By numerical simulation it is shown that in nonstationary, switching environments, STAR usually outperforms classical variable structure automata such as L/sub R-P/, L/sub R-I/, and L/sub R-/spl epsi/P/.

Learning Automata update their action probabilites on the basis of the response they get from a random environment. They use a reward adaptation rate for a favorable environment's response and a penalty adaptation rate for an unfavorable environment's response. In this correspondence, we introduce Multiple Response learning automata by explicitly classifying the environment responses into a reward (favorable) set and a penalty (unfavorable) set. We derive a new reinforcement scheme which uses different reward or penalty rates for the corresponding reward (favorable) or penalty (unfavorable) responses. Well known learning automata, such as the L(R-P);L(R-I); L(R-eP) are special cases of these Multiple Response learning automata. These automata are feasible at each step, nonabsorbing (when the penalty functions are positive), and strictly distance diminishing. Finally, we provide conditions in order that they are ergodic and expedient.

The multiobjective routing problem in multiple-class integrated services networks is presented. A multiserver two-class queuing model is introduced, where packets from the first class can be queued, while packets from the other class are blocked when the number of packets in the system exceeds some threshold. Therefore, the first class wants to minimize its average packet delay, while the other class wants to minimize its blocking probability. The resulting multiobjective routing problem is formulated as a Nash game, where each class tries to minimize its own cost function in competition with the other class. The routing policy for a two-server parallel system is derived and the strategy and performance of each class are shown.>

The problem of routing virtual circuits according to dynamical probabilities in virtual-circuit packet-switched networks is considered. Queueing network models are introduced and performance measures are defined. A decentralized asynchronous adaptive routing methodology based on learning automata theory is presented. Every node in the network has a stochastic learning automaton as a router for every destination node. The routing probabilities that are assigned to the network paths are updated asynchronously on the basis of current network conditions. A learning algorithm suitable for routing is used. Some initial simulation experiments, for a simple network, show convergence to optimal routing.>