Using mouse dynamics to assess stress during online exams

"Lecture notes in computer science series", ISSN 0302-9743, vol. 9121Stress is a highly complex, subjective and multidimensional phenomenon. Nonetheless, it is also one of our strongest driving forces, pushing us forward and preparing our body and mind to tackle the daily challenges, independently of their nature. The duality of the effects of stress, that can have positive or negative effects, calls for approaches that can take the best out of this biological mechanism, providing means for people to cope effectively with stress. In this paper we propose an approach, based on mouse dynamics, to assess the level of stress of students during online exams. Results show that mouse dynamics change in a consistent manner as stress settles in, allowing for its estimation from the analysis of the mouse usage. This approach will allow to understand how each individual student is affected by stress, providing additional valuable information for educational institutions to efficiently adapt and improve their teaching processes.This work is part-funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-028980 (PTDC/EEI-SII/1386/2012) and project PEst-OE/EEI/UI0752/2014

EUStress: A human behaviour analysis system for monitoring and assessing stress during exams

In today’s society, there is a compelling need for innovative approaches for the solution of many pressing problems, such as understanding the fluctuations in the performance of an individual when involved in complex and high-stake tasks. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. Human stress can be viewed as an agent, circumstance, situation, or variable that disturbs the normal functioning of an individual, that when not managed can bring mental problems, such as chronic stress or depression. In this paper, we propose a different approach for this problem. The EUStress application is a non-intrusive and non-invasive performance monitoring environment based on behavioural biometrics and real time analysis, used to quantify the level of stress of individuals during online exams.FCT - Fuel Cell Technologies Program(NORTE-01-0247-FEDER-017832)info:eu-repo/semantics/publishedVersio

Predicting completion time in high-stakes exams

For the majority of students, assessment moments are associated with significant levels of stress and anxiety. While a certain amount of stress motivates the individual and improves performance, too much stress will have the contrary effect. Stress has therefore a fundamental role on student performance. It should be the educational organizations’ mission to understand the underlying mechanisms that lead to performance anxiety and provide their students with the best coping tools and strategies. In the present study we analyze student behavior during e-assessment in terms of mouse dynamics. Two major behavioral patterns can be identified, based on ten features that quantify the performance of the student’s interaction with the computer: (1) students who are able to sustain performance during the exam and (2) students whose performance varies significantly. Data shows that the behavior of each student during the exam correlates strongly with the time it takes the student to complete it. Several classifiers were trained that predict the completion time of each exam based on the students’ interaction patterns. Two of them do it with an average error of around twelve minutes. Results show that there are still mechanisms that can be explored to better understand the complex relationship between stress, performance and human behavior, that can be used for the implementation of better stress detection, monitoring and coping strategies.This work has been supported by COMPETE, Portugal: POCI-01- 0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia, Portugal within the Project Scope: UID/CEC/00319/2013. This work was funded by ‘‘EUSTRESS – Sistema de Informação para a monitorização e avaliação dos níveis do stress e previsão de stress crónico, Portugal’’, No. 2015/017832 P2020 SI I&DT, (NUP, Portugal, NORTE-01-0247-FEDER-017832) in co-promotion between Optimizer-Lda and ICVS/3B’s-Uminho

X3S: A multi-modal approach to monitor and assess stress through human-computer interaction

Stress evaluation is nowadays gaining an increasing importance in a time in which inter-individual competition continuously pushes us to be better. Indeed, in the workplace, in the academia or in many other contexts there is increasing pressure for better performance, which pushes us forward but also wears us out. This phenomenon has been studied from many different angles, including psychology, medicine or occupational dynamics. In a medical or biological context, stress is a physical, mental, or emotional factor that causes bodily or mental tension, which can cause or influence the course of many medical conditions including psychological conditions such as depression and anxiety. In these cases, individuals are under an increasing demand for performance, driving them to be under constant pressure, and consequently to present variations in their levels of stress. To mitigate this condition, this paper proposes to add a new dimension in human–computer interaction through the development of a distributed multi-modal framework approach entitled X3S, which aims to monitor and assess the psychological stress of computer users during high-end tasks, in a non-intrusive and non-invasive way, through the access of soft sensors activity (e.g. task performance and human behaviour). This approach presents as its main innovative key the capacity to validate each stress model trained for each individual through the analysis of cortisol and stress assessment survey data. Overall, this paper discusses how groups of medical students can be monitored through their interactions with the computer. Its main aim is to provide a stress marker that can be effectively used in large numbers of users and without inconvenienceThis work is part-funded by ERDF–European Regional Development Fund and by National Funds through the FCT–Portuguese Foundation for Science and Technology within project NORTE-01-0247-FEDER-017832. FCT grant with the reference ICVS-BI-2016-005info:eu-repo/semantics/publishedVersio

Intrusion Detection Using Mouse Dynamics

Compared to other behavioural biometrics, mouse dynamics is a less explored area. General purpose data sets containing unrestricted mouse usage data are usually not available. The Balabit data set was released in 2016 for a data science competition, which against the few subjects, can be considered the first adequate publicly available one. This paper presents a performance evaluation study on this data set for impostor detection. The existence of very short test sessions makes this data set challenging. Raw data were segmented into mouse move, point and click and drag and drop types of mouse actions, then several features were extracted. In contrast to keystroke dynamics, mouse data is not sensitive, therefore it is possible to collect negative mouse dynamics data and to use two-class classifiers for impostor detection. Both action- and set of actions-based evaluations were performed. Set of actions-based evaluation achieves 0.92 AUC on the test part of the data set. However, the same type of evaluation conducted on the training part of the data set resulted in maximal AUC (1) using only 13 actions. Drag and drop mouse actions proved to be the best actions for impostor detection.Comment: Submitted to IET Biometrics on 23 May 201

e-Authentication for online assessment: A mixed-method study

Authenticating the students’ identity and authenticity of their work is increasingly important to reduce academic malpractices and for quality assurance purposes in Education. There is a growing body of research about technological innovations to combat cheating and plagiarism. However, the literature is very limited on the impact of e-authentication systems across distinctive end-users because it is not a widespread practice at the moment. A considerable gap is to understand whether the use of e-authentication systems would increase trust on e-assessment, and to extend, whether students’ acceptance would vary across gender, age and previous experiences. This study aims to shed light on this area by examining the attitudes and experiences of 328 students who used an authentication system known as adaptive trust-based e-assessment system for learning (TeSLA). Evidence from mixed-method analysis suggests a broadly positive acceptance of these e-authentication technologies by distance education students. However, significant differences in the students’ responses indicated, for instance, that men were less concerned about providing personal data than women; middle-aged participants were more aware of the nuances of cheating and plagiarism;while younger students were more likely to reject e-authentication, considerably due to data privacy and security and students with disabilities due to concerns about their special needs

Combining mouse and keyboard events with higher level desktop actions to detect mild cognitive impairment

We present a desktop monitoring application that combines keyboard, mouse, desktop and application-level activities. It has been developed to discover differences in cognitive functioning amongst older computer users indicative of mild cognitive impairment (MCI). Following requirements capture from clinical domain experts, the tool collects all Microsoft Windows events deemed potentially useful for detecting early clinical indicators of dementia, with a view to further analysis to determine the most pertinent. Further requirements capture from potential end-users has resulted in a system that has little impact on users? daily activities and ensures data security from initial recording of events through to data analysis. We describe two experiments: firstly, volunteers were asked to perform a short set of known tasks; the second (ongoing) experiment is a longitudinal study, with the software currently successfully running on participants? computers

Intelligent solution for automatic online exam monitoring

E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording

Using computer peripheral devices to measure attentiveness

Attention is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms. The learner’s attention affects learning results and can define the success or failure of a student. The negative effects are especially significant when carrying out long or demanding tasks, as often happens in an assessment. This paper presents a monitoring system using computer peripheral devices. Two classes were monitored, a regular one and an assessment one. Results show that it is possible to measure attentiveness in a non-intrusive way.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio