Browsing by Author "Lim, Yee Mei"

Now showing 1 - 12 of 12
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    Continuous Stress Monitoring under Varied Demands Using Unobtrusive Devices
    (Taylor & Francis, 2019-07-22) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    This research aims to identify a feasible model to predict a learner’s stress in an online learning platform. It is desirable to produce a cost-effective, unobtrusive and objective method to measure a learner’s emotions. The few signals produced by mouse and keyboard could enable such solution to measure real world individual’s affective states. It is also important to ensure that the measurement can be applied regardless the type of task carried out by the user. This preliminary research proposes a stress classification method using mouse and keystroke dynamics to classify the stress levels of 190 university students when performing three different e-learning activities. The results show that the stress measurement based on mouse and keystroke dynamics is consistent with the stress measurement according to the changes of duration spent between two consecutive questions. The feedforward back-propagation neural network achieves the best performance in the classification.
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    Designing Learning Management System to Encourage ELearning Sustainability
    (Tunku Abdul Rahman University College, 2013) Lim, Yee Mei; Chee, Keh Niang; Ayesh, Aladdin, 1972-; Stacey, Martin
    Many universities have been employing Learning Management System (LMS) in their educational programs for many years. However, sustaining the e-learning environment has become a great challenge for these institutes. Although there was much research conducted to study the success factors of a LMS, understanding the impact of user interface, navigation and usability designs, which may affect the user experience in virtual learning environment, is equally important. It is suggested that during the design stage the instructor should plan and structure the resources to assure interactions that assist in the transfer of skills and knowledge. In addition we can use tools such as email, chat rooms, and discussion boards to provide learners the opportunities to interact and add a new level of depth into their learning. It is also necessary to conduct a complete series of evaluations for testing the accuracy, effectiveness and clarity of the e-learning system. Therefore this research aims to evaluate the effectiveness and clarity of LMS design to encourage e-learning sustainability. We investigate the effectiveness of the LMS in assisting knowledge transfer and interactivity in the virtual learning environment, based on three areas: navigation design, user interface design and usability of the discussion board. An online questionnaire survey was conducted to collect data from students and instructors regarding their experiences with the LMS, and their satisfaction levels in these three areas, as well as to suggest areas of improvements.
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    Detecting and Modelling Stress Levels in E-Learning Environment Users
    (De Montfort University, 2017-03) Lim, Yee Mei
    A modern Intelligent Tutoring System (ITS) should be sentient of a learner's cognitive and affective states, as a learner’s performance could be affected by motivational and emotional factors. It is important to design a method that supports low-cost, task-independent and unobtrusive sensing of a learner’s cognitive and affective states, to improve a learner's experience in e-learning, as well as to enable personalized learning. Although tremendous related affective computing research were done in this area, there is a lack of empirical research that can automatically measure a learner's stress using objective methods. This research is set to examine how an objective stress measurement model can be developed, to compute a learner’s cognitive and emotional stress automatically using mouse and keystroke dynamics. To ensure the measurement is not affected even if the user switches between tasks, three preliminary research experiments were carried out based on three common tasks during e-learning − search, assessment and typing. A stress measurement model was then built using the datasets collected from the experiments. Three stress classifiers were tested, namely certainty factors, feedforward back-propagation neural network and adaptive neuro-fuzzy inference system. The best classifier was then integrated into the ITS stress inference engine, which is designed to decide necessary adaptation, and to provide analytical information of learners' performances, which include stress levels and learners’ behaviours when answering questions.
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    Detecting Cognitive Stress from Keyboard and Mouse Dynamics during Mental Arithmetic
    (IEEE, 2014-08-27) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    Much research has been done to detect human emotion using various computational methods, such as physiological measures and facial expression recognition. These methods are effective but they could be expensive or intrusive as special setups of equipment are needed. Some researchers have utilized nonintrusive methods by using mouse or keyboard analyses and presented comparable effectiveness in detecting human emotion. This paper investigates how both keyboard and mouse features can be combined to detect human stress, particularly cognitive stress induced by time pressure and mental arithmetic problems. The results show that the complexity of the mental arithmetic problem and time pressure affect user behaviour, mouse behaviour and keyboard behaviour significantly. This indicates that automatic analysis of human stress from keyboard and mouse input is potentially useful for providing adaptation in interactive systems such as an e-learning system.
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    Detecting Emotional Stress during Typing Task with Time Pressure
    (IEEE, 2014-08-27) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    Automated stress detection is important in developing adaptive e-learning systems. Empirical evidence suggests that mouse dynamics and keyboard dynamics analyses can be both effective in user behaviour modelling as well as emotion detection compared to physiological measures and facial expression recognitions, and yet they are far less expensive and considered non-intrusive. This paper investigates how mouse dynamics and keyboard dynamics can be affected by emotional stress, particularly stress induced by time pressure, text length and language familiarity. Our research findings show that longer text and unfamiliar language raise users’ stress perceptions. Demanding job such as long typing task could result in anomalous behaviours once the users have lost motivation. Language familiarity mainly affects keyboard behaviour but text length change mouse behaviour. This shows that there is a good potential of developing an adaptive e-learning system by detecting learners’ emotional stress from keyboard and mouse input.
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    The Effects of Menu Design on Users’ Emotions, Search Performance and Mouse Behaviour
    (IEEE, 2014-08-18) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    One should not ignore the fact that affect (or emotion) plays an important role in cognition and learning. For instance, badly designed interface brings negative impact on user’s performance if the user does not find enjoyment in his or her overall experience with the system. Automatic analyses of user behaviour in adaptive e-learning system development is important and it would be good to have an effective yet flexible computation metrics to learn user’s emotion, so that necessary adaptation could be provided to enhance user experience. The introduction of keyboard and mouse analyses shed a light to the development of a non-intrusive and inexpensive automated emotion detection method, as these peripherals are part of the computer system. This research investigates the effects of menu design on users’ emotion, search task performance and their mouse behaviours. The results show that the effects of menu design on users’ search task performance and their mouse behaviours are statistically significant. Menu design factors do affect users’ emotions, which they feel uncomfortable with bad combination of colours, smaller font size, text without code, abbreviated text, use of ambiguous term, random display and the need to scroll. However, their discomfort with the bad menu design does not necessarily affect their search job performance.
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    The Effects of Task Demand and External Stimuli on Learner's Stress Perception and Performance
    (Tunku Abdul Rahman University College, 2016-10-17) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin; Tan, Li Peng
    Over the past decades, research in e-learning has begun to take emotions into account, which is also known as affective learning. It advocates an education system that is sentient of learner's cognitive and affective states, as learners' performance could be affected by emotional factors. This exploratory research examines the impacts of task demand and external stimuli on learner's stress perception and job performance. Experiments are conducted on 160 undergraduate students from a higher learning institution. The results show that the impacts are significant.
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    The Effects of Typing Demand on Emotional Stress, Mouse and Keystroke Behaviours
    (Springer International Publishing, 2015) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    Past research found that cognitive effort is related to emotion, which negative emotion may influence task performance. To enhance learning experience, it is important to have an effective technique to measure user’s emotional and motivational affects for designing an adaptive e-learning system, rather than using a subjective method that is less reliable and accurate. Keystroke and mouse dynamics analyses shed light on a better automated emotion recognition method as compared to physiological methods, as they are cheaper, non-invasive and can be easily set up. This research shows that unification of mouse and keyboard dynamics analyses could be useful in detecting emotional stress, particularly stress induced by time pressure, text length and language familiarity. The changes of mouse and keystroke behaviours of the students are found cohere with their task performance and stress perception. However anomalies in mouse and keystroke behaviours present when the students are pushed beyond their capabilities.
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    The effects of typing demand on learner's Motivation/Attitude-driven Behaviour (MADB) model with mouse and keystroke behaviours
    (IEEE, 2017-07) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    It would be desirable to have an automated means of assessing a learner's motivation and stress levels in an e-learning system, which would give impact on his or her learning performance. This preliminary research examines the effects of typing task demand on Motivation/Attitude-driven Behavior (MADB) model. The model is adapted from what was proposed by Wang [1], which is used to describe how the motivation process drives human behaviours and actions, and how the attitude and decision-making process help to regulate and determine the action to be taken by the learner. The effects of typing demand are tested on learners' stress perceptions, motivation, attitudes, decision, as well as their mouse and keystroke behaviours. The typing demand is varied by the pre-defined text length and language familiarity. The results of Multivariate Analysis of Variance and correlation tests are generally congruent with the MADB model proposed by Wang, but with minor difference. We also found that a learner's behaviour is significantly correlated to his or her mouse and keystroke behaviours. A revised version of MADB model based on e-learning environment is proposed.
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    Exploring Direct Learning Instruction and External Stimuli Effects on Learner's States and Mouse/Keystroke Behaviours
    (IEEE, 2016-08-23) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    A modern Intelligent Tutoring System (ITS) should be sentient of learner's cognitive and affective states, as learner’s performance could be affected by motivational and emotional factors. It is important to design a low-cost and unobtrusive computational method for ITS to determine learner's states automatically. The automated learner's data sensing is useful in the development of personalized e-learning that can adapt learning content according to individual's emotion. Although many past findings relate stressor such as task demand to learner's emotion, however there is a lack of existing studies that examine the impact of stressor on learners’ cognitive states and their mouse and keystroke dynamics. This exploratory research, conducted on 160 undergraduate students, found that the impacts of direct learning instruction and external stress stimuli, such as timer display, on learner's cognitive and affective states are significant. The correlations between direct instruction, external stimuli, learners’ cognitive and affective states, as well as their mouse and keystroke behaviours are also significant.
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    The Motivation/Attitude-Driven Behavior (MADB) Model in E-Learning and the Effects on Mouse Dynamics
    (IGI Global, 2016) Lim, Yee Mei; Ayesh, Aladdin, 1972-; Stacey, Martin
    This paper presents the application of Motivation/Attitude-driven Behavior (MADB) model proposed by Wang (Wang, 2007a) in the e-learning context. The authors’ work demonstrates how mathematical models and formal cognitive processes is developed based on menu search task. The effects of menu design on stress perceptions, motivation and attitudes during the search tasks are being examined. The correlations between emotion (stress perception), motivation, attitude, decision, behavior and mouse behavior are studied. The authors’ findings are consistent with what was proposed by Wang. They also found that behavior is significantly correlated to mouse dynamics, such as mouse speed, mouse idle duration and mouse left click rate. Generally strong behavior strength results in slower mouse movements. The significant effect of behavior on mouse dynamics may be caused by motivation and decision, but not attitude and stress perception.
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    Using Mouse and Keyboard Dynamics to Detect Cognitive Stress During Mental Arithmetic
    (Springer International Publishing, 2015) Ayesh, Aladdin, 1972-; Stacey, Martin; Lim, Yee Mei
    To build a personalized e-learning system that can deliver adaptive learning content based on student’s cognitive effort and efficiency, it is important to develop a construct that can help measuring perceived mental state, such as stress and cognitive load. The construct must be able to be quantified, computerized and automated. Our research investigates how mouse and keyboard dynamics analyses could be used to detect cognitive stress, which is induced by high mental arithmetic demand with time pressure, without using intrusive and expensive equipment. The research findings suggest that when task demand increased, task error, task duration, passive attempt, stress perception and mouse idle duration may increase, while mouse speed, left mouse click and keystroke speed decreased. The significant effects of task demand and time pressure on mouse and keystroke behaviours suggest that stress evaluation from these input devices is potentially useful for designing an adaptive e-learning system.