Christos N. Moridis

The effect of emotional feedback on behavioral intention to use computer based assessment

This study introduces emotional feedback as a construct in an acceptance model. It explores the effect of emotional feedback on behavioral intention to use Computer Based Assessment (CBA). A female Embodied Conversational Agent (ECA) with empathetic encouragement behavior was displayed as emotional feedback. More specifically, this research aims at investigating the effect of Emotional Feedback on Behavioral Intention to Use a CBA system, Perceived Playfulness, Perceived Usefulness, Perceived Ease of Use, Content and Facilitating Conditions. An appropriate survey questionnaire was completed by 134 students. Results demonstrate that Emotional Feedback has a direct effect on Behavioral Intention to Use a CBA system and on other crucial determinants of Behavioral Intention. Finally, the proposed acceptance model for computer based assessment extended with the Emotional Feedback variable explains approximately 52% of the variance of Behavioral Intention.

Continuance acceptance of computer based assessment through the integration of user's expectations and perceptions

The Information Systems (IS) community has put considerable effort on identifying constructs that may explain the initial/continuance use of computer based learning or assessment systems. This study is a further step toward IS continuance acceptance delivered in Computer Based Assessment (CBA) context. Specifically, it aims at the exploration of continuance acceptance in CBA context and the development of a new approach for continuance use. This approach is applied by measuring users expectations before the interaction with the system and users perceptions after the interaction. The sign (?) and the intensity of the difference between pre and post measures is actually the strength that drives users to continuance intention to use. Through these measurements it is possible to estimate students level of confirmation for each essential determinant of CBA continuance acceptance. Results underline Confirmed Ease of Use and Confirmed Playfulness as the direct determinants of continuance acceptance. Through Confirmed Playfulness important determinants, such as Confirmed Usefulness and Confirmed Content influence Continuance Behavioral Intention. Confirmed Facilitating Conditions and Confirmed Goal determine Confirmed Ease of Use and Confirmed Usefulness respectively. Important implications of these results are discussed. Highlights? A new approach is proposed for continuance use in Computer Based Assessment (CBA). ? The difference between pre and post measures used to explain continuance intention. ? Pre and post measures estimate students level of confirmation for each determinant. ? Confirmed Ease of Use and Confirmed Playfulness define CBAs continuance acceptance.

How student's personality traits affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM

Researchers in the Information Systems (IS) field have put considerable effort on identifying how personality affects technology acceptance. This study is a further step towards this direction within the context of Computer Based Assessment (CBA). Particularly, it investigates how the five personality factors affect the most important variables regarding CBAs acceptance. For this purpose, 117 participants were required to complete a survey questionnaire. The questionnaire included the Big Five Inventory (BFI) questions in order to measure the five personality traits, and 23 items regarding students perceptions. Partial Least Squares (PLS) was used to test the measurement and the structural model. Results underline that Neuroticism has significant negative effect on Perceived Usefulness and on Goal Expectancy, Agreeableness determines Social Influence and Perceived Ease of Use, Conscientiousness defines Perceived Ease of Use, while Extroversion and Openness explain Perceived Importance. Important implications of these results are discussed.

Measuring instant emotions based on facial expressions during computer-based assessment

Emotions are very important during learning and assessment procedures. However, measuring emotions is a very demanding task. Several tools have been developed and used for this purpose. In this paper, the efficiency of the FaceReader during a computer-based assessment (CBA) was evaluated. Instant measurements of the FaceReader were compared with the researchers’ estimations regarding students’ emotions. The observations took place in a properly designed room in real time. Statistical analysis showed that there are some differences between FaceReader’s and researchers’ estimations regarding Disgusted and Angry emotions. Results showed that FaceReader is capable of measuring emotions with an efficacy of over 87% during a CBA and that it could be successfully integrated into a computer-aided learning system for the purpose of emotion recognition. Moreover, this study provides useful results for the emotional states of students during CBA and learning procedures. This is actually the first time that student’s instant emotions were measured during a CBA, based on their facial expressions. Results showed that most of the time students were experiencing Neutral, Angry, and Sad emotions. Furthermore, gender analysis highlights differences between genders’ instant emotions.

Toward Computer-Aided Affective Learning Systems: A Literature Review

The aim of this survey is to provide an overview of the various components of “computer aided affective learning systems.” The research is classified into 3 main scientific areas that are integral parts of the development of these kinds of systems. The three main scientific areas are: I) emotions and their connection to learning; ii) affect recognition; and iii) emotional instruction and design. Affective learning instructional technology is a new, multi-disciplinary research area, which has been developed during the last decade. This article depicts the development of the core relevant areas and describes the concerns.

Mood Recognition during Online Self-Assessment Tests

Individual emotions play a crucial role during any learning interaction. Identifying a students emotional state and providing personalized feedback, based on integrated pedagogical models, has been considered to be one of the main limits of traditional tools of e-learning. This paper presents an empirical study that illustrates how learner mood may be predicted during online self-assessment tests. Here a previous method of determining student mood has been refined based on the assumption that the influence on learner mood of questions already answered declines in relation to their distance from the current question. Moreover, this paper sets out to indicate that ldquoexponential logicrdquo may help produce more efficient models, if integrated adequately with affective modelling. The results show that these assumptions may prove useful to future research.

The impact of audio-visual stimulation on alpha brain oscillations: An EEG study

Many studies investigated the brain responses as a reaction in auditory or visual stimuli separately. However a few studies have been published so far investigating the interactions of the two aforementioned stimuli. The current study comes to examine the impact of the audio-visual stimulation with binaural beats and flickering light in four different colors on low and upper alpha oscillations. For this purpose electroencephalogram (EEG) was adopted and Event Related Desynchronization/Event Related Synchronization (ERD/ERS) has been used as an index in order to investigate the alpha brain responses. Statistically significant results suggest that the combination of audio-visual stimuli with binaural beats and flickering light color at 8 and 10 Hz respectively can evoke significant Following Frequency Response (FFR) of the low and upper alpha oscillations.

Modeling Student's Mood during an Online Self-assessment Test

Student’s emotional state is crucial during learning. When a student is in a very negative mood, learning is unlikely to occur. On the other hand too positive mood can also impair learning. Thus a key issue for instructional technology is to recognize student’s mood, so as to be able to provide appropriate feedback. This paper introduces a model of student’s mood during an online self-assessment test. The model was evaluated using data emanated from experiments with 153 high school students from 3 different regions of Greece. The results confirm the model’s ability to estimate a student’s mood.

Affective learning: Empathetic embodied conversational agents to modulate brain oscillations

Integrating emotional feedback to educational systems has become one of the main concerns of the affective learning research community. This paper provides evidence that Embodied Conversational Agents (ECAs) could be effectively used as emotional feedback to improve brainwave activity towards learning. Further research, integrating ECAs into tutoring systems is essential to confirm these results.