The role of co-occurring emotions and personality traits in anger expression

The main aim of the current study was to examine the role of co-occurring emotions and their interactive effects with the Big Five personality traits in anger expression. Everyday anger expression ("anger-in" and "anger-out" behavior) was studied with the experience-sampling method in a group of 110 participants for 14 consecutive days on 7 random occasions per day. Our results showed that the simultaneously co-occurring emotions that buffer against anger expression are sadness, surprise, disgust, disappointment, and irritation for anger-in behavior, and fear, sadness and disappointment for anger-out reactions. While previous studies have shown that differentiating one's current affect into discrete emotion categories buffers against anger expression (Pond et al., 2012), our study further demonstrated the existence of specific interactive effects between the experience of momentary emotions and personality traits that lead to higher levels of either suppression or expression of anger behavior (or both). For example, the interaction between the trait Openness and co-occurring surprise, in predicting anger-in behavior, indicates that less open people hold their anger back more, and more open people use less anger-in behavior. Co-occurring disgust increases anger-out reactions in people low in Conscientiousness, but decreases anger-out reactions in people high in Conscientiousness. People high in Neuroticism are less likely to engage in anger-in behavior when experiencing disgust, surprise, or irritation alongside anger, but show more anger out in the case of co-occurring contempt. The results of the current study help to further clarify the interactions between the basic personality traits and the experience of momentary co-occurring emotions in determining anger behavior

Knowledge Elicitation Methods for Affect Modelling in Education

Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners’ affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners’ affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy

Personality representation: predicting behaviour for personalised learning support

The need for personalised support systems comes from the growing number of students that are being supported within institutions with shrinking resources. Over the last decade the use of computers and the Internet within education has become more predominant. This opens up a range of possibilities in regard to spreading that resource further and more effectively. Previous attempts to create automated systems such as intelligent tutoring systems and learning companions have been criticised for being pedagogically ineffective and relying on large knowledge sources which restrict their domain of application. More recent work on adaptive hypermedia has resolved some of these issues but has been criticised for the lack of support scope, focusing on learning paths and alternative content presentation. The student model used within these systems is also of limited scope and often based on learning history or learning styles.This research examines the potential of using a personality theory as the basis for a personalisation mechanism within an educational support system. The automated support system is designed to utilise a personality based profile to predict student behaviour. This prediction is then used to select the most appropriate feedback from a selection of reflective hints for students performing lab based programming activities. The rationale for the use of personality is simply that this is the concept psychologists use for identifying individual differences and similarities which are expressed in everyday behaviour. Therefore the research has investigated how these characteristics can be modelled in order to provide a fundamental understanding of the student user and thus be able to provide tailored support. As personality is used to describe individuals across many situations and behaviours, the use of such at the core of a personalisation mechanism may overcome the issues of scope experienced by previous methods.This research poses the following question: can a representation of personality be used to predict behaviour within a software system, in such a way, as to be able to personalise support?Putting forward the central claim that it is feasible to capture and represent personality within a software system for the purpose of personalising services.The research uses a mixed methods approach including a number and combination of quantitative and qualitative methods for both investigation and determining the feasibility of this approach.The main contribution of the thesis has been the development of a set of profiling models from psychological theories, which account for both individual differences and group similarities, as a means of personalising services. These are then applied to the development of a prototype system which utilises a personality based profile. The evidence from the evaluation of the developed prototype system has demonstrated an ability to predict student behaviour with limited success and personalise support.The limitations of the evaluation study and implementation difficulties suggest that the approach taken in this research is not feasible. Further research and exploration is required –particularly in the application to a subject area outside that of programming

Using Student Mood And Task Performance To Train Classifier Algorithms To Select Effective Coaching Strategies Within Intelligent Tutoring Systems (its)

The ultimate goal of this research was to improve student performance by adjusting an Intelligent Tutoring System\u27s (ITS) coaching strategy based on the student\u27s mood. As a step toward this goal, this study evaluated the relationships between each student\u27s mood variables (pleasure, arousal, dominance and mood intensity), the coaching strategy selected by the ITS and the student\u27s performance. Outcomes included methods to increase the perception of the intelligent tutor to allow it to adapt coaching strategies (methods of instruction) to the student\u27s affective needs to mitigate barriers to performance (e.g. negative affect) during the one-to-one tutoring process. The study evaluated whether the affective state (specifically mood) of the student moderated the student\u27s interaction with the tutor and influenced performance. This research examined the relationships, interactions and influences of student mood in the selection of ITS coaching strategies to determine which strategies were more effective in terms of student performance given the student\u27s mood, state (recent sleep time, previous knowledge and training, and interest level) and actions (e.g. mouse movement rate). Two coaching strategies were used in this study: Student-Requested Feedback (SRF) and Tutor-Initiated Feedback (TIF). The SRF coaching strategy provided feedback in the form of hints, questions, direction and support only when the student requested help. The TIF coaching strategy provided feedback (hints, questions, direction or support) at key junctures in the learning process when the student either made progress or failed to make progress in a timely fashion. The relationships between the coaching strategies, mood, performance and other variables of interest were considered in light of five hypotheses. At alpha = .05 and beta at least as great as .80, significant effects were limited in predicting performance. Highlighted findings include no significant differences in the mean performance due to coaching strategies, and only small effect sizes in predicting performance making the regression models developed not of practical significance. However, several variables including performance, energy level and mouse movement rates were significant, unobtrusive predictors of mood. Regression algorithms were developed using Arbuckle\u27s (2008) Analysis of MOment Structures (AMOS) tool to compare the predicted performance for each strategy and then to choose the optimal strategy. A set of production rules were also developed to train a machine learning classifier using Witten & Frank\u27s (2005) Waikato Environment for Knowledge Analysis (WEKA) toolset. The classifier was tested to determine its ability to recognize critical relationships and adjust coaching strategies to improve performance. This study found that the ability of the intelligent tutor to recognize key affective relationships contributes to improved performance. Study assumptions include a normal distribution of student mood variables, student state variables and student action variables and the equal mean performance of the two coaching strategy groups (student-requested feedback and tutor-initiated feedback ). These assumptions were substantiated in the study. Potential applications of this research are broad since its approach is application independent and could be used within ill-defined or very complex domains where judgment might be influenced by affect (e.g. study of the law, decisions involving risk of injury or death, negotiations or investment decisions). Recommendations for future research include evaluation of the temporal, as well as numerical, relationships of student mood, performance, actions and state variables

Let's Set Up Some Subgoals: Understanding Human-Pedagogical Agent Collaborations and Their Implications for Learning and Prompt and Feedback Compliance

International audienceResearch on collaborative learning between humans and virtual pedagogical agents represents a necessary extension to recent research on the conceptual, theoretical, methodological, analytical, and educational issues behind co-and socially-shared regulated learning between humans. This study presents a novel coding framework that was developed and used to describe collaborations between learners and a pedagogical agent (PA) during a subgoal setting activity with MetaTutor, an intelligent tutoring system. Learner-PA interactions were examined across two scaffolding conditions: prompt and feedback (PF), and control. Learners' compliance to follow the PA's prompts and feedback in the PF condition were also examined. Results demonstrated that learners followed the PA's prompts and feedback to help them set more appropriate subgoals for their learning session the majority of the time. Descriptive statistics revealed that when subgoals were set collaboratively between learners and the PA, they generally lead to higher proportional learning gains when compared to less collaboratively set goals. Taken together, the results provide preliminary evidence that learners are both willing to engage in and benefit from collaborative interactions with PAs when immediate, directional feedback and the opportunity to try again are provided. Implications and future directions for extending co-and socially-shared regulated learning theories to include learner-PA interactions are proposed

The Effects of Cognitive Disequilibrium on Student Question Generation While Interacting with AutoTutor

AbstractThe purpose of this study was to test the effects of cognitive disequilibrium on student question generation while interacting with an intelligent tutoring system. Students were placed in a state of cognitive disequilibrium while they interacted with AutoTutor on topics of computer literacy. The students were tutored on three topics in computer literacy: hardware, operating system, and the internet. During the course of the study a confederate was present to answer any questions that the participant may have had. Additional analyses examined any potential influence the confederates had on student question asking. Lastly, the study explored the relationship between emotions and cognitive disequilibrium. More specifically, the study examined the temporal relationship between confusion and student generated questions. Based on previous cognitive disequilibrium literature, it was predicted that students who were placed in a state of cognitive disequilibrium would generate a significantly higher proportion of question than participants who were not placed in a state of cognitive disequilibrium. Additionally, it was predicted that students who were placed in a state of cognitive disequilibrium would generate “better” questions than participants who were not in a state of cognitive disequilibrium. Results revealed that participants who were not placed in a state of cognitive disequilibrium generated a significantly higher proportion of questions. Furthermore, there were no significant differences found between participants for deep or intermediate questions. Results did reveal significant main effects as a function of time for certain action units. Lastly, it was discovered that certain measures of individual differences were significant predictors of student question generation

Modeling The Influences Of Personality Preferences On The Selection Of Instructional Strategies Inintelligent Tutoring Systems

This thesis hypothesizes that a method for selecting instructional strategies (specifically media) based in part on a relationship between learning style preference and personality preference provides more relevant and understandable feedback to students and thereby higher learning effectiveness. This research investigates whether personality preferences are valid predictors of learning style preferences. Since learning style preferences are a key consideration in instructional strategies and instructional strategies are a key consideration in learning effectiveness, this thesis contributes to a greater understanding of the relationship between personality preferences and effective learning in intelligent tutoring systems (ITS). This research attempts to contribute to the goal of a truly adaptive ITS by first examining relationships between personality preferences and learning style preferences; and then by modeling the influences of personality on learning strategies to optimize feedback for each student. This thesis explores the general question what can personality preferences contribute to learning in intelligent tutoring systems? So, why is it important to evaluate the relationship between personality preferences and learning strategies in ITS? While one-on-one human tutoring is still superior to ITS in general, this approach is idiosyncratic and not feasible to deliver to [any large population] in any cost-effective manner. (Loftin, 2004). Given the need for ITS in large, distributed populations (i.e. the United States Army), it is important to explore methods of increasing ITS performance and adaptability. Findings of this research include that the null hypothesis that there is no dependency between personality preference variables and learning style preference variables was partly rejected. Highly significant correlations between the personality preferences, openness and extraversion, were established for both the active-reflective and sensing-intuitive learning style preferences. Discussion of other relationships is provided

Reframing the L2 learning experience as narrative reconstructions of classroom learning

In this study we investigate the situated and dynamic nature of the L2 learning experience through a newly-purposed instrument called the Language Learning Story Interview, adapted from McAdams’ life story interview (2007). Using critical case sampling, data were collected from an equal number of learners of various L2s (e.g., Arabic, English, Mandarin, Spanish) and analyzed using qualitative comparative analysis (Rihoux & Ragin, 2009). Through our data analysis, we demonstrate how language learners construct overarching narratives of the L2 learning experience and what the characteristic features and components that make up these narratives are. Our results provide evidence for prototypical nuclear scenes (McAdams et al., 2004) as well as core specifications and parameters of learners’ narrative accounts of the L2 learning experience. We discuss how these shape motivation and language learning behavior

Overcoming foreign language anxiety in an emotionally intelligent tutoring system

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning. Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct and less regulated by the learner, but it is uncomfortable and requires the learner to be in the lab. Employing self-reports is scalable because it is easy to administer in the lab and online. However, it interrupts the learning flow, and people sometimes respond inaccurately. Using sensor-free human behavioral metrics is a scalable and practical measurement because it is feasible online or in class with minimum adjustments. To overcome FLA, researchers have studied the use of robots, games, or intelligent tutoring systems (ITS). Within these technologies, they applied soothing music, difficulty reduction, or storytelling. These methods lessened FLA but had limitations such as distracting the learner, not improving performance, and producing cognitive overload. Using an animated agent that provides motivational supportive feedback could reduce FLA and increase learning. It is necessary to measure FLA effectively with minimal interruption and then successfully reduce it. In the context of an e-learning system, I investigated ways to detect FLA using sensor-free human behavioral metrics. This scalable and practical method allows us to recognize FLA without being obtrusive. To reduce FLA, I studied applying emotionally adaptive feedback that offers motivational supportive feedback by an animated agent