A Tutor Scooter was added to a Cognitive Tutor for Scatter plots in order to obtain an intelligent tutoring system which teaches students how to create and ...
Towards a Fuzzy Multiagent Tutoring System for M-Learners' Emotion Regulation
1
Mouna Abdelkefi1, Ilhem Kallel1,2 : REGIM-Lab: Research Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, Sfax 3038, Tunisia
2
: Higher Institute of Computer Science and Multimedia of Sfax, Pôle technologique de Sfax, ISIMS, BP 242, Sfax 3021, Tunisia
{mouna.abdelkefi; ilhem.kallel}@ieee.org
Abstract—An intelligent Tutoring System (ITS) is a computerbased instructional system, involving intelligent algorithms and strategies, which brings a remarkable progress in the learning processes when the internet is "always on". In fact, ITS makes inferences about a student’s proficiency level in order to dynamically adapt the learning content or the tutoring style. Around the same time, emotions, whether positive or negative, affect the learning process. Accordingly, the regulation of learners' negative emotional state, is an imperious factor in learning, in order to optimize learners’ performances and to improve learning productivity during an ITS episode. Firstly, this paper presents a systematic literature review (SLR) on strategies for regulating the emotional state of learners in the point of view of (a) emotion recognition, (b) emotion modelling, (c) emotion recognition theories, and (d) emotion recognition approaches and methods. Secondly, the paper proposes a new intelligent method, and presents its architecture as a Fuzzy Emotional Regulation for ITS distributed under the Multiagent Approach. This architecture will be used to generate an Emotionally Intelligent Tutoring Sys-tem (EITS) allowing learner to achieve his educational purpose adaptively, anywhere and at any time.
with ITS [3]. Recent research has shown that emotions are closely related to any cognitive process. In the context of learning, for example, the learner, either in front of a teacher or in front a machine or a device, experiences many emotions, whether positive or negative, that will strongly influence his cognitive abilities. Emotions have increasingly been used in educational settings and are thought to affect students’ learning and performance and are considered part of everyday school life [4] [5] [6] [7]. This paper presents a systematic literature review to determine strategies for regulating the emotional state of learners. We have reviewed the researches that apply the emotion regulation methods in an intelligent tutoring environment. Hope-fully, through the findings of this review, we propose a new a model for emotions regulation that benefits the learners during the learning process.
Keywords-component; systematic literature review (SLR); Intelligent Tutoring Systems (ITS); M-learning; Emotion Regulation; Multiagent system; Fuzzy Logic
In this paper, we carried out an SLR [6] in order to identify the current and original articles applying different strategies for managing the negative emotional state of the learner such as boredom, sadness, anger, hopelessness, anxiety, disgust, to improve the learning productivity and the performance of the learner while learning in an e-learning environment. Its aim is to evaluate and interpret the available relevant studies relating to our research area. Fig. 1 shows a representation of the evolution of emotion regulation publications from the years 2010 to 2016.
I.
INTRODUCTION
Today, it became inconceivable to continue to adopt the same traditional methods in our teaching practice, especially to the digital offspring generation. Therefore, it will be indispensable to include digital pedagogy in the daily practice of the students. In fact, they were born in the 1990s and 2000s and have grown up in the digital age and the Internet "always on". Indeed, Information and Communication Technologies (ICT) and Intelligent Tutoring Systems (ITS) also have an important role in reducing the rate of academic failure. One of the ICT service qualities is the functionalities of M-learning technology which can be improved by the addition of existing courses with valueadded features such as alerts, personalized agents or communication aids such as conversational agent [1] as well as access to interaction or discussion utilities that help learners convert their dead-time to productive activities and dynamically adapt the content or style of instruction [2]. Besides, Emotional Intelligence was beginning to rise as a new and exciting research area. The regulation of the learners' negative emotional state is an important factor in learning, in order to optimize learners’ performances and to improve the learning productivity of the learner during a learning episode
II.
PLANNING AND CONDUCTING THE REVIEW OF EMOTION REGULATION IN EDUCATION
16000 14000 Emotion régulation in ELearning
12000 10000 8000
Fuzzy Emotion Regulation
6000 4000
MultiAgent Emotion regulation
2000 0 2010 2011 2012 2013 2014 2015 2016
Fig 1. Evolution of Emotion regulation publications (Google scholar)
978-1-5386-3968-9/17/$31.00 ©2017 IEEE
A. Research Questions The following four research questions guide our review to give answers in the rest of this paper: • • • •
RQ1: What is the impact of positive and negative
emotions in ITS learner outcomes? RQ2: What are the appropriate intelligent methods for regulating learners’ negative emotions?
RQ3: Can we distribute and parallelize the problem of emotion recognition and regulation under Multiagent approach? RQ4: How to evaluate the adaptation of the proposed tutoring process?
B. Study selection
Gross divides emotion regulation strategies into two categries: antecedent-focused ones and response-focused ones [10]. Antecedent-focused strategies (i.e., situation selection, situation modification, attentional deployment, and cognitive change) occur before an emotional response is fully generated to influence an emotional state. Responsefocused strategies (i.e., response modulation) occur after an emotional response is fully generated. The fig.2 describes these emotion regulation strategies [10].
Situation
For purposes of this study, we have studied the electronic databases that are relevant to education, psychology, information technology and social science: (a) IEEE XPLORE, (b) Science Direct, (c) Springer, ACM Digital Library, (d) Springer Link, (e) ERIC (Educational Resources Information Center) and (f) Scopus. Moreover, we used the following steps to create the search strings: Listing keywords mentioned in the defined problem, using words, synonyms and derivatives, and using the Boolean OR and AND to link the major terms. The defined search terms are as follows: Emotion Regulation, Education, Intelligence Tutoring System, Elearning, Conversational Agent, Multi-agent, fuzzy logic, Intelligent Methods. As a result, the search strings identified by Eq (1) were used for the search of the literature: Emotion Regulation AND [(Education OR ITS OR E-learning) OR (Multi-agent OR Fuzzy OR Artificial Intelligence)]
III.
(appraisal). (b) Emotion suppression strategies (c) emotional responding-based strategies.
(1)
OVERVIEW OF SOME RELEVANT WORKS ON EMOTION
REGULATION IN ITS Emotions play a major role in cognitive processes, especially in learning tasks. Learning is a fundamental component of everyone’s life. Not until recently has psychology turned its attention to the study of negative emotion or negative affect, including: depression, sadness, anger, stress and anxiety [8]. Moreover, several neurological researches have shown the interrelation between cognition and emotions [9]. The research in Cognitive Psychology and Education reveals that the concept of emotion regulation enjoys a substantial attention in research with adults [10] and children [11] as well as with the neuroscience perspective [12]. The emotion regulation can be defined as a process by which a person modifies the experience of emotional expression as well as situations that stimulate such emotions in order to improve his welfare or to better respond to environmental issues. Three types of emotion regulation strategies are generally distinguished in cognitive psychology: (a) the situation of cognitive re-evaluation strategies
• Situation selection
Attention
• Situation modification
• Attentional deployment
Appraisal
• Cognitive change
Response
• Response Modulation
Fig.2 The process model of emotion regulation According to the literature, research efforts go to integrate the regulation of emotions into an ITS. This latter, or another advanced learning environments began in the late 1970s and have implemented sophisticated instructional procedures. ITS were able to induce the characteristics of individual learners at a fine-grained level, to attribute problems or tasks which are learner’s profile sensitive, and to generate specific tutoring actions to optimize learning according to scientific principles [13]. In traditional ITS, most researchers investigate on the automatic identification of the learner's emotional state. However, the present generation of ITS which is related to the regulation of negative emotional states in the users’ learning process, has gone a giant field in affective computing and is beginning to be intensified [14]. This section presents some reviews of the prominent research in the area of ITS based on the regulation of the learners’ negative emotion sate. However, it is worth noting that in most research studies, researchers have not specifically stated the emotion regulation strategies they have applied in their cognitive models for managing the learner' negative emotions.
In [15], the authors designed an interactive software agent. A Tutor Scooter was added to a Cognitive Tutor for Scatter plots in order to obtain an intelligent tutoring system which teaches students how to create and interpret scatter plots of
data. The Scatter Plot Tutor was originally designed as part of the Middle School Mathematics Tutor [16]. They focused their analyses on the impact of an agent on the students’ affect, specifically an agent which makes requests of students and responds emotionally to their behaviours (such as boredom, confusion, delight, engaged concentration, frustration, neutral, and surprise). Scooter responded to gaming behaviour with a combination of meta-cognitive messages, expressions of positive and negative emotion, and supplementary exercises covering the material the student bypassed through gaming. Indeed, Rodrigo et al. in [15] had studied the student affect while using Scooter in a fine-grained fashion, focusing on the dynamics of affect when using Scooter and when using the same tutor without Scooter. Some test experiences in US classrooms, showed that Scooter successfully reduced gaming and significantly improved gaming students’ learning relative to the basic tutor. Furthermore, the Scooter tutor has a positive impact on students in the Philippines (participating students’ ages ranged from approximately 12 to 14) based on their subjective evaluation. Whereas students in US did not like Scooter and it did not have a significant influence on students’ emotional states or their dynamics such as boredom, confusion, and engaged concentration.
AutoTutor and Affective AutoTutor [17] are interactive intelligent systems that promote learning and engagement. AutoTutor is an intelligent tutoring system that helps students compose explanations of difficult concepts in Newtonian physics and enhances computer literacy and critical thinking by interacting with them in natural language with adaptive dialog moves similar to those of human tutors. Affective AutoTutor takes the individualized instruction and human-like interactivity to a new level by automatically detecting and responding to students’ emotional states in addition to their cognitive states. Mao and Li [18] proposed a new IETS capable to identify the students’ affective state and able to react accordingly by expressing the pedagogical movements in an affectively suitable way. To be specific, based on ALICE, they provided the agent tutor with emotion detection capability through facial expression, speech and text produced by the AIML (Artificial Intelligence Markup Language). They used the model of affective information processing to provide the ability of reasoning. However, the agent tutor can analyze the facial expression, speech and text input by the student to sense the underpinned affective qualities. Then, they consulted more than 100 excellent human teachers to construct a database of common-sense knowledge (by human tutor common sense system) to let the agent tutor know what to do with the affective state of leaner for each scenario applied in different learning situations. Mao and Li applied the emotional-aware agents (facial expression, synthetic emotional speech, and animated pedagogical emotions) in their ITS to optimize the
learner behaviour towards learners’ enjoyment of the learning situation. Moreover, they searched to influence a particular mood state to the learner, or at least a positive impression.
In Tables 1, we tried to present a synthesis matrix of literature review about relevant methods on emotion regulation in ITS. This table let us corroborate that this new research field is still open and we have not yet enough experimental results to be able to compare the weaknesses and the strengths of each method. Table 2 offers another critical overview of research works in emotion regulation. In fact, it is based of emotion regulation theories and approaches or methods. As far as that goes, we still cannot perceive a research tendency in emotion regulation for ITS. IV.
TOWARDS A FUZZY MULTIAGENT METHOD FOR EMOTION REGULATION IN ITS
Emotions have much to do with the learners’ performances. Consequently, e-learning systems should integrate emotional capabilities in order to optimize learners' experience. For inter-actionist theorists, the success of any learning process depends greatly on the learner's emotional predispositions. Indeed, according to them, intelligence depends closely on emotion. Daniel Goleman [25] was one of the first to draw the attention of the general public to the theory of emotional intelligence and to argue that emotions drive us day after day. Emotions thus play a fundamental role in any cognitive process and prove to be essential during learning. They influence the psychological and biological states and they act on the learner's attention and on his abilities to understand and memorize [26]. A learner experiencing emotional distress (fear of failure, fear, boredom, psychological pressures of diverse natures, feeling of incompetence, sadness, anger, hopelessness, anxiety, disgust, etc.) sees his learning performance reduced [27]. Alternatively, if it is emotionally supported, learning works better and the learner presents better results. For all these reasons, several Intelligent Tutoring Systems (ITS) now take into consideration the emotional aspects of the learner. Tutoring systems such as Scooter, AutoTutor, Affective AutoTutor, Alice and other systems purport to test the assumption that, ITS taking into account the emotions of learners, increase learning performance. These ITS are based merely on the detection of facial expressions, speech recognition and/or textual recognition. However, to date, there is no system capable of taking into consideration the environmental situation of learning. Therefore, emotional regulation will have to be influenced, which has led us to propose a new hypothesis in this direction. To over-come this major deficiency, we have sought to develop an ITS with some capacities of emotional regulation.
We propose a fuzzy emotion regulation method as a kernel module in a regulation agent. The figure 3 present the multiagent model of our proposal with respect of the emotion process model in Figure 2.
TABLE I.
SYNTHESIS MATRIX OF LITERATURE REVIEW ABOUT RELEVANT METHODS ON EMOTION REGULATION IN ITS
Emotion
Emotion
Recognition
Modelling
Agent
Context
Virtual enEmotional (Facial) Expression:
Five basic emotions (happy, sad, anger, fear, and surprise) as defined by Ekman
Intelligent
vironment/
agent
Driver as
(EMIA)
An agent on the road
Interactive Textual Interaction
McGill Friendship Questionnaire (MFQ)
Sad, anger, hopeless, anxiety, and disgust
Questionnaire (PSSQ) Emotional experience (relies on experience and intuition) Emotional expression (relies on logic and rationality)
folds - decomposing a task into small sub-tasks, without hindering user’s self-exploration.
mous vir-
lation in friendship functions
tual agents
Emotion Regulation Agent framework (ERA)
Learning agent
Personality, emotional arousal, emotion regulation,
narrative or game environment
Virtual learning Environment (VLE) Singapore
Regulation architecture
Web system based on ----
learning performance evaluation
S.JAIN and K.ASAWA,
2015 [19]
Reduce the complexity of tasks by: - reducing time and mental effort
text-based applications
sonal Emotion Regu-
decision making styles, and problem solving styles
- reason backwards about emotions by using two emotional meta-operators, - perform interpersonal emotion regulation.
Chat
Autono-
Hyper fear emotional response
- generate emotions,
- providing hints or automating task scaf-
influence of Interper-
Mood Evaluation
agents that is able to:
Chinese
Interactive
Ref.
A generic model for emotionally intelligent
Emotion-
Determine the overall
Negative emotion state
Results’ Strengths
- Antecedent-focused emotion regulation: regulates the emotion before emotion generation, - Response-focused emotion regulation: regulates the emotion after its generation.
Comfort/relax the e-learner’s negative emotions.
- Action Outperforms the performance of the non-fuzzy model by providing a more realistic and smoother regulation process. - more adaptivity to the environmental changes - more consistency with Gross theory.
- A person with a high in emotional experience and emotional expression scales will have a tendency towards more emotional and less rational styles. - The behaviour is altered by affective elements in decision making and problem solving routines as its performance in cognitive processing tasks
Tian et al,2014 [20]
Dias and Paiva, 2013 [21]
Chanet al,2015 [22]
A. Soleimani and Z.Kobti, 2013 [24]
Zacharis et al.,2013 [23]
TABLE II.
SYNTHESIS MATRIX OF RELATED INTELLIGENT TUTORING SYSTEM BASED ON EMOTION REGULATION THEORIES AND STRATEGIES S.JAIN and K.ASAWA, 2015
[19] Gross model
EMOTION REGULATION
OCC Ortony, Clore & Colins PAD Emotion Theories
Tian et al,2014
[20]
Dias and Paiva, 2013 [21]
Chanet al, 2015 [22]
Zacharis et al.,2013 [23]
A. Soleimani and Z.Kobti, 2013 [24]
*
*
*
*
*
* *
Roseman Lazarus
*
Scherer
*
Text-based
Approaches or Strategies
Social approach/ Interpersonal (IER)
*
Computer based approach
*
Active listening approach
*
Fuzzy Inference System
*
*
Fig 3. Architecture of the Proposed Method: Fuzzy Emotional Regulation for ITS distributed under the Multiagent Approach
*
First, the Situation phase is managed by an agent group responsible of the emotion recognition. In fact, the identification of the student’s emotional state is performed according to the generation of speech, facial expressions, and other gestures as well as the influence of the M-learning environment. Second, in the Attention and the Appraisal phases, two Emotion agents (Negative and Positive) perform in parallel and interact in order to classify the perceived emotion. These emotions are deployed as an affective learner state about the corresponding agent. As a response, the latter agent interacts with a Fuzzy emotion regulation agent in order to regulate the cognitive representation of the M-learner in the learning episode. The multiagent system is able to go further by completing the process through the integration of the tutoring strategies and leaner-tutor conversations.
V.
CONCLUSION AND FUTURE WORKS
In this paper, we analyzed and categorized the most common studies about emotion regulation in different context for Intelligent Tutoring System and learning environment. We proposed a new architecture of Fuzzy Emotional Regulation for ITS, distributed under the Multiagent approach. This architecture will be used to generate an Emotionally Intelligent Tutoring System (EITS) allowing learner to achieve his educational purpose adaptively. Currently we are developing the regulation agent behaviour as a fuzzy system and working on tuning its fuzzy database and rule base. ACKNOWLEDGMENT The authors would like to acknowledge the financial support of this work by grants from the General Direction of Scientific Research (DGRST), TUNISIA, under the ARUB program. [1]
REFERENCES Abdelkefi, M., & Kallel, I. (2016). Conversational agent for mobilelearning: A review and a proposal of a multilingual text-to-speech agent,“MobiSpeech”. Research Challenges in Information Science (RCIS), 2016 IEEE Tenth Int. Conference (pp. 1-6). IEEE.
[2]
Jacob, S. M., & Issac, B. (2014). The mobile devices and its mobile learning usage analysis. ArXiv preprint arXiv:1410.4375. Proceedings of the International MultiConference of Engineers and Computer Sci-entists 2008 Vol I IMECS 2008, 19-21 March, 2008, Hong Kong
[3]
Malekzadeh, M., Mustafa, M. B., & Lahsasna, A. (2015). A Review of Emotion Regulation in Intelligent Tutoring Systems. Educational Technology & Society, 18 (4), 435-445. Zeidner, M., Matthews, G., & Roberts, R. D. (2012). What we know about emotional intelligence: How it affects learning, work, relationships, and our mental health. MIT press. Petrovica, S., Anohina-Naumeca, A., & Ekenel, H. K. (2017). Emotion Recognition in Affective Tutoring Systems: Collection of Ground-truth Data. Procedia Computer Science, 104, 437-444. Abid, A., Kallel, I., & Ben Ayed, M. (2016). Teamwork construction in E-learning system: A systematic literature review. In Information Technology Based Higher Education and Training (ITHET), 2016 15th International Conference on (pp. 1-7). IEEE. Gross, J. J. (Ed.). (2013). Handbook of emotion regulation. Guilford publications. Marroquín, B. (2011). Interpersonal emotion regulation as a mechanism of social support in depression. Clinical psychology re-view, 31(8), 1276-1290.
[4]
[5]
[6]
[7] [8]
[9]
Pessoa, L. (2008). On the relationship between emotion and cognition. Nature reviews neuroscience, 9 (2), 148-158. [10] , J. J. (1998). The emerging field of emotion regulation: An integrative review. Review of general psychology, 2(3), 271. [11] Cole, P. M., Martin, S. E., & Dennis, T. A. (2004). Emotion regulation as a scientific construct: Methodological challenges and directions for child development research. Child development, 75(2), 317-333.
[12] Ochsner, K. N., Bunge, S. A., Gross, J. J., & Gabrieli, J. D. (2002). Rethinking feelings: an FMRI study of the cognitive regulation of emo-tion. Journal of cognitive neuroscience, 14(8), 1215-1229. [13] Marinov, M., & Valova, I. (2016). Application of planning techniques in knowledge-managed tutoring systems. In Information Technology Based Higher Education and Training (ITHET), 2016 15th International Conference on (pp. 1-5). IEEE. [14] Ibrahim, M. S., & Hamada, M. (2016). Adaptive learning framework. In Information Technology Based Higher Education and Training (ITHET), 2016 15th International Conference on (pp. 1-5). IEEE. [15] Rodrigo, M. M. T., Baker, R. S., Agapito, J., Nabos, J., Repalam, M. C., Reyes, S. S., & San Pedro, M. O. C. (2012). The effects of an inter-active software agent on student affective dynamics while using; an intelligent tutoring system. IEEE Transactions on Affective Compu-ting, 3(2), 224-236. [16] D Baker, R. S., Corbett, A. T., Koedinger, K. R., Evenson, S., Roll, I., Wagner, A. Z., ... & Beck, J. E. (2006). Adapting to when students game an intelligent tutoring system. In International Conference on In-telligent Tutoring Systems (pp. 392-401). Springer Berlin Heidelberg.
[17] D'mello, S., & Graesser, A. (2012). AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems (TiiS), 2(4), 23. [18] Mao, X., & Li, Z. (2009). Implementing emotion-based user-aware elearning. In CHI'09 Extended Abstracts on Human Factors in Computing Systems (pp. 3787-3792). ACM. [19] Jain, S., & Asawa, K. (2015). EMIA: emotion model for intelligent agent. Journal of Intelligent Systems, 24(4), 449-465. [20] Tian, F., Gao, P., Li, L., Zhang, W., Liang, H., Qian, Y., & Zhao, R. (2014). Recognizing and regulating e-learners’ emotions based on interactive chinese texts in e-learning systems. Knowledge-Based Systems, 55, 148-164. [21] Dias, J., & Paiva, A. (2013). I want to be your friend: establishing relations with emotionally intelligent agents. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems (pp. 777-784). International Foundation for Autonomous Agents and Multiagent Systems. [22] Shun, M. C. Y., Yan, M. C., Bo, A., & Cyril, L. (2015). Modeling Learner's Emotions with PAD. In Advanced Learning Technologies (ICALT), 2015 IEEE 15th International Conference on (pp. 49-51). IEEE. [23] Lekkas, Z., Germanakos, P., Tsianos, N., Mourlas, C., & Samaras, G. (2013, July). Personality and emotion as determinants of the learning experience: how affective behavior interacts with various components of the learning process. In International Conference on Human-Computer Interaction (pp. 418-427). Springer Berlin Heidelberg. [24] Soleimani, A., & Kobti, Z. (2013). A fuzzy logic computational model for emotion regulation based on Gross theory. In Proceedings of Flor-ida Artificial Intelligence Research Society Conference. AAAI Press,
[25] Goleman, D., Boyatzis, R., & McKee, A. (2013). Primal leadership: Unleashing the power of emotional intelligence. Harvard B. Press. [26] Oxford, R. L., & Bolaños-Sánchez, D. (2016). A Tale of Two Learners: Discovering Mentoring, Motivation, Emotions, Engagement, and Perseverance. In New Directions in Language Learning Psychology (pp. 113134). Springer International Publishing.
[27] Gross, J. J. (2002). Emotion regulation: Affective, cognitive, and social consequences. Psychophysiology, 39(3), 281-291 .
