Lecture Notes in Computer Science- P8 pot

Exploring a Computer-Assisted Managing System with Competence Indicators 25 positive effect on students’ calculation ability. It should be the result that the system is effective to the training of the numeric fluency. After Subtest Three’s original scores are transformed into the percentage scores. The scores in the pre-tests (M = 49.38, SD = 19.71) are significantly better than those in the post-test ones (M = 41.60, SD = 23.01) .The α value of t-test is 0.483. It shows that the scores in pre-tests and post-tests are different (t = 0.04, α < 0.05). It indicates that the system is helpful for students in the ability of mathematical application. After the total original scores are transferred into the percentage scores, the scores in the pre-tests (M = 43.25, SD = 18.87) are significantly better than those in the post-test ones (M =36.88, SD =20.42) .The α value of t test is 0.02. It shows that the scores in pre-tests and post-tests are different (t = 0.2, α < 0.05). It indicates that the system can upgrade the students’ learning effects. Teaching of mathematical concept might not be a field where computer system can develop. The elements provided by the system can not complete teaching of mathe- matical concept. Improvement of students’ calculation ability might be pushed under the training of the system and pressure of time. Most of the evaluation elements are application questions and solving more application questions helps students solve questions. Feasible learning resource categorization method is established in order to build the networked learning environment. 5 Conclusion Using ICTs on teaching activities cannot guarantee the upgrading of teaching quality and learning effects [15] [16]. This study proposes CIs of Grade 1-9 Curriculum as the learning resource classification in the system and further constructs networking learning environment. System feasibility analysis and empirical study prove that this system can allow the students to upgrade their math capacities by self-learning. Thus, we infer that using detailed subitems at different grades of CIs in Grade 1-9 Curriculum as the teaching material classification and control of learning progress is feasible. As to the advantages of computer supporting teaching, for the students with low degree of academic performance, in particular, as long as they absorb supplement teaching, their academic performance can be upgraded. This system uses the detailed subitems at different grades of CIs which the teachers are familiar with to classify the learning components and employs XML language to re-packaging raw data file (teaching materials) uploaded by the teachers so that these teaching materials become the learning components LCMS or LMS can manage or re-arrange. It is thus easier for the teachers to look for teaching materials. The system uses SCORM standard to re-seal the learning components and allows the learning components to be shared at different LMS or LCMS that fulfills the goal of resource share. Through the mechanism of learning process control and feedback, the students can learn on line on their own and facilitate their academic performance. Acknowledgement. We would like to thank the National Council of Taiwan for sup- porting this research under Contract number NSC 96-2520-S-194-002-MY3. 26 Y S. Lai et al. References 1. MOE.: General Guidelines of Grade 1-9 Curriculum of Elementary and Junior High School Education. Taipei, Ministry of Education, Taiwan (1998), http://140.111.1.22/ english/home_policy.htm 2. Bigge, J.L., Stump, C.S., Spagna, M.E., Silberman, R.K.: Curriculum, Assessment, and Instruction for Students with Disabilities. Wadsworth, Belmont (1999) 3. Rogoff, B.: Apprenticeship in Thinking: Cognitive Development in Social Context. Oxford University Press, NY (1990) 4. 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Summary, R., Summary, L.: The Effectiveness of the World Wide Web as an Instructional Tool, Instructional Technology Conference (1998), http://www.mtsu.edu/~tconf/proceed98/rsummary.html F. Li et al. (Eds.): ICWL 2008, LNCS 5145, pp. 27–38, 2008. © Springer-Verlag Berlin Heidelberg 2008 e-Learning Issues under an Affective Perspective Makis Leontidis, Constantine Halatsis, and Maria Grigoriadou Department of Informatics and Telecommunications, University of Athens Panepistimiopolis, GR-15784 Athens, Greece {leon,halatsis,gregor}@di.uoa.gr Abstract. The aim of this paper is to present an Affective Educational Module for distance learning which is called MENTOR. MENTOR constitutes of three main components, the Emotional Component, the Teacher Component and the Visualization Component and its main purpose is to motivate appropriately the student in order to accomplish his learning goals. The basic concern of MEN- TOR is to retain the student’s emotional state positive during the learning proc- ess. To achieve this, it recognizes the emotions of the students and takes them under consideration to provide them with the suitable learning strategy. This kind of strategy is based both on the cognitive abilities and the affective prefer- ences of the student and is stored in the student’s model. The student model supplies the educational system with necessary information with the aim to adapt itself successfully to the student’s needs. Keywords: e-Learning, Affective Computing in Education, Pedagogical Issues. 1 Introduction During the learning process in the real class, a creative teacher usually invests a sig- nificant amount of his efforts and time to identify the personality and the mood of his students in order to find the suitable ways of increasing their motivation [1]. The intrinsic ability of a good teacher to balance subtly and accurately their students’ emotional predisposition, their individual needs and preferences and their current disposition, while he directs them adroitly to their goals’ achievement, is one of the major factors to the student’s progress and successful attainment of learning. Despite the importance of the affective factor, in most educational systems, this crucial parameter seems to have been ignored, since the significant process of learn- ing is supported by methods which are mainly concentrating on the cognitive abilities of the student. Indeed, these systems in their majority develop their educational di- mension, based only on cognitive parameters such as learning styles, without taking into consideration the emotional factors that are related to the mood and the personal- ity of the student. Many Web learning designers realize that this omission deprives the education from a very important pedagogical dimension. Thus, they conceive the necessity to turn their attention to affective subjects that influence learning. As a result, few contemporary educational systems began to consider their opera- tion under an affective perspective with the aim of modelling the emotional processes which are taking place during the educational session [2], [5], [7]. Corresponding 28 M. Leontidis, C. Halatsis, and M. Grigoriadou affective techniques are being incorporated more frequently in educational systems with the aim of recognising student’s emotions, mood and personality [10], [13], [15]. The traditional student model starts to be modified in order to be capable of storing affective information. According to this point of view, we developed the MENTOR which is an Affective Educational Module capable of supporting the learning in the distance education [11]. Although, this Module consists of three main components, which are the Emotional, the Teacher and the Visualisation Components respectively, in this paper we are con- centrating particularly on the Emotional and the Teacher Component, demonstrating the tasks which are taking place during their interaction. As it has already been stated, MENTOR takes into account the personality and the emotional state of the student, in order to decide which is the appropriate affective tactic for him. In traditional learning we refer to teaching as denoting mainly the method which is followed by the teacher for the development of the student’s cogni- tive abilities. This definition implies also, however without stating it clearly, that the teacher is responsible for the emotional control and support of their students [4]. The architecture of the MENTOR is designed with equal respect to the cognitive and the emotional dimension of teaching as well. So, we consider that the Teacher Component which is in charge of the formation of teaching consists of two sub- components, the Teaching Generator and the Pedagogical Generator which are re- sponsible for providing the cognitive and emotional tactic respectively. Therefore, we use the term affective tactic so as to denote that the learning method which is sug- gested by the Teacher Component is a two-dimensional combination of cognitive and emotional guidance and support. Taking the above points into consideration, it seems clearly that the main pur- pose of the MENTOR is to create or to maintain a positive mood to the student, keeping him in track of his learning goals. To achieve this, we need to be aware of the student’s emotional state in every moment. That is stored in the affective stu- dent model, which consists of cognitive and emotional information, and it is pro- vided by the Emotional Component. In accordance with this plan, the model selects and supplies accurately the student with the proper affective tactics. In this manner, it involves effectively the student into the learning process under a fruitful peda- gogical perspective. In the next section, we analyse the significant role of the student’s personality and emotions in learning. In the following section we present the architecture of the MEN- TOR demonstrating how it takes advantage of the Emotional and the Teacher Compo- nents to select the appropriate affective tactic and engage consequently the student efficiently into the learning process. We describe the basic structure of these compo- nents and their operation as well. Finally, we cite the conclusions and further work. 2 Basic Issues of Affective Computing The term Affective Computing involves the intention of Artificial Intelligence re- searchers to model emotions in intelligent systems. According to Picard [18] an affec- tive system must be capable of recognizing emotions, respond to them and react “emotionally”. In fact, the affective computing area could be considered from four e-Learning Issues under an Affective Perspective 29 major perspectives. The first one comprises methods for the automatic recognition of the affective state of a person or mechanism in order for a computerised system to express emotional behaviour in human-computer interaction. The second studies the relationship between cognitive and affective factors which characterize processes such as learning. The third deals with the use of the affective information in order for the system’s adaptation to be achieved. Finally, the affective computing relates to the designing and simulation of lifelike agents which are software entities capable to exhibit believably emotional behaviour optimizing in this way the effectiveness of human-computer interactions. 2.1 Personality and Five-Factor Model The personality determines all those characteristics that distinguish one human being from another. It is related to its behaviour and mental processes and has a permanent character. The most known model of personality is the Five Factor Model (FFM) [14] and results from the study of Costa and McCrae [6]. It is a descriptive model with five dimensions: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroti- cism. Due to these dimensions the model is also called OCEAN model. This model describes an Openness person as accessible to new experiences, creative, imaginative, intellectual, interested in culture, social, emotional aware, with a significant sense of freedom and exploration. According to the intensity of these characteristics a person who belongs to the Openness category is characterized either as Explorer, or as Moder- ate or as Preserver. Conscientiousness refers to a person who is well-organised, dutiful, responsible, persistent in achieving goals, thinking and planning in detail before acting, controlling his impulses, with consolidated points of view. According to the intensity of these characteristics, a person who belongs to the Conscientiousness category is charac- terized either as Focused, or as Balanced or as Flexible. Extroversion refers to a social, energetic, talkative person who is liable to make new acquaintances easily and to dem- onstrate positive emotional behaviour. According to the intensity of these characteristics a person who belongs to the Extroversion category is characterized either as Extrovert, or as Ambivalent or as Introvert. Agreeableness refers to a person who is cooperative, modest, friendly, accommodating, trusting, positive motivated in his interactions with other people and lucks antagonistic intentions. According to the intensity of these char- acteristics a person who belongs to the Agreeableness category is characterized either as Adapter, or as Negotiator or as Challenger. Finally, a negative emotionality is predomi- nant in a Neuroticism person, so this person usually feels nervous, anxious, in pressure, insecure, emotionally unstable and prone to pessimist thoughts. According to the inten- sity of these characteristics a person who belongs to the Neuroticism category is charac- terized either as Reactive, or as Responsive or as Resilient. The descriptive character of FFM and the particular characteristics that accompany each type of personality (traits) allow us to model the student’s personality [15] and use this information in educational applications [5]. The FFM provides us with a reliable way in order to connect a student’s personal- ity with his mood and emotions that he possibly develops during the learning process. This is very useful because we are able to initiate a student’s emotional state and select the suitable pedagogical strategy. . responsible, persistent in achieving goals, thinking and planning in detail before acting, controlling his impulses, with consolidated points of view. According to the intensity of these characteristics,. Issues of Affective Computing The term Affective Computing involves the intention of Artificial Intelligence re- searchers to model emotions in intelligent systems. According to Picard [18] an affec- tive. Apprenticeship in Thinking: Cognitive Development in Social Context. Oxford University Press, NY (1990) 4. English, F.W.: Deciding What to Teach and Test: Developing, Aligning, and Auditing the Curriculum.