2
2.1
ITS Currently, there are several types of tutors, however, these tutors have not completely achieved the desired objectives, since they are either autonomous or adaptable, but not both. Besides, they do not consider in realtime an important element that a ects users' learning: their emotional state. There are some of these tutors who assess the user's emotional state only at the end of the work sessions, which is not enough to improve the learning environment [DTGN19b].
The typical architecture of an ITS has the following components: Expert Model, Student Model, Tutor Model, and Interface [AS13].
The Expert Model contains all the concepts, facts, rules, and strategies for solving problems in a given pedagogical domain. Also, it serves as a source of specialized knowledge, which is, a standard for assessing user performance and diagnosing their errors [AS13]. Finally, it performs data analysis and can also make predictions about the knowledge of a given user, as it observes the actions performed by that user.
The Student Model is an overlay of the Expert Model. This model contains the user's cognitive and a ective states in association with their evolution as the learning process progresses. As the user works step by step in the problem-solving process, the system analyzes the user's interaction with the system [AS13]. This model contains the dynamic monitoring of the user's emerging knowledge and skills.
The Tutor Model is the part of the ITS that designs and regulates interactions with the user. This model accepts information from Student Model and Expert Model. Also, it is closely linked to the Student Model, since it makes use of knowledge about the user and its structure of tutorial objectives, to design the pedagogical activity to be introduced. It also monitors student progress, creating a pro le of strengths and weaknesses concerning production rules [AS13].
The Interface is the front-end interaction with the ITS. This system integrates all types of information necessary to interact with the user, through graphics, text, multimedia, video, menus, etc. The Interface is the communication component of the ITS that controls the interaction between the user and the system. The interface translates the internal representation of the interface system to an understandable language for the user and vice versa [AS13]. 2.2
According to [HAN04], the evolution of a ective computing is related to the need to put computers interacting, thinking, receiving and transmitting people's personalities.
Picard and Hassin [Pic99], [HAN04] highlight a ective computing as a research area, which explores how computer systems can identify, classify and prove human personality.
A ective computing can increase the capabilities of con ict management with the customer, as well as increase the e ciency of recommendation systems. A ective computing is about: (a) understanding how emotions play vital roles in persons; (b) regulating our intention; (c) helping people make good decisions; and (d) changing the way we emphasize and prioritize things. Consequently, it is possible to build a personalized computer system with the ability to perceive and interpret the feelings of the human being, providing intelligent, sensitive and adapted responses to situations [PPB+04].
Based on the state of the art section, the idea is to create an ITS adapted to each user, established on behaviour characteristics. In this rst phase, a general structure of an ITS was developed, based on the traditional ITS general framework, which is shown in Figure1. The ITS is composed by 4 main parts: Expert Model, Student Model, Tutor Model, and Interface. With this general framework, we proposed a new framework for every four main parts.
The Expert Model, presented in Figure 2 contains the domains with program content. This domain its divide .in two parts: the content area and the performance area. The content area includes all concepts, facts, and problem-solving strategies for a given domain. Furthermore, the model contains the rules for each domain. The performance area has a standard assessment performance for evaluating user performance and allows diagnosing errors. Also, it is in the expert model that data analysis is performed and it is also possible to make predictions about the knowledge of a given user, as he observes the actions performed by that user.
The Student Model, presented in Figure 3 is subdivided into four levels: student style, student cognition, student emotion and monitor knowledge and skills. In the rst level, student style is based on the user's learning style and user interaction with the systems, where type of learning is classi ed.
In the second level, student cognition, based on the users' cognitive states during learning, the user's cognitive state is classi ed. In the third level, student emotion, based on the user's emotions during learning, the user's current emotion is classi ed. Finally, at the fourth level, monitor knowledge and skills, the user's pro le is created concerning their learning evolution. All of this information is stored in its database. As the user works step by step in the problem solving process, the system analyzes the user's interaction with the system.
The Tutor Model, presented in Figure 4 accepts information from the Student Model and the Expert Model. Besides, it is closely linked to the Student Model, since it makes use of knowledge about the user and its structure of tutorial objectives, to design the pedagogical activity to be introduced. It also monitors user progress, creating a pro le of strengths and weaknesses concerning production rules.
The Interface, presented in Figure 5 is the front-end interaction of an ITS. This system integrates all types of information necessary to interact with the user, through graphics, text, multimedia, video, menus. The Interface is the communication component of the ITS that controls the interaction between the user and the system. The Interface captures data from the user's interaction with the ITS. Data capture is done using a non-invasive and non-intrusive approach. There is a log application that runs in the background, saving the user's necessary events with ITS. This application has a device that generates raw data that describe the user's interaction with the system: mouse, keyboard, and activity. There are also exible sensors that use the information available from other measurements and process parameters to calculate and estimate the amount of raw data. The raw data generated is stored locally until it is synchronized with the web server in the cloud at regular intervals, usually every 5 minutes. In the present study, we only experimented the ITS to monitor the patterns user' behaviour. This is behaviour patterns based on mouse dynamics, keystroke, and user's activity. The idea is monitoring biometric behavioural variations for each activity and bases the set of attributes relevant and machine learning classi ers obtain user' learning preference. This approach used a non-intrusive method to assess the preferences of user while interacting with the computer. 4.1
All involved participants presented computing pro ciency and the rooms were equipped with similar computers, where each participant was randomly assigned to a computer. Information regarding each assessment's duration is presented in Table1.
When the Tutor Model receives the data from sensing Interface, it transforms it so its features can be extracted. Speci cally, it goes through the list of pairs and computes the time during which each window was active (Figure 6). When a user does not change applications for a large amount of time, which are represented by a pair with an empty AppName, the time is added to the last known AppName (since this means that the user is still interacting with it). 4.2
The biometrics features captured of each case study were labeled with the respective activity. Besides, based on the biometric features recorded from di erent soft sensors, the distribution of each feature (e.g. mean, median, standard deviation, etc.) are displayed in di erent scales. To solve this problem, it was important to apply features scaling (i.e. normalisation techniques). In this study, the two methods adopted were max-min normalisation and Z-score normalisation.
Max-min normalisation technique is a normalisation strategy which linearly scales feature value to range [0,1], based on the minimum and maximum values of the set of observed values [TGDN18]. In other words, the minimum value of the feature value is mapped to 0 while the maximum value is mapped to 1. As for Z-score, this technique is a stand-in for the actual measurement, and they represent the distance of a value from the mean measured in standard deviations [TGDN18]. This distribution technique is useful when relating di erent measurement distributions to each acting as a `common denominator'. With this, several machine learning categorisation methods were used to predict the user's activity, through the analysis of his/her behaviour.
Several classi ers were trained and tested to determine the most e cient method to categorise the student's activity. The set of classi cation methods trained and tested were: Support Vector Machine, Nearest Neighbour, Naive Bayes, Neural Network and Random Forest. As for the validation method, a split validation method was used to determine the classi cation performance, where 2/3 of the study cases were used for training the classi ers while the remaining 1/3 was used to test it [TGDN18].
In the end, the model with the lowest error rate was the selected one. Figure 7 presents the set of results from this process, where it shows that the model displays an average minimised error when the number of decision trees is 80.
This paper presents the rst approach to ITS. The approach aims to make the ITS non-invasive and non-intrusive. The entire ITS scheme is proposed, from the Expert Model, through the Student Model, through the Tutor Model to the Interface. Besides, it presents a lot of detail of the modules that make up each model.
It was also presented some results made from real tests that were applied to a group of students in a real context. These tests analyzed the biometric part of the behavior of di erent students with di erent learning styles. The system monitors and analyzes the dynamics of the mouse, keyboard, and tasks to determine the student's interaction with the computer.
In the future, we will continue to develop ITS, namely the Student Model, where we intend to create a system that aggregates all the information. 5.0.1
This work has been supported by FCT - Fundac~ao para a Ci^encia e a Tecnologia within the RD Units project scope UIDB/00319/2020 [AS13]
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