Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

https://www.healio.com/psychiatry/journals/psycann/1976-12-6-12/{806b8887-c02e-40c8-a228-57f1cdb2bdba}/the-stress-of-life.pdf

The stress of life

One distinctive feature of any adaptive system is the user model that represents essential information about each user This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling

User models for adaptive hypermedia and adaptive educational systems

Computer games are very popular among children and adolescents. In this respect, they could be exploited by educational software designers to render educational software more attractive and motivating. However, it remains to be explored what the educational scope of educational software games is. In this paper, we explore several issues concerning the educational effectiveness, appeal and scope of educational software games through an evaluation study of an Intelligent Tutoring System (ITS) that operates as a virtual reality educational game. The results of the evaluation show that educational virtual reality games can be very motivating while retaining or even improving the educational effects on students. Moreover, one important finding of the study was that the educational effectiveness of the game was particularly high for students who used to have poor performance in the domain taught prior to their learning experience with the game.

Combining Software Games with Education: Evaluation of its Educational Effectiveness

Artificial Intelligence and Tutoring Systems

Initializing a student model for individualized tutoring in educational applications is a difficult task, since very little is known about a new student. On the other hand, fast and efficient initialization of the student model is necessary. Otherwise the tutoring system may lose its credibility in the first interactions with the student. In this paper we describe a framework for the initialization of student models in Web-based educational applications. The framework is called ISM. The basic idea of ISM is to set initial values for all aspects of student models using an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm. In particular, a student is first assigned to a stereotype category concerning her/his knowledge level of the domain being taught. Then, the model of the new student is initialized by applying the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. ISM has been applied in a language learning system, which has been used as a test-bed. The quality of the student models created using ISM has been evaluated in an experiment involving classroom students and their teachers. The results from this experiment showed that the initialization of student models was improved using the ISM framework.

http://homepage.cem.itesm.mx/juresti/ITS/Articulos/Modelado%20del%20estudiante/Tsiriga-2004.pdf

A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

This paper describes an intelligent multimedia tutoring system for the passive voice of the English grammar. The system may be used to present theoretical issues about the passive voice and to provide exercises that the student may solve. The main focus of the tutor is on the student's error diagnosis process, which is performed by the student modelling component. When the student types the solution to an exercise, the system examines the correctness of the answer. If the student's answer has been erroneous it attempts to diagnose the underlying misconception of the mistake. In order to provide individualised help, the system holds a profile for every student, the long term student model. The student’s progress and his/her usual mistakes are recorded to this long term student model. This kind of information is used for the individualised error diagnosis of the student in subsequent sessions. In addition, the information stored about the student can also be used for the resolution of an arising ambiguity, as to what the underlying cause of a student error has been.

https://www.jstor.org/stable/jeductechsoci.3.4.139

Student Modelling in an Intelligent Tutoring System for the Passive Voice of English Language

We describe Web Passive Voice Tutor (Web PVT), an adaptive teaching Web-based intelligent computer assisted language learning (ICALL) program that is aimed at teaching non-native speakers the passive voice of the English language. The design of the system has been largely based on the results of an empirical study that was conducted at schools with the collaboration of human teachers. Web PVT incorporates techniques from intelligent tutoring systems (ITS) and adaptive hypermedia (AH) technologies to provide students with individualised instruction and feedback. The system uses a combination of stereotypes and the overlay technique for the initialisation of the student model, which is then refined by observing the student while working with the system. The resulting student model is used for the annotation of the links to topics presented to the student. In addition, it is also used in the process of error diagnosis and the adaptation of feedback and advice provided to the student.

Web Passive Voice Tutor: an intelligent computer assisted language learning system over the WWW

In this paper we describe the method for initializing the student model in a Web-based Algebra Tutor, which is called Web-EasyMath. The system uses an innovative combination of stereotypes and the distance weighted k-nearest neighbor algorithm to initialize the model of a new student. In particular, the student is first assigned to a stereotype category concerning her/his knowledge level based on her/his performance on a preliminary test. The system then initializes all aspects of the student model using the distance weighted k-nearest neighbor algorithm among the students that belong to the same stereotype category with the new student. The basic idea of the application of the algorithm is to weigh the contribution of each of the neighbor students according to their distance from the new student; the distance between students is calculated based on a similarity measure. In the case of Web-EasyMath the similarity measure is estimated taking into account the school class students belong to, their degree of carefulness while solving exercises as well as their proficiency in using simple arithmetic operations.

Initializing the student model using stereotypes and machine learning

In this paper we describe the techniques used for student modelling in an adaptive web-based intelligent computer assisted language learning (ICALL) system. The system teaches the domain of the passive voice of the English language and is called Web Passive Voice Tutor (Web-PVT). It dynamically models a student's progress in learning and is able to provide individualised tutoring and advice, tailored to a student's knowledge level and individual weaknesses. The initialisation of the student model is based on a multidimensional stereotype approach. However, after the individual student has interacted with the system sufficiently, the initial values provided by the stereotype are overwritten to reflect the individual student. This allows the system to adapt to each particular student.

Victoria Tsiriga

Papers

A Framework for the Initialization of Student Models in Web-based Intelligent Tutoring Systems

Student Modelling in an Intelligent Tutoring System for the Passive Voice of English Language

Web Passive Voice Tutor: an intelligent computer assisted language learning system over the WWW

Initializing the student model using stereotypes and machine learning

Modelling the student to individualise tutoring in a web-based ICALL