Ақылды оқыту жүйелері: Білім берудегі жаңа тәсілдер

Computer system to provide instruction to learners
An intelligent tutoring system (ITS) is a computer system that imitates human tutors and aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one to one, personalized tutoring, in contexts where students would otherwise have access to one to many instruction from a single teacher (e. g., classroom lectures), or no teacher at all (e. g., online homework). These early works inspired later developments. The concept of intelligent machines for instructional use date back as early as 1924, when Sidney Pressey of Ohio State University created a mechanical teaching machine to instruct students without a human teacher. His machine resembled closely a typewriter with several keys and a window that provided the learner with questions. The Pressey Machine allowed user input and provided immediate feedback by recording their score on a counter. Pressey was influenced by Edward L. Thorndike, a learning theorist and educational psychologist at the Columbia University Teachers' College of the late 19th and early 20th centuries. Thorndike posited laws for maximizing learning. Thorndike's laws included the law of effect, the law of exercise, and the law of recency. By later standards, Pressey's teaching and testing machine would not be considered intelligent as it was mechanically run and was based on one question and answer at a time, Early implementations in education focused on programmed instruction (PI), a structure based on a computerized input output system. Although many supported this form of instruction, there was limited evidence supporting its effectiveness. A new perspective would emerge that focused on the use of computers to intelligently coach students called Intelligent Computer Assisted Instruction or Intelligent Tutoring Systems (ITS). Where CAI used a behaviourist perspective on learning based on Skinner's theories (Dede & Swigger, 1988), ITS drew from work in cognitive psychology, computer science, and especially artificial intelligence. Computers were still expensive and not as available as expected. Developers and instructors were reacting negatively to the high cost of developing CAI programs, the inadequate provision for instructor training, and the lack of resources. The LISP Tutor could identify mistakes and provide constructive feedback to students while they were performing the exercise. The system was found to decrease the time required to complete the exercises while improving student test scores (Corbett & Anderson, 1992). and PARNASSUS created in Carnegie Mellon University in 1989 for language instruction.

After the implementation of initial ITS, more researchers created a number of ITS for different students. In the late 20th century, Intelligent Tutoring Tools (ITTs) was developed by the Byzantium project, which involved six universities. The ITTs were general purpose tutoring system builders and many institutions had positive feedback while using them. (Kinshuk, 1996) This builder, ITT, would produce an Intelligent Tutoring Applet (ITA) for different subject areas. Different teachers created the ITAs and built up a large inventory of knowledge that was accessible by others through the Internet. Once an ITS was created, teachers could copy it and modify it for future use. This system was efficient and flexible. However, Kinshuk and Patel believed that the ITS was not designed from an educational point of view and was not developed based on the actual needs of students and teachers (Kinshuk and Patel, 1997). Recent work has employed ethnographic and design research methods to examine the ways ITSs are actually used by students and teachers across a range of contexts, often revealing unanticipated needs that they meet, fail to meet, or in some cases, even create. Modern day ITSs typically try to replicate the role of a teacher or a teaching assistant, and increasingly automate pedagogical functions such as problem generation, problem selection, and feedback generation. However, given a current shift towards blended learning models, recent work on ITSs has begun focusing on ways these systems can effectively leverage the complementary strengths of human led instruction from a teacher or peer, when used in co located classrooms or other social contexts. There were three ITS projects that functioned based on conversational dialogue: AutoTutor, Atlas (Freedman, 1999), and Why2. The idea behind these projects was that since students learn best by constructing knowledge themselves, the programs would begin with leading questions for the students and would give out answers as a last resort. AutoTutor's students focused on answering questions about computer technology, Atlas's students focused on solving quantitative problems, and Why2's students focused on explaining physical systems qualitatively. (Graesser, VanLehn, and others, 2001) Other similar tutoring systems such as Andes (Gertner, Conati, and VanLehn, 1998) tend to provide hints and immediate feedback for students when students have trouble answering the questions. They could guess their answers and have correct answers without deep understanding of the concepts. Research was done with a small group of students using Atlas and Andes respectively. The results showed that students using Atlas made significant improvements compared with students who used Andes. However, since the above systems require analysis of students' dialogues, improvement is yet to be made so that more complicated dialogues can be managed.

During the rapid expansion of the web boom, new computer aided instruction paradigms, such as e learning and distributed learning, provided an excellent platform for ITS ideas. Areas that have used ITS include natural language processing, machine learning, planning, multi agent systems, ontologies, Semantic Web, and social and emotional computing. In addition, other technologies such as multimedia, object oriented systems, modeling, simulation, and statistics have also been connected to or combined with ITS. Historically non technological areas such as the educational sciences and psychology have also been influenced by the success of ITS. In recent years, ITS has begun to move away from the search based to include a range of practical applications. ITS have expanded across many critical and complex cognitive domains, and the results have been far reaching. ITS systems have cemented a place within formal education and these systems have found homes in the sphere of corporate training and organizational learning. ITS offers learners several affordances such as individualized learning, just in time feedback, and flexibility in time and space. While Intelligent tutoring systems evolved from research in cognitive psychology and artificial intelligence, there are now many applications found in education and in organizations. Intelligent tutoring systems can be found in online environments or in a traditional classroom computer lab, and are used in K 12 classrooms as well as in universities. There are a number of programs that target mathematics but applications can be found in health sciences, language acquisition, and other areas of formalized learning. Reports of improvement in student comprehension, engagement, attitude, motivation, and academic results have all contributed to the ongoing interest in the investment in and research of theses systems. The personalized nature of the intelligent tutoring systems affords educators the opportunity to create individualized programs. Within education there are a plethora of intelligent tutoring systems, an exhaustive list does not exist but several of the more influential programs are listed below.

Algebra Tutor PAT (PUMP Algebra Tutor or Practical Algebra Tutor) developed by the Pittsburgh Advanced Cognitive Tutor Center at Carnegie Mellon University, engages students in anchored learning problems and uses modern algebraic tools in order to engage students in problem solving and in sharing of their results. The aim of PAT is to tap into a students' prior knowledge and everyday experiences with mathematics in order to promote growth. The success of PAT is well documented (ex. Miami Dade County Public Schools Office of Evaluation and Research) from both a statistical (student results) and emotional (student and instructor feedback) perspective. SQL Tutor SQL Tutor is the first ever constraint based tutor developed by the Intelligent Computer Tutoring Group (ICTG) at the University of Canterbury, New Zealand. SQL Tutor teaches students how to retrieve data from databases using the SQL SELECT statement. EER Tutor EER Tutor is a constraint based tutor (developed by ICTG) that teaches conceptual database design using the Entity Relationship model. An earlier version of EER Tutor was KERMIT, a stand alone tutor for ER modelling, which was shown to results in significant improvement of student's knowledge after one hour of learning (with the effect size of 0.6). COLLECT UML COLLECT UML is a constraint based tutor that supports pairs of students working collaboratively on UML class diagrams. The tutor provides feedback on the domain level as well as on collaboration. StoichTutor StoichTutor is a web based intelligent tutor that helps high school students learn chemistry, specifically the sub area of chemistry known as stoichiometry. It has been used to explore a variety of learning science principles and techniques, such as worked examples and politeness. Mathematics Tutor The Mathematics Tutor (Beal, Beck & Woolf, 1998) helps students solve word problems using fractions, decimals and percentages. The tutor records the success rates while a student is working on problems while providing subsequent, lever appropriate problems for the student to work on. The subsequent problems that are selected are based on student ability and a desirable time in is estimated in which the student is to solve the problem. eTeacher eTeacher (Schiaffino et al., 2008) is an intelligent agent or pedagogical agent, that supports personalized e learning assistance. It builds student profiles while observing student performance in online courses. eTeacher then uses the information from the student's performance to suggest a personalized courses of action designed to assist their learning process. ZOSMAT ZOSMAT was designed to address all the needs of a real classroom. It follows and guides a student in different stages of their learning process. This is a student centered ITS does this by recording the progress in a student's learning and the student program changes based on the student's effort. ZOSMAT can be used for either individual learning or in a real classroom environment alongside the guidance of a human tutor. REALP REALP was designed to help students enhance their reading comprehension by providing reader specific lexical practice and offering personalized practice with useful, authentic reading materials gathered from the Web. The system automatically build a user model according to student's performance. After reading, the student is given a series of exercises based on the target vocabulary found in reading. CIRCSlM Tutor CIRCSIM Tutor is an intelligent tutoring system that is used with first year medical students at the Illinois Institute of Technology. It uses natural dialogue based, Socratic language to help students learn about regulating blood pressure. Why2 Atlas Why2 Atlas is an ITS that analyses students explanations of physics principles. The students input their work in paragraph form and the program converts their words into a proof by making assumptions of student beliefs that are based on their explanations. In doing this, misconceptions and incomplete explanations are highlighted. The system then addresses these issues through a dialogue with the student and asks the student to correct their essay. A number of iterations may take place before the process is complete. SmartTutor The University of Hong Kong (HKU) developed a SmartTutor to support the needs of continuing education students. Personalized learning was identified as a key need within adult education at HKU and SmartTutor aims to fill that need. SmartTutor provides support for students by combining Internet technology, educational research and artificial intelligence. AutoTutor AutoTutor assists college students in learning about computer hardware, operating systems and the Internet in an introductory computer literacy course by simulating the discourse patterns and pedagogical strategies of a human tutor. AutoTutor attempts to understand learner's input from the keyboard and then formulate dialog moves with feedback, prompts, correction and hints. ActiveMath ActiveMath is a web based, adaptive learning environment for mathematics. This system strives for improving long distance learning, for complementing traditional classroom teaching, and for supporting individual and lifelong learning. ESC101 ITS The Indian Institute of Technology, Kanpur, India developed the ESC101 ITS, an intelligent tutoring system for introductory programming problems. AdaptErrEx is an adaptive intelligent tutor that uses interactive erroneous examples to help students learn decimal arithmetic.

Assessing the effectiveness of ITS programs is problematic. ITS vary greatly in design, implementation, and educational focus. When ITS are used in a classroom, the system is not only used by students, but by teachers as well. This usage can create barriers to effective evaluation for a number of reasons; most notably due to teacher intervention in student learning. Teachers often have the ability to enter new problems into the system or adjust the curriculum. In addition, teachers and peers often interact with students while they learn with ITSs (e. g., during an individual computer lab session or during classroom lectures falling in between lab sessions) in ways that may influence their learning with the software. may add additional sources of variation across evaluation contexts. All of these variables make evaluation of an ITS complex, and may help explain variation in results across evaluation studies. Despite the inherent complexities, numerous studies have attempted to measure the overall effectiveness of ITS, often by comparisons of ITS to human tutors. Reviews of early ITS systems (1995) showed an effect size of d = 1.0 in comparison to no tutoring, where as human tutors were given an effect size of d = 2.0. Subsequent studies found that these results were particularly pronounced in students from special education, non native English, and low income backgrounds. A more recent meta analysis suggests that ITSs can exceed the effectiveness of both CAI and human tutors, especially when measured by local (specific) tests as opposed to standardized tests. "Students who received intelligent tutoring outperformed students from conventional classes in 46 (or 92%) of the 50 controlled evaluations, and the improvement in performance was great enough to be considered of substantive importance in 39 (or 78%) of the 50 studies. The median ES in the 50 studies was 0.66, which is considered a moderate to large effect for studies in the social sciences. It is roughly equivalent to an improvement in test performance from the 50th to the 75th percentile. This is stronger than typical effects from other forms of tutoring. C. L. C. Kulik and Kulik's (1991) meta analysis, for example, found an average ES of 0.31 in 165 studies of CAI tutoring. ITS gains are about twice as high. The ITS effect is also greater than typical effects from human tutoring. As we have seen, programs of human tutoring typically raise student test scores about 0.4 standard deviations over control levels. Developers of ITSs long ago set out to improve on the success of CAI tutoring and to match the success of human tutoring. Our results suggest that ITS developers have already met both of these goals Although effects were moderate to strong in evaluations that measured outcomes on locally developed tests, they were much smaller in evaluations that measured outcomes on standardized tests. Average ES on studies with local tests was 0.73; average ES on studies with standardized tests was 0.13. This discrepancy is not unusual for meta analyses that include both local and standardized tests local tests are likely to align well with the objectives of specific instructional programs. Off the shelf standardized tests provide a looser fit. Our own belief is that both local and standardized tests provide important information about instructional effectiveness, and when possible, both types of tests should be included in evaluation studies." Some recognized strengths of ITS are their ability to provide immediate yes/no feedback, individual task selection, on demand hints, and support mastery learning.