An intelligent tutoring system (ITS) is any computer system that provides direct customized instruction or feedback to students, i.e. without the intervention of human beings, whilst performing a task.[1] Thus, ITS implements the theory of learning by doing. An ITS may employ a range of different technologies. However, usually such systems are more narrowly conceived of as artificial intelligence systems, more specifically expert systems made to simulate aspects of a human tutor. Intelligent Tutor Systems have been around since the late 1970s, but increased in popularity in the 1990s.
The concept of intelligent machines for instructional use date back as early as 1924-1926. Sidney Pressey of Ohio State University created a mechanical machine to instruct students without a human teacher[2]. His machine resembled closely a typewriter with only five keys. There was a window that revealed a question. The learner would select their answer and the score was recorded on a counter at the back of the machine giving the learner immediate feedback[3]. There was also a lever that when shifted, would only allow for the next question to be revealed if the learner responded correctly to the previous question. Pressey’s machine was created for rote and drill-learning. Pressey was influenced by Edward L. Thorndike, a learning theorist and educational psychologist at the Columbia University Teacher College of the late 19th and early 20th century. Thorndike posited laws for maximizing learning. Pressey created his teaching machines based on Thorndike’s several laws including the law of effect, which states that if an association is followed by a “satisfying state of affairs” it will be strengthened and if it is followed by an “annoying state of affairs “ it will be weakened (wikipedia); the law of exercise, which explains that stimulus-response associations are strengthened through repetition; and the law of recency which explains that the most recent response is most likely to reoccur (wikipedia 14). Later, Pressey’s teaching and testing machine was not considered intelligent as it was mechanically run and was based on one question and answer at a time[3]. By the 1950's and 1960's, Burrhus Frederic "B.F." Skinner at Harvard University did not agree with Thorndike's learning theory of connectionism or Pressey's teaching machine. Rather, Skinner was a behaviourist who believed that learners should construct their answers and not rely on recognition [2]. He too, constructed a teaching machine. It was a mechanical system that would reward students for correct responses to a list of questions (wikipedia). The information presented to learners was structured and incremental (wikipedia).
By the mid 1900’s mechanical binary systems gave way to binary based electronic machines. These machines were considered intelligent when compared to their mechanical counterparts as they had the capacity to make logical decisions. However, defining and recognizing a machine's intelligence was difficult at the time.
Alan Turing, a mathematician, logician and computer scientist, linked computing systems to thinking. He developed the Turing test to test the intelligence of a machine. Essentially, the test consisted of having a person communicate with a human and a computer. A person poses questions to a computer and another human. A computer passes the test if the human posing the questions perceives the responses to be from a human. The Turing test has been used in its essence for more than two decades as a model for current ITS development. The main ideal for ITS systems is to effectively communicate. [3]
The latter part of the 1960's and 1970's saw many new CAI projects that built on advances in computer science. With the creation of BASIC programming language in 1958 many schools and universities had begun developing Computer Assisted Instruction (CAI) programs. This allowed for programmed instruction (PI) and was being implemented in education. It was based on an input - output system on a computer. Many who supported this form of instruction thought it could enhance learning, however, there was not much evidence supporting this belief. [3]. Major computer vendors and federal agencies in the US such as IBM, HP, and the National Science Foundation funded the development of these projects.[4] The programming language LOGO was created in 1967 by Wally Feurzeig and Seymour Papert as a language for educating. PLATO, an educational terminal featuring displays, animations, and touch controls that could store and deliver large amounts of course material, was developed by Donald Bitzer in the University of Illonois in the early 1970's. Many other CAI projects were started in many countries including the US, the UK, and Canada.[4]
At the same time that CAI was gaining interest, Jaime Carbonell suggested that computers could act as a teacher rather than just a tool (Carbonell, 1970). A new perspective would emerge that focused on the using computers to intelligently coach students called Intelligent Computer Assisted Instruction or Intelligent Tutoring Systems. Where CAI used a behaviourist perspective on learning based on Skinner's theories (Dede & Swigger, 1988) within a specific knowledge domain[5], ITS drew from work in cognitive psychology and computer science.[5] A key goal of ITS was to adapt dynamically to user input by both being able to teach a task, but also perform that task in a general way. The technical requirements of ITS, however, proved to be higher and more complex than CAI systems and ITS systems would not be developed at this time. Towards the latter part of the 70's interest in CAI technologies began to wane.[6] [4]. 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.[6]
Intelligent tutoring systems consist of four different subsystems or modules: the interface module, the expert module, the student module, and the tutor module. The interface module provides the means for the student to interact with the ITS, usually through a graphical user interface and sometimes through a rich simulation of the task domain the student is learning (e.g., controlling a power plant or performing a medical operation). The expert module references an expert or domain model containing a description of the knowledge or behaviors that represent expertise in the subject-matter domain the ITS is teaching—often an expert system or cognitive model. An example would be the kind of diagnostic and subsequent corrective actions an expert technician takes when confronted with a malfunctioning thermostat. The student module uses a student model containing descriptions of student knowledge or behaviors, including his misconceptions and knowledge gaps. An apprentice technician might, for instance, believe a thermostat also signals too high temperatures to a furnace (misconception) or might not know about thermostats that also gauge the outdoor temperature (knowledge gap). A mismatch between a student's behavior or knowledge and the expert's presumed behavior or knowledge is signaled to the tutor module, which subsequently takes corrective action, such as providing feedback or remedial instruction. To be able to do this, it needs information about what a human tutor in such situations would do: the tutor model.
An intelligent tutoring system is only as effective as the various models it relies on to adequately model expert, student and tutor knowledge and behavior. Thus, building an ITS needs careful preparation in terms of describing the knowledge and possible behaviors of experts, students and tutors. This description needs to be done in a formal language in order that the ITS may process the information and draw inferences in order to generate feedback or instruction. Therefore a mere description is not enough; the knowledge contained in the models should be organized and linked to an inference engine. It is through the latter's interaction with the descriptive data that tutorial feedback is generated.
All this is a substantial amount of work, even if authoring tools have become available to ease the task.[7] This means that building an ITS is an option only in situations in which they, in spite of their relatively high development costs, still reduce the overall costs through reducing the need for human instructors or sufficiently boosting overall productivity. Such situations occur when large groups need to be tutored simultaneously or many replicated tutoring efforts are needed. Cases in point are technical training situations such as training of military recruits and high school mathematics. One specific type of intelligent tutoring system, Cognitive Tutors, has been incorporated into mathematics curricula in a substantial number of United States high schools, producing improved student learning outcomes on final exams and standardized tests.[8] Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc. Intelligent Language Tutoring Systems (ILTS), e.g. this[9] one, teach natural language to first or second language learners. ILTS requires specialized natural language processing tools such as large dictionaries and morphological and grammatical analyzers with acceptable coverage.
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, machine learning, planning, multi-agent systems, ontologies, semantic Web, and social and emotional computing. Besides these, other technologies such as multimedia, object-oriented systems, modeling, simulation and statistics have also been applied or combined with ITS. Non-technological areas like education sciences and psychology are also attracted by the success of ITS[10].
In recent years, ITS has moved away from the theoretical (research labs) and into a wide range of practical application. ITS have expanded across many critical and complex cognitive domains, and the results have been dramatic and far reaching. ITS systems have cemented a place within formal education and has found a home in the sphere of corporate training and organizational learning. ITS has several affordance, such as individualized learning, just in time feedback and flexibility in time and space.
While Intelligent tutoring systems were first introduced within the corporate world (need a reference), there are now many applications in the educational arena as well. 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 at the university level. 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 an opportunity to create individualized programs. Within education there are a plethora of intelligent tutoring systems, an exhaustive list does not exist but highlighted below are several of the more influential programs.
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 activate students to problem solve and share results. The aim of PAT is to able to tap into a students prior knowledge and their day to day experiences with mathematics in order to promote growth. The success of PAT is well documented (exp. Miami-Dade County Public Schools Office of Evaluation and Research) from both a statistical (student results) and emotional (student and instructor feedback) perspective.
Carnegie Learning Evaluation of the Cognitive Tutor Algebra I Program A Shneyderman - Miami–Dade County Public Schools, Office of Evaluation and Research, Miami Fl. September 2001
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 when student is working on the problems and subsequent problems that are predicted to fit student’s level will be selected and an estimated desirable time will be given to the student to solve the problem[11].
eTeacher eTeacher (Schiaffino et al., 2008) is an intelligent agent that supports personalized e-learning assistance. It builds student’s profile while observing student performing in online courses. Then eTeacher uses the information from student’s profile to suggest their personalized courses of action that assist their learning process[12].
ZOSMAT ZOSMAT was designed to address all the needs in a real classroom. It follows and guides a student in different stages of their learning process. This is a student-centered ITS that records the progress in student’s learning progress depends on their effort. ZOSMAT can be used for either individual learning or real classroom environment with the guidance of a human tutor[13].
REALP REALP was designed to help students enhance reading comprehension by providing reader-specific lexical practice and offering personalized practice in useful, authentic reading materials gathered from the Web. The system automatically build a user model according to student’s performance. After reading, the student will be given a series of exercises based on the target vocabulary found in reading[14].
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. http://www.cs.iit.edu/~circsim/
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 them into a proof by making assumptions of student beliefs based on their explanations. In doing, 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. aroque.bol.ucla.edu/pubs/vanLehnEtAl-its02-architectureWhy.pdf
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 student by combining Internet technology, educational research and artificial intelligence[15].
AutoTutor Auto Tutor assists college students in learning hardware, operating systems and Internet in an introductory computer literacy course by simulating the discourse patterns and pedagogical strategies of a human tutor. Auto Tutor attempts to understand learner’s input from keyboard and formulate dialog moves with feedback, prompts, correction and hints[16].
ActiveMath ActiveMath is a web-based, adaptive learning environment for mathematics. These systems strive for improving long-distance learning, for complementing traditional classroom teaching, and for supporting individual and life-long learning[17].
SHERLOCK “SHERLOCK” is used to train Air Force technicians to diagnose problems in the electrical systems of F-15 jets. The ITS creates faulty schematic diagrams of systems for the trainee to locate and diagnose. The ITS provides diagnostic readings allowing the trainee to decide whether the fault lies in the circuit being tested or if it lies elsewhere in the system. Feedback and guidance are provided by the system and help is available if requested[18].
Cardiac Tutor The Cardiac Tutor's aim is to support advanced cardiac support techniques to medical personnel. The tutor presents cardiac problems and, using a variety of steps, students must select various interventions. Cardiac Tutor provides clues, verbal advice, and and feedback in order to personalize and optimize the learning. Each simulation, regardless of whether the students were successful able to help their patients, results in a detailed report which students review[19].
The Intelligent Tutoring Systems conference was typically held every other year in Montréal (Canada) by Claude Frasson and Gilles Gauthier in 1988, 1992, 1996 and 2000; in San Antonio (US) by Carol Redfield and Valerie Shute in 1998; in Biarritz (France) and San Sebastian (Spain) by Guy Gouardères and Stefano Cerri in 2002; in Maceio (Brazil) by Rosa Maria Vicari and Fábio Paraguaçu in 2004; in Jhongli (Taiwan) by Tak-Wai Chan in 2006. The conference was recently back in Montreal in 2008 (for its 20th anniversary) by Roger Nkambou and Susanne Lajoie. ITS'2010 was held in Pittsburgh (US) by Jack Mostow, Judy Kay, and Vincent Aleven. The International Artificial Intelligence in Education (AIED) Society (http://iaied.org) publishes The International Journal of Artificial Intelligence in Education (IJAIED) and produces the International Conference on Artificial Intelligence in Education every odd numbered year. The American Association of Artificial Intelligence (AAAI)(www.aaai.org) sometimes has symposia and papers related to intelligent tutoring systems. A number of books have been written on ITS including three published by Lawrence Erlbaum Associates.
- Nkambou, Roger; Bourdeau, Jacqueline; Mizoguchi, Riichiro, eds. (2010). Advances in Intelligent Tutoring Systems. Springer. ISBN 3-642-14362-8.
- Woolf, Beverly Park (2009). Building Intelligent Interactive Tutors. Morgan Kaufmann. ISBN 978-0-12-373594-2.
- Evens, Martha; Michael, Joel (2005). One-on-one Tutoring by Humans and Computers. Routledge. ISBN 978-0-8058-4360-6.
- Polson, Martha C.; Richardson, J. Jeffrey, eds. (1988). Foundations of Intelligent Tutoring Systems. Lawrence Erlbaum. ISBN 0-8058-0053-0.
- Psotka, Joseph; Massey, L. Dan; Mutter, Sharon, eds. (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum. ISBN 0-8058-0023-9.
- Wenger, Etienne (1987). Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann. ISBN 0-934613-26-5.
- Brown, D.; Sleeman, John Seely, eds. (1982). Intelligent Tutoring Systems. Academic Press. ISBN 0-12-648680-8.
- ^ Joseph Psotka, Sharon A. Mutter (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates. ISBN 0-8058-0192-8.
- ^ a b Fry, E. (1960). Teaching Machine Dichotomy: Skinner vs. Pressey. Pshychological Reports(6) 11-14. Southern University Press.
- ^ a b c d Shute, V. J., & Psotka, J. (1994). Intelligent Tutoring Systems: Past, Present, and Future. Human resources directorate manpower and personnel research division. pp. 2-52
- ^ a b c Chambers, J., & Sprecher, J. (1983). Computer-Assisted Instruction: Its Use in the Classroom. Englewood Cliffs, New Jersey: Prentice-Hall Inc.
- ^ a b Larkin, J, & Chabay, R. (Eds.). (1992). Computer Assisted Instruction and Intelligent Tutoring Systems: Shared Goals and Complementary Approaches. Hillsdale, New Jersey: Lawrence Erlbaum Associates.
- ^ a b Anderson, K. (1986) Computer-Assisted Instruction. Journal of Medical Systems, 10(2), 163-171. — Preceding unsigned comment added by Sethtee (talk • contribs) 23:02, 26 May 2012 (UTC)
- ^ For an example of an ITS authoring tool, see Cognitive Tutoring Authoring Tools
- ^ Koedinger, K. R.; Corbett, A. (2006). "Cognitive Tutors: Technology bringing learning science to the classroom". In Sawyer, K.. The Cambridge Handbook of the Learning Sciences. Cambridge University Press. pp. 61–78. OCLC 62728545.
- ^ Shaalan, Khalid F. (February 2005). "An Intelligent Computer Assisted Language Learning System for Arabic Learners". Computer Assisted Language Learning: An International Journal (Taylor & Francis Group Ltd.) 18 (1 & 2): 81–108. DOI:10.1080/09588220500132399. http://www.informaworld.com/smpp/content~db=all?content=10.1080/09588220500132399.
- ^ Ramos, C., Ramos, C., Frasson, C., & Ramachandran, S. (2009). Introduction to the special issue on real world applications of intelligent tutoring systems. , 2(2) 62-63.
- ^ Beal, C. R., Beck, J., & Woolf, B. (1998). Impact of intelligent computer instruction on girls’ math self concept and beliefs in the value of math. Paper presented at the annual meeting of the American Educational Research Association.
- ^ Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to e-learning students. Computers & Education , 51 , 1744-1754
- ^ Keles, A., Ocak, R., Keles, A., & Gulcu A. (2009). ZOSMAT: Web-based Intelligent Tutoring System for Teaching-Learning Process. [Elsevier.]. Expert Systems with Applications , 36 , 1229-1239.
- ^ Heffernan, N. T., Turner, T. E., Lourenco, A. L. N., Macasek, M. A., Nuzzo-Jones, G., & Koedinger, K. R. (2006). The ASSISTment Builder: Towards an Analy- sis of Cost Effectiveness of ITS creation. Presented at FLAIRS2006, Florida.
- ^ Cheung, B., Hui, L., Zhang, J., & Yiu, S. M. (2003). SmartTutor: An intelligent tutoring system in web-based adult education. Journal of Systems and Software , 68 , 11-25
- ^ Graesser, A.C., Wiemer-Hastings, K., Wiemer-Hastings, P., & Kreuz, R., & TRG. (1999). AutoTutor: A simulation of a human tutor. Journal of Cognitive Systems Research , 1 , 35-51
- ^ Melis, E., & Siekmann, J. (2004). Activemath: An Intel- ligent Tutoring System for Mathematics. In R. Tadeus- iewicz, L.A. Zadeh, L. Rutkowski, J. Siekmann, (Eds.), 7th International Conference “Artificial Intelligence and Soft Computing” (ICAISC) Lecture Notes in AI LNAI 3070 . Springer-Verlag 91-101
- ^ Lajoie, S. P., & Lesgold, A. (1989). Apprenticeship training in the workplace: Computer coached practice environment as a new form of apprenticeship. Machine- Mediated Learning , 3 , 7-28
- ^ Eliot, C., & Woolf, B. (1994). Reasoning about the user within a simulation-based real-time training system. In Proceedings of the fourth international conference on user modeling , 121-126.
