An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without 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 aims to solve the problem of over-dependency of students over teachers for quality education. It aims to provide access to high quality education to each and every student, thus reforming the entire education system.
Intelligent tutoring systems consist of four basic components based on a general consensus amongst researchers (Nwana,1990; Freedman, 2000; Nkambou et al., 2010):
The Domain model
The Student model
The Tutoring model
The User interface model
The domain model:
The domain model (also known as the cognitive model or expert knowledge model) is built on ACT-R theory which tries to take into account all the possible steps required to solve a problem. More specifically, this model "contains the concepts, rules, and problem-solving strategies of the domain to be learned. It can fulfill several roles: as a source of expert knowledge, a standard for evaluating the student's performance or for detecting errors, etc." (Nkambou et al., 2010, p. 4).
The student model:
The student model can be thought of as an overlay on the domain model. It is considered as the core component of an ITS paying special attention to student's cognitive and affective states and their evolution as the learning process advances. As the student works step-by-step through their problem solving process the system engages in a process called model tracing. Anytime the student model deviates from the domain model the system identifies, or flags, that an error has occurred.
The tutoring model:
The tutor model accepts information from the domain and student models and makes choices about tutoring strategies and actions. At any point in the problem-solving process the learner may request guidance on what to do next, relative to their current location in the model. In addition, the system recognizes when the learner has deviated from the production rules of the model and provides timely feedback for the learner, resulting in a shorter period of time to reach proficiency with the targeted skills. The tutor model may contain several hundred production rules that can be said to exist in one of two states, learned or unlearned. Every time a student successfully applies a rule to a problem, the system updates a probability estimate that the student has learned the rule. The system continues to drill students on exercises that require effective application of a rule until the probability that the rule has been learned reaches at least 95% probability.
Knowledge tracing tracks the learner's progress from problem to problem and builds a profile of strengths and weaknesses relative to the production rules. The cognitive tutoring system developed by John Anderson at Carnegie Mellon University presents information from knowledge tracing as a skillometer, a visual graph of the learner's success in each of the monitored skills related to solving algebra problems. When a learner requests a hint, or an error is flagged, the knowledge tracing data and the skillometer are updated in real-time.
The user interface model:
The user interface component "integrates three types of information that are needed in carrying out a dialogue: knowledge about patterns of interpretation (to understand a speaker) and action (to generate utterances) within dialogues; domain knowledge needed for communicating content; and knowledge needed for communicating intent" (Padayachee, 2002, p. 3).
Nkambou et al. (2010) make mention of Nwana's (1990) review of different architectures underlining a strong link between architecture and paradigm (or philosophy). Nwana (1990) declares, "[I]t is almost a rarity to find two ITSs based on the same architecture [which] results from the experimental nature of the work in the area" (p. 258). He further explains that differing tutoring philosophies emphasize different components of the learning process (i.e., domain, student or tutor). The architectural design of an ITS reflects this emphasis, and this leads to a variety of architectures, none of which, individually, can support all tutoring strategies (Nwana, 1990, as cited in Nkambou et al., 2010). Moreover, ITS projects may vary according to the relative level of intelligence of the components. As an example, a project highlighting intelligence in the domain model may generate solutions to complex and novel problems so that students can always have new problems to work on, but it might only have simple methods for teaching those problems, while a system that concentrates on multiple or novel ways of teaching a particular topic might find a less sophisticated representation of that content sufficient.
The following is a list of existing ITS for reference:
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.
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 (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 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 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.
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 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.
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 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 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.
Evaluation of the Cognitive Tutor Algebra I Program A Shneyderman - Miami–Dade County Public Schools, Office of Evaluation and Research, Miami Fl. September 2001
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
Joseph Psotka, Sharon A. Mutter (1988). Intelligent Tutoring Systems: Lessons Learned. Lawrence Erlbaum Associates. ISBN 0-8058-0192-8.
Nwana, H. S. (1990). Intelligent tutoring systems: An overview. Artificial Intelligence Review, 4, 251-277.
Freedman, R. (2000). What is an intelligent tutoring system? Intelligence, 11(3), 15–16.
Nkambou, R., Mizoguchi, R., & Bourdeau, J. (2010). Advances in intelligent tutoring systems. Heidelberg: Springer.
Anderson, H. & Koedinger, M. (1997). Intelligent tutoring goes to school in the Big City. International Journal of Artificial Intelligence in Education, 8, 30-43.
Corbett, Albert T. and Anderson, John R., "Student Modeling and Mastery Learning in a Computer-Based Programming Tutor" (2008). Department of Psychology. Paper 18. http://repository.cmu.edu/psychology/18
Padayachee I. (2002. Intelligent Tutoring Systems: Architecture and Characteristics.