Intelligent Tutoring System

Probably the most extensive use of such componential analysis is for intelligent tutoring systems (Sleeman & Brown, 1982). These computer systems inter- act with students while they are learning and solving problems, much as a hu- man tutor would. An example of such a tutor is the LISP tutor (J. R. Anderson, Conrad, & Corbett, 1989; J. R. Anderson & Reiser, 1985; Corbett & Anderson,
1990), which teaches LISP, the main programming language used in artificial intelligence in the 1980s and 1990s. The LISP tutor continuously taught LISP to students at Carnegie Mellon University from 1984 to 2002 and served as a prototype for a generation of intelligent tutors, many of which have focused on teaching middle-school and high-school mathematics. The mathematics tutors are now distributed by a company called Carnegie Learning, spun off by Car- negie Mellon University in 1998. The Carnegie Learning mathematics tutors have been deployed to about 3,000 schools nationwide and have interacted with over 600,000 students each year (Koedinger & Corbett, 2006; Ritter, Anderson, Koedinger, & Corbett, 2007; you can visit the Web site www.carnegielearning .com for promotional material that should be taken with a grain of salt). A large-scale study conducted by the Rand Corporation (Pane, Griffin, McCaffrey, & Karam, 2013) indicates that the tutor does provide real, if modest, gains for high-school students.
A motivation for research on intelligent tutoring is the evidence showing that private human tutoring is very effective. The results of studies have shown that giving students a private human tutor enables 98% of them to do better than the average student in a standard classroom (Bloom, 1984). An ideal pri- vate tutor is one who is with the student at all times while he or she is studying a particular subject matter. To use the terms of Ericsson et al. (1993), a private tutor guarantees the deliberate practice that is essential for learning. Having the tutor present while solving problems in domains, such as LISP and mathemat- ics, which require complex problem-solving skills, is particularly important. In LISP, problem solving takes the form of writing computer programs, or func- tions, as they are often called in LISP. Therefore, in developing the LISP tutor, we chose to focus on providing students with tutoring while they were writ- ing computer programs. Note how carefully the tutor monitors the student’s performance in solving the problem. It can do so because it knows how to write LISP functions. As the student is writing the function, the tutor is simultaneously trying to solve the same problem that the student is working on. As soon as it sees the student making a mistake, the tu- tor can intervene with remedial instruction.
Underlying the tutor’s ability to solve problems and monitor the student’s problem solving is a set of rules that can solve the same LISP programming problems that we expect students to be able to solve. In all, there are about 500 rules that encode the knowledge relating to LISP. A typical rule in the LISP tutor is:
If the goal is to multiply one number by another, Then use * and set subgoals to code the two numbers.
The basic goal of the LISP tutor is to communicate these 500 rules to the student, monitor performance to see whether he or she possesses these rules in correct form, and provide the student with practice on these rules. The success of the LISP tutor is one piece of evidence that these 500 rules indeed underlie coding skill in LISP.
Besides providing an instructional tool, the LISP tutor is a research tool for studying the course of skill acquisition. The tutor can monitor how well a student is doing on each of the 500 rules, recording statistics such as the number of errors that a student is making and the time taken by a student to type the code corresponding to each of these rules. These data have indicated that students acquire the skill of LISP by independently acquiring each of the 500 rules. The two dependent measures are the number of errors made on a rule and the time taken to write the code corresponding to a rule (when that rule is correctly coded). These statistics are plotted as a function of learning opportunities, which present themselves each time the student comes to a point in a problem where that rule can be applied. As can be seen, performance on these rules dramatically improves from first to second learning opportunity and improves more gradually thereafter. These learning curves are similar to those identified for the learning of simple associations.
There were substantial differences in the speed with which different stu- dents learned the material. Students who have already learned a program- ming language are at a considerable advantage compared with students for whom their first programming language is that of the LISP tutor. The “identical elements model” of transfer, in which rules for programming in one language transfer to programming in another language, can account for this advantage.
We also analyzed the performance of individual students in the LISP tutor and found evidence for two factors underlying individual differences. Some students

were able to learn new rules in a lesson quite rapidly, whereas other students had more difficulty. More or less independent of this acquisition factor, students could be classified according to how well they retained rules. Thus, students differ in how rapidly they learn with the LISP tutor. However, the tutor employs a mastery learning system in which slower students are given more prac- tice and so are brought to the same level of mastery achieved by other students.
Students emerge from their interactions with the LISP tutor having acquired a complex and sophisticated skill. Their enhanced programming abilities make them appear more intelligent among their peers. However, when we examine what underlies that newfound intelligence, we find that it is the methodical ac- quisition of some 500 rules of programming. Some students can acquire these rules more easily than others because of past experience and specific abilities. However, when they graduate from the LISP course, all students have learned the 500 new rules. With the acquisition of these rules, few differences remain among the students with respect to ability to program in LISP. Thus, we see that, in the end, what is important with respect to individual differences is how much information students have previously learned, and not their native ability.
■ By carefully monitoring individual components of a skill and pro- viding feedback on learning, intelligent tutors can help students rap- idly master complex skills.