Intelligent Tutoring Systems
Intelligent Tutoring Systems (ITS) are computer-based training programs that use artificial intelligence to tailor multimedia learning by providing individualized instruction (Ong & Ramachandran, 2000). Intelligent Tutoring Systems try to imitate the help that a live tutor would provide to an individual student (Johnson, 2005). ITS offer a way to identify, remediate, and track all students separately (Johnson, 2005). The goal of ITS is to provide the benefits of individualized instruction without the cost and time it takes to provide personalized instruction with teachers (Ong & Ramachandran, 2000).
Keywords Acquisition; Computer Aided Instruction (CAI); Computer-Based Training (CBT); Distance Learning; Individualized Instruction; Instructional Technology; Intelligent Tutoring Systems (ITS); Module; No Child Left Behind Act of 2001(NCLB); Retention
The concept of intelligent tutoring systems (ITS) has been studied for more than thirty years by researchers in education, psychology, and artificial intelligence (Ong & Ramachandran, 2000). Schools and universities have been looking for ways to increase learning for students and improve test scores (Wijekumar, 2006). Since computer technologies have made their ways into both homes and schools, computer tutoring systems are viewed as a potential solution to this problem (Wijekumar, 2006). In the early 1990s, Jay Liebowitz wrote a book called The Explosion of Intelligent Systems by the Year 2000 in which he made some apt predictions about the future of technology (cited in Karlin, 2007). He imagined a world in which students of all grade levels and abilities used video-conferencing in schools, employed voice-activated programs, and enrolled in distance learning courses (cited in Karlin, 2007). It turns out he was right. By the early years of the twenty-first century, ITS were providing interactive instruction to support K-12 education, college education, corporate training and military preparation.
What are Intelligent Tutoring Systems?
Intelligent Tutoring Systems (ITS) are computer-based training programs that use artificial intelligence to tailor multimedia learning by providing individualized instruction (Ong & Ramachandran, 2000). They are also referred to as intelligent computer-aided instruction (ICAI) and have been a major breakthrough in the field of instructional technology. Before, ITS, computer-based training (CBT) and computer aided instruction (CAI) were the only computer teaching systems (Beck, Stern, & Haugsjaa, 2004). In systems like these, the directions were not specified to meet the individual needs of each learner, and transitioning a student through the material was formulaic and inflexible, “such as ‘if question 21 is answered correctly, proceed to question 54; otherwise go to question 32’” (Beck, Stern, & Haugsjaa, 2004, ¶ 1). Prior knowledge and learning style were not taken into account. As a result, their impact on learning was mediocre (Wijekumar, 2006).
Intelligent Tutoring Systems are more advanced, allowing learners to hone their abilities by completing assignments within interactive academic settings. ITS can answer questions and provide personalized assistance to the learner. ITS, unlike other educational technologies, evaluate every student’s response in order to assess his/her knowledge and skills (Ong & Ramachandran, 2000). ITS can then modify instructional strategies, give explanations, examples, demonstrations, and practice exercises where necessary (Ong & Ramachandran, 2000). ITS offer more options in the presentation of material and have the capability to specialize information to cater to a student's needs (Beck, Stern, & Haugsjaa, 2004).
The typical ITS model does the following:
• Identifies learning objectives and their context;
• Acknowledges gaps in individual student's knowledge;
• Trains each student according to the areas in which they lack knowledge;
• Guides the student through the relevant parts of the book, or provided material;
• Assesses students on the learning objectives;
• Gives the student feedback on his/her responses and provides explanation as to why an answer is correct or incorrect; and
• Provides each student with more questions in the specific areas where they lack knowledge (Sessink, Beeftink, Tramper, & Hartog, 2007).
The inception of the No Child Left Behind Act has put pressure on schools to deliver high-quality instruction to all their students (Wijekumar, 2006). As a result, schools are trying to utilize technological advancements such as Intelligent Tutoring Systems to teach their subjects, practice tests, and track progress (Wijekumar, 2006). Intelligent Tutoring Systems provide motivation, modeling, interactivity, feedback, and consistency like no other tool before (Wijekumar, 2006).
Why Use Intelligent Tutoring Systems?
Many academic courses are attended by a heterogeneous group of students who have come from different backgrounds and have attended different courses in the past; some even speak in different languages. Some students may simply be at varying skill levels. (Sessink, Beeftink, Tramper, & Hartog, 2007). Effectively teaching a heterogeneous student population is a challenge in education because most traditional methods target the average student. This is a definite disadvantage for advanced students, students with disabilities, and students who lack certain prior knowledge (Sessink, Beeftink, Tramper, & Hartog, 2007). Unfortunately, class sizes and instructor loads often make it impossible for teachers to tutor every student individually. Intelligent Tutoring Systems try to imitate the help that a live tutor would provide to an individual student (Johnson, 2005). ITS offer a way to identify, remediate, and track all students separately (Johnson, 2005).
The main initiative of ITS is giving personalized instruction without the cost and time it takes to provide personalized instruction with teachers (Ong & Ramachandran, 2000). ITS can be thought of as a virtual training assistant that collects the wisdom and experience of trained teaching professionals and distributes the content to students electronically (Ong & Ramachandran, 2000). Adaptive Intelligent Tutoring systems may help to support learning for a heterogeneous group of students.
Research on prototype Intelligent Tutoring Systems indicated that students who used ITS generally learned faster and demonstrated improved performance compared to classroom-trained participants. In the 1980s, Benjamin Bloom determined “that students who receive one-on-one instruction perform two standard deviations better than students who receive traditional classroom instruction. An improvement of two standard deviations means that the student performed in the top 2 percent of those receiving instruction” (Ong & Ramachandran, 2000, ¶ 18).
In a more recent study conducted at Carnegie Mellon University, college students used an ITS called the LISP Tutor to learn computer programming skills (Ong & Ramachandran, 2000). “Students who used the ITS scored 43 percent higher on the final exam than the control group” (Ong & Ramachandran, 2000, ¶ 4). In addition, the control group needed 30 percent more time to solve complex programming problems (Ong & Ramachandran, 2000). In another example, students using Smithtown, an ITS for economics, did not perform better than students in a traditional learning environment, but they required less time to cover the material (Beck, Stern, & Haugsjaa, 2004). Performance was equal but more efficient.
In 2011 VanLehn compared computer tutors and human tutors for their impact on learning gains, particularly focusing on experiments that compared one type of tutoring to another while attempting to control all other variables, such as the content and duration of the instruction (VanLehn, 2011). He concluded that “ITS should be used to replace homework, seatwork, and perhaps other activities but not to replace a whole classroom experience. Nonetheless, within their limited area of expertise, currently available ITS seem to be just as good as human tutors” (VanLehn, 2011, p. 214).
How Intelligent Tutoring Systems Work
Many traditional teaching methods introduce learners to facts and concepts and follow up with test questions to assess understanding (Ong & Ramachandran, 2000). “These methods are effective in exposing people to large amounts of information and testing their recall but learners often” are not taught how to correctly apply their new knowledge (Ong & Ramachandran, 2000, ¶ 5). By contrast, Intelligent Tutoring Systems use highly interactive learning environments, including simulations, that require students to use the skills they have just acquired. This type of learning is effective in helping students retain and apply knowledge more effectively in the future (Ong & Ramachandran, 2000).
In order to provide such specialized guidance to students, ITS systems typically use three kinds...
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