Intelligent tutoring system
At this time the mathematician and philosopher Gottfried Wilhelm Leibniz envisioned machines capable of reasoning and applying rules of logic to settle disputes.
[7] 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.
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,[7] but it set an early precedent for future projects.
Burrhus Frederic "B.F." Skinner at Harvard University did not agree with Thorndike's learning theory of connectionism or Pressey's teaching machine.
Turing's work as well as later projects by researchers such as Allen Newell, Clifford Shaw, and Herb Simon showed programs capable of creating logical proofs and theorems.
Major computer vendors and federal agencies in the US such as IBM, HP, and the National Science Foundation funded the development of these projects.
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 Illinois in the early 1970s.
Where CAI used a behaviourist perspective on learning based on Skinner's theories (Dede & Swigger, 1988),[10] ITS drew from work in cognitive psychology, computer science, and especially artificial intelligence.
Basically, early specifications for ITS or (ICAI) require it to "diagnose errors and tailor remediation based on the diagnosis" (Shute & Psotka, 1994, p. 9).
A key breakthrough in ITS research was the creation of The LISP Tutor, a program that implemented ITS principles in a practical way and showed promising effects increasing student performance.
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[20] or peer,[21] when used in co-located classrooms or other social contexts.
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.
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.
Corbett et al. (1997) summarized ITS design and development as consisting of four iterative stages: (1) needs assessment, (2) cognitive task analysis, (3) initial tutor implementation and (4) evaluation.
[42] The second stage, cognitive task analysis, is a detailed approach to expert systems programming with the goal of developing a valid computational model of the required problem solving knowledge.
[43] The third stage, initial tutor implementation, involves setting up a problem solving environment to enable and support an authentic learning process.
[51] Intelligent tutoring systems have been constructed to help students learn geography, circuits, medical diagnosis, computer programming, mathematics, physics, genetics, chemistry, etc.
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 processing, machine learning, planning, multi-agent systems, ontologies, Semantic Web, and social and emotional computing.
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.
[90] Kurt VanLehn's much more recent overview (2011) of modern ITS found that there was no statistical difference in effect size between expert one-on-one human tutors and step-based ITS.
[93] Subsequent studies found that these results were particularly pronounced in students from special education, non-native English, and low-income backgrounds.
"[95] Some recognized strengths of ITS are their ability to provide immediate yes/no feedback, individual task selection, on-demand hints, and support mastery learning.
There are also factors that limit the incorporation of intelligent tutors into the real world, including the long timeframe required for development and the high cost of the creation of the system components.
To get the full experience of dialogue there are many different areas in which a computer must be programmed; including being able to understand tone, inflection, body language, and facial expression and then to respond to these.
[108] In addition, some current research has focused on modeling the nature and effects of various social cues commonly employed within a dialogue by human tutors and tutees, in order to build trust and rapport (which have been shown to have positive impacts on student learning).
[115] With regard to teenagers, Ogan et al. draw from observations of close friends tutoring each other to argue that in order for an ITS to build rapport as a peer to a student, a more involved process of trust building is likely necessary which may ultimately require that the tutoring system possess the capability to effectively respond to and even produce seemingly rude behavior in order to mediate motivational and affective student factors through playful joking and taunting.
Evidence suggests that learning by teaching can be an effective strategy for mediating self-explanation, improving feelings of self-efficacy, and boosting educational outcomes and retention.
