User:EDUC892 G4/sandbox

Intelligent Tutoring Systems consist of four basic components based on a general consensus amongst researchers: (Nwana (1990),,    ):

(1) The Domain Model

(2) The Student Model

(3) The Tutoring Model, and

(4) The User Interface Model

The Domain Model (also known as the Cognitive Model, or Expert Knowledge Model) is built on ACT-R theory (link: http://en.wikipedia.org/wiki/ACT-R) which tries to take into account all the possible steps required to solve a problem. "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. 4-5)

As the student works step-by-step through their problem solving process in the Student Model (core component of an ITS), the system engages in a process called Model Tracing. Think of the student model as an overlay on the Domain Model. The Student Model system traces the student progress through the problem solving process and knows exactly where the learner is in that process. The system keeps track of which steps in the problem solving process were most problematic for the learner, and continues to drill the learner with questions that require application of that specific cognitive step until the deficient skill is developed to the point of being automatic.

The Tutor Model accepts information from the domain and student models and makes choices about tutoring strategies and actions. In the tutor model a process called Model Tracing is used to track the learner's to track the learner's progress through the production rules in the Domain Model. Critical to the success of cognitive tutors is the immediacy of feedback. 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 contains several hundred production rules called the ideal student model. The Tutor Model receives input from both the domain and student models and makes decisions about tutoring strategies and actions. Each of the production rules for solving a problem 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. Students continue to work 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 uses knowledge tracing in the form of a 'skillometer'; a visual graph of the learner's success in each of the monitored skills related to solving algebra problems. The system continues to present the learner with problems that allow for weaker skills to be developed through practice.

User interface “This 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, p.3)

ITS structure

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).

Design and development methods

Apart from the discrepancy amongst ITS architectures each emphasizing different elements, the development of an ITS is much the same as any instructional design procedure comprising of four iterative design stages: (1) needs assessment, (2) cognitive task analysis, (3) initial tutor implementation and (4) evaluation.

The first stage know as needs assessment, is common in any instructional design project, especially software development. This involves getting to know the users, the subject matter experts and/or the teacher(s). This step fits into the expert/knowledge and student domain. The goal is to specify learning goals and to outline a general plan for the curriculum; it is imperative not to computerize traditional concepts but develop a new curriculum structure by defining the task in general and understanding learners possible behaviours dealing with the task and to a lesser degree the tutor’s behavior. In doing so, three crucial dimensions needs to be dealt with: (1) the probability a student is able to solve problems; (2) the time it takes to reach this performance level and (3) the probability the student will actively use this knowledge in the future. Another important aspect that needs analysis is cost effectiveness of the interface. Moreover, teachers and student entry characteristics such as prior knowledge must be assessed since both groups are going to be system users.

The second stage, cognitive task analysis, is a more detailed approach to expert systems programming with the goal of developing a psychologically valid computational model of the problem solving knowledge. There are three chief methods for developing a domain model: (1) interviewing domain experts, (2) conducting “think aloud” protocol studies with domain experts and (3) conducting “think aloud” studies with novices. Although the first method is most commonly used, experts are usually incapable of reporting cognitive components, therefore the preferred method to obtain cognitive model evidence is the think aloud study in which the expert is asked to report aloud what s/he is thinking when solving typical problems.

The third stage, initial tutor implementation involves setting up a problem solving environment to enable and support an authentic learning process. This stage is followed by a series of evaluation activities as the final stage which is again similar to any software development project.

The fourth stage, evaluation includes (1) pilot studies to confirm basic usability and educational impact; (2) formative evaluations of the system under development, including (3) parametric studies that examine the effectiveness of system features and finally, (4) summative evaluations of the final tutor’s effect: learning rate and asymptotic achievement levels.

Eight principles of ITS design and development

Principle 0: Enable the student to work to the successful conclusion of problem solving.

(1) Represent student competence as a production set.

(2) Communicate the goal structure underlying the problem solving.

(3) Provide instruction in the problem solving context.

(4) Promote an abstract understanding of the problem-solving knowledge.

(5) Minimize working memory load.

(6) Provide immediate feedback on errors.

(7) Adjust the grain size of instruction with learning.

(8) Facilitate successive approximations to the target skill.

other issues of problems - expertise reversal effect???

....................................................................................................................................................................................................................................................................................

A look at the future of intelligent tutoring systems

“Many software systems would significantly improve performance if they could adapt to the emotional state of the user… Many researchers now feel strongly that ITSs would be significantly enhanced if computers could adapt to the emotions of students. This idea has spawned the developing field of affective tutoring systems (ATSs): ATSs are ITSs that are able to adapt to the affective state of students.” (Sarrafzadeh, Alexandaer, Dadgostar, Fan and Bigdeli, 2008). By observing human tutors working with students, data was generated that could be used in the development of an ATS called Easy with Eve, designed at Massey University. Facial analysis is currently used to detect student emotions, which in turn causes the ATS to adjust to the needs of the student. In the future, gesture recognition, vocal and physiological input will also be considered. Unlike ITSs of the past, ATSs will not only be aware of the cognitive states of the students but also the affective state.

- other possible areas to explore: Authoring tools that are used to create ITSs. Different authoring tools are used to create different ITSs. EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network