An Intelligent Tutoring System for Automata Theory : A Proposed Framework

With the advancement in technology and computers superseding humans in almost every territory of their life on this earth. The concept of Intelligent computers trying to replicate the human brain has evolved since the discovery of Artificial computers which work on the same concept as human brain which learn with change in experience over time. Intelligent tutors have been a boon for learners because it is humanely impossible for the human tutor to concentrate on each and every learner in the traditional classroom scenario. This paper summarizes the Intelligent tutoring systems available for the Undergraduate course of Automata Theory for Computer Science students and proposes a new solution based on the shortcomings in the existing systems. Keyword: Intelligent Tutoring System, Automata Theory, FLAT. Introduction Research in education has led to the use of Computers for education which then evolved into delivering instruction online which was an idea that emerged from distance education and made learning possible anytime, anywhere and at whatever pace the learner wants to. The augmentation and advancement in the domains of Education and Artificial Intelligence is the work of researchers who worked in both the domains. Intelligent tutoring systems have been evolved from the traditional computer assisted instruction systems (CAI). When the CAI were developed to behave intelligently they were termed as Intelligent Computer Assisted Instruction (ICAI) which further were modified to be renamed as Intelligent tutoring systems (ITS). [11] Intelligent tutoring systems are computer-based learning environments that incorporate computational modules on cognitive sciences, learning sciences, computational linguistics, artificial intelligence, mathematics, regular language and other fields that develop intelligent systems that are well-specified computationally. [1] In [21], researchers presented an initial outline of the ITS requirements, the following are (a) Knowledge of the domain (Expert model) (b) knowledge of the learner (Student model) (c) Knowledge of the teaching strategies (Tutor model) The outline has not changed over the years, but a new module has been added to it. The four model architecture [3] preserved the three models but added one more model, and now this architecture has become a standard for intelligent tutoring systems The following are the four models: (a) Knowledge base (b) Student Model (c) Pedagogical model (d) User Interface Knowledge base: This component is similar to the domain model of the previous architectures. A tutor or coach consolidates declarative, procedural and metacognitive knowledge. Student model: An internal model which defines the cognitive processes, meta-cognitive strategies and psychological attributes for each learner. International Journal of Research Volume VIII, Issue II, February/2019 ISSN NO:2236-6124 Page No:32 Muneza Khatoon Pedagogical model: This module is similar to its counterpart in the other architectures. It uses a model of the learner’s present comprehension to select an efficient path through its knowledge representation to generate expert behavior by the learner. User Interface: This module integrates three types of information: knowledge about patterns of interpretations and actions within dialogues; domain knowledge needed for communicating content and knowledge needed for communicating intent. The Constructivist theory claims that knowledge’ is actively constructed by the student, not passively absorbed from textbooks and lectures. Since the construction builds recursively on knowledge (facts, ideas and beliefs) that the student already has, each student will construct an idiosyncratic version of knowledge. Teaching techniques derived from the theory of constructivism are supposed to be more successful than traditional techniques, because they explicitly address the inevitable process of knowledge construction. [24] Application of the constructivist theory to the Computer Science education is difficult for the following reasons • The construction of CS concepts using the constructivist theory is, because sensory data, from class must be integrated into a student’s existing framework that is too superficial. • Frustration and the perception that computer science is ‘hard’ is due to the fact that models must be selfconstructed from the ground up. • Autodidactic programming experience is not necessarily correlated with success in academic CS studies. These students, like physics students, probably come with firmly held constructions that are not viable for academic studies. • The reality feedback obtained by working on a computer can be discouraging to students who prefer a more reflective or social style of learning. [24] The subject of FLAT is included in most of Computer Science Curricula since it discusses the fundamental theories of the discipline and help students understand the mathematical foundation of computation, its power and limitations [20] [10] [19]. Many Computer Science programs teach a course in Theory of Computation (also called Theoretical Computer Science, Formal Languages and Automata Theory(FLAT), etc.) These courses typically require students to design Finite Automata (FAs), Pushdown Automata(PDAs) and Turing Machines (TMs) to recognize languages of the relevant class Students often find mastering these foundational FLAT concepts challenging (as they can be very abstract, and thus overwhelming), and even boring (as there is a perception that the field is not only dated, but that it has little current applicability in the real world). [4] [7] [28] [30] [34] [40] This can lead to demotivated and frustrated students [31] who retain little of what they have learned [28]. Problem-solving skills, or rather the lack thereof, have been identified as one of the major contributes to the difficulties associated with building FAs [30]. This poor level of problem-solving ability can lead to the logic errors that were found to be prevalent. Furthermore, students could be using mechanical ‘plug-and-chug’ approaches without understanding the logic of the process. In response to this, several teaching approaches have been proposed to overcome these challenges, as well as to assist in the building of better mental models of FAs [5] [38] • Constructivist teaching strategies and active learning: This includes the need to help students with immediate feedback at each stage of the process of building FAs so that they can better develop problem-solving skills (something that is very difficult to implement in a distance learning environment) • Visualization tools: The point of these is to assist students in conceptualizing FAs and how they work by providing an alternative view, allowing them to experiment with the concepts by testing them on various input strings and receiving immediate feed-back. Such approaches build on the principle that abstract models can become concrete when students can interact with them. [9] • Link to current Computer Science applications: This could include using knowledge of programming as motivation for the exploration of FAs as well as the use of its application to real-world problems. • Intelligent tutoring systems: These provide students with individualized assistance, tailoring the route through the required material based on how the student is progressing. International Journal of Research Volume VIII, Issue II, February/2019 ISSN NO:2236-6124