Abstract-This paper presents a new philosophy to model the behavior of a student in a tutorial-like system using learning automata (LAs). The model of the student in our system is inferred using a higher level LA, referred to as a meta-LA, which attempts to characterize the learning model of the students (or student simulators), while the latter use the tutorial-like system. The meta-LA, in turn, uses LAs as a learning mechanism to try to determine if the student in question is a fast, normal, or slow learner. The ultimate long-term goal of the exercise is the following: if the tutorial-like system can understand how the student perceives and processes knowledge, it will be able to customize the way by which it communicates the knowledge to the student to attain an optimal teaching strategy. The proposed meta-LA scheme has been tested for numerous environments, including the established benchmarks, and the results obtained are remarkable. Indeed, to the best of our knowledge, this is the first published result that infers the learning model of an LA when it is externally treated as a black box, whose outputs are the only observable quantities. Additionally, our paper represents a new class of multiautomata systems, where the meta-LA synchronously communicates with the students, also modeled using LAs. The meta-LA's environment "observes" the progress of the student LA, and the response of the latter to the meta-LA actions is based on these observations. This paper also discusses the learning system implications of such a meta-LA.Index Terms-Learning automata (LAs), modeling of adaptive systems, student modeling, tutorial-like systems.

The question of whether leadership can be taught has received much attention in the literature. While many authors believe that it can be taught, other disagree. Leadership is a process which is different from the term leader. In that regard, faculty can teach leadership as a process but they may not be able to provide their students with positions as leaders. The purpose of this article is to discuss the role of faculty in teaching leadership studies. The focus will be on why to teach leadership, when to teach leadership, what to teach about leadership, and how to teach it. Since leadership is a process, students are better served if they learn how this process works and use it in their daily interactions with others. Important leadership behaviors can be learned and practiced early on in life. They can be learned from parents, teachers, peers or significant others. Leadership skills may be successfully taught and learned by using the interactive approach. Thus, through instructional integrity, intellectual humility, relevant equality, critical thinking, specific class structure, and self-directed humor, faculty may create the appropriate climate for teaching leadership skills.

Unlike the field of tutorial systems, where a real-life student interacts and learns from a software system, our research focuses on a new philosophy in which no entity needs to be a real-life individual. Such systems are termed as tutorial-like systems, and research in this field endeavors to model every component of the system using an appropriate learning model [in our case, a learning automaton (LA)].1 While models for the student, the domain, the teacher, etc., have been presented elsewhere, the aim of this paper is to present a new approach to model how the teacher, in this paradigm, of our tutorial-like system "learns and improves his "teaching skills" while being himself an integral component of the system. We propose to model the "learning process" of the teacher by using a higher level LA, referred to as the metateacher, whose task is to assist the teacher himself. Ultimately, the intention is that the latter can communicate the teaching material to the student(s) in a manner customized to the particular student's ability and progress. In short, the teacher will infer the progress of the student and initiate a strategy by which he can "custom-communicate" the material to each individual student. The results that we present in a simulated environment validate the model for the teacher and for the metateacher. The use of the latter can be seen to significantly improve the teaching abilities of the teacher.

Almost all of the learning paradigms used in machine learning, learning automata (LA), and learning theory, in general, use the philosophy of a Student (learning mechanism) attempting to learn from a teacher. This paradigm has been generalized in a myriad of ways, including the scenario when there are multiple teachers or a hierarchy of mechanisms that collectively achieve the learning. In this paper, we consider a departure from this paradigm by allowing the Student to be a member of a classroom of Students, where, for the most part, we permit each member of the classroom not only to learn from the teacher(s) but also to "extract" information from any of his fellow Students. This paper deals with issues concerning the modeling, decision-making process, and testing of such a scenario within the LA context. The main result that we show is that a weak learner can actually benefit from this capability of utilizing the information that he gets from a superior colleague-if this information transfer is done appropriately. As far as we know, the whole concept of Students learning from both a teacher and from a classroom of Students is novel and unreported in the literature. The proposed Student-classroom interaction has been tested for numerous strategies and for different environments, including the established benchmarks, and the results show that Students can improve their learning by interacting with each other. For example, for some interaction strategies, a weak Student can improve his learning by up to 73% when interacting with a classroom of Students, which includes Students of various capabilities. In these interactions, the Student does not have a priori knowledge of the identity or characteristics of the Students who offer their assistance.

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