Evaluation of a Scenario-Based Socratic Style of Teaching and Learning Practice

Evaluation of a Scenario-Based Socratic Style of Teaching and Learning Practice

DOI: 10.4018/978-1-7998-7172-9.ch007
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This chapter presents the central features of a knowledge-based system, evaluation method, which is deeply rooted to the principle of the Socratic style learning in law school. Software system evaluation is placed in the context of a hybrid legal intelligent tutoring system, Guidance for Business Merger and Acquisition (GBMA) process. The legal knowledge for GBMA is presented in two forms, as rules and previously decided cases. Besides distinguishing the two different forms of knowledge representation, the chapter outlines the actual use of these forms in a computational framework designed to generate a plausible solution for a given case by using rule-based reasoning (RBR) and case-based reasoning (CBR) in an integrated environment. The nature of the suitability assessment of a solution has been considered as a multiple-criteria decision-making process in GBMA evaluation. The evaluation was performed by a combination of discussions and questionnaires with different user groups in a scenario-based teaching and learning practice.
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A vast body of academic literature on student employment has examined the conditions of employment among higher education students and its effects on academic progression and labour market success (Martin, 1999). It is worth noting that students’ employment may enhance with progress in higher education since qualifications acquired from university studies facilitate access to skilled jobs. This way, higher education has become so prized in the job market. A combination of political, social, and economic reforms has triggered changes in the higher education sector. On the other hand, educators (e.g., module leaders, lecturers, teachers) are under tremendous pressure to deliver teaching. This pressure is due to the change in student and staff ratio (Kogan et al., 1994), educators' work and life balance, teaching professionals reward structure, and socio-economic value of the higher education market.

Market or market-like economic concepts are responsible for an essential facet in higher education, with prominent effects on the regulation of higher education systems and the governance mechanisms of individual institutions. Since the advent of economics or market-based economy as an independent concept of knowledge in the eighteenth century, economists have attracted education-providing institutions, particularly universities. It is helpful to find out the history of economists’ views on the role of education, from Adam Smith, John Stuart Mill, Alfred Marshall, and Milton Friedman to present-day debates about the relevance of market economies to higher education policy (Watson & Taylor, 1988) (Williams, 1991) (Wolf, 1993). Also, higher education systems' rising costs have contributed to funding to increase competition between higher education institutions. Recent higher education policy changes show the dominant power of market mechanisms worldwide and a certain ambivalence about these changes.

Also, educators have been dealing with a hugely large number of students (Watson & Taylor, 1988) (Williams, 1991) (Wolf, 1993) in recent years. This expansion brought with it a non-homogenous student population. It has made the educators reconsider their teaching style and change their teaching in a more befitting way to accept this societal tendency. Also, educators can no longer assume that all students will achieve their educational competence by being taught in the same manner. Therefore, new teaching practices are essential to support the services for the non-homogenous student population.

In this way, research on learning styles' theory plays a vital role in modern higher education. The main idea behind the learning styles is that students learn in different ways (e.g., by seeing and hearing, reflecting and acting, reasoning logically and intuitively, memorizing, visualizing, and drawing analogies) in fits and starts (Parry, 1995) (Gregory & Graham, 1971). Teaching methods also vary; some educators lecture and others use practical exercises. A student can learn in a class if guided by that student's native ability and prior preparation. Moreover, the student learning style and the educator's teaching style are deciding factors in today's higher education teaching and learning practice.

There is evidence that students learn better when engaging in authentic, motivating, and appropriate learning activities pertinent to their requirements and hopes. Exercise-based tutorials, from a pedagogical standpoint, can address these characteristics and present the subject matter. Besides, course design is also part and parcel of higher education teaching and learning practice. Four-course design approaches (i.e., systematic, intellectual, scenario-based, and workshop-based) were advocated by D'Andrea (D’Andrea, 2001) She also mentioned that an outcome-based learning system provides flexible teaching, learning, and assessment strategies for course design. It is also essential to realize the difference between deep and surface approaches (Biggs, 1987) to learning and necessary influences on levels of outcomes in a course. Biggs (Biggs, 1999) also mentioned that the following four components are essential:

  • Motivational context: In intrinsic motivation, students must consider both learning objectives and learning activities as suitable to feel ownership of the subject and course.

  • Learning activity: students need to be active, not passive; deep learning is associated with doing rather than passively receiving.

  • Interaction with others: discussion with peers requires students to explain their understanding, enhancing their thought process.

  • A well-structured knowledge base: the beginning point for a few learning should be based on existing knowledge and experience. Learning programs should have a clear structure and should relate to other knowledge and not present in isolation.

Key Terms in this Chapter

Intelligent Turning System: An intelligent tutoring system is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher.

Fuzzy Set Theory: In mathematics, fuzzy sets are somewhat like sets whose elements have degrees of membership. Fuzzy sets were introduced independently by Lotif A. Zadeh and Dieter Klaua in 1965 as an extension of the classical notion of set.

Knowledge-Based System: A knowledge-based system is a computer program that reasons and uses a knowledgebase to solve complex problems.

Merger and Acquisition: In corporate finance, mergers and acquisitions are transactions in which the ownership of companies, other business organizations, or their operating units are transferred or consolidated with other entities. As an aspect of strategic management merger and acquisition can allow enterprises to grow or downsize and change the nature of their business or competitive position.

Software System Evaluation: The evaluation of decision quality in an automated intelligent tutoring system is an important and complex task. In particular, the decision quality is dependent on the belief of the perceived evaluation of the end-users.

Rule-Based Reasoning: In conventional rule-based reasoning, both common sense knowledge and domain specific domain expertise are represented in the forms of plausible rules (e.g., IF < precondition (s)> THEN < conclusion (s)>). For example, an instance of a particular rule: IF {( Sam has a driving license ) AND ( Sam is drunk ) AND ( Sam is driving a logistic distribution track ) AND ( Sam is stopped by police )} THEN {( Sam’s driving license will be revoked by the transport authority )}. Moreover, rule-based reasoning requires an exact match on the precondition(s) to predict the conclusion(s). This is very restrictive, as real-world situations are often fuzzy and do not match exactly with rule preconditions. Thus, there are some extensions to the basic approach that can accommodate partial degrees of matching in rule preconditions.

Case-Based Reasoning: Case-based reasoning (CBR) is one of the useful mechanisms for both modeling human reasoning and building intelligent software application systems. The basic principle of case-based reasoning systems is that of solving problems by adapting the solution of similar problems solved in the past. A CBR system consists of a case base , which is the set of all cases that are known to the system. The case base can be thought of as a specific kind of knowledge base that contains only cases. When a new case is presented to the system, it checks the case base for similar cases that are most relevant to the case in hand, in a selection process . If a similar case is found, then the system retrieves that particular case and attempts to modify it (if necessary) to produce a potential solution for the new case. The process is known as adaption .

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