The primary contributions of this research lie in the fields of Artificial Intelligence in Education and Intelligent Tutoring Systems, and these particular sub-fields of AIED and ITS: ill-defined domains, metacognition, open group learner modelling, visualization, collaborative learning environments, domains requiring natural language interaction. More specifically, the following contributions have been made:
• Discovery of effective instructional strategies for the ill-defined domain of professional ethics. Ill-defined domains are an under-investigated area in AIED research. There is no consensus on the best instruc- tional strategies for them. This research has shown (at least for the professional ethics domain) what instructional strategies work well, and what don’t work that well in the individual case analysis for stimulating learners’ reflection, and broadening their perspectives. In particular, system hints to learn- ers on new arguments that they haven’t yet considered for a given case, turned out to be an effective strategy for broadening the learners’ perspectives.
• Development of insight into ways that collaborative environments could be organized for a case analysis.
This research demonstrated the effectiveness of an organization based on highlighting similarities and differences between students’ positions through the system suggestions, and a specifically designed circle visualization. The proposed organization proved to stimulate students’ productive interactions, and metacognitive processes.
• Development of an open group learner model based on a circle visualization. This research proposed
an open group learner model that visualizes, in the form of a circle visualization, the differences be- tween students’ positions, along with the quality of students’ positions, and the quality of students’ interactions. Throughout the course of the research we experimented with different features of the vi- sualization, and came up with the features that are the most intuitive for learners, such as representing the number of arguments by the size of a circle, representing different case resolutions by different color, etc.
• Finding techniques to foster important metacognitive skills for the professional ethics domain. Metacog- nitive skills play a crucial role in ill-defined domains, often constituting the very subject matter to be learned in these domains. This research demonstrated how the circle visualization is able to encourage
students’ practices of metacognitive skills of reflection, assessment and revision of their own positions on a given case study.
• Development of alternative methods to measure students’ learning in ill-defined domains. Measuring
learning in ill-defined domains is an open issue. The standard ITS procedure in well-defined domains of measuring learning gains through pretest and posttest doesn’t seem to work well here. This research offered alternative methods to measure learning in systems for ill-defined domains through the mixed assessment of students’ behaviours, students’ attitudes, changes in students’ answers, and a comparison with other systems.
• Identification of methods to diagnose learners’ textual arguments. Domains requiring natural language
interaction, and analysis of students’ answers in textual form are an on-going research challenge in the AIED field. In this research we have developed methods for diagnosing students’ arguments based on calculating similarities and differences between them and predefined system arguments using a combination of interface features with LSA or WTMF. This research has demonstrated that effective tutoring support can be built based on the identified methods.
• Discovery of correlations between students’ interactions, particularly interactions with peers of different points of views, and students’ expansion of their own positions on a given case. This suggests that the better a tutoring system is able to organize these interactions, the more will students learn from them, and the more will they expand their initial positions.
The secondary contributions of this research are concerned with professional ethics education. In this research we surveyed literature on pedagogy in professional ethics education and best teaching practices both in traditional classroom settings and in computer-based learning environments. Based on the literature review on pedagogy in ethics and moral development, and based on the results of experiments we ran in several ethics courses, we have formulated techniques for building computer-based learning systems for the development of skills necessary for the professional ethics domain. And here lies the secondary contributions of our research — to professional ethics education. In Section 2.2.4 we have identified the limitations of the previous ethics ITSs as supporting only some small parts of ethical decision making, having very constrained interfaces for students’ input, or using other human raters for the assessment of students’ input. Umka advances beyond these limitations by offering support to students during all stages of ethical analysis, containing an interface that allows students to provide their analysis in natural language, and organizing an automatic assessing of students’ arguments based on text similarity algorithms.
All these contributions have a goal of expanding of AIED’s repertoire of techniques for supporting learning in ill-defined domains. We hope that the techniques developed in this research will allow the building of more systems, and more robust systems for supporting human learning in ill-defined domains.