Dynamic learning need reflection system for academic education and its applicability to intelligent agents

This paper suggests a new concept DLNR (Dynamic Learning Need Reflection) and its system practically used in the education at Japanese University. The effects, particularly on the learning of software agents, are analyzed. DLNR’s goal is to increase students' learning motivation through dynamically clarifying and reflecting their learning need. To achieve this goal, DLNR includes “prerequisite conditions”, “no compulsory subjects”, “payment for each learning subject”, and “GPA (Grade Point Average)” for estimating learning results. Using a tool developed for realizing DLNR, students design their learning need, namely their own graduation timeline by themselves to achieve their academic goal towards their job after graduation. Through taking classes, students dynamically modify the timeline reflectively according to the intermediate results such as shown by GPA. DLNR’s effects are evaluated. Particularly, DLNR was found applicable to the learning of software agents for intelligent system assistants, through incorporating more general tool such as Story board

Modeling Academic Education Processes by Dynamic Storyboarding

In high-level education such as university studies, there is a flexible but complicated system of subject offerings and registration rules such as prerequisite subjects. Those offerings, connected with registration rules, should be matched to the students’ learning needs and desires, which change dynamically. Students need assistance in such a maze of dynamically changing opportunities and limitations. To cope with this problem, a new storyboard concept for academic education, called “dynamic storyboarding” is proposed to assist university students. Dynamic storyboarding is based on the idea of semi-formally representing, processing, evaluating, and refining didactic knowledge. This storyboarding is more appropriate in managing high-level education than is general artificial intelligence knowledge representations such as frames. This is because the structure of dynamic storyboarding is driven by the semi-formal and multilayered nature of didactic knowledge in university education. A feasibility study showed that storyboarding can be used to supplement an academic educational system, such as the dynamic learning need reflection system (DLNRS) of Tokyo Denki University (TDU) in Japan. Concretely speaking, didactic knowledge in the university curricula was proven to be easily and clearly represented by dynamic storyboarding. This contributed to the students’ dynamic learning activities by supporting features that help students review and adapt their own individual curricula

Managing academic education through dynamic storyboarding

Complex long term learning activities may be exhausting, tiring and sometimes even frustrating. In high level education such as university studies, there is a system of offers, rules, requests and prerequisites, which need to be matched with students' needs and desires. University students need assistance in the jungle of opportunities and limitations at today's universities. Here, we employ our formerly developed storyboard concept to face this problem and introduce a storyboard to develop, maintain, and evaluate academic education. Storyboarding is based on the idea of formally representing, processing, evaluating and refining didactic knowledge. It is more powerful in managing education than general AI knowledge representations such as frames, because the syntax of storyboards is driven by the particular nature of didactic knowledge. The concept is a supplement to the educational system (called Dynamic Learning Needs Reflection System: DLNRS) of the School of Information Environment of Tokyo Denki University, Japan. Concretely speaking, the didactic knowledge of DLNRS can be represented by storyboard and used for supporting dynamic learning activities of students

A CASE OF RUPTURED MITRAL VALVE ANEURYSM DUE TO INFECTIVE ENDOCARDITIS

58-year-old woman with aortic regurgitation was admitted to our hospi- tal because of high grade fever. She had infective endocarditis and an aneurysm of the anterior mitral leaflet. Doppler echocardiography indicated a ruptured mitral valve aneurysm. Aortic regurgitant flow along the anterior mitral leaflet was suspected to have contributed to mitral valve endocarditis, aneurysm formation and rupture. She was initially treated with high-dose intravenous penicillin, but congestive heart failure worsened. Mitral valve replacement was then successfully performed

Personalized curriculum composition by learner profile driven data mining

The paper is focused on modeling, processing, evaluating and refining processes with humans involved like (not only, but also e-) learning. A formerly developed concept called storyboarding has been applied at Tokyo Denki University (TDU) to model the various ways to study at this university. Along with this storyboard, we developed a Data Mining Technology to estimate success chances of curricula. Here, we introduce a learner profiling concept that represents the students’ individual properties, talents and preferences personalized data mining

Using storyboarding and data mining to estimate success chances of curricula

In university studies, there is a flexible but complicated learning system of subject offers, enrollment rules for particular subject combinations, and prerequisites to meet for taking particular subjects, which need to be matched with students' needs and desires. Students need assistance in the jungle of such learning opportunities and limitations at today's universities. To face this problem, we employed our formerly developed storyboard concept and used it to develop, maintain, and evaluate curricula. Storyboarding is based on the idea of formally representing, processing, evaluating and refining didactic knowledge. This concept is utilized to supplement an educational system called Dynamic Learning Needs Reflection System (DLNRS) of the School of Information Environment of Tokyo Denki University, Japan. Concretely speaking, didactic knowledge of DLNRS can be represented by storyboarding and used for supporting dynamic learning activities of students. Here, we introduce an additional benefit of the storyboard concept. By using data mining - like methods to evaluate storyboard paths, we are able to estimate success chances of storyboard paths. Based on such an evaluation we will be able to rate planned (future) paths and thus, to prevent students from failing by non-appropriate curricula. Moreover, besides the evaluation, the estimation can be used for computer enforced suggestions to complete a path towards optimal success chances

Knowledge mining for supporting learning processes

AI technologies for knowledge mining are commonly used in technical environments. Their application for social processes like learning processes, for example, is a quite a new challenge, which is characterized by having "humans in the loop". Humans’ desires, preferences and decisions may be unpredictable and thus, not appropriate for modeling - at a first glance. However, in learning processes didactic variants can be anticipated and can become a subject of AI technologies. A semiformal modeling approach called storyboarding, is outlined here. A storyboard represents various opportunities for composing a learning process according to individual circumstances, such as topical prerequisites (educational history), mental prerequisites (preferred learning styles, etc.), performance prerequisites (a requested success level in former learning activities, etc.), and personal aspects (needs, wishes, talents, aims). By storyboarding, various didactic variants can be validated by considering the average learning success associated with the different paths through a storyboard in a case study. Based on validation results, success chances can be derived for the different paths. Here, a concept and an implementation to pre-estimate success chances of intended (future) learning paths through a storyboard are introduced. They are based on a Data Mining technology, and construct a decision tree by analyzing former learners’ paths and their degrees of success. Furthermore, this technology generates a supplement to a submitted path, which is optimal according to the success chances. This technology has been tested at a Japanese university, in which students had to compose their individual plan (subject sequences) in advance, and the technology helped them by predicting success chances and suggesting alternatives

Personalized curriculum composition by learner profile driven data mining

The paper is focused on modeling, processing, evaluating and refining processes with humans involved like (not only, but also e-) learning. A formerly developed concept called storyboarding has been applied at Tokyo Denki University (TDU) to model the various ways to study at this university. Along with this storyboard, we developed a Data Mining Technology to estimate success chances of curricula. Here, we introduce a learner profiling concept that represents the students’ individual properties, talents and preferences personalized data mining

Optimal Protocol for Contrast-enhanced Free-running 5D Whole-heart Coronary MR Angiography at 3T.

Free-running 5D whole-heart coronary MR angiography (MRA) is gaining in popularity because it reduces scanning complexity by removing the need for specific slice orientations, respiratory gating, or cardiac triggering. At 3T, a gradient echo (GRE) sequence is preferred in combination with contrast injection. However, neither the injection scheme of the gadolinium (Gd) contrast medium, the choice of the RF excitation angle, nor the dedicated image reconstruction parameters have been established for 3T GRE free-running 5D whole-heart coronary MRA. In this study, a Gd injection scheme, RF excitation angles of lipid-insensitive binominal off-resonance RF excitation (LIBRE) pulse for valid fat suppression and continuous data acquisition, and compressed-sensing reconstruction regularization parameters were optimized for contrast-enhanced free-running 5D whole-heart coronary MRA using a GRE sequence at 3T. Using this optimized protocol, contrast-enhanced free-running 5D whole-heart coronary MRA using a GRE sequence is feasible with good image quality at 3T

SIGMOID SEPTUM CAUSING AORTIC REGURGITATION 1 A CASE REPORT

An 86-year-old woman with a sigmoid septum that caused aortic regurgita- tion (AR) is described. The patient visited our hospital because of dyspnea and leg edema. On auscultation, a characteristic Levine 3/6 diastolic musical murmur was head in Erb's area. A 12-lead electrocardigram showed atrial fibrillation (heart rate=68/min) but no evidence of ischemic change. Chest radiography showed cardiomegaly but no pulmonary congestion. Two-dimensional echocardiography revealed a basal interventricular septum (IVS) markedly protruding into the left ventricular outflow tract (sigmoid septum). The angle formed by the aorta and the IVS (aorto-septal angle) was about 70 degrees. Furthermore, prolapse of the right coronary cusp toward the left ventricle accompanied by paradoxical motion of the basal IVS during diastole was also observed. Color Doppler echocardiography detected a localized and distinct regurgitant jet flow from the right coronary cusp toward the base of the anterior mitral leaflet. According to Seller's classifi- cation, the AR was grade 2. These findings suggest that AR may develop in patients with a sigmoid septum due to prolapse of the right coronary cusp leading to paradoxical motion of the basal lVS