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Qingrong Ouyang, Lei Xu, Yunwei Zhang, Luwen Huang, Linlin Li, Ming Yu Department of Neurology, Suining Central Hospital, Suining, 629000, People’s Republic of ChinaCorrespondence: Ming Yu, Tel +86 18008258574, Email ym1376@sns120.comBackground: Cognitive impairment, a prevalent non-motor symptom in advanced Parkinson’s disease (PD), has been associated with hyperhomocysteinemia, an important risk factor for PD progression and cognitive decline in PD. However, evidence regarding the association between homocysteine (Hcy) and cognitive function during early PD remains insufficient. Therefore, this study aims to examine the correlation between Hcy levels and cognitive function in the early stage of PD.Methods: The study included 218 individuals in the early stages of PD who were consecutively admitted to the Suining Central Hospital Neurology Department. All the individuals completed the Parkinson’s Disease Cognitive Rating Scale (PD-CDR). The Unified Parkinson’s Disease Rating Scale part III (UPDRS-III) was employed for measuring the severity of motor symptoms, while the Hoehn-Yahr scale was used to measure the clinical symptom stage. Fasting venous blood samples were also drawn to measure the Hcy concentration, red blood cell folate, and vitamin B12.Results: In this cross-sectional study, 47 (21.5%) patients with PD showed cognitive dysfunction. The serum Hcy levels were significantly higher in the cognitive impairment PD (PDCI) group compared with the cognitive normal PD group (P< 0.001). The Generalized Additive Model (GAM) analysis revealed a nonlinear relationship between Hcy and the risk of PDCI. Multiple logistic regression analyses demonstrated a positive relationship between elevated Hcy and the risk of PDCI in the fully adjusted model ([OR]:3.1, 95% CI, 1.1– 8.5, P=0.028). Segmented linear regression analysis showed that when Hcy levels were above 17.7 umol/l, the risk of PDCI increased by 1.6 times for every 1 unit elevated in Hcy (95% CI:1.1â

The flexibility of student pathways in today’s university programs can make it difficult for students to perceive the relationships between the courses they take and to integrate their knowledge and skills in a useful way. In this paper we describe a tool we have developed to help students cope with this challenge.

Educational mapping is the process of analyzing an educational system to identify entities, relationships and attributes. This paper proposes a network modeling approach to educational mapping. Current mapping processes in education typically represent data in forms that do not support scalable learning analytics. For example, a curriculum map is usually a table, where relationships among curricular elements are represented implicitly in the rows of the table. The proposed network modeling approach overcomes this limitation through explicit modeling of these relationships in a graph structure, which in turn unlocks the ability to perform scalable analyses on the dataset. The paper presents network models for educational use cases, with concrete examples in curriculum mapping, accreditation mapping and concept mapping. Illustrative examples demonstrate how the formal modeling approach enables visualization and learning analytics. The analysis provides insight into learning pathways, supporting design of adaptive learning systems. It also permits gap analysis of curriculum coverage, supporting student advising, student degree planning and curricular design at scales ranging from an entire institution to an individual course.

Mapping Outcomes in an Undergraduate Aerospace Engineering Program Student learning outcomes are well known to be an important component of developing subject content, communicating expectations to students, and designing effective assessments. This project focuses on mapping the relationships among outcomes across an entire undergraduate aerospace engineering program. In many domains, outcomes are presented simply as lists of capabilities students should gain by the end of a learning activity. This project expands upon the standard presentation by placing all outcomes from the undergraduate program in an interconnected contextual map. To bring further structure to the map, we group outcomes within modules, where we define a module as a learning unit comprised of a set of outcomes.This new outcomes framework provides a structured process for reevaluation and assessment of a curriculum, specifically with regard to intra and interconnectedness of subjects. Several specific motivations drove this project: first, to inspire discussion among faculty about the current and ideal shape of the undergraduate curriculum and potential gaps or overlap among classes; second, to highlight modularity inherent in the curriculum; third, to provide an educational roadmap to students so that they are able to view their progress through the program and self-assess as necessary; and fourth, to provide a framework for dynamic, integrated assessment within subjects.As a part of this project, faculty revised and expanded upon existing outcomes or generated new outcomes. In addition, faculty identified the relationships among outcomes using prerequisite structures and they grouped outcomes in modules. This led to a set of over 700 outcomes for subjects taught within the Aerospace department, over 150 outcomes for prerequisite mathematics and physics subjects taught outside the department, and almost 1000 prerequisite links between outcomes. These outcomes and links, along with their associat