...Case #: 117090 Cord Care (Normal Delivery) Name: Baby Boy Mabasa Mother: Judic Mabasa Date: 11-25-2010 Time of Birth: 3:35pm Weight: 3kg N.C: 34cm C.C: 33cm A.C: 34cm Temp.: 36.5cm Apgrr: 8/9 Length: 52cm Pedia: Br. OB.gyne: Dr. Vivian lachaona Case # 669537 Cord Care NSD Name: Baby Boy Milorpis Mother: Grace 26 years old Date: 6-21-11 Time of Birth: 8:00am Weight: 2.85kg. N.C: 33cm C.C: 33cm A.C: 30cm Temp.: 35.3'c Apgar: 9'9 Legth: 53 Case # 669559 Cord Care: CS Name: Baby Mirabuenoz Mother: Rosemarie 41 years old Date: 6-22-11 Time of Birth: 11:45am Weight: 2.6kg N.C: 35cm C.C: 34cm A.C: 32cm Apgar: 9'9 Legth: 47cm Case # 669655 NSD Name: Baby Boy Tonculas Mother: Jessa 24 years old Date : 6-27-11 Time of Birth: 11:18am Weight: 2.5kg N.C: 33cm C.C: 32cm A.C: 30cm Temp.: 35.6cm Legth: '17 Apgar: 9'9 Case # 3 PBE 6-27-11 Name: Baby Girl Ongay Barto Mother: fatima 24 years old Date: 6-28-11 Time of Birth: Weight: 2.4kg N.C: 33cm C.C: 32cm A.C: 30cm Temp.: 36.1'c Length: 47 Apgar: 9'9 Case # 1630682 (handle) G1 P0 /PU “NSD”/RMLE Name: Bonajas, Kimberly Guerreno Date: 6-29-11 Age: 17 years old AOG: 32 4/7 weeks B.B: Girl Time: 8:30am Case # 2327710 (Assist) G1 P0/PU “NSD”/RMLE Name: cabanda,Corena Yana Date: 6-29-11 Age: 26 years old AOG: 40 4/7 weeks B.B: Girl Time: 11: 50am Case # 2319913 (Handle) G4 P0/PU “NSD”RMLE Name: Tayo, Garcia Victoria Date: 6-29-11 Age:...
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...* Personal Responsibility * Day-to-day student performance and behavior are the best indicators that a student is having trouble. The challenge is knowing which students are struggling and then getting this information into the hands of those at the institution who can provide the right help. Some studend find different ways to learn easy for them.Some examples: * Human and Electronic. The system seamlessly combines feedback about a student's performance from people on campus (e.g., instructors) as well as automatic analysis of student activity as recorded in academic (LMS) and administrative (SIS) systems. * Academic and Personal. The system incorporates feedback about a student's academic performance as well as social/behaviorial indicators. * Find and Fix. The system not only identifies issues but also enables student success staff to address the issues to resolution. * Know What's Working and What Isn't How do you know if you have the right services to assist the diverse set of students enrolled in your institution? The best administrative decisions are made when your institution can analyze relevant information. Positions your institution to know what's working and where there is an opportunity for change. For example, are students who attend tutoring more than twice a term for a class more likely to pass the class? With the ability to assess student outcomes as a result of your student success programs, you are able to create actionable plans for continuous...
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...CHAPTER I A. Introduction Learning is a very complex process which involves organizing, modifying existing and newly acquired knowledge, skills, behaviours and values. Thus learning and academic performace of a student can be easily determined by the grade they earned by the time alloted to learn. Good grades imply that the student learned a lot, while poor grades imply less learning. However learning process is also affected by several factors, like heredity, IQ level, age, year level, study habbits, social status, emotions, and many more. The researchers are interested on the factors that affect learning process to find solutions on the common academic problems “the failing grades of students”. The researcher believed that it is study habbits that has the greatest effect on the learning process and academic achievements of the first year students of Bachelor of Science in Mathematics of University of SouthEastern Philippines. Study habbits also has several factors to be considered to make the learning very effective and useful and it has the greatest issue on all students, thus giving the researchers the motivation to conduct this study. B. Conceptual Framework INDEPENDENT VARIABLES CONCEPTUAL FRAMEWORK OF THE STUDY LEARNING ACADEMIC PERFORMANCE DEPENDENT VARIABLES Time alloted for studying Time alloted for the homework How works on a subject are organized Time alloted for each subject How the mind of the student is set to learn new skills STUDY...
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...INTERNATIONAL JOURNAL of ACADEMIC RESEARCH Vol. 6. No. 2. March, 2014 T. Kafadar, B. Tay. Learning strategies and learning styles used by students in social studies. International Journal of Academic Research Part B; 2014; 6(2), 259-267. DOI: 10.7813/2075-4124.2014/6-2/B.39 Library of Congress Classification: L7-991 LEARNING STRATEGIES AND LEARNING STYLES USED BY STUDENTS IN SOCIAL STUDIES* Tugba Kafadar , Bayram Tay 1 2 1 2 Marmara University, Institute of Education Sciences, Istanbul Ahi Evran University, Faculty of Education, Kirsehir (TURKEY) E-mails: tugbakafadar@gmail.com, bayramtay@gmail.com DOI: 10.7813/2075-4124.2014/6-2/B.39 Received: 28 Sept, 2013 Accepted: 15 Mar, 2014 ABSTRACT It can be important to be known students’ learning features to increase efficiency of learning process in social studies lesson that aims educating efficient citizens. Therefore, in this study the learning strategies used by students, their learning styles and whether or not their learning strategies are changing according to their learning styles are researched. The data in this study, which is a cross sectional survey, were collected through the learning strategies developed by Tay (2002) on the basis of the classification of learning strategies performed by Gagne and Dricscoll (1988) and Kolb learning style inventory III which was adopted into Turkish by Evin Gencel (2006). As a result of the research it was identified that while students mostly use affective strategies...
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...University of Florence Faculty of Economy Master’s Degree in Bank, Insurance and Financial Markets Thesis in Applied Statistics for Banks and Insurances Credit Risk Models: Single Firm Default and Contagion Default Analysis Supervisor: P rof essor Fabrizio Cipollini Student: Marco Gambacciani Academic Year 2009/2010 Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Structural Models 1.1 Terminal Default . . . . . . . . . . . . 1.2 First Passage Models . . . . . . . . . . 1.2.1 The Black and Cox’s Model . . 1.2.2 Longstaff and Schwartz’s Model 1.2.3 Leland and Toft’s Model . . . . 1.2.4 Zhou’s Model . . . . . . . . . . 1.2.5 Random Threshold Model . . . 2 5 5 11 11 15 19 24 30 35 36 39 41 45 48 50 51 56 67 76 77 79 79 82 83 84 94 114 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Modelli reduced form 2.1 Approach With An Homogenous Poisson Process . . 2.2 Approach With a Non-Homogenous Poisson Process 2.3 Approach with a Cox’s Process . . . . . . . . . . . . 2.4 Bond and Spread Valuation . . . . . . . . . . . . . . Models For The Correlation Between Defaults 3.1 Bottom-Up Models . . . . . . . . . . . . . 3.1.1 Structural Apporach . . . . . . . . 3.1.2 Intensity Models Approaches . . . 3.1.3 Approaches with...
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