Entrepreneurial learning from failure : an interpretative phenomenological analysis

This paper develops a deeper conceptualisation of venture failure from a learning perspective. Moving beyond the causes of failure, I seek to develop a richer picture of the impact and outcomes of failure and the learning processes by which entrepreneurs actively grieve for, and recover from, the loss of a business. Based on interpretative phenomenological research with eight entrepreneurs, this paper adds valuable empirical weight to extant conceptual discussions of failure. Marrying emergent literature on entrepreneurial learning with theories of failure, I propose distinctive higher-level learning processes triggered by failure that prove fundamental in personal and business terms. These learning outcomes provide entrepreneurs with invaluable insights into the 'pressure points' of the entrepreneurial process, significantly augmenting levels of entrepreneurial preparedness for future enterprising activity

DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.Comment: Technical Report, 12 page

Kecemasan Menghadapi Tes (Test Anxiety) Dan Dampaknya Terhadap Aktivitas Belajar

In education process, the objective of test given to the students is to find out the change of attitude and also new skill that achieve by the students after teaching and learning process. Many times, test in students’ mind is a problem that cause test anxiety. The anxiety theory differentiates between cognitive component which is known as worry component and emotional component or affective component, as two components of test anxiety. Cognitive component or worry component administers mind that comes with anxiety such as test failure, and the consequence of the test failure is potential to cause the anger of parents, drop out, and also embarrassed feeling. Whereas the emotional component or affective component describes the physiological reaction and emotion that appear when students face the test. It’s important to note that most of people feel the anxiety when they have to prove their productivity. Test anxiety that encountered by people will be influenced by their ability to finish the test. Some researches prove that test anxiety can influence academic productivity, self-esteem, related to students’ self-defense and the fear of negative valuation. Hence, to anticipate the negative effect that caused by test anxiety, it is important to held anxiety management and learning skill

Why University Students Fail in Most Computer Programming Courses: The Case of Wachemo University-Student-Teacher Perspective

In this research, a study was conducted to investigate and explore the views of students for the failure and difficulties they faced in learning fundamental programming courses. There are many factors that influence the high rate of failure of students in most computer programming courses. This paper focuses on the teaching and learning methodologies and strategies that are implemented in teaching of all computer programming courses. This is a major factor for consideration; hence an investigation into the causes of failure of students in most computer programming courses from all perspectives with regard to the teaching methodology used by teachers to teach these courses is relevant and very important concept. Most computer programming courses form part of the core concentration areas for students especially studying in school of computing and informatics as an undergraduate degree program. All computer programming students are expected to prove capabilities in the principles of programming and logic that are being taught in the courses; even though some of these concepts are highly intellectual and multifaceted. Their opinions to the usefulness of the teaching methods being implemented in computer programming courses were required for. The needs and concerns about the teaching and learning methods are highlighted in the study and discussed thereby leading to the making of suggestions about the ways to improve the teaching and learning methods that are used in computer programming courses in order to advance understanding of computer programming, when studied by students thereby minimizing failure rates of those students. Keywords: Computer programming; Failure; School of Computing and Informatics; Student and Teacher Perspective DOI: 10.7176/CEIS/11-2-02 Publication date: February 29th 2020