The Social Benefits Of Education

The Social Benefits Of Education Education has been considered an investment value. Those who acquire additional schooling generally earn more over their lifetimes, achieve higher level of employment, and enjoy more satisfying careers. It also enable people to more fully enjoy life, appreciate literature and culture and be more socially involved citizens. Private returns to education refer to the benefits received by the individual who acquires additional schooling. These include economic benefits such as higher lifetime earnings, lower level of unemployment, and greater job satisfaction, improve health and longevity. Social returns refer to positive or possibly negative consequences that accrue to individuals other than the indivudal or family making the decision. About how much schooling to acquire. These are the benefits not taken into account by the decision-maker. II. Rationales for Government involvement In Post Secondary Education Efficiency gains result in an increase in societys total output of goods and services, and thus allow achievement of higher average living standards Equity considerations relate not to the average standards of living but how societys total output is distributed among citizens. Second argument to intervention is that in the absence of interventions such as student loan programs individuals who might benefit from higher education but who do not have the financial resources to finance the investment are typically unable to use their potential human capital as collateral for loan.. The talent of the population may not be fully utilized and the total output of goods and services may fall short of its potential. Both of these efficiency rationales involve a potential market failure. The first arises because of positive external benefits associated with education -social benefits that exceed private benefits. The second arises because of a failure in credit market that results in some individuals being unable to finance productive investments. III. Estimating Private and Social Returns to Education Education is one of the best predictors of success in the labour market. More educated workers earn higher wages, have greater earnings growth over their lifetimes, experience less unemployment and work longer Higher education is also associated with higher longer life expectancy, better health and reduced participation in crime. According to human capital theory, schooling raises earnings because it enhances workers skills thus making employees more productive and more valuable to employers. III. Social Returns to Education positive or possibly negative consequences that accrue to individuals other than the indivudal or family making the decision. About how much schooling to acquire. These are the benefits not taken into account by the decision-maker. A. Innovation, knowledge creation and economic growth new growth theory: emphasizes the contribution of knowledge creation and innovation in fostering advances in living standards over time.. education plays an important role in economic growth . knowledge creation and innovation respond to economic incentives, and thus can be influenced by public policy. The education and skill formation systems play an important role in fostering innovation and advancing knowledge. There are 3 main dimensions to this role related to research function of educational institutions esp. universities can be an important source of new ideas. Accgd. To this perspective the human capital of the workforce is a crucial factor facilitating the adoption of new and more productive technologies. Human capital of the workforce is a crucial factor facilitating the adoption of new and more productive technologies. The transfer of knowledge function msut be reflected to the returns to education. Those receiving educ will become more prodictive and thus more valuable to the employers. Post sec educ in oecd countries is relatively more important than with primary and secondary educ in developing countries B. Knowledge spillovers Static knowledge spillovers arise if more education raises not only the productivity of those receinvg the education but alos the productivity of those they work with and interact with. Jacobs argue that cities are engine of growth bec they facilitate the exchange of ideas esp. between entrepreneurs and managers Such knowledge spillovers can take place thorugh the echange of ideas, imitation and learning by doing C. Non-market effects of education Other forms of benefits other than higher wages or non-wage benefits from working. This includes improved onw health or child dev. private in nature and thus may be taken into account by individuals in cjoosing the amount of educ to acquire. Authors find considerable impact of educ on a wide variety of non-0market and social benefits even after controlling income, age, health and race. This includes effect of wifes schooling on husband earnings effect of parents educ on child outcomes (intergenerational effects): education, cognitive ability, health and fertility choices effect of educ on own health and spouse health effect of educ on consumer choice efficiency, labour market etc effect of educ on charitable giving and volunteeractivity higher ave of educ levels in the community lower school dropout rates of children D. Intergenerational effect parents education has strong effects on children, resulting in large intergenerational effects parental educ on a number of child outcomes including higher parental educ is associated with lower fertility via increased efficiency of contraception as well as via raising the age of both marriage and first pregnancy. The resulting of lower pop growth is positive for economic growth in dev countires incidence of teenage childbearing is much higher for children of less educated parents child abuse and neglect are also associarted with parents educ high parental educ more subs family investments in children , loer criminal propensities , improved child health children of less educated poarents generally cost more to educate intergenerational benefits of educ to society: lower educ cost, less ue of foster care and juvenile diversion, lower crime, lower heakth cost and lower dependence on welfare transfers E. Health and longeivity child health is posivitve related to parents educ results to superior health behaviors: reduced smoking, more exercise and low incidence of heavy drinking educ people adopt newer drugs due to ability to learn and more info thus educ leads to better health F. Criminal Activity high educ levels may lower crime byb raising wage rates, which increase the opportunity cost of crime lower crime rates G. Civic participation correlation between educ and voting is high . higher educ is also associated with greater charitable giving and volunteerism trust and participation educ raises the quality of peoples involvement in the society H. Tax and transfer returns more educ are less likely to return on public transfers wven when elgivible for benefits FLEMISH EDUCATION, BETWEEN MERTIOCRACY AND EGALITARANISM By: Ides Nicaise I. A Century of Reforms- without much success social inequality in education still exist in flanders compulsory educ until the age of 18 90s began with an experimentation on positive discrimination schools with a large number of pupils from underprivileged groups (immigrants, disadvantaged pupils) received additional funding What is lacking is a clear choice in favour of a more egalitarian of educ Two Basic Views of Equality Meritocracy Egalitarianism Both visions to a certain extent share the same concern: out an end to the unjustified passing on of power , prestige, and wealth based on a persons descent. Allocation of social positions should no longer be ascribed to individuals based on their origins (the principle of ascription), rather these positions should be acquired based on achievement Every member of society should regardless of social origin have the same opportunities to prove himself Meritocracy an ideology of equal opportunities .. and unequal treatment Principle of individual merit which boils down to a combination of talent and effort False justice theory, results in a disguised reproduction of the existing inequalities Tony Blair- ambition to make his country a meritocratic society. Nederlands and Sweden were the first to achieve the higest stage of a meritocratic educ society Social positions to be distribuited on the basis of merit (talent and effort) The existing social inequality can essentially be explained by three set of factors innate abilities genetically determined social background- transfer of matrial assets, social networks, and cultural capital. This is regarded as unfair ; these are the mechanisms that have to be eliminated as much as possible , eg by the provision of free and freely accessible educ. Accdg. To meritocratic thinking, society is not responsible for the two other sets of factors. Innate ability (for the time being) a question of coincidence, personal effect-responsibility of every individual personal preferences and effort opposed to the social transfer of power and prosperity, but inequality exists in merit . the merit talent . it is implicitly assumed that tlents are purely randomly distributed among the pop. And tehrfore have nothing to do with social origins The meritocratic recipe for educ can be summarized in 3 major principles everyone must have equal access to education according to innate ability . equal opportunities : opportunities refer to coincidental factor which is not within our power and which helps determine the outcomes of educ and future social pos. The aim is not therefore equal outcomes, but a particular distribution of possible outcomes which are unrelated to a persons social background equal access educ is not unconditional. Everyone should have access to educ accdg to his innate ability. It is accepted that not everyone gains access to the same extent to a given level or type of educ. Specifically, financial obstacles in education will have to be eliminated as much as possible but that admission tests or intelligence tests can be accepted a legitimate selection criteria. Unequal treatment of individuals based on merit is regarded as legitimate. In other words it is accepted that more is invested in persons who display a greater innate ability and or more personal effort. . moral to economic interà ¢Ã¢â€š ¬Ã‚ ¦ regarded as fair community invest more resources in people with more talent, perhaps they have merited this, but bec they are expected to contribute more in the future to collective prosperity to those who have shall be given inequality based on social background will disappear if the two previous principles are consistently applied Principle of equal opportunites has been translated into compulsory education and free educ. Compulsory educ is a way of legally limiting parents freedom of choice regarding educational participation Second principle- differential treatment accdg to talent and effort, forms the counterbalance to this mildness at the entrance gate . Flemish educ is extremely selective and achievement -oriented What is wrong with meritocracy? John Goldthorpe inherited talents are in no way an element of merit and as a result the ethical justification for this social model is immediately negated Dick pels- adds a number of arguments to demonstrate that even on a labour market regarded as competitive and meritocratic Youn- meritocracy in its most perfect form eventually leads to a new type of class-based society Egalitarianism: a relic from the communist era? Egalitarianism is the basic percepts of human rights, ie the equal dignity and freedom of people The right of educ may not depend on the talents of an individual but is, to a certain extent, an absolute right Absolute rights do indeed apply to basic goods John Rawls- people will agree that distribution of basic goods must be strictly egalitarian and may not be dependent on something like talents, precisely bec. Talents are unearned Inequalities that contribute to an improvement in the position of the poorest citizens gradation differences exist within egalitarianism: at the level of elem educ., it refers to equal outomes (a level that everyone should attain), at the higher level- equal opportunities The emphasis on equal outcomes (elem and sec) forms a second critical area of difference bet. Egalitarianism and meritocracy. Amartya Sen emphasis the basic right is only effective if the result is achieved, not if it is written down in law. This means that authorities bear the responsibility for guaranteeing the implementation of basic rights for all. Principle of positive discrimination- priority given to disadvantage Egalitarianism implicitly assumes that equal outcomes are possible. Students in the primary and sec levels are in the position to achieve the targets Traces of egalitarianism in Flemish educ: attainment targets in guaranteeing pupils with the same min skill level remains limited. Study grants from merit.. to egalitarian vision Trojan Horse of the Lower Classes Protagonist of greater equality are not infrequently accused but face with some questions: A society cannot consist solely of university graduates . labour market also needs semi-skilled workers. . the egalitatain base refers to basic education. equal outcomes can be interpreted in 2 ways: strict def.: same target level is applied for every individual , broader def. accepts certain variation in individuals. In other words, individual differences are tolerated but the average outcomes among children from various social environments must be equalized resistnace to egalitarianism: postivie discrimination in favour of the underprivileged groups could be flipside of negative discrimination against them (white person with high score over black with low score- black gets priorty- contest educ is not a zero sum game in which better outcomes for one group are achived at the expense of poore results for another group. The key is to adapt reform and strategies that more equal outcomes go hand in hand with a sin-win sit for every one (ex. R3educed referral to SPED Educational Strategies for disadvantaged youth in 6 European countries By : I. Nicaise Intro Gen. level of educ is increased but has demonstrated that in most countries inequality is passed on unrelentingly .. social exclusion Social Equality in Education Current educ system filters, segregates and reproduces social inequality Dream of democratic educ sys- the dream of equal opportunites and unhindered social mobility. Everyone is entitled to benefit to a resonalbe extent from their education . Whether consciously or not, many harbour meritocratic view of education, it is assumed that everyone has equal opportunites but equal porofit is certainly not an aim because aaacdg to the theory, the unequal benefit from educ merely reflects the efforts and talents of each individual . As Goldthrope demosntatres, meritocratic ideology expliclty perceives unequal educational outcomes as fair. .. it hastily passess over the issue of the unequal socity in which education is rooted A priori opportunities are not equal and unequal outcomes are not fair 2. Equal Opportunity Strategies Integrated approach to poverty, inequality and social exc

Comparing Extreme Programming and Waterfall Project Results

Comparing Extreme Programming and Waterfall Project Results Feng Ji Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 [email  protected] com Todd Sedano Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 todd. [email  protected] cmu. edu Abstract Waterfall and Extreme Programming are two software project methods used for project management. Although there are a number of opinions comparing the two methods regarding how they should be applied, none have used project data to clearly conclude which one is better.In this paper, we present the results of a controlled empirical study conducted at Carnegie Mellon University in Silicon Valley to learn about the effective transition from traditional development to agile development. We conducted a comparison research against these two approaches. Multiple teams were assigned a project; some used Waterfall development, others used Extreme Programming. The purpose of this research is to look at advantages and disadvantages based upon the outcomes, generated artifacts, and metrics produced by the teams. 1. Introduction 1. 1.Agile vs Traditional Since the early 1970s, numerous software managers have explored different ways of software development methods (such as Waterfall model, evolutionary model, spiral model etc. ) those have been developed to accomplish these goals and have been widely used by the software industry [1]. Methodologists often describe the Waterfall method as a stereotypical traditional method whereas they describe Extreme Programming as the stereotypical agile method. The Waterfall model, as the oldest traditional software development method, was cited by Winston W.Royce in 1970 [2]. He divided the software development lifecycle into seven sequential and linear stages: Conception, Initiation, Analysis, Design, Construction, Testing, and Maintenance. The Waterfall model is especially used for large and complex engineering projects. Waterfall's lasting imp ression upon software engineering is seen even in the Guide to Software Engineering Body of Knowledge which introduces the first five knowledge areas based upon their sequence in the Waterfall lifecycle even though the Guide does not recommend any particular lifecycle [3].Although the Waterfall model has been adopted in many large and complex projects, it still has some inherent drawbacks, like inflexibility in the face of changing requirements [1]. If large amounts of project resources have been invested in requirements and design activities, then changes can be very costly later. High ceremony documentation is not necessary in all projects. Agile methods deal well with unstable and volatile requirements by using a number of techniques of which most notable are: low ceremony documents, short iterations, early testing, and customer collaboration.Kent Beck and Cynthia Andres define Extreme Programming 2. 0 with many practices [4], like Pair Programming, Test First Programming, and Co ntinuous Integration and so on. These characteristics enable agile methods to obtain the smallest workable piece of functionality to deliver business value early and continually improving it while adding further functionality throughout the life of the project [5]. 1. 2. PET project background Carnegie Mellon University Silicon Valley students start their masters program with the Foundations of Software Engineering course. This course is team-based, project-based, and mentored.Each team builds The Process Enactment Tool (PET). The user personas are software developers and managers. The tool helps users plan, estimate, and execute a project plan while analyzing historical data. The tool's domain encourages students to learn about software lifecycles and methods while understanding the benefit of metrics and reflection. 1. 2. 1. PET 1. 0: In 2001, Carnegie Mellon had one of the largest outsourcing firms in the world develop Pet 1. 0. Later the student teams were brought in to do the n ext release. The initial offerings of the course had the teams follow a Waterfall lifecycle.The faculty decided to use Extreme Programming as the method for the Foundations course because it was an agile method, it had good engineering practices, and it was a safe sandbox environment for engineers to try paired programming since many managers in industry were initially skeptical about its benefits. In 2005, the faculty allowed three of the sixteen teams tried our new curriculum to see if there were any serious issues in the switch, while other thirteen teams continued to follow a start point in 2004. The feedback was extremely positive so in 2006, all teams followed Extreme Programming.For the project plan duration, Waterfall teams needed fifteen weeks to finish their tasks where as Extreme Programming teams were given only thirteen weeks, a 13% reduction in time. 1. 2. 2. PET 1. 1: In 2005, the VP of Engineering advised the three teams that rewriting the code from scratch would be easier than working with the existing code base. Team 30:1 decided to use the latest in Java technologies including Swing and Hibernate. PET 1. 1, the team's product became the starting point for the students in the following year. 1. 2. 3. PET 1. 2: In 2008, the faculty switched the core technology from Java to Ruby on Rails.Ruby on Rails' convention over configuration, afforded a lower learning curve for students. For Pet 1. 2, students would build their projects from scratch. 2. Related work Much research has been done as to when to use an agile method and when to use a traditional method. For example, Boehm Turner's home grounds look at several characteristics, criticality, culture, and dynamism [6]. Our paper aims to extend these limitations to some degree by estimating Waterfall and XP in an academic case study, which provide a substantive ground for researchers before replicating their ideas in industry.Basili [7] presented a framework for analyzing most of the experimental w ork performed in software engineering. We learned that how to conduct a controlled experiment. Andrew and Nachiappan [8] reported on the results of an empirical study conducted at Microsoft by using an anonymous web-based survey. They found that one third of the study respondents use Agile methodologies to varying degrees and most view it favorably due to improved communication between team members, quick releases and the increased flexibility of agile designs.Their findings that we will consider in our future work is that developers are most worried about scaling Agile to larger projects, and coordinating agile and traditional teams. Our work is closely related to the work by Ming Huo et al [9]. They compared the Waterfall model with agile processes to show how agile methods achieve software quality. They also showed how agile methods attain quality under time pressure and in an unstable requirements environment. They presented a detailed Waterfall model showing its software qualit y support processes.Other work has only illustrates one or some Agile practices such as pair programming [10]. 3. Experimental methodology Our research was conducted primarily using Glaser's steps [11] in the constant comparison method of analysis. Step1: Begin collecting data. We collected more than 50 teams’ detailed data during a five year period as Table 1 shows. Table 1. Team building the same project 2004 2005 2005 2006 2007 2008 Method Waterfall Waterfall XP XP XP XP Language Java Java Java Java Java Ruby Project PET1. 0 PET1. 0 PET1. 0 PET1. 1 PET1. 1 PET1. 2 Numbers of Teams 10 13 3 9 6 11Step2: Look for key issues, recurrent events, or activities in the data that become categories for focus. The approach in software design makes us categorize the data into two distinctive software development methods, namely Waterfall and Extreme Programming. Step3: Collect data that provides many incidents of the categories of focus with an eye to seeing the diversity of the dimens ions under the categories. According to Basili[7], we provided some metrics to compare these two categories, Waterfall and XP. Requirements Metrics M1: Numbers of UI screens (ie. mockup) M2: Numbers of use cases (story cards)M3: Pages of Software Requirements Specification (SRS) documents M4: Pages of User Requirements Documents (URD) Design Metric M5: Pages of detailed design documents Implementation Metrics M6: Lines of code M7: Percentage of lines of comments to lines of source code M8: Lines of test cases M9: Ratio of lines of test code to lines of program code Step4: Write about the categories that we are exploring, attempting to describe and account for all the incidents we have in our data while continually searching for new incidents. Step5: Work with the data and emerging model to discover basic social processes and relationships.Step6: Engage in sampling, coding, and writing as the analysis focuses on the core categories. During 2005, there were 13 teams following Waterfal l and 3 teams following XP during the same period of time. These three teams, team Absorb, GT11 and 30:1 are interesting teams to examine as we can compare their data against the Waterfall teams doing the exact same project. 4. Experimental results 4. 1. UI screens (M1) and Story cards (M2) comparison These wide ranges can be seen in Table 2 and Table 3 where the standard deviation of the UI mockups is often half the document size.Comparing use cases to story cards in Table 3, we see that the standard deviation for use cases is much lower than the standard deviation for story cards. This is expected since use cases are a higher ceremony document when compared to story cards. Teams might give little consideration to how to represent each feature on a story card whereas a team writing a use case step by step how a user will use the system will spend much more time thinking about the coupling and cohesion of each use case. Table 2. Average numbers and Standard Deviation of mockups Year 004 2005 Absorb GT11 30:1 2006 2007 2008 Average mockups 15. 5 11. 8 17 18 9 15 12. 8 17. 7 Standard Deviation of mockups 6. 6 6. 3 5. 4 3. 1 8. 8 Table 3. Average numbers and Standard Deviation of use cases/story cards Year Average Number Standard Deviation 2004 User cases 18. 7 2005 User cases 18. 9 2. 3 Absorb Story cards 15 1. 6 GT11 Story cards 13 30:1 Story cards 18 2006 Story cards 16. 6 2007 Story cards 18. 3 2008 Story cards 16. 6 7. 5 6. 8 8. 0 4. 2. Requirement documents (M3&M4) Starting with PET 1. 0, Waterfall teams on average add 1. 7 use cases and modified 2. use cases. Teams were given a 28 page System Requirements Specification (SRS) and on averaged finished with a 34 page SRS. XP teams starting with PET 1. 0 were given the same starting documents. Instead of modifying them, the teams created story cards that represented each new feature. Instead of spending time on writing use cases, XP teams started coding sooner. Because XP has an emphasis on low ceremony docume nts, they had more time to code resulting in an effort savings for the teams. 4. 3. Comparing the size of the detail design documents (M5) There are some insights from Table 4.Waterfall teams using Pet 1. 0 started with a 21 page Detailed Design Document (DDD), which they altered to reflect their new use cases. Waterfall teams typically did not update their design documents at the end of the project. Given the scope of the project, Waterfall teams’ final code matched the original design with respect to new classes. Table 4. Average pages and Standard Deviation of Detail Design Documents Year 2004 2005 Absorb GT11 30:1 2006 2007 2008 Starting Point 21 21 21 21 0 14 14 0 Average DDD 25. 8 31. 1 18 22 14 18. 3 12. 5 9. 5 Standard Deviation 8. 39 7. 48 7. 70 7. 8 5. 19 XP teams increased their design documents with each iteration. Because the XP teams followed Test-Driven Development, they wrote their code and had an emergent design. At the end of each iteration, the teams were a sked to update the design document to reflect important design decisions they had made during that iteration. Therefore, the design document serves a different purpose in XP. It is not a template or blueprint for future construction. Instead, it can be a guide for understanding why certain decisions were made. In this regard, it is a biography of the development, ot a plan of action. 4. 4. New lines of source code and comments, Percentage of comments in codes Table 5 shows that Waterfall teams starting with Pet 1. 0 produced lines of code with a wide variance. The two XP teams starting with Pet 1. 0 fell right within the middle of the average. Because instead of producing some documents up front, the XP teams spent a longer time coding, one would expect them to produce more lines of code. The research results also show that XP Teams had a higher percentage of comments in source code. Table 5. Average and Standard Deviation of new lines in code YearLanguage Average new lines in code Standard Deviation Lines of test codes Ratio of test codes to program code 2004 2005 Absorb GT11 30:1 2006 2007 2008 Java Java Java Java Java Java Java Ruby 9,429 11,910 13,288 14,689 0 9,628 8,572 3,670 7,946 9,851 4,920 5,465 1,507 3378 4164 1380 3186 947 3555 2212 3,255 8% 13% 4% 8% 8% 16% 10% 90% 4. 5. Submitted lines of test codes and ratio of test code to program code The observation of these two metrics in Table 5 shows that the amount of test code written by the Waterfall teams equals the amount of test code written by the XP teams.Initially the faculty thought that Test-Driven Development would increase the amount of testing code, however, given a slow adoption rate of Test-Driven Development, programmers resorted to what was familiar and thus produced similar results. 5. Conclusion In this paper, we observed and presented the data from five years of 50 teams developing the same project each year and the affects of transitioning from Waterfall to Extreme Programming. The ch aracteristics between these two methods were evaluated and compared.Waterfall teams spent more time creating high ceremony documents where as Extreme Programming teams spent more time writing code and documenting their design in their code. Surprisingly, the amount of code and features completed were roughly the same for both methods suggesting that on a three month project with three to four developers it doesn't matter the method used. It is challenging to conduct this kind of analysis of the data in hindsight. Given that this is not a toy problem, and the freedom teams have in the execution of their projects, setting up this kind of experiment properly in advance is also challenging. . References [1] Sommerville, Software engineering, 8th ed. , New York: Addison-Wesley, Harlow, England, 2006. [2] W. Royce, Managing the Development of Large Software Systems, IEEE WESTCON, Los Angeles, 1970. [3] A. Abran and J. W. Moore, Guide to the software engineering body of knowledge: trial ve rsion (version 0. 95) IEEE Computer Society Press, Los Alamitos, CA, USA, 2001. [4] Kent Beck and Cynthia Andres, Extreme programming eXplained: embrace change, Second Edition, MA: Addison-Wesley, 2004. 5] Mike Cohn, Agile estimating and planning, Prentice Hall Professional Technical Reference, Nov 11, 2005. [6] Barry, Boehm and Richard Turner, Balancing Agility and Discipline: A Guide for the Perplexed, Addison Wesley, August 15, 2003. [7] Basil, V. R. , Selby, R. and Hutchens, D. , Experimentation in Software Engineering, IEEE Transactions on Software Engineering (invited paper), July 1986. [8] Andrew Begel and Nachiappan Nagappan, Usage and Perceptions of Agile Software Development in an Industrial Context: An Exploratory Study, MiIEEE Computer Society MSR-TR-2007-09, no. 2007): 10. [9] Ming Huo, June Verner, Muhammad Ali Babar, and Liming Zhu, How does agility ensure quality? , IEEE Seminar Digests 2004, (2004):36. [10] Jan Chong, Robert Plummer, Larry Leifer, Scott R. Klemmer, and George Toye. Pair Programming: When and Why it Works, In Proceedings of Psychology of Programming Interest Group 2005 Workshop, Brighton, UK, June 2005. [11] Glaser, Barney G, Strauss, and Anselm L. , The Discovery of Grounded Theory: Strategies for Qualitative Research, Aldine Publishing Company, Chicago, 1967. Comparing Extreme Programming and Waterfall Project Results Comparing Extreme Programming and Waterfall Project Results Feng Ji Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 [email  protected] com Todd Sedano Carnegie Mellon University Silicon Valley Campus Mountain View, CA, 94035 todd. [email  protected] cmu. edu Abstract Waterfall and Extreme Programming are two software project methods used for project management. Although there are a number of opinions comparing the two methods regarding how they should be applied, none have used project data to clearly conclude which one is better.In this paper, we present the results of a controlled empirical study conducted at Carnegie Mellon University in Silicon Valley to learn about the effective transition from traditional development to agile development. We conducted a comparison research against these two approaches. Multiple teams were assigned a project; some used Waterfall development, others used Extreme Programming. The purpose of this research is to look at advantages and disadvantages based upon the outcomes, generated artifacts, and metrics produced by the teams. 1. Introduction 1. 1.Agile vs Traditional Since the early 1970s, numerous software managers have explored different ways of software development methods (such as Waterfall model, evolutionary model, spiral model etc. ) those have been developed to accomplish these goals and have been widely used by the software industry [1]. Methodologists often describe the Waterfall method as a stereotypical traditional method whereas they describe Extreme Programming as the stereotypical agile method. The Waterfall model, as the oldest traditional software development method, was cited by Winston W.Royce in 1970 [2]. He divided the software development lifecycle into seven sequential and linear stages: Conception, Initiation, Analysis, Design, Construction, Testing, and Maintenance. The Waterfall model is especially used for large and complex engineering projects. Waterfall's lasting imp ression upon software engineering is seen even in the Guide to Software Engineering Body of Knowledge which introduces the first five knowledge areas based upon their sequence in the Waterfall lifecycle even though the Guide does not recommend any particular lifecycle [3].Although the Waterfall model has been adopted in many large and complex projects, it still has some inherent drawbacks, like inflexibility in the face of changing requirements [1]. If large amounts of project resources have been invested in requirements and design activities, then changes can be very costly later. High ceremony documentation is not necessary in all projects. Agile methods deal well with unstable and volatile requirements by using a number of techniques of which most notable are: low ceremony documents, short iterations, early testing, and customer collaboration.Kent Beck and Cynthia Andres define Extreme Programming 2. 0 with many practices [4], like Pair Programming, Test First Programming, and Co ntinuous Integration and so on. These characteristics enable agile methods to obtain the smallest workable piece of functionality to deliver business value early and continually improving it while adding further functionality throughout the life of the project [5]. 1. 2. PET project background Carnegie Mellon University Silicon Valley students start their masters program with the Foundations of Software Engineering course. This course is team-based, project-based, and mentored.Each team builds The Process Enactment Tool (PET). The user personas are software developers and managers. The tool helps users plan, estimate, and execute a project plan while analyzing historical data. The tool's domain encourages students to learn about software lifecycles and methods while understanding the benefit of metrics and reflection. 1. 2. 1. PET 1. 0: In 2001, Carnegie Mellon had one of the largest outsourcing firms in the world develop Pet 1. 0. Later the student teams were brought in to do the n ext release. The initial offerings of the course had the teams follow a Waterfall lifecycle.The faculty decided to use Extreme Programming as the method for the Foundations course because it was an agile method, it had good engineering practices, and it was a safe sandbox environment for engineers to try paired programming since many managers in industry were initially skeptical about its benefits. In 2005, the faculty allowed three of the sixteen teams tried our new curriculum to see if there were any serious issues in the switch, while other thirteen teams continued to follow a start point in 2004. The feedback was extremely positive so in 2006, all teams followed Extreme Programming.For the project plan duration, Waterfall teams needed fifteen weeks to finish their tasks where as Extreme Programming teams were given only thirteen weeks, a 13% reduction in time. 1. 2. 2. PET 1. 1: In 2005, the VP of Engineering advised the three teams that rewriting the code from scratch would be easier than working with the existing code base. Team 30:1 decided to use the latest in Java technologies including Swing and Hibernate. PET 1. 1, the team's product became the starting point for the students in the following year. 1. 2. 3. PET 1. 2: In 2008, the faculty switched the core technology from Java to Ruby on Rails.Ruby on Rails' convention over configuration, afforded a lower learning curve for students. For Pet 1. 2, students would build their projects from scratch. 2. Related work Much research has been done as to when to use an agile method and when to use a traditional method. For example, Boehm Turner's home grounds look at several characteristics, criticality, culture, and dynamism [6]. Our paper aims to extend these limitations to some degree by estimating Waterfall and XP in an academic case study, which provide a substantive ground for researchers before replicating their ideas in industry.Basili [7] presented a framework for analyzing most of the experimental w ork performed in software engineering. We learned that how to conduct a controlled experiment. Andrew and Nachiappan [8] reported on the results of an empirical study conducted at Microsoft by using an anonymous web-based survey. They found that one third of the study respondents use Agile methodologies to varying degrees and most view it favorably due to improved communication between team members, quick releases and the increased flexibility of agile designs.Their findings that we will consider in our future work is that developers are most worried about scaling Agile to larger projects, and coordinating agile and traditional teams. Our work is closely related to the work by Ming Huo et al [9]. They compared the Waterfall model with agile processes to show how agile methods achieve software quality. They also showed how agile methods attain quality under time pressure and in an unstable requirements environment. They presented a detailed Waterfall model showing its software qualit y support processes.Other work has only illustrates one or some Agile practices such as pair programming [10]. 3. Experimental methodology Our research was conducted primarily using Glaser's steps [11] in the constant comparison method of analysis. Step1: Begin collecting data. We collected more than 50 teams’ detailed data during a five year period as Table 1 shows. Table 1. Team building the same project 2004 2005 2005 2006 2007 2008 Method Waterfall Waterfall XP XP XP XP Language Java Java Java Java Java Ruby Project PET1. 0 PET1. 0 PET1. 0 PET1. 1 PET1. 1 PET1. 2 Numbers of Teams 10 13 3 9 6 11Step2: Look for key issues, recurrent events, or activities in the data that become categories for focus. The approach in software design makes us categorize the data into two distinctive software development methods, namely Waterfall and Extreme Programming. Step3: Collect data that provides many incidents of the categories of focus with an eye to seeing the diversity of the dimens ions under the categories. According to Basili[7], we provided some metrics to compare these two categories, Waterfall and XP. Requirements Metrics M1: Numbers of UI screens (ie. mockup) M2: Numbers of use cases (story cards)M3: Pages of Software Requirements Specification (SRS) documents M4: Pages of User Requirements Documents (URD) Design Metric M5: Pages of detailed design documents Implementation Metrics M6: Lines of code M7: Percentage of lines of comments to lines of source code M8: Lines of test cases M9: Ratio of lines of test code to lines of program code Step4: Write about the categories that we are exploring, attempting to describe and account for all the incidents we have in our data while continually searching for new incidents. Step5: Work with the data and emerging model to discover basic social processes and relationships.Step6: Engage in sampling, coding, and writing as the analysis focuses on the core categories. During 2005, there were 13 teams following Waterfal l and 3 teams following XP during the same period of time. These three teams, team Absorb, GT11 and 30:1 are interesting teams to examine as we can compare their data against the Waterfall teams doing the exact same project. 4. Experimental results 4. 1. UI screens (M1) and Story cards (M2) comparison These wide ranges can be seen in Table 2 and Table 3 where the standard deviation of the UI mockups is often half the document size.Comparing use cases to story cards in Table 3, we see that the standard deviation for use cases is much lower than the standard deviation for story cards. This is expected since use cases are a higher ceremony document when compared to story cards. Teams might give little consideration to how to represent each feature on a story card whereas a team writing a use case step by step how a user will use the system will spend much more time thinking about the coupling and cohesion of each use case. Table 2. Average numbers and Standard Deviation of mockups Year 004 2005 Absorb GT11 30:1 2006 2007 2008 Average mockups 15. 5 11. 8 17 18 9 15 12. 8 17. 7 Standard Deviation of mockups 6. 6 6. 3 5. 4 3. 1 8. 8 Table 3. Average numbers and Standard Deviation of use cases/story cards Year Average Number Standard Deviation 2004 User cases 18. 7 2005 User cases 18. 9 2. 3 Absorb Story cards 15 1. 6 GT11 Story cards 13 30:1 Story cards 18 2006 Story cards 16. 6 2007 Story cards 18. 3 2008 Story cards 16. 6 7. 5 6. 8 8. 0 4. 2. Requirement documents (M3&M4) Starting with PET 1. 0, Waterfall teams on average add 1. 7 use cases and modified 2. use cases. Teams were given a 28 page System Requirements Specification (SRS) and on averaged finished with a 34 page SRS. XP teams starting with PET 1. 0 were given the same starting documents. Instead of modifying them, the teams created story cards that represented each new feature. Instead of spending time on writing use cases, XP teams started coding sooner. Because XP has an emphasis on low ceremony docume nts, they had more time to code resulting in an effort savings for the teams. 4. 3. Comparing the size of the detail design documents (M5) There are some insights from Table 4.Waterfall teams using Pet 1. 0 started with a 21 page Detailed Design Document (DDD), which they altered to reflect their new use cases. Waterfall teams typically did not update their design documents at the end of the project. Given the scope of the project, Waterfall teams’ final code matched the original design with respect to new classes. Table 4. Average pages and Standard Deviation of Detail Design Documents Year 2004 2005 Absorb GT11 30:1 2006 2007 2008 Starting Point 21 21 21 21 0 14 14 0 Average DDD 25. 8 31. 1 18 22 14 18. 3 12. 5 9. 5 Standard Deviation 8. 39 7. 48 7. 70 7. 8 5. 19 XP teams increased their design documents with each iteration. Because the XP teams followed Test-Driven Development, they wrote their code and had an emergent design. At the end of each iteration, the teams were a sked to update the design document to reflect important design decisions they had made during that iteration. Therefore, the design document serves a different purpose in XP. It is not a template or blueprint for future construction. Instead, it can be a guide for understanding why certain decisions were made. In this regard, it is a biography of the development, ot a plan of action. 4. 4. New lines of source code and comments, Percentage of comments in codes Table 5 shows that Waterfall teams starting with Pet 1. 0 produced lines of code with a wide variance. The two XP teams starting with Pet 1. 0 fell right within the middle of the average. Because instead of producing some documents up front, the XP teams spent a longer time coding, one would expect them to produce more lines of code. The research results also show that XP Teams had a higher percentage of comments in source code. Table 5. Average and Standard Deviation of new lines in code YearLanguage Average new lines in code Standard Deviation Lines of test codes Ratio of test codes to program code 2004 2005 Absorb GT11 30:1 2006 2007 2008 Java Java Java Java Java Java Java Ruby 9,429 11,910 13,288 14,689 0 9,628 8,572 3,670 7,946 9,851 4,920 5,465 1,507 3378 4164 1380 3186 947 3555 2212 3,255 8% 13% 4% 8% 8% 16% 10% 90% 4. 5. Submitted lines of test codes and ratio of test code to program code The observation of these two metrics in Table 5 shows that the amount of test code written by the Waterfall teams equals the amount of test code written by the XP teams.Initially the faculty thought that Test-Driven Development would increase the amount of testing code, however, given a slow adoption rate of Test-Driven Development, programmers resorted to what was familiar and thus produced similar results. 5. Conclusion In this paper, we observed and presented the data from five years of 50 teams developing the same project each year and the affects of transitioning from Waterfall to Extreme Programming. The ch aracteristics between these two methods were evaluated and compared.Waterfall teams spent more time creating high ceremony documents where as Extreme Programming teams spent more time writing code and documenting their design in their code. Surprisingly, the amount of code and features completed were roughly the same for both methods suggesting that on a three month project with three to four developers it doesn't matter the method used. It is challenging to conduct this kind of analysis of the data in hindsight. Given that this is not a toy problem, and the freedom teams have in the execution of their projects, setting up this kind of experiment properly in advance is also challenging. . References [1] Sommerville, Software engineering, 8th ed. , New York: Addison-Wesley, Harlow, England, 2006. [2] W. Royce, Managing the Development of Large Software Systems, IEEE WESTCON, Los Angeles, 1970. [3] A. Abran and J. W. Moore, Guide to the software engineering body of knowledge: trial ve rsion (version 0. 95) IEEE Computer Society Press, Los Alamitos, CA, USA, 2001. [4] Kent Beck and Cynthia Andres, Extreme programming eXplained: embrace change, Second Edition, MA: Addison-Wesley, 2004. 5] Mike Cohn, Agile estimating and planning, Prentice Hall Professional Technical Reference, Nov 11, 2005. [6] Barry, Boehm and Richard Turner, Balancing Agility and Discipline: A Guide for the Perplexed, Addison Wesley, August 15, 2003. [7] Basil, V. R. , Selby, R. and Hutchens, D. , Experimentation in Software Engineering, IEEE Transactions on Software Engineering (invited paper), July 1986. [8] Andrew Begel and Nachiappan Nagappan, Usage and Perceptions of Agile Software Development in an Industrial Context: An Exploratory Study, MiIEEE Computer Society MSR-TR-2007-09, no. 2007): 10. [9] Ming Huo, June Verner, Muhammad Ali Babar, and Liming Zhu, How does agility ensure quality? , IEEE Seminar Digests 2004, (2004):36. [10] Jan Chong, Robert Plummer, Larry Leifer, Scott R. Klemmer, and George Toye. Pair Programming: When and Why it Works, In Proceedings of Psychology of Programming Interest Group 2005 Workshop, Brighton, UK, June 2005. [11] Glaser, Barney G, Strauss, and Anselm L. , The Discovery of Grounded Theory: Strategies for Qualitative Research, Aldine Publishing Company, Chicago, 1967.

Good practice for Managing Learning and Development in Groups

â€Å"In group work the aim is not simply the transmission of content (the content focus) but the need to work with that content (the process focus). Students use and develop two sets of overlapping skills.† Staff and Education Development Unit, LSHTMIt is important to encourage our students to learn in the groups. There are just some of the skills they can develop through the group work:†¢Thinking aloud – putting thoughts into words†¢Active learning – learning through action and reaction†¢Defending your position – the power of debate†¢Going deeper into the subject – creativity, originality and critical judgement†¢Professional skills – learning how to work productively with others†¢Learning how to learn – personal growthTo summarise: To create current good practice for Managing Learning and Development in Groups , the first we need to understand the principles and practices of managing learning and develop ment in groups: †¢strategies to manage group behavior and dynamics;†¢techniques which facilitate the delivery of learning and development in groups;†¢characteristics of group environments that foster learning and development,†¢risks to consider when managing learning and development in groups;†¢ways to involve learners in the management of their own learning and development in groups †¢barriers to management of individual learning in groupsThen we need to create environments that are suitable for group learning and development. To do that, we need to consult with group members to adapt their learning and development environments to improve their learning outcomes. We need to use deferent motivational methods to engage the group and its individual members in the learning and development process. We need to facilitate communication, collaboration and learning between group members. We need manage the risks associated with group learning and development.We also need to use different methods and techniques to manage learning and development in groups: e.g.: Involve learners in agreeing group learning objectives; adapt and implement delivery methods, use activities and resources to meet the learning and development objectives of the group; manage group learning strategies and delivery methods to reflect changing requirements; provide individual advice to learners to assist their decision-making about future learning needs.We need minimize risks to safety, health, wellbeing and security of learners and comply with legal and organizational requirements: Support learners’ rights in relation to equality, diversity and inclusion, manage confidentiality in relation to learners and the organization, and maintain learning and development records in accordance with organizational procedures.But where are always some barriers we will face while teaching in the groups. These are some of the things my students say they dislike while learnin g in group: †¢A small group can easily be dominated by one person.Finding a way to channel student misbehaviour into something productive is your first line of attack. Students who misbehave have talents that school does little to bring out. Students who are ringleaders have leadership qualities that we’d be wise to nurture. We want them to use their talents for good instead of bad so we need to give them that opportunity. Sitting and being quiet is not appealing to a leader. E.g.: Then I’m presenting a slide show, every five minutes or so we’d need it to be quiet so that groups of students could hear me and the slideshow.I had one student who I knew was going to have a hard time being quiet. So I made him the engineer. He was the one who pushed the button to start the recording and pressing the next slide show. It was totally quiet in my room. Instead of allowing B.H. to be the guy who ruined our class projects by yapping, he became our trusted engineer. H e felt good about it and the class appreciated him for it. Sometimes if students have a problem with talking in the classroom, you might arrange your seats in groups rather than isolated tables so that learning can be more social and project based. †¢When members of the group  wonders around the classroom.Teachers who have students who have trouble wandering around the room might make those kids the paper or door monitors so they have a reason to wander and wander with a purpose that’s productive for the classroom. †¢Students who say â€Å" I don’t care†Some students say they don’t care about missing out. I found it it is usually because they really do care. If it doesn’t bother students to miss out on your activities then your activities aren’t interesting for them, but because they are in my lesson because they chose to be that is usually not true. I try not to send students out of the room for misbehaving. A student often misb ehaves because he’s bored†¦he then misbehaves†¦you send him away. Student got what he wanted. I try not to reward bad behaviour in this way. It diminishes your own power and gives another incentive to misbehave.As I stated earlier, I believe a good Classroom Management is the key to an environment where learning can take place and students can feel safe participating. I hope to create an environment that is conducive to learning and involves all my students. I believe the most important part of classroom management is not the behaviour problems but creating a good rapport with the students, encouraging them to succeed and setting high expectations for them. As well as using an engaging a curriculum, I believe you can create this environment and it will limit the behaviour problems in your classroom from the start.

Cloning Technology With Respect to Faith and Reason Essay

Cloning Technology: With Respect to Faith and Reason Church VS Science The idea of sitting in an airport and seeing someone walk past that looks identical to you may seem absurd, but due to new scientific development it may not stay that way for long. In 1953 two scientists by the names of James Watson and Francis Crick discovered the structure of DNA.1 DNA can be defined as the makeup of chromosomes, which carry genetic information. DNA is present in nearly every living organism and can be found in a living organisms body tissue, hair, and blood.2 The discovery of DNA has led to amazing advances in the medical field. When the structure was first discovered society did not fully understand some of the possible outcomes†¦show more content†¦Many religious people believe that some things should not be changed and messed with. Whereas scientists believe that anything can be changed and anything is possible. Scientists explain that through genetic research many human diseases can be cured. James Watson writes In combating disease, genetics helps enormously if it is a bad gene that contributes to the cause. Ignoring genes is like trying to solve a murder without finding the murderer. Watson also reports that DNA research is solely responsible for the small steps that have been taken in better understanding cancer. Furthermore he explains that it is only through DNA research that cancer may ever be cured.4 A g enetic scientist from Stanford University by the name of Paul Berg also writes Scientist argue that the opportunity to learn more about the processes of early development and to capture scientific and medical promise that cloning technology offers†¦are also of paramount importance.5 Through statements such as these scientists are explaining that they generally agree with the public in the idea that too many questions remain to allow creation of a human being by cloning. Scientists are much more concerned with using cloning technology to cure many human diseases. 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