Max Weber Economic History, Theory of Bureaucracy, and Politics as a Vocation

Free Essays on The History Of The Nun

The Affects of a Broken Vow Aphra Behn’s The History of the Nun illustrates the importance of decision making and keeping vows, particularly the â€Å"Sacred Vow, made to God only,† through the life of the main character Isabella (Behn). The reader witnesses the perfect, simplistic nature of Isabella changing as she attempts to conquer the misfortunes that accompany her broken vow to eternally â€Å"serve him [God] with all Chastity and Devotion† (Behn). It is through Isabella’s interactions with other characters that she acquires more realistic qualities. From beginning to end, this virtuous woman is just another victim of a sinful world. Isabella’s upbringing in a nunnery influences the woman she becomes. As a young girl, she is exceptionally civil and affable as the nuns teach her everything possible; however, she only sees a nun’s perspective of life. The nuns mold her view of the world, causing the secular outside to become less appealing to her. In the nunnery, she is an ideal person with perfect virtures and is full of knowledge. Her character, in this stage of her life, is too innocent and righteous. Her first big mistake occurs as she enters the sisterhood. This, in actuality, ignites a chain of events that leads to many of her downfalls. Isabella is unaware of her other options while she makes her decision; one that will affect the rest of her life. Isabella’s personality becomes more realistic as she journeys through life and faces consequences rooted in her initial vow to become a nun. The nun undergoes changes in her personality from being a perfect little girl, to becoming a nun, to breaking her sacred vow, and killing both of her husbands, and finally losing her own life. At first, Isabella questions her morals as she confesses to her best friend, Katteriena, that she loves Katteriena’s brother, Henault. Deceiving Katteriena, Isabella convinces her that she can see Henault without loving ... Free Essays on The History Of The Nun Free Essays on The History Of The Nun The Affects of a Broken Vow Aphra Behn’s The History of the Nun illustrates the importance of decision making and keeping vows, particularly the â€Å"Sacred Vow, made to God only,† through the life of the main character Isabella (Behn). The reader witnesses the perfect, simplistic nature of Isabella changing as she attempts to conquer the misfortunes that accompany her broken vow to eternally â€Å"serve him [God] with all Chastity and Devotion† (Behn). It is through Isabella’s interactions with other characters that she acquires more realistic qualities. From beginning to end, this virtuous woman is just another victim of a sinful world. Isabella’s upbringing in a nunnery influences the woman she becomes. As a young girl, she is exceptionally civil and affable as the nuns teach her everything possible; however, she only sees a nun’s perspective of life. The nuns mold her view of the world, causing the secular outside to become less appealing to her. In the nunnery, she is an ideal person with perfect virtures and is full of knowledge. Her character, in this stage of her life, is too innocent and righteous. Her first big mistake occurs as she enters the sisterhood. This, in actuality, ignites a chain of events that leads to many of her downfalls. Isabella is unaware of her other options while she makes her decision; one that will affect the rest of her life. Isabella’s personality becomes more realistic as she journeys through life and faces consequences rooted in her initial vow to become a nun. The nun undergoes changes in her personality from being a perfect little girl, to becoming a nun, to breaking her sacred vow, and killing both of her husbands, and finally losing her own life. At first, Isabella questions her morals as she confesses to her best friend, Katteriena, that she loves Katteriena’s brother, Henault. Deceiving Katteriena, Isabella convinces her that she can see Henault without loving ...

Reasons why the two United States Embassies in Africa in 1998 were so Essay

Reasons why the two United States Embassies in Africa in 1998 were so vulnerable to terrorists. What went into the planning of those attacks - Essay Example The department of state gathered intelligence on this attacks prior to them occurring but were dismissed as too vague to be useful. (The washington Post, 1999) The second reason is that state agencies such as the FBI and CIA failed to predict the consequences of their pressure on the bin Laden network. In 1997 and 1998 the state agencies put a lot of pressure on the network plus other affiliated groups such as the Al-Haramain thinking that such pressure will make the network to stop its activities. They did not think past the consequences of this which led to the bin laden declaration of war on America especially on embassies in Nairobi and Dar es Salaam. (IPP Media, 2011) The third reason is that the two countries Kenya and Tanzania were chosen by the bin laden network because they were convenient for the terrorist organization. A look at both countries shows as that they lacked adequate security during this time especially in Tanzania. The two countries have also a sizeable number of Muslims and people of Arab origin and therefore it was easy to blend in to accomplish their mission. The two nations especially Kenya are known to have close ties with America and therefore bombing such nations meant successful bombing on US territory. The above mentioned factors contributed largely to the vulnerability of the US embassies to Terrorist attacks. (James M. Lutz, 2004) The two bombing attacks in Nairobi and Dar es salaam are believed by many to be a revenge mission for the bin laden network on America for its involvement in the extradition and alleged torture of some members of the Egyptian Islamic Jihad that are said to have been arrested in Albania two months before the attacks. The four men were said to be involved in the assassination of Rifaat el-Mahgoub and a plot against the Khan el-Khalili market in Cairo. There was a communiquà © prepared by the bin

Documentary Movie Analysis-Who Killed Vincent Chin Essay

Documentary Movie Analysis-Who Killed Vincent Chin - Essay Example Documentaries are more non-fictitious and real-life based, unlike commercial flicks; their purpose varying from showcasing reality to enlightening and educating the rural masses. Whatever be the case, documentaries are real life depictions on screen. Documentary films fall under varied genres, such as Authored Documentaries, Fly on the Wall, Docu-soaps, Docudramas, and the like. The first two are captures of real life instances, while the latter two are enactments of what has really transpired. Thus, documentaries stand for ‘Truth’. The documentary, ‘Who killed Vincent Chin’ was directed by Christine Choy and Renee Tajima. It is in the form of a docu-drama, exploring the real incident, by using characters to play the real-life ones. The cast includes Renee Tajima and Ron Ebens. The documentary explores the concept of racial discrimination in America, against an Asian-American immigrant, the flaws in the judicial system in America and above all, the sheer struggle that immigrants undergo in the process of assimilation and adaptation to the American culture and falling in line to conquer and realize their ‘American Dream’. This paper attempts to analyse the documentary, the various techniques used and how the documentary has been shot, to add value to the concept and the idea in particular. The story-line of the documentary reflects the real-life incident, wherein an Asian-American, Vincent Chin was murdered haplessly, by two Americans, Ron Ebens and his step-son, Michael Nitz. The murder happens after a scuffle between the two, at Fancy Pants, a Detroit topless bar. The murder takes place outside the bar, in a corner, with eye-witnesses watching the gruesome act, where Chin is shown beaten with baseball bats. While on the surface, the concept is that of a hapless murder occurring in the midst of

Managing services Essay Example | Topics and Well Written Essays - 1000 words

Managing services - Essay Example The researcher then targeted UK websites in which people complained about food. These were ideal for the investigation because such parties are already quite willing to seek remedies for their problems. They were requested to take part in the survey and asked to click on a link which would provide them with access to the forms. After completion, volunteers were supposed to click on another link which would allow them to submit their responses to the researcher. The link was centrally placed in the form. Evans and Mathur (2005) notes that one of the advantages of online surveys is the ease with which one can access a large sample space. It is easy to obtain contacts if one already has a target audience. In this research, persons who complain about food services are already tried and tested clients of the service industry under analysis. Therefore, they are ideal for this food service investigation. The study involved an analysis of the responses obtained through mathematical methods. Currently, some elements of analysis are not complete but will be completed in the coming weeks. Respondents were to select specific answers from a set of four possible choices. It was relatively easy to analyse these outcomes. Duffy, et al. (2005) explains that one of the reasons why researchers are attracted to this method of market research is its speedy and relatively unproblematic response times. The research demonstrated that several customers paid attention to the degree of cleanliness in fast food restaurants. Simple things like whether a waiter dipped their fingers in the salad or used different tongs for different food items affected customer perceptions of the quality. Barber and Scarcelli (2010) echo these sentiments in their US survey which found that cleanliness was of primary significance to clients. A number of respondents also cited their interactions with service providers as a key indication of value. Some believed that fast franchises are too keen on

Optimization of Benchmark Functions using VTS-ABC Algorithm

Optimization of Benchmark Functions using VTS-ABC Algorithm Performance Optimization of Benchmark Functions using VTS-ABC Algorithm Twinkle Gupta  and Dharmender Kumar Abstract  A new variant based on tournament selection called VTS-ABC algorithm is provided in this paper. Its performance is compared with standard ABC algorithm with different size of data on several Benchmark functions and results show that VTS-ABC provides better quality of solution than original ABC algorithm in every case. Keywords— Artificial Bee Colony Algorithms, Nature-Inspired Meta-heuristics,Optimizations, Swarm Intelligence Algorithms, Tournament selection. NOMENCLATURE ABC – Artificial Bee Colony ACO – Ant Colony Optimization BFS – Blocking Flow-Shop Scheduling DE – Differential Evolution EA – Evolutionary Algorithm GA – Genetic Algorithm MCN – Maximum Cycle Number PSO – Particle Swarm Optimization TS – Tournament size TSP – Travelling Salesman Problem 1.INTRODUCTION For optimization problems, various algorithms havebeendesigned which are basedonnature-inspiredconcepts [1].Evolutionary algorithms(EA) and swarmoptimizationalgorithmsare two different classes in which nature inspired algorithms are classified.Evolutionary algorithms like Geneticalgorithms (GA)andDifferentialevolution (DE) attempt to carry out the phenomenon ofnaturalevolution [2]. However, a swarm like ant colony, a flock of birds can be described as collection of interacting agents and their intelligence lieintheir way of interactions with other individuals andtheenvironment [3]. Swarm optimization includes Particle swarm optimization (PSO) modelon socialbehaviorofbirdflocking [4], Antcolony optimization (ACO) model on swarmofants and Artificial Bee Colony (ABC) model on the intelligent foraging behaviour of honey bees [5]. Some important characteristics of ABC algorithm which makesitmoreattractivethanotheroptimizationalgorithms are: Employs only three control parameters (population size, maximum cycle number and limit) [6]. Fastconvergencespeed. Quite simple, flexible and robust [7] [8]. Easyintegrationwithotheroptimizationalgorithms. Therefore, ABC algorithm is a very popular nature inspired meta-heuristic algorithm used to solve various kinds of optimization problems. In recent years, ABC has earned so much popularity and used widely in various application such as: Constrained optimization, Image processing, Clustering, Engineering Design, Blocking flow shop scheduling (BFS), TSP, Bioinformatics, Scheduling and many others [9]-[18].Similar to other stochastic population-based approaches like GA, Ant Colony etc. ABC algorithm also applied Roulette Wheel selection mechanism which chooses best solution always with high selection pressure and leads the algorithm into premature convergence. With ever-growing size of dataset, optimization of algorithm has become a big concern. This calls for a need of better algorithm. The aim of this paper is to create such an algorithm named VTS-ABC algorithm. This new variant is based on tournament selection mechanism and selects variable tournament size each time in order to select the employed bees sharing their information with onlooker bees. Onlooker bees select solution from selected tournament size of solutions with less selection pressure so that high fitness solutions can’t dominate and give better quality of solutions with large data set as well. A worst solution is also replaced by better solution generated randomly in each cycle. Rest of the paper is divided in different sections as follows: Introduction to standard ABC algorithm is described in section 2. Section 3 describes the proposed VTS-ABC algorithm. Experiments and its simulation results to show performance on several Benchmark functions are described in section 4 and in the last; Conclusion of the paper is discussed. 2.ARTIFICIAL BEE COLONY ALGORITHM In 2005, Karaboga firstly proposed Artificial Bee Colony algorithm for optimizing numerical problems [19] which includes employed bees, onlooker bees and scouts. The bee carrying out search randomly is known as a scout. The bee going to the food source visited by it before and sharing its information with onlooker bees is known as employed bee and the bee waiting on the dance area called onlooker bee. ABC algorithm as a collective intelligence searching model has three essential components: Employed bees, Unemployed bees (onlooker and scout bees) and Food sources. In the view of optimization problem, a food source represents a possible solution. The position of a good food source indicates the solution providing better results to the given optimization problem. The quality of nectar of a food source represents the fitness value of the associated solution. Initially, a randomly distributed food source position of SNsize, the size of employed bees or onlooker bees is generated. Each solution xi is a D-dimensional vector that represents the number of optimized parameters and produced usingthe equation 1: where,xmaxandxminare the upper and lower bound of the parameterxi,respectively and j denotes the dimension. The fitness of food sources to find the global optimal is calculated by the following formula: where, fm(xm)is the objective function value of xm. Then the employed bee phase starts. In this phase, each employed bee xi finds a new food source viin its neighborhood using the equation 3: where, t: Cycle number; : Randomly chosen employed bee and k is not equal to i ; ( ): A series of random variable in the range [-1, 1]. The fitness of new solution produced is compared with that of current solution and memorizes the better one by means of a greedy selection mechanism. Employed bees share their information about food sources with onlooker bees waiting in the hive and onlooker bees probabilistically choose their food sources using fitness based selection technique such as roulette wheel selection shown in equation 4: where, Pi: Probability of selecting the ith employed bee, S: Size of employed bees, ÃŽ ¸i: Position of the ith employed bee and F : Fitness value. Afterthatonlookerbeescarried outrandomly searchintheirneighborhood similar to employed bees and memorize the better one. Employed bees whose solutions can’t be improved through a predetermined number of cycles, called limit, become scouts and their solutions are abandoned. Then, they find a new random food source position using the following equation 5: Where, r: A random number between 0 and 1 and these steps are repeated through a predetermined number of cycles called Maximum Cycle Number (MCN). 3.PROPOSED WORK: VTS-ABC ALGORITHM In every meta-heuristic algorithm mainly two factors need to be balanced for global optimization outcome i.e. Exploration and Exploitation but ABC is a poor balance of these two factors. Various variants of ABC have been modelled for its improvement in different phases by number of researchers like Sharma and Pant have proposed a variant of ABC called RABC for solving the numerical optimization problem [20] and Tsai et al. have presented an interactive ABC optimization algorithm to solve combinational optimization problem [21] in which the concept of universal gravitational force for the movement of onlooker bees is introduced to enhance the exploration ability of the ABC algorithm. D. Kumar and B. Kumar also reviewed various papers on ABC and give a modified RABC algorithm based on topology for optimization of benchmark functions [22] [23]. Intelligence of ABC algorithm mainly depends upon the communication between individual agents. Employed beesshare their information with onlooker bees waiting in the hive and flow of this information from one individual to another depends on the selection mechanism used. Different selection schemes select different individuals to share the information which affect the communication ability of individuals and primarily the outcome of the algorithm. ABC algorithm uses Roulette wheel selection mechanism in which each onlooker bee selects the food source based on certain probability. Each onlooker bee selects the best food source with high selection pressure and lead to premature convergence. To overcome this problem, its new variant is proposed in which Tournament Selection method is applied based on Cycle number and number of employed bees. In Tournament selection, a tournament size (TS) is chosen to select the number of employed bees sharing the information with onlooker bees. For better exploration, TS=2 i.e. Binary Tournament is applied in early stages and for better exploitation, variable tournament size is applied based on the current cycle number (CYL) and size of employed bee in middle stages. As the stages grow, this method works similar to Roulette wheel method in the end. Hence, the selection pressure is less in early stages and more in final stages which provide us better quality of solution. As variable size of tournament is used at different stages of the algorithm, hence the algorithm named VTS-ABC (Variable Tournament Size Artificial Bee Colony) algorithm. Method used for calculating TS is shown in equation 6 and equation 7: If SN >= 20 If SN Where Here, two equations are shown for calculating tournament size of tournament selection method. The purpose of using these two equations is to increase the speed of algorithm. When the size of employed bee i.e. given population of food source positions is small like 10, a solution can be easily found by changing the tournament size by 1 but as the size grows i.e. when best food source position is to be found in large set of population for example when SN=40 or more than 40, increasing size of tournament by 1 and 2 only is a very tedious task as it will take more time to run the algorithm. Hence, in order to increase speed of algorithm, the tournament size based on current cycle and size of population is increased. One more concept is applied to increase its convergence speed. At each iteration or cycle, a new solution is generated randomly similar to scout and its fitness value is calculated. Greedy selection mechanism is applied between new solution and worst one and the better solution is memorized. Hence, it helps in finding good quality of solution as well as improving the convergence speed and provides better balance between exploration and exploitation. 4.experiments and simulation results 4.1 Benchmark Functions The Benchmark Functions used to compare the performance of VTS-ABC algorithm with original ABC algorithm are illustrated below: Sphere Function: Schwefel Function: Griewank Function: Where Ackley Function: Here, ObjVal is the function value calculated for each food source position. A food source is represented by X and population size is taken of n*p matrix where n is the no. of possible food source positions and p represents the dimension of each position. 4.2 Performance Measures Simulation Result The experimental results of VTS-ABC and ABC algorithm in MATLAB are taken under the parameter of size of food source positions (n*p) i.e. different size of population with different dimensions are taken to run and compare both algorithms. MCN is set as 2000 and each algorithm is run for 3 iteration i.e. Runtime=3. Limit for scouts is set equals to 300. In order to provide the quantitative assessment of the performance of an optimization algorithm, Mean of Global Minimum i.e. mean of minimum objective function value at each cycle of all iterations are taken as performance measure whose values are shown in table1and figure 1-4. Table1: Mean of Global minimum on different size of data Fig. 1: Mean of Sphere function values on different size of data Fig. 2: Mean of Schwefel function values on different size of data Fig. 3: Mean of Griewank function values on different size of data Fig. 4: Mean of Ackley function values on different size of data Figure 1 to 4 show simulation results of ABC and VTS-ABC algorithm with different size of data on Sphere, Schwefel, Griewank, Ackley respectively and reveal that VTS-ABC algorithm provides us better quality of solution than original ABC algorithm by minimizing objective function value or producing higher fitness solutions. 5. DISCUSSION AND CONCLUSION In this paper, a new algorithm VTS-ABC is presented. In this algorithm, firstly variable tournament size (TS) is applied to select the food source position for onlooker bees which helps to achieve diversity in solution. Then to increase convergence speed, a new solution is generated in each cycle which replaced the worst one. In order to demonstrate the performance of proposed algorithm, it is applied on several Benchmark functions with different size of data set as input. Simulation results show that it provides better quality of solution than original ABC algorithm in every case. Therefore, it can be applied in different fields of optimization with large and higher dimensions data set efficiently. References Yugal Kumar and Dharmender Kumar, â€Å"Parametric Analysis of Nature Inspired Optimization Techniques†International Journal of Computer Applications, vol. 32, no. 3, pp. 42-49, Oct. 2011. P. J. Angeline, J. B. Pollack and G.M. Saunders, â€Å"An evolutionary algorithm that constructs recurrent neural networks,† Neural Networks in IEEE Transactions on, vol. 5, no. 1, 1994, pp. 54-65. J. Kennedy and R. Eberhart, â€Å"Particle swarm optimization,† in Proceedings of IEEE international conference on neural networks, 1995, vol. 4, pp. 1942–1948. E. Bonabeau, M. Dorgio, and G. Theraulaz, â€Å"Swarm intelligence: from neural network to artificial intelligence,† NY: oxford university press, New York, 1999. D. Karaboga, â€Å"An idea based on honey bee swarm for numerical optimization,† Techn.Rep. TR06, Erciyes Univ. Press, Erciyes, 2005. D. Karaboga and B. Akay, â€Å"A comparative study of artificial bee colony algorithm,† Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009. R. S. Rao, S. V. L. Narasimham, and M. Ramalingaraju, â€Å"Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm,† International Journal of Electrical Power and Energy Systems Engineering, vol. 1, no.2, pp. 116–122, 2008. A. Singh, â€Å"An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem,† Applied Soft Computing, vol. 9, no. 2, pp. 625–631, Mar. 2009. D. Karaboga and B. Basturk, â€Å"Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems,† in Foundations of Fuzzy Logic and Soft Computing, Springer, 2007, pp. 789–798. C. Chidambaram and H. S. Lopes, â€Å"A new approach for template matching in digital images using an Artificial Bee Colony Algorithm,† in World Congress on Nature Biologically Inspired Computing, 2009. NaBIC 2009, IEEE, 2009, pp. 146–151. N. K. Kaur Mann, â€Å"Review Paper on Clustering Techniques,† Global Journal of Computer Science and Technology, vol. 13, no. 5, 2013. S. Okdem, D. Karaboga, and C. Ozturk, â€Å"An application of Wireless Sensor Network routing based on Artificial Bee Colony Algorithm,† in 2011 IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 326–330. T. K. Sharma, M. Pant, and J. C. Bansal, â€Å"Some modifications to enhance the performance of Artificial Bee Colony,† in 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 1–8. L. Bao and J. Zeng, â€Å"Comparison and analysis of the selection mechanism in the artificial bee colony algorithm,† in Hybrid Intelligent Systems, 2009. HIS’09. Ninth International Conference on, 2009, vol. 1, pp. 411–41. C. M. V. Benà ­tez and H. S. Lopes, â€Å"Parallel Artificial Bee Colony Algorithm Approaches for Protein Structure Prediction Using the 3DHP-SC Model,† in Intelligent Distributed Computing IV, M. Essaaidi, M. Malgeri, and C. Badica, Eds. Springer Berlin Heidelberg, 2010, pp. 255–264. D. L. Gonzà ¡lez-à lvarez, M. A. Vega-Rodrà ­guez, J. A. Gà ³mez-Pulido, and J. M. Sà ¡nchez-Pà ©rez, â€Å"Finding Motifs in DNA Sequences Applying a Multiobjective Artificial Bee Colony (MOABC) Algorithm,† in Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, C. Pizzuti, M. D. Ritchie, and M. Giacobini, Eds. Springer Berlin Heidelberg, 2011, pp. 89–100. L. Wang, G. Zhou, Y. Xu, S. Wang, and M. Liu, â€Å"An effective artificial bee colony algorithm for the flexible job-shop scheduling problem,† Int J Adv Manuf Technol, vol. 60, no. 1–4, pp. 303–315, Apr. 2012. S.-W. Lin and K.-C. Ying, â€Å"Increasing the total net revenue for single machine order acceptance and scheduling problems using an artificial bee colony algorithm,† J Oper Res Soc, vol. 64, no. 2, pp. 293–311, Feb. 2013. D. Karaboga, â€Å"An idea based on honey bee swarm for numerical optimization,† Techn.Rep. TR06, Erciyes Univ. Press, Erciyes, 2005. T. K. Sharma, M. Pant, and J. C. Bansal, â€Å"Some modifications to enhance the performance of Artificial Bee Colony,† in 2012 IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 1–8. TSai, Pei-Wei, et al. , Enhanced artificial bee colony optimization.International Journal of Innovative Computing, Information and Control,vol. 5, no. 12, 2009, pp.5081-5092. B. K. Verma and D. Kumar, â€Å"A review on Artificial Bee Colony algorithm,† International Journal of Engineering Technology, vol. 2, no. 3, pp. 175–186, 2013. D. Kumar and B. Kumar, â€Å"Optimization of Benchmark Functions Using Artificial Bee Colony (ABC) Algorithm,† IOSR Journal of Engineering, vol. 3, no. 10, pp. 09-14, October 2013.

Making Dreams Come True :: Teaching Education Careers Teachers Essays

Making Dreams Come True â€Å"What do you want to be when you grow up,† asked the second grade teacher. Little boys and girls raised their hands with enthusiasm and responded with, â€Å"ballerina, football player, princess, race car driver† as the teacher wrote the dreams on the board. One little girl in the middle of the room had different dreams: she said, â€Å"I want to be a teacher when I grow up!† I was that little girl. When I was eight years old, I realized what I wanted to do for the rest of my life. I wanted to teach. In Mrs. White’s second grade class I made a discovery. If my peers were having trouble with their work and Mrs. White was busy, I would help. The joy I experienced from helping others, especially my friends, was amazing. I felt very good about myself while I was helping and even better when the classmate received a good grade. I concluded that teaching was the job for me. I believe that like my peers in second grade, all students want to learn. Some may rebel and act out, they may skip school a lot, but I feel that deep down they want to succeed just like the excellent students. Students want responsibility, work (yes, I said work), encouragement, support, and a mentor. The rebelling students try to deny themselves these values because of low self-esteem. They may believe they cannot succeed and therefore act as though they do not care. My job as a teacher will be to make all students want to learn and broaden their knowledge base. Relative knowledge is the only true knowledge because it is dependent on the person and the environment. Howard Gardner’s multiple intelligences show that knowledge is different for different people. There is no set guide for being a knowledgeable person and there should not be. I do agree with essentialists that in mastering the basic core subjects is very important, but that is not all a child needs to learn. The basics are a strong foundation for children to broaden knowledge to fit their interests.