The Transition Of High School - 1256 Words

Time For a Change The transition from high school to college is typically a big step in a teenager’s life. We go from being with people we have grown up with and gotten to know for many years, to a whole new environment with strangers. It can be scary because it is such a huge change. Making friends in high school is a lot different from making friends in college because it takes less effort. Usually, when you meet someone in high school, you already have known â€Å"of â€Å" them, but have not gotten to know them as an individual. For example, I met one of my closest friends, Nikki, in fourth grade because we were neighbors and basically grew up together. We became friends not only because of being neighbors but because we went to the same†¦show more content†¦Coming into St. John Fisher as a freshmen, I only knew one person. This was intimidating because I was basically all on my own. A difficulty I had in this transition to college was that I loved my friends from home and did n ot want to leave them. I simply did not want to make any new friends because I had such amazing ones at home.I wanted them to come to college with me! Having all of us part our separate ways really did take a toll on my friend-making process. I was not ready to make friends because I already had such wonderful friends at home. My transition to St. John Fisher started with my selfishness pouring out. I came into college with the wrong mind set on making friends and I was too worried my friends at home were going to create better bonds with their college friends. Another hindrance I had, was that I was generally only attentive to those who had the same interests as I do. Within a friendship the bond should have a certain degree of mutuality. Common ideas, values and likes are often what drive people to connect and create a friendship. This coherence is good but a degree of differences is also important in a friendship. In college so far, I tend to try to be friends with only nursing majors being that I am a nursing major. Certainly, I am in fact still meeting new people and creating new friendships, but they are all too similar. I noticed we all have the same idea that we want

The Music And Arts Festival - 1322 Words

Go with the flow I have been to multiple concerts before but nothing compares to the Resonance Music and Arts Festival, a weekend long camp out of like-minded, motived, music loving individuals. In simpler terms, modern day hippies. These hippies focus more on medication, dancing, and yoga as a way to reach the ultimate state of joy. The people around me, the clothing they wore, the smell of weed that filled the air, and the music that blared through the speakers were all things I will never forget about that summer weekend. The life style of hundreds of individuals in a crowd was different from anything I had ever laid my eyes on. Although I never imagined to be in a new environment, I had no choice. I My dad works for Budweiser, the†¦show more content†¦Suddenly I could not take my eyes off what was in front of me. Hammocks were hung from trees and even hung from car mirrors. There were hippies sleeping in, and on their cars. I saw people cooking on small grills, selling f ood right from the trunk of their car. A simple sign that read, â€Å"Fish tacos.† I was memorized by all the booths that lined the entrances of Legend Valley. People sold paintings, jewelry, tie dyed bags, shirts, skirts, and head bands. I made my dad stop at one booth because I saw a dress that I absolutely loved. It was priced at $30. When I paid for my purchase the woman expressed how grateful she was that I was buying her clothing because that is how she made a living. She told me the story of how she lives in her car, and travels to these festivals. I felt sad for a brief moment until I realized this was her calling and she was truly happy. It inspired me to appreciate the little things that surround me. Also there were hundreds of tents on the opposite side of our camper, each one a bright color with some article of tie dye draped over it. In front of the tents sat chairs, empty. I asked my dad, â€Å"Where is everyone?† He just chuckled and said, â€Å"You’ll see them when we attend the concert.† When we made our way back to the camper, there was a car full of young modern day hippies parking next to us. My eyes wondered to eachShow MoreRelatedThe Taj Mahal or The Golden Temple820 Words   |  3 PagesCONTENTS Painted Art 1 History 1 Murals and Miniatures 1 Folk and Tribal Art 1 Religious Art 1 Christian Art 1 Buddhist Art 2 Islamic Art 2 Architectural Art 2 Taj Mahal 2 The Golden Temple 2 Humayun’s Tomb 2 Festivals 3 Dance 3 Theater 3 Music 3 India has some of the world’s greatest arts. For example the Taj Mahal, the Golden Temple, and Sri Ranganathaswamy Temple. In India, art is expressed in many different forms. India has one of the world’s largest collections of songs, music, dance, theatreRead MoreCultural Event.786 Words   |  4 Pageslife in Cameroon Diaspora and to assist in the transfer of brain gain to our motherland Cameroon and Africa. 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Security and Privacy Issues in Predictive Analytics †Free Samples

Question: Discuss about the Security and Privacy Issues in Predictive Analytics. Answer: Technology Description Predictive Analytics is a branch within advanced analytics used to make predictions about future events that are still unknown. Predictive analytics makes use of several techniques including statistics, data mining, machine learning, modeling, as well as AI (artificial intelligence) to analyze present data and make predictions concerning the future. Predictive analytics makes use of various predictive modeling, data mining, and analysis techniques to bring together information technology, management, and business modeling processes to make future predictions. The technology works on the basis of transactional and historical data that are used for purposes such as identifying future risks or identifying future opportunities. Models used in predictive analysis capture relationships among several factors so as to assess risks using a specific set of conditions to assign weights or scores (Waller Fawcett 2013). Business users, utilizing text analytics, data mining, as well as statistics crate predictive intelligence through the uncovering of relationships and patterns in both unstructured and structured data. Structured data, such as gender, age, marital status, and sales, can be readily used for predictive analysis. Unstructured data, such as social media content, text data in call center information/ notes, sentiment such as those in social media or open text that must be extracted are also used for predictive analytics in the process of model building (Umachandran Ferdinand-James 2017). Using predictive analytics, organizations become forward looking, proactive, and anticipate behaviors and outcomes based on data and not on assumptions or just hunches. The result is better decision making and planning, which among other things confer competitive advantages to the organization (Rickman Cosenza 2007). Technology Solution Assessment While predictive analytics greatly helps businesses such as to plan for the future for example in projecting future demand, consumption patterns among different age groups, and possible new products, it comes with risks, especially with regard to consumer data and information (Crawford Schultz 2014). To achieve predictive analytics, organizations inevitably must make use of consumer data, such as customer gender, age, and sentiments in social media in which case both structured and unstructured data is used for predictive analytics. Big data is, and continues to carry enormous potential for revolutionizing people's lives through predictive power for example, it is possible to accurately predict weather to 95% accuracy 48 hours before the time (Murphy 2015). The sheer scales of people that have been involved in security incidents of big data, the stakes are even higher. For instance, the breach of eBay data in 2014 resulted in a breach to personal information for 145 million people w hose e-mail and home addresses as well as birth dates were exposed/ breached (Finkle, Chatterjee and Maan, 2014). Because of the sheer size, the different aspects of the data as well as its diverse sources, it is also a daunting task to protect this amount of data. Vulnerability to unauthorized access is multiplied because of the broad and distributed range of access (Terzi, Terzi Sagiroglu 2015). Privacy Issues Breaches to privacy that cause embarrassments and other risks: Actions that organizations take in using big data for analytics can easily breach the privacy of the users data and result in embarrassments and law suits, as well as loss of jobs. Some retailers, for instance, use big data on customers, making use of such details as pregnant customers due dates, or even monthly menstrual cycles for women, or the color of lingerie most bought by a customer (Crawford Schultz 2014). Data masking being defeated so as to reveal personal information: If data masking is used inappropriately, predictive analytics of big data can reveal persons whose data was masked. The newness of predictive analytics of big data means that organizations still remain aware of such risks, greatly exposing individual private information to unwanted audiences, such as hackers Individuals have no (or there are very few) legal protections for them. While authorities and regulators have expressed risks to privacy due to predictive analytics of bid data, legal requirements for protecting privacy when big data analytics are being undertaken are not yet existent, or remain unclear and opaque (Paulson Scruth 2017). Risk of unethical decisions from predictive analytics of big data Predictive analytics of big data can be employed to influence behavior which is unethical and this happens when organizations use big data analytics to make decisions that do not take cognizance of the value of human life/ health. The potential for revealing peoples personal information since it is not illegal through this revelation can damage the lives/ health of the concerned persons poses another privacy hazard (Kshetri 2014). Discriminatory tendencies: Predictive analytics of big data can be used to provide promotions, develop courses among other uses; the results can back fire if there is no objectivity. Big data can make discrimination more prevalent and pervasive, for instance, in human resource planning. A financial institution cannot determine the sexual orientation or race of a loan applicant since this is illegal in the first place; however, using big data and predictive analytics, the race, gender, age, financial situation, even address of the prospective applicant can be mined through big data analytics by mining such information from the Internet of Things or from on-line platforms such as social media (Loehr 2017). With big data analytics, a loan request can be turned down based simply on discriminatory decision making or algorithms that are inherently discriminatory. Security concerns Predictive analytics not always accurate: Despite the use of predictive analytics of big data, some issues may not be unearthed since the technology is not fully accurate. The data files employed in predictive analysis can contain inaccurate information and data on individuals or the algorithms can be flawed. Predictive analytics are only as good as the computations used for generating results. The risk of inaccuracies increases proportionately to the addition of data files to existing datasets along with the use of complex models for data analysis. Financial firms such as VISA rely a great deal on predictive big data analytics, for among other things, detecting security breaches and fraud, but there are problems with the models, fraud will still happen (Armerding 2017). The large amounts of data organizations collect about individuals and store distributive, for instance Amazon, creates enhanced risks to the security of the data. The data can be stolen or hacked and be used for further malicious attacks. Predictive analytics is being used, and offers huge promises for organizations in predicting employee behavior; the risk of flight can be predicted early and appropriate measures taken. However, a new element is introduced with such applications; speculative data on employees. Beyond the standard financial and personal information on employees, predictive analysis crates a new problem of future behavior estimation, speaking to the mind, heart, and intentions of this employee. The questions that arise are ethical and practical; what is the predictive analysis on flight risks are wrong? The prediction of behavior can result in targeted responses by HR which borders in mind control and behavioral modification, a grave ethical issue. If the analytics ar e wrong, an employee can be wrongly labeled as being disloyal and have their reputation blemished, yet in reality, they may just have different behavior and this can result in unfair actions from management. Apart from possibly giving wrong conclusions, it is a pervasive privacy invasion; information that can be used maliciously even by insiders in the organization. Organizational Change Assessment A survey shows that several big organizations still believe that big data has several huge untapped opportunities for the future. Big data is being viewed from the context of different datasets integration to uncover or drive specific insights. A third of the respondents believe that analytics will be an integral driver or organizational change and transformation within organizations, while also forming a significant part of day to day activities in the running of organizations (Klein 2014). Predictive analytics of big data or any other data will have a significant impact in organizational processes, including in product development. For instance, a software or application developer will change how they develop software and the speed with which this software is developed and delivered based on predictive analytics. Predictive analytics is presently used widely in the financial services sector, for instance to predict credit risk of a client, by insurance firms for predicting losses, by law enforcement agencies to predict the nature and kinds of criminal acts, and by organizations to predict employee behavior and flight risk, as HP already does (Siegel 2013) . Predictive analytics can be expanded to prescriptive analytics so an individual can know what is likely to cause them problems in future and what they can do to overcome such problems. Using predictive analytics to determine future customer behavior, product and market trends, or employee behavior, organizations will (and already are) gaining strategic competitive advantages in their markets and beating the competition not yet using predictive analytics comprehensively. Already, predictive analytics has been used to predict winners of elections; Nate Silver predicted, back in 2012, the winner in all the fifty DC states and has also accurately predicted nine presidential elections before 2012, even before predictive analytics of big data came into the mainstream. For any commercial organization, there is in formation related to the customer, such as customer satisfaction, expectations, social media activity, referrals, and service levels; there is also financial information including profitability, revenue, margins, and competitor performance. There is also operational information such as cycle time, productivity, errors, waste, among others, while information related to the workforce such as skill acquisition and effectiveness of training also exist. Predictive analytics can be applied to help solve some of the most challenging aspects of an organization; one prominent area where predictive analytics can be beneficial is in procurement (Galvan 2015). The need for efficiency and getting the best value through procurement is a pressing one for both private and public organizations. Traditionally, legacy systems have been used to manage procurement; however, the ERP systems provide few if any, insights into business processes and usually are the cause of-instead of the solution to-bottlenecks in the procurement chain (order to delivery). This happens because traditional processes of procurement do not have real time data analysis capacity that provides valuable insights to help with decision making. The traditional procurement methods rely heavily on human intelligence, which has its challenges and imperfections to drive performance, cost savings, and efficiency. In contrast, predictive analytics gathers large volumes of data in rea l time on delivery networks, supply chains, customer sales, and billing and uses powerful computer algorithms to mine trends, insights, and other forms of intelligence. Predictive analytics manages to undertake these analytics continuously and in real time and this implies that firms can apply them immediately to reduce costs, improve performance, and attain higher levels of efficiency in their procurement processes. Using predictive analytics, organizations will gain better intelligence into supply chains; these chains provide tons of useful data from several tracking systems, audits, and inspections. The generated data is difficult to analyze by hand because of the high volume but through the use of computer analytics, holistic and meaningful conclusions can be derived. With this information, firms can better plan and optimize procurement scenarios, undertake more accurate demand forecasting, and collaborate better with suppliers, consequently enabling better planning to be integr ated into the whole organization. Any business organization can be transformed through the use of predictive analytics because of knowing likely outcomes, trends, scenarios with regard to the success of the organization. Predictive analytics can significantly change and transform any organization across any industry. In the health care sector, predictive analytics is transforming health care by enabling organizations (hospitals) to spot trends in diverse areas, from staffing to needs for readmission. Health organizations are hiring specialist data analysts to transform health care by applying certain algorithms to collected data on health care and generating useful insights from such data, according to Hede (2016). The health care industry can especially benefit from predictive analytics in a great way because most organizations in this industry long ago adopted the concept of EHR (electronic health records) and so have large treasure troves of highly useful data. In the crucial cyber security sector, players are rea lizing the power of harnessing predictive analytics by evaluating aspects like Internet chatter using specific algorithms to identify patterns and analyzing past attacks and incidents to identify and root out/ prepare better for possible attacks (Amjad 2016). The construction industry too is not left behind; being organizations that undertake some of the biggest and most complex construction project in the world; number crunching comes naturally to these organizations. However, nearly 35% of wastes in the construction industry are attributed to material waste; using big data analytics, construction companies can, and have been able to significantly lower costs through the use of predictive analytics in project management (Marr 2016). Research shows that organizational transformation wrought about by big data and predictive analytics is field executives are taking more seriously and planning for the inevitable transformation of their organizations due to this phenomenon (Klein 2014). Conclusions Predictive analytics is a form of advanced analytics used to make predictions about events that are still unknown. Techniques used in predictive analytics of big data include statistics, data mining, machine learning, modeling, as well as AI (artificial intelligence). Its therefore beneficial to organizations for future planning, responding to market changes, projecting future demand and consumption patterns among other myriad benefits. Predictive analytics brings the risks of privacy, data security, and even discriminatory tendencies when used for predicting employee loyalty, for example. Predictive analytics is forcing companies to change their organizational culture; rather than the firms changing their culture because of the huge benefits from predictive analytics. Organizations across industries, ranging from health care, security, cyber security, manufacturing, retail, financial and construction are being transformed by predictive analytics. This paper concludes therefore, that predictive analytics is new and high disruptive phenomenon that has numerous benefits for industries, from planning operations to responding to future market threats. However, it has some issues with regard to security and privacy of personal data and information, which is what, is used for analysis and making predictions. Predictive analytics is however forcing organizations to change their processes just to stay competitive. References Armerding, T. (2017). The 5 worst big data privacy risks (and how to guard against them). [online] CSO Online. Available at: https://www.csoonline.com/article/2855641/privacy/the-5-worst-big-data-privacy-risks-and-how-to-guard-against-them.html [Accessed 1 Oct. 2017]. Crawford, K., Schultz, J. (2014). Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms. Boston College Law Review. Boston College Law School. https://lawdigitalcommons.bc.edu/bclr/vol55/iss1/4. Demurjian, S. A., Moussa, M. A. (2017). Differential Privacy Approach for Big Data Privacy in Healthcare. Finkle, J., Chatterjee, S. and Maan, L. (2014). EBay asks 145 million users to change passwords after cyber attack. [online] Reuters. Available at: https://www.reuters.com/article/us-ebay-password/ebay-asks-145-million-users-to-change-passwords-after-cyber-attack-idUSBREA4K0B420140521 [Accessed 1 Oct. 2017]. Galvan, J. (2015). Predictive analytics can transform any type of organization. [online] IBM Big Data Analytics Hub. Available at: https://www.ibmbigdatahub.com/blog/predictive-analytics-can-transform-any-type-organization [Accessed 1 Oct. 2017]. Kshetri, Nir B. (2014). Big Data's Impact on Privacy, Security and Consumer Welfare. https://libres.uncg.edu/ir/uncg/f/N_Kshetri_Big_2014.pdf. Klein, L. (2014). Future of Analytics: Big Data Integration, Transforming Organizations and Processes, Providing Speed and Foresight - Predictive Analytics Times - machine learning data science news. [online] Predictive Analytics Times. Available at: https://www.predictiveanalyticsworld.com/patimes/future-analytics-big-data-integration-transforming-organizations-processes-providing-speed-foresight/4175/ [Accessed 1 Oct. 2017]. Loehr, A. (2017). Big Data for HR: Can Predictive Analyltics Decrease Discrimination?. [online] Anneloehr.com. Available at: https://www.anneloehr.com/2015/03/12/big-data-for-hr-predictive-analytics-help-decrease-discrimination-workplace/ [Accessed 1 Oct. 2017]. Marr, B. (2016). How Big Data And Analytics Are Transforming The Construction Industry. [online] Forbes.com. Available at: https://www.forbes.com/sites/bernardmarr/2016/04/19/how-big-data-and-analytics-are-transforming-the-construction-industry/#38bb88a233fc [Accessed 1 Oct. 2017]. Murphy, M. (2015). IBM is going to change how we forecast the weather with Watson. [online] Quartz. Available at: https://qz.com/535345/ibm-is-going-to-change-how-we-forecast-the-weather-with-watson/ [Accessed 1 Oct. 2017]. Paulson SS, Scruth E. (2017). Legal and Ethical Concerns of Big Data: Predictive Analytics. Clinical Nurse Specialist CNS. 31, 237-239. Rickman, T. A., Cosenza, R. M. (2007). The changing digital dynamics of multichannel marketing: The feasibility of the weblog: text mining approach for fast fashion trending. Journal Of Fashion Marketing And Management. 11, 604-621. Siegel, E. (2013). Predictive Analytics: The privacy pickle - Hewlett-Packards prediction of employee behavior - Analytics Magazine. [online] Analytics Magazine. Available at: https://analytics-magazine.org/predictive-analytics-the-privacy-pickle-hewlett-packards-prediction-of-employee-behavior/ [Accessed 1 Oct. 2017]. Terzi, D. S., Terzi, R., Sagiroglu, S. (2015). A survey on security and privacy issues in big data. 202-207. Umachandran, K., Ferdinand-James, D. S. (2017). Affordances of Data Science in Agriculture, Manufacturing, and Education. InfoSci-Books Waller M.A., Fawcett S.E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics. 34, 77-84.

Mentoring and Supervision for Professionals

Question: Discuss about theMentoring and Supervision for Professionals. Answer: Introduction Mentoring is an employee training system in which an experienced or a senior person acts as a guide, advisor, or counselor to a trainee or a junior. With time the definition and practice of mentoring has evolved. The focus is on facilitation of learning and growth of partnership. Mentoring is, in fact a collaborative and reciprocal relationship between two or more individuals sharing a mutual relationship and responsibility. A mentor is accountable for helping a mentee to achieve his goals. There must be growth in the relationship between a mentor and a mentee in order to stay focused (Opengart Bierema, 2015). Mentoring involves a self-directed learning relationship and is driven by the needs of the mentee and is process- oriented rather than service driven. It focuses more on intangible, softer, and broader issues as well as tangible and harder goals. It may seem that mentoring and coaching are the same but it is not so (Barsh, 2013). Mentoring relationship is mutually more account able compared to coaching relationship. Both coaching and mentoring focus on expansion of individual potential through enhancement of performance and development. However, while mentoring focuses on achievement of professional or personal developmental goals, the focus of coaching is upon enhancing skills and boosting the performance of an individual. This article shall evaluate the mentoring relationship between Morris and Emily. Morris had been the badminton coach of Emily. While Morris is an Australian native, Emily is a European (Johnson Ridley, 2015). Mentoring or Supervising the Mentee of a Different Culture Culture has an omnipresent influence and affects human behavior both consciously and unconsciously. Culture impacts the thought process, language, behavior, and attitude of individuals. The values and philosophy is affected by culture. Culture often sets limitations and boundaries. However, a culture is required in order to implement the values of mentoring. There must be cultural congruence between the partners (Williams et al., 2013). Both the mentor and the mentee must be sensible and sensitive to each others cultural backgrounds. They must understand that their language, behavior, mode of communication may differ due to their varying cultures. The differences must be openly discussed without any hesitation. Morris must be given the opportunity to meet Emily informally so that they can become familiar with each other. This can be done by going to lunch or dinner. This would prepare them and help them to know their culture and personality. The mentee must understand and appreciate the values of the partners culture. Morris must be able to overcome his fears, stereotypes and biases if any. Being from a minor culture, the mentor must not be afraid to express what Morris wants to say. He must guide Emily without considering the background or history of their cultures. Cultural norms and customs must be kept aside for a budding and flourishing relationship. The mentor must see the mentee as a dualistic individual (Kerry Mayes, 2014). It means that the mentee must be viewed as both an individual and a person belonging to a larger social context. It is important to record factual materials, reactions, goals, and feelings on both sides. The ROS model may be helpful to facilitate movement through each phase. The ROS model comprises Readiness, Opportunity, and Support. Receptivity involves openness and receptivity to the experience of learning. It tries to address the issue of preparedness. Opportunity reflects the situations that are available to hold meetings etc. It refers to the situations, venues, and settings. Support emphasizes the adequate and relevant assistance to promote learning. It builds on the concept of support. The ROS tool helps the mentors and the mentees to diagnose and analyze the missing elements (Kleiman et al., 2016). Phases of Mentoring The relationship of mentoring undergoes four phases- preparing, negotiating, enabling, and coming to closure. These phases come together to form a developmental sequence and are part of both formal and informal mentoring relationship. However, these phases vary in length and be considered for they may have negative consequences if ignored (Zachary Fischler, 2014). Preparing Since each mentoring relationship is unique within itself, both the mentee and the mentor must be prepared individually as well as in partnership every time a new mentoring relationship begins. Just as a number of processes such as fertilizing, aerating, cultivating, and plowing, etc are required before planting, similarly, various processes take place in the preparing phase. Mentors in this phase explore their readiness to become a mentor. They also explore their personal motivation and try to identify their areas of development and learning. To establish the fecundity of a relationship, it is highly essential to have clarity about the role and expectation from both sides. A prospective conversation between the mentor and the mentee is very helpful to set the tone of relationship. Meeting after several years, Emily and Morris decide to reestablish their long lost contact. The history of their relationship determined their interest in continuing their relationship (Pekerti et al., 20 14). Negotiating The negotiating phase can be compared to the phase of planting seeds in the soil. This phase would determine the fruition of the mentoring relationship. Just as good soil determines proper growth and high productivity, a proper negotiation between the mentor and the mentee determines whether the relationship would yield positive or negative results. This phase is considered as the business phase. This is when the partners come together to agree on goals of learning and define the process and content of relationship. Negotiating is not mere drawing up agreement but a phase for developing the ground rules as well. It is also known as the detail phase for it is in this phase that the details regarding meeting the responsibilities, accountability, and closure of the relationship is mutually articulated. Since the mentor Morris was at the last stage of his life, Emily and Morris decide to meet often on Sundays (Suffrin et al., 2016). Enabling The enabling phase is the longer phase compare to the other phases for this phase involves implementation of the learning relationship. It is in this phase that the contact between the partners takes place. This phase provides opportunity to nurture, develop, and learn. Also, it is in this phase that the mentor mentee relationship is the most vulnerable and is prone to derailment. The relationship must be able to find its own path even when the milestones are identified, goals are well defined, and the processes are clearly articulated. Trust must be developed in the mentoring relationship in this phase. The mentor at this stage must nurture the growth of the mentee by promoting learning and developing the quality of the relationship by building trust and through effective communication. The mentor must be open, candid, thoughtful, and must have the ability to receive a constructive feedback. After spending fifteen Sundays with each other, Emily shared the knowledge and wisdom of her coach that he had gathered over the years (Orland-Barak et al., 2013). Coming to Closure The last and the final phase is an evolutionary process and has a beginning, a middle, and an end. This phase involves evaluating, acknowledging, and celebrating the achievement of learning outcomes. Both the mentor and the mentee can benefit from the closure. In fact, the closure may be seen as an opportunity to evaluate learning and implement that learning in other relationships and situations of life. Throughout the relationship, they were knowingly or unknowingly preparing for closure (Orland-Barak et al., 2013). They knew well that the closure would happen with the death of Morris. However, the values taught by the coach shall remain with Emily for the rest of her life. Figure 1: Phases of Mentoring (Source: Created by Author) Evaluating the Effectiveness of the Mentoring Design Assessing the mentoring relationship Professional Development and Role of Mentor Characteristics of a good mentor Mentee Outcomes of Mentoring Relationship Contact frequency Critiques work Provides support Research activity Mode of Communication Mentor facilitates opportunities Treated as a colleague Grants publications presentations Length of Relationship Makes connections Cares about the mentee as a person Academic appointments Accessibility Provides guidance and support Active listening skills Promotion Mentee Satisfaction Responsive National recognition (Graf Edelkraut, 2016). The framework would be fruitful and beneficial for the teachers as well as the students. Once trust is established between the mentor and the mentee, the student would be able to share more with his mentor. Proper values would be imparted to the mentee once trust is established. Setting goals and measurement of progress in those goals would help both the mentor and the mentee to work on the areas that need attention. Constant evaluation of the mentee by the mentor would ultimately lead to progress and development of the individual. Research shows that proper mentoring has positive influence on youths as it increases the self-esteem of youngsters. Mentoring has a significant amount of positive impact on the perception of adults. It is at a very early stage of their life that adolescents develop their perception about their environment and the society they live in. Mentors play a crucial role in developing their sense of perception. However, termination of mentoring relationship may ha ve a negative impact on the psychology, self esteem and perception of a person. Conclusion Culture acts as one of the major hindrances in the mentor mentee relationship as there is bound to be differences in the background of the two individuals. The individuals involved in a relationship must share common beliefs and attitudes to procure a fruitful relationship. To reach fruition, the similarities and differences must not be too much highlighted or completely ignored (Mullen Schunk, 2012). The more the similarities are appreciated and accepted, the more the relationship becomes stronger. In such a situation, both the mentor and the mentee must examine ones own mind first honestly. He/she must look if any prejudices or stereotypes exist in the mind. It is essential to acknowledge what has been taught and learnt during the tenure of the relationship. It is extremely important to know the reasons behind the biases formed. Perspectives can be broadened by acknowledging the similarities and differences. In fact, cultural differences can be seen as an opportunity to learn. References Barsh, A. (2013). The Mentor's Guide: Facilitating Effective Learning Relationships by Lois J. 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