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  • Fri, 15 Apr 2022 01:38:00 -0500 en text/html
    Killexams : College of Business & Technology

    MBA Program Highlights

    The College of Business and Technology offers graduate work leading to the Master of Business Administration (MBA). MBA courses are offered by the Schools of Management and Marketing; School of Accounting, Finance, Economics and Decision Sciences (AFED); and the School of Computer Sciences. The WIU MBA is open to business graduates and those in liberal arts, engineering, mathematics, science, and other fields. The MBA is 33 hours of graduate coursework; however, individual degree program requirement are based on previously completed coursework and may range between 33 and 54 semester hours for those without a strong business background.


    • Diverse Class Delivery:
      • On Campus 
      • 100% Online
      • Hybrid-Livestream
    • AACSB accredited signifying highly qualified faculty and significant engagement with industry partners
    • Flexible schedules that fit into busy lifestyles.
    • Affordable tuition rates that can put an MBA degree within reach.
    • Internship and assistantship opportunities allow students to combine work and education.
    • Faculty with doctoral and law degrees from more than 40 internationally recognized universities.
    • Accessible faculty who pride themselves on student mentoring.

    Course Formats

    The MBA curriculum is uniform across the various formats which allows students the option of taking courses in more than one format during the program.


    The online MBA is designed for those who are unable to attend any on-campus classes. There are no face-to-face requirements for any of the online MBA courses. WIU’s course delivery system is Western Online which uses the course management software system, Desire2Learn (D2L).

    On Campus

    Traditional face-to-face classes are available in Macomb. Class meetings, time of day, and length of class are dependent upon each class.

    Hybrid/Livestream Classes

    These are courses designed for the lifestyle and career needs of working professionals and those who are unable to commit to the on-campus option. This configuration blends online classes with face-to-face class meetings to deliver an attractive and efficient educational experience. The face-to-face meeting may be remotely accessed if the course is available as livestream option. 75% of the course is online delivery and 25% is meetings. In many instances, the class meetings may be recorded for those needing a completely asynchronous experience.


    Western’s MBA has earned accreditation in both business and accounting by the AACSB International, designating Western Illinois University as among the best business schools in the world. Less than one-third of U.S. business schools and only 15% of business schools worldwide meet the rigorous standards of AACSB International accreditation. As a member institution, Western’s MBA has confirmed a commitment to quality and continuous improvement through a rigorous and comprehensive peer review process. AACSB International accreditation ensures that Western’s MBA degree program is committed to providing the highest-calibre education and experiences for our student professionals. Additional information is available at

    Frequently Asked Questions (FAQs)

    Is the length of the program 1 or 2 years?

    The length of the MBA program is 33 semester hours. There are required background courses which could increase the hours a student may need to complete. You can learn more about the degree requirements through the Online Graduate Catalog. Additionally, an individual’s course load per semester varies based on their study habits, learning abilities, time management skills, other commitments, and course work requirements. It is possible to complete the Online program in one year, but most working professionals take two years.

    What is the cost of the tuition, books, fees for this?

    You will find that as an AACSB accredited institution, our MBA program is among the most affordable, providing great value at our price point. Tuition varies depending on status (domestic vs. international; Macomb vs. Online). Here is our schedule of the current tuition/fees schedule and a cost estimator to assist you in determining the total program cost based on current conditions.

    How often is class typically per week? Time of day? Length of class? 

    Class meetings, time of day, and length of class are dependent upon each class. For details of each class, you can visit the STARS system.

    Online core courses and hybrid/livestream course are mostly 8 week classes. See the course schedule on STARS for days and times.

    How do you accommodate students who are working full-time?

    The online and hybrid/livestream formats are best for students working full time. Traditional on campus classes in Macomb are generally in the daytime, and thus more difficult for people who work full time. The MBA director can assist you will a plan that fits your individual needs.

    If a student is unable to attend a scheduled class due to work commitments, how does this affect their overall grade and success for the course and program?

    The attendance policy for each course is up to the instructor. Most are very understanding when it comes to work schedules for career professionals. However repeated absence, even if excused, often does affect performance.

    Online class format

    There are no face-to-face requirements for any of the online MBA courses. WIU’s course delivery system is Western Online which uses the course management software system, Desire2Learn (D2L).

    Admission Questions

    The catalog says that a GMAT score of 500 and undergraduate overall GPA of 3.0 qualifies for automatic admission. Can I still be admitted if one or both of those is lower?

    Yes, it is possible. What we mean by “automatic” admission is that if you exceed both of these criteria that you do not need to provide any other documentation of work experience, certifications, or qualifications. If one of these is slightly lower and the other higher, that may work too. For example, if you fall just a little short on the GMAT but have a GPA that is well above a 3.0, we will take that into consideration.

    The importance we place on GPA is somewhat less for those applicants who have been in the workforce for a number of years since completing their undergraduate degree. A GPA from 10 or 20 years ago may not be the best indicator of success in the program. In those cases, we would want to know what you have been doing lately. Do you have any professional certifications or significant work experience that is relevant to MBA studies?

    Applicants not meeting the criteria for automatic admission should proceed as follows:

    1. Apply online
    2. Request official transcripts to be sent to the School of Graduate Studies.
    3. Fill out the request form and attached the following:
      • a current resume
      • an essay detailing the petitioner’s educational goals, work experience, and any extenuating circumstances affecting any deficiencies in past academic performance
    Do you accept the GRE instead of the GMAT?

    Yes! As a general rule, we would look for a 150 on both the verbal and quantitative sections of the GRE coupled with at least a 3.0 GPA for automatic admission. If your scores are slightly lower, see the previous question as the same provisions apply.

    Can the GMAT/GRE requirement be waived?

    Yes, under certain circumstances. Exceptions to the GMAT/GRE requirement will be considered on a case-by-case basis. Applicants wishing to request a GMAT waiver should proceed as follows:

    1. Apply online
    2. Request official transcripts to be sent to the School of Graduate Studies.
    3. Fill out the request form and attached the following:
      • a current resume
      • evidence of a *graduate degree in a relevant field or at least five years of relevant **professional experience with increasing responsibility
      • strong verbal, quantitative, and analytical skills as substantiated by graduate transcripts, ***professional certifications , or other qualifications.

    * Examples of graduate degrees in a related field include, but are not limited to: economics, accountancy, finance, organizational leadership, and law (J.D.). Persons with these degrees qualify automatically for a GMAT/GRE waiver. Certain other graduate degrees, while not being closely related to business, may qualify if supplemented by other qualifications. Contact the MBA director to discuss your individual situation.

    ** To be considered on the basis of professional experience , you must have at least 5 years of professional experience (in a position generally requiring a bachelor’s degree) with increasing responsibility. The idea is that the experience you claim should represent evidence of your verbal, quantitative, and analytical skills necessary for success in the program. Please submit a resume highlighting your specific skills. Simply having 5 years of experience is not by itself sufficient to waive the GMAT/GRE. The MBA committee will review your entire portfolio (transcripts, certifications, etc.) in their consideration of the waiver. Only applications that are complete (except for GMAT/GRE scores) will be reviewed.

    *** Examples of professional certifications that would automatically qualify to waive the GMAT/GRE include, but are not limited to: CPA, CFA, and Series 7 licenses. Additionally, passing scores actuarial exams and other exams given in the insurance industry may also satisfy the requirement. Contact the MBA director to discuss your individual situation.

    Integrated BB/MBA Students

    Undergraduate students accepted into the integrated BB/MBA program will automatically have a status change to MBA student upon completion of their bachelor’s degree.

    International Student Questions

    Visit International Admissions for details.

    Please note: The TOEFL and IELTS requirements are higher for the MBA program than for the university. The TOEFL requirement is 550 (paper) or 79 (internet based) and must be less than two years old at the time of matriculation. The IELTS requirement is 6.5 overall and 6.5 speaking subscore.

    Thu, 19 May 2022 02:54:00 -0500 en text/html
    Killexams : MSc Quantitative Finance


    Degree awarded
    12 months full-time
    Entry requirements

    We require a First or Upper Second class honours degree (2:1, with 60% average) from a UK university or the overseas equivalent.

    • You ideally need a a degree in finance, economics, mathematics, statistics, physics, engineering, actuarial or decision sciences and have taken or be taking a significant number of modules in quantitative subjects, such as differential equations, econometrics or mathematical statistics in the final year of your degree, with excellent results
    • We highly recommend GMAT or GRE and anticipate a well-balanced score with a strong performance in the quantitative sections
    • When assessing your academic record, we take into account your grade average, position in class and the standing of the institution where you studied your qualification. 

    Full entry requirements

    How to apply

    Apply online

    Applications for this programme are now closed for September 2022 entry.

    Course options

    Full-time Part-time Full-time distance learning Part-time distance learning
    MSc Y N N N

    Course overview

    This specialist course enables you to develop quantitative skills in finance, providing training in programming, numerical methods and statistics. It provides a thorough grounding in pricing and risk management techniques. It allows you to develop the power of inquiry, critical analysis and logical thinking and to apply theory to current issues of policy.

    You will gain a good understanding of:

    • asset pricing and investment
    • pricing financial securities (equities, bonds, and derivatives)
    • the main theories of finance, the CAPM and the Black-Scholes model
    • interest rate models and interest rate derivatives
    • the development of a research enquiry
    • research issues and methodologies

    Open days

    Meet us at virtual events

    Meet us at a virtual event to find out more about our master's degree courses.

    Meet us >>  


    For entry in the academic year beginning September 2022, the tuition fees are as follows:

    • MSc (full-time)
      UK students (per annum): £20,000
      International, including EU, students (per annum): £29,000

    Further information for EU students can be found on our dedicated EU page.

    The fees quoted above will be fully inclusive for the course tuition, administration and computational costs during your studies.

    Refund Policy

    Due to the competition for places and limited availability, this course requires a deposit of £2,000 to cover non-recoverable costs and secure your place. The deposit will be deducted from your tuition fees when you register on the course.

    The deposit is non-refundable, except in the following situations:

    • you fail to meet the conditions of your offer (see below for further information); and/or
    • you are refused a visa or entry clearance to enter the UK (proof must be submitted)

    If an offer has been made specifying an English Language condition which you do not meet, the Admissions Team will require the official certificate of an English Language test taken after the date of offer as evidence that you have attempted to meet your offer conditions for a refund to be approved. The English Language test certificate provided with your application documents will not be accepted as proof that you have attempted to meet your offer conditions as such a certificate will predate the offer.

    If an offer has been made specifying an academic condition, the Admissions Team will require the official university documentation showing that you have not met this academic condition from the institution at which you have studied, as evidence for a refund to be approved.

    The Admissions Team reserves the right to refuse to refund of any deposit that does not meet with the requirements outlined above.

    Policy on additional costs

    All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

    Contact us for further information about the new Accounting and Finance scholarship for UK/EU students and other  scholarships available .

    Courses in related subject areas

    Use the links below to view lists of courses in related subject areas.

    Entry requirements

    Academic entry qualification overview

    We require a First or Upper Second class honours degree (2:1, with 60% average) from a UK university or the overseas equivalent.

    • You ideally need a a degree in finance, economics, mathematics, statistics, physics, engineering, actuarial or decision sciences and have taken or be taking a significant number of modules in quantitative subjects, such as differential equations, econometrics or mathematical statistics in the final year of your degree, with excellent results
    • We highly recommend GMAT or GRE and anticipate a well-balanced score with a strong performance in the quantitative sections
    • When assessing your academic record, we take into account your grade average, position in class and the standing of the institution where you studied your qualification. 

    English language

    For the latest information on demonstrating your English proficiency for those whose first language is not English, please see our  language requirements .

    English language test validity

    Some English Language test results are only valid for two years. Your English Language test report must be valid on the start date of the course.

    Other international entry requirements

    We accept a range of qualifications from different countries. For these and general requirements see  entry requirements for your country .

    Application and selection

    How to apply

    Apply online

    Applications for this programme are now closed for September 2022 entry.

    Advice to applicants

    Your statement of purpose should cover the areas outlined below: 

    • Tell us why you are interested in the MSc Quantitative Finance course at Alliance MBS and how the course will impact on your future
    • List any other economics, mathematics, quantitative, or finance courses or qualifications you have taken in addition to your undergraduate degree
    • Describe what makes you an outstanding applicant and describe your potential to contribute to all aspects of the course.

    How your application is considered

    We can only process applications with the following documents:

    • valid English language qualification
    • first and second year transcript (scanned copies are accepted at the time of application)
    • list of final year modules (where possible this should be included within the same document as your first and second year transcript)
    • statement of purpose (this is included as part of your application form, you do not need to email your statement of purpose directly to the Admissions Team)


    To defer your offer to the following year, you must contact your admission officer to get a copy of the deferral form.  You can only defer your place for one year.


    If you applied in the previous year and your application was not successful you may apply again. Your application will be considered against the standard course entry criteria for that year of entry. In your new application you should demonstrate how your application has improved. We may draw upon all information from your previous applications or any previous registrations at the University as a student when assessing your suitability for your chosen course.

    Course details

    Course description

    The course is particularly suitable for students with a degree in Finance, Financial Economics, Actuarial Science, Engineering, and Mathematics (see  entry requirements ), as well as relevant work experience (non-compulsory). It is also suited to those wishing to gain salary enhancements, or students wishing to pursue advanced studies in quantitative finance.

    • Learn how to forecast and manage risk and return
    • Gain the skills to price any financial instrument
    • Learn how to engineer new methods and financial products
    • Build advanced knowledge of the main theoretical and applied concepts in quantitative finance, financial engineering and risk management, using current issues to stimulate your thinking
    • Prepare for careers involving the design and management of new financial instruments, the development of innovative methods for measuring, or predicting and managing risk.

    CFA training

    We have teamed up with Kaplan Schweser to facilitate student preparation for the CFA exams.

    • MSc Accounting , MSc Accounting and Finance , MSc Finance and MSc Quantitative Finance course students who perform well in Semester one and show a strong interest in CFA will get free access to Kaplan's online training materials for CFA Part One.
    • Gain student support from Kaplan with access to tuition material, online question banks and progress tests, and mock test runs. Plus tutor support and 1-2-1 sessions.
    • The CFA Program builds a strong foundation of advanced investment analysis and real-world portfolio management skills.
    • Take the first step to earning the Chartered Financial Analyst ® (CFA) credential to prepare for the Level I exam
    • The CFA is the most respected and recognised investment designation in the world.

    Trading BootCamp Week

    Together with Amplify Trading , a global financial trading and training firm, we offer a week long Trading BootCamp for MSc Accounting, MSc Accounting and Finance, MSc Finance and MSc Quantitative Finance course students.

    • experience a live trading floor provided by two experienced traders with specialist software
    • engage with contemporary financial markets and apply your classroom theory in practice
    • watch live breaking news, economic data and geo-political events, and use your knowledge and skills in trading different asset classes
    • a fantastic opportunity to prepare you for successful careers after graduation.

    The Finance Zone   

    Special features

    Worshipful Company of International Bankers Affiliation

    Our accounting and finance division courses are now affiliated with the Worshipful Company of International Bankers (WCIB). Every year the best dissertation out of the following four courses will receive the WCIB Prize worth £300.

    • MSc Accounting 
    • MSc Accounting and Finance
    • MSc Finance
    • MSc Quantitative Finance

    The winner will also enter the competition for the prestigious WCIB Lombard Prize

    Coursework and assessment

    The course teaching is shared by Alliance Manchester Business School and the School of Mathematics and Statistics and delivered through lectures, case studies, seminars and group project-based work. Assessment varies depending on course units taken.  It may include a combination of course work and examination.  The dissertation normally ranges between 30 and 50 double spaced pages.

    Course unit details

    During the course you will be taking 180 credits in all. The eight taught units during semester one and two total 120 credits and consists of both compulsory and optional taught units which can be viewed in the list below.  

    Over the summer period, you will carry out your Research Dissertation, worth 60 credits. The dissertation gives you the opportunity to apply what you have learned in the taught part of the course.  Our subjects are aligned with the research interests of leading financial institutions from the City of London and internationally.

    Examples of accurate dissertation project subjects include:

    • Investigating dynamics and determinants of risk-neutral PDs
    • Using hazard models to forecast corporate bankruptcy
    • Analysing asset pricing implications from real options models
    • Pricing sovereign CDS contracts
    • Estimating liquidation probabilities of hedge funds

    Course unit list

    The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.

    Scholarships and bursaries

    Masters scholarships for Accounting and Finance programmes for UK/EU students

    We are delighted to be able to offer five scholarships available across the four Accounting and Finance programmes with a value of £10,000 towards the cost of the tuition fee. The scholarships will be awarded in line with our scholarship deadlines so early application is advisable.  Read more >>

    Disability support

    Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email:


    Career opportunities

    This course provides ideal preparation for a career in the finance industry specialising in designing and trading financial instruments. These financial engineers are expected to be proficient in computer programming and strong in financial mathematics. They have to be at ease with complex derivatives and creative enough to find solutions for pricing and hedging. The course also provides research skills for those who wish to pursue an academic career by studying at doctoral level.

    Recent recruiters

    Armacell, Bank of Thailand, Barclays Capital, Bloomberg, China Merchants Bank, CIBC World Markets, Citigroup, Hewitt Associates, KPMG, Matrix, MFC Fund and Schlumberger.

    Read more about graduate career destinations >>

    Read about our Postgraduate Careers Service >>

    Read the latest information on visa changes and opportunities in the UK for international students >>

    Sat, 03 Apr 2021 14:51:00 -0500 en text/html
    Killexams : Frequently asked questions

    How do I apply to the MPA programme?

    All applications are made through LSE’s Graduate Admissions Office. Applications open in mid-October each year. Full details of how to apply are available on the how to apply page. This includes information about the entry requirements and the documents applicants are required to submit with their application.

    What are the entry requirements?

    Please visit the entry requirements section on the how to apply page for further details.

    Is there a Graduate Open Day?

    LSE offers a virtual open day where you can watch a wide range of talks on applying to LSE, accommodation, careers, financial support, support services and life at LSE. This virtual open day can be viewed at anytime, from anywhere in the world.

    In addition, the MPA programme holds regular Online Information Sessions which provide you with the opportunity to learn more about study support, future careers and extra-curricular activities. Each session ends with time for your questions, which are answered live by our team.

    If you would like to visit the LSE campus, please see 'Can I visit the campus?'.

    How many applications do you receive each year?

    On average each year we typically receive around 420 applications. Our target intake is 100 students per year, meaning at any one time there may be more than 160 MPA students studying at LSE across both years.

    What are the application deadlines?

    The MPA does not have a deadline for applications; Graduate Admissions begin accepting applications in mid-October. The MPA has a limited number of offers to make each year and once this limit has been reached, no further applications can be considered. To find out about current programme availability, go to the LSE Graduate Admissions homepage and click "Available programmes" in the menu on the left of the page. We recommend that applicants submit their applications as early as possible to maximise their chance of being considered.

    What do you look for in an applicant?

    The Selectors will consider the application as a whole before making a decision. They are looking for:

    • Proven academic ability and strong academic grades. Economics and/or quantitative course work is particularly helpful but there is no specific subject requirement for the first degree.

    • Applicants are normally required to have a minimum of one year’s relevant professional work experience at the point of entry to the programme. However, applicants with an exceptional and outstanding academic background may use this to compensate for less than one year’s work experience.

    • A personal statement that is well written and clearly explains why you have chosen this professionally-oriented policy programme. It is also important that your statement explains how your prior professional and educational experiences make you a good candidate for the programme.

    • Strong references in support of your application.

    Please note that we cannot advise on individual applications or supply any indication if an applicant should apply. Full details of the entry requirements are available from the Graduate Admissions webpages.

    Can I submit a professional reference?

    Please select the ‘two academic references’ item from the list on this webpage for reference requirements.

    How long should my personal statement be?

    Please select the 'personal statement' item from the list on this webpage for more information.

    Where do I send my application documents?

    All application documents must be sent to Graduate Admissions. If you are unable to upload your documents, please contact Graduate Admissions for advice.

    What is your GRE/GMAT policy?

    We do not require applicants to have taken GRE/GMAT tests. However, if you have taken one of these, and you feel your results will support your application, you are welcome to include it. As this is not a requirement for the MPA we cannot advise what the Selectors would consider a good score. We are also unable to advise applicants what the average GRE/GMAT score is as only a minority of applicants choose to include this information.

    Can I apply even if my English language score is lower than required?

    The requirements for English language test scores are available here, please note that we require 'higher'. You can apply if you have not yet achieved the required score. If your application is successful you will receive a conditional offer, which means that you will only be able to join the MPA if you achieve the required test scores before the programme starts. If you will require a student visa, please note that you will need to hold an unconditional offer in order to receive the Confirmation of Acceptance for Studies (CAS) you will need for your visa application so will need to achieve the required English test score leaving enough time to apply for your visa and wait for a decision on your visa application.

    Do you accept transfer students?

    No, the MPA does not accept transfer students. It is also not possible for students to attend individual courses without being registered for a degree programme at LSE.

    What happens after I submit my application?

    The LSE Graduate Admissions Office will process your application. They will confirm to you that it has been received and if any further action or documents are required from you. Graduate Admissions receive applications and documentation for all graduate programmes at LSE. This means that it may take some time for your application to be processed. You can see the current processing times on-line.

    The MPA Team will not be able to tell you when you will receive a decision on your application as all decisions are processed and sent by Graduate Admissions.

    Please note that all queries relating to application documents should be directed to Graduate Admissions.

    Mon, 09 May 2022 16:12:00 -0500 en-GB text/html
    Killexams : MS in Professional Studies

    NOTE: Applications are no longer being accepted for this program.

    The Master of Science (MS) in Professional Studies program provides students with the highly valued skills — such as writing, strategic planning, persuasion, ethics, intercultural consciousness, critical thinking, and self-awareness — needed to collaborate, negotiate and lead effectively in today’s workplace. Through an interdisciplinary blend of quantitative and qualitative courses, students can apply their knowledge and skills across multiple industries.

    The students who will benefit from the MS in Professional Studies will have a minimum of three years in the workforce. They will be in lower management or looking to move into management, but whose options are limited without the quantitative and qualitative skills necessary to succeed in today’s workplace. Our master's in Professional Studies is offered online, giving you the flexibility to complete your master’s program your way.

    What is a Master of Science in Professional Studies Degree?

    Our Master of Science (MS) in Professional Studies teaches you transferable skills like communication, ethics, leadership and strategic decision-making and then solidifies them with the hands-on experience you can speak to in an interview. This program is similar to a Masters of Professional Studies in that they are both intended for working professionals and teach you applicable skills. However, the MS in Professional Studies program goes a step further by encouraging you to practice the research skills and theoretical approaches of a master of science degree with the flexibility and applied skills of a Master’s of Professional Studies.

    Read our FAQ for more info.

    MS in Professional Studies Program Goals

    The MS in Professional Studies program aims to equip students with the following demonstrable skills:

    • Communication — enhancing oral, written, and non-verbal communication skills to allow students to easily relate to, collaborate with, and lead others in the work place - both in person and virtually
    • Leadership — providing students with the necessary tools to lead people and organizations through the acts of negotiation, attracting and sustaining talent, and thinking and acting strategically to achieve results
    • Critical Inquiry — training students to conduct research and collect data using proven scientific methods and then evaluate and analyze that data to make profitable decisions in the workplace
    • Ethics — developing a moral and ethical framework from which organizational decisions can be made

    Hear from a student

    “The Professional Studies program perfectly aligned with what I was looking to gain from a degree program. The focus on experiential learning and discussion, coupled with the opportunities to take real-life problems and utilize the research methods to discover and apply solutions, allowed me to take what I was learning over the course of the program and apply it directly back to my role.

    This program is an excellent choice for anyone looking to develop and refine their skills as an effective leader or strengthen their 'power' skills to prepare them for success as they take the next steps in their careers.”

    — Jessica Barr, Assistant Director, Enrollment Services, Drexel University Online.

    What Can You do with a Master of Science in Professional Studies?

    The skills students obtain in our Master of Science in Professional Studies degree program are transferable across a wide variety of industries like business and finance, marketing and communications, government, service and education, Students who complete the program will be well versed in essential skills that are highly sought after by employers. These skills include:

    • Communication 
    • Ethics 
    • Team management 
    • Leadership 
    • Influencing others 
    • Creativity 
    • Analysis 

    Career Placement

    Our professors in the MS in Professional Studies are career professionals who bring real-world workplace situations to our virtual classrooms. This better prepares you to apply your learned skills in the following industries: communications, human resources, finance, insurance, marketing, utilities, pharmaceuticals, among many others. How you apply this degree is up to you! Regardless of the career path you choose, the MS in Professional Studies degree will hone your skills allowing you to leverage yourself into various levels of middle and upper management with continued possibilities to advance your career.

    Admissions Requirements

    • 3.0 GPA on undergraduate or other completed master’s transcripts
    • Two professional recommendations (three preferred)
    • Resume (minimum of three years work experience)
    • Statement of Purpose (250-500 words)
      • Why is the individual pursuing a Master’s degree?
    • Students may also submit (if they choose – not required) any of the following:
      • GRE/GMAT scores
      • Example of a work project that demonstrates his/her current skill
    • Up to 6 transfer credits may be accepted if the courses taken closely align with the core courses in the MS in Professional Studies curriculum
    Sat, 24 Feb 2018 08:24:00 -0600 en text/html
    Killexams : Graduate Admission FAQ

    Effective September 1, 2022, all Ph.D. candidates at the Carroll School of Management will receive a stipend of $38,000 per year in addition to full tuition remission. A student in good standing may receive this support for a maximum of four years. In return for this support, the student acts as a research assistant for 16 hours per week for the first two years of the program, then acts as a research and/or teaching assistant in the following years of the program.

    This level of support is based on the fact that students are expected to devote their full energies to the Ph.D. program during the entire calendar year, not just the academic year.

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    Killexams : Neural automated writing evaluation for Korean L2 writing

    1. Introduction

    Technology is applied to all aspects of foreign language learning and teaching including assessments. Among these technologies, there has been an increase in the use of automated writing evaluation (AWE) for writing assessment. Natural language processing (NLP) and machine learning are employed in AWE systems to provide language learners with automated corrective feedback (Li, Dursun, and Hegelheimer Reference Li, Dursun and Hegelheimer2017) and more accurate and objective scoring, which can otherwise be biased when performed by test raters. Because automated scoring is faster and more cost-effective compared to human scoring, it is used to help language teachers easily assess endless essays. Owing to these benefits, many scholars developed and implemented AWE systems for various languages including English (Shermis and Burstein Reference Shermis and Burstein2003), Japanese,Footnote a Bahasa Malay, Chinese, Hebrew, Spanish, and Turkish.Footnote b

    Despite the large number of pre-existing AWE systems, AWE for Korean L2 writing remains unexplored. Based on the Modern Language Association (MLA) report, Korean is the only language that demonstrated a sharp increase in enrollment over the past few years compared to other foreign languages. Furthermore, Korean has been consistently ranked as the 15th most commonly taught foreign languages in US colleges and universities between 2013 and 2016. Therefore, it is necessary to develop AWE for Korean to provide innovative resources in the growing field of Korean language education.

    In the most basic terms, AWE is defined as “the process of evaluating and scoring written prose via computer programs” (Shermis and Burstein Reference Shermis and Burstein2003). With the advent of automatic scoring in the 1960s (Page Reference Page1966), advanced language processing technologies and statistical methods led to the development of various AWE systems (Li et al. Reference Li, Dursun and Hegelheimer2017). The first computerized scoring system called project essay grader ${}^{\text{TM}}$ (PEG ${}^{\text{TM}}$ ) could detect syntactic errors and predict scores that were comparable to those of human raters (Page and Petersen Reference Page and Petersen1995).

    More advanced AWE systems were developed in the 1990s; the intelligent essay assessor ${}^{\text{TM}}$ (IEA) utilized latent semantic analysis to move beyond the capability of scoring and include feedback on semantics (Foltz, Laham, and Landauer Reference Foltz, Laham and Landauer1999). Recently, several scoring engines with more sophisticated language processing techniques and statistical methods have been developed (Li et al. Reference Li, Link, Ma, Yang and Hegelheimer2014). E-rater ® , Knowledge analysis technologies ${}^{\text{TM}}$ , and IntelliMetric ${}^{\text{TM}}$ analyze a wide range of text features at lexical, semantic, syntactic, and discourse levels.

    E-rater (developed by ETS) is an early AWE scoring engine designed to evaluate essays written by nonnative English learners; it is still widely used for TOEFL and GMAT, which are high-stakes tests for undergraduate admission or graduate business admission in the United States (Burstein, Tetreault, and Madnani Reference Burstein, Tetreault and Madnani2013). E-rater identifies and extracts several feature classes for model building and scoring using statistical and rule-based NLP (Attali and Burstein Reference Attali and Burstein2006). Some of the feature classes include (1) grammatical errors (e.g., subject–verb agreement errors); (2) word usage errors (e.g., here versus hear); (3) errors in mechanics (e.g., spelling and punctuation); (4) presence of discourse elements (e.g., thesis statement, supporting details, and concluding paragraphs); (5) development of discourse elements; (6) style (e.g., repeated use of the same word); (7) content-vector analysis (CVA)-based features to evaluate topical word usage; (8) features associated with the correct usage of prepositions and collocations (e.g., powerful versus strong); and (9) a variety of sentence structure formation (Burstein et al. Reference Burstein, Tetreault and Madnani2013). After measuring these features, the e-rater provides a holistic score that corresponds with human-rated scores. A randomly selected sample of human-scored essays is run through the e-rater, after which a variety of linguistic features are extracted and converted to numerical values. Using a regression modeling approach, the values obtained from this sample are used to determine the weight for each feature. To score a new essay, the e-rater extracts the set of features and converts the features to a vector value, and then, these values are multiplied by the weights relevant to each feature. Finally, the sum of the weighted feature is computed to predict the final score, which represents the overall quality of an essay (Attali, Bridgeman, and Trapani Reference Attali, Bridgeman and Trapani2010).

    Another important scoring engine is IntelliMetric, which uses the same holistic scoring approach employed by human raters (Schultz Reference Schultz2013). Similar to the training requirements for human raters to score a specific prompt, the IntelliMetric system needs to be trained with a set of previously scored responses from human raters. The system then internalizes the features of the responses linked to each score point and applies it to score essays with unknown scores. The IntelliMetric system uses a multistage process to score essays. First, the essays need to be provided in an electronic form. After the information is received and prepared for analysis, the text is then parsed to understand the grammatical and syntactic structure of the language. Each sentence is identified in terms of parts of speech, vocabulary, sentence structure, and expression. After all the information is collected from the text, statistical techniques are employed to translate the text into a numerical form. Then, IntelliMetric uses virtual raters (mathematical models) to assign scores. Each virtual rater attempts to link the features extracted from the text to the scores assigned in the training set to ensure accurate scoring for essays with unknown scores. IntelliMetric finally integrates the information received from the virtual rates to present a single and reliable score.

    Powered by these above-mentioned scoring engines, AWE tools such as Criterion and MYAccess! have been developed. These AWE tools can provide writing scores and feedback instantly, and students can benefit from these tools by practicing writing and receiving immediate feedback from the tools. In the context of writing instructions, AWE tools can assist instructors by providing immediate scoring and feedback, especially in large classroom scenarios.

    In general, AWE studies have focused on the validity and reliability of AWE tools (Dikli and Bleyle Reference Dikli and Bleyle2014). Previous validation studies reported high agreement rates between the AWE tools and human raters (Burstein et al. Reference Burstein, Braden-Harder, Chodorow, Hua, Kaplan, Kukich, Lu, Nolan, Rock and Wolff1998; Landauer, Laham, and Foltz Reference Landauer, Laham and Foltz2003; Chodorow, Gamon, and Tetreault Reference Chodorow, Gamon and Tetreault2010). For example, Shermis et al. (Reference Shermis, Koch, Page, Keith and Harrington2002) showed that PEG ${}^{\text{TM}}$ achieved scores that were highly correlated with human scores ( $r = 0.82$ ) compared with human inter-rater reliability ( $r = 0.71$ ). Furthermore, Enright and Quinlan (Reference Enright and Quinlan2010) found high agreement indices between ratings provided by two human raters and those provided by e-rater and one human in TOEFL iBT. E-rater proved to be a reliable complement to human ratings under specific testing contexts (Burstein et al. Reference Burstein, Braden-Harder, Chodorow, Hua, Kaplan, Kukich, Lu, Nolan, Rock and Wolff1998; Powers et al. Reference Powers, Burstein, Chodorow, Fowles and Kukich2000; Burstein Reference Burstein2003; Chodorow and Burstein Reference Chodorow and Burstein2004; Attali Reference Attali2007; Lee, Gentile, and Kantor Reference Lee, Gentile and Kantor2008).

    Neural models have dominated current AWE systems. Ke and Ng (Reference Ke and Ng2019), Ramesh and Sanampudi (Reference Ramesh and Sanampudi2021), and Uto (Reference Uto2021) have summarized accurate neural models well. For automatic essay scoring, there are two main model types. Firstly, in RNN-based models, the RNN output is sent to mean-over-time to aggregate the input to the fixed length vector and a linear layer for the scalar value (Taghipour and Ng Reference Taghipour and Ng2016) or a simple BiLSTM to the linear layer is used for predicting essay scores (Alikaniotis, Yannakoudakis, and Rei Reference Alikaniotis, Yannakoudakis and Rei2016). Secondly, transformer-based models, for example, BERT with BiLSTM with attention (Nadeem et al. Reference Nadeem, Nguyen, Liu and Ostendorf2019) or BERT concatenated with handcrafted features (Uto, Xie, and Ueno Reference Uto, Xie and Ueno2020), can be used to predict the score. Fine-tuning BERT using multiple losses including regression loss and reranking loss for constraining automated essay scores has been shown to produce state-of-the-art results (Yang et al. Reference Yang, Cao, Wen, Wu and He2020).

    Although there are many studies that explore AWE tools and their validation, a majority of the studies focus on AWE systems developed for native English-speaking writers (Powers et al. Reference Powers, Burstein, Chodorow, Fowles and Kukich2001; Rudner, Garcia, and Welch Reference Rudner, Garcia and Welch2006; Wang and Brown Reference Wang and Brown2007) or English as a second language (ESL) writers (Chen and Cheng Reference Chen and Cheng2008; Choi and Lee Reference Choi and Lee2010). Only a few studies investigate the use of the AWE system for less commonly taught languages, and to the best of our knowledge, there are no studies that investigate AWE for Korean as a foreign language (KFL) because of the lack of available AWE tools. This study aims to extend the scope of research in this area by introducing a state-of-the-art AWE system that is developed based on the Korean learner corpus for Koreans.

    The goal of this study is to develop a neural Korean AWE engine and validate it in terms of its capacity to distinguish the developmental level of second language learners. In this paper, we address the question of how accurate advancements in neural network models can help Boost automatic writing evaluation, and how neural network models can use different linguistic features to Boost AWE performance using linguistic features for AWE in a complementary manner. This paper includes a description of the automated essay scoring system, its natural language processing-centered approach within the neural system, and details on the validation of the AWE system in terms of predicting the proficiency level and holistic score simultaneously of the learners.

    The rest of this paper is organized as follows. First, the paper presents the Korean learner corpus used to develop the Korean AWE program and discusses how we define features in the learner corpus (Section 2). Next, the basic AWE model is presented (Section 3), followed by a proposed neural AWE model that was designed to compensate for the limitations of the basic model (Section 4). Finally, the results from an experiment are reported with detailed discussions (Section 5) and future perspectives for the AWE model in the conclusion (Section 6).

    2. Korean learner corpus

    2.1 Learner corpus dataset

    We use the dataset from the Korean learner corpus (Park and Lee Reference Park and Lee2016); this database contains proficiency levels (from Level 1 to Level 6) (<level>), native language by nationality (<nationality>), gender (<gender>), teacher-attributed score (<score>), and text. Figure 1 shows an example of the Korean learner corpus dataset which indicates the learner’s proficiency level = Level 1 (A1), L1 = Chinese, gender = F, and score = 70. Furthermore, it shows the title of the text (<topic>) and the entire text where the sentence is delimited using s (the beginning of a sentence) and /s (the end of a sentence), and the paragraph using p (the beginning of a paragraph) and /p (the end of a paragraph).

    Figure 1.Example of the Korean learner corpus: <level> = Level 1, <nationality> = Chinese, <gender> = female, <term> = final examination, <date> = Fall 2013, <topic> = my weekend, and <score> = 70. The present example of Korean writing can roughly be translated into I went to the library on Sunday. I went to the library with a friend. There was a book in the library. I studied in the library. I read a book. So it’s fun on Sunday.

    The Common European Framework of Reference for Languages (CEFR) suggest common reference levels divided into three level groups: A1 and A2 (basic), B1 and B2 (independent), and C1 and C2 (proficient) users. The Korean proficiency test divides students into beginner, intermediate, and advanced groups, which are further divided into levels based on each student’s ability. These groups are subdivided into Levels 1 (A1) and 2 (A2) for the beginner levels ( chogeub, literally “beginner”), Levels 3 (B1) and 4 (B2) for the intermediate levels ( junggeub, “intermediate”), and Levels 5 (C1) and 6 (C2) for the advanced levels ( gogeub, “advanced’’). The minimum requirement in universities for foreign students whose first language is not Korean should be at least Levels 3 and 4 respectively admission and completing their university degree regardless of their major. For students in Korean studies, Levels 5 and 6 are required for admission and degree completion, respectively.

    Although they hailed from over 80 different countries, the majority of the learners were from Asian countries where Chinese and Japanese are the first and second most spoken languages. Writing examples for L1 Mandarin Chinese and Japanese in the corpus represent 38.27% and 21.09%, respectively. If we place students from China, Hong Kong, and Taiwan together, the percentage of learners who speak Chinese as L1 increases to 49.72%, and thus, half of the writing tests can be said to be produced by Chinese L1 learners.

    A total of 2523 learners participated in a writing examination to produce 4094 writing examples. All examinees provided their native language (L1) and gender; there were 700 men, 1822 women, and a participant who did not specify their gender. The corpus also specified that all students were high school graduates, and over 60% were university graduates. In the learner corpus, the beginner levels (Levels 1 and 2) represent almost 50% of the corpus. Writing examples represent about 75% of the corpus if Level 3 (intermediate level) is also considered. Table 1 presents the most frequently used prompts in the learner corpus. While some writing prompts are given only to learners at a specific proficiency level (e.g., My weekend requested only for Level 1), other subjects can be used for different proficiency levels (e.g., The day that I remember the most for Level 3 and Level 5).

    Table 1. Examples of most frequent prompts and their number of instances in the learner corpus

    There are over 100 writing prompts. Twenty-one writing prompts are given to multiple proficiency levels, and these prompts represent 42.96% of the dataset. For the proposed AWE system, we use <level> and <score> as target classes, and extract various linguistic features only from sentences. Although other annotations in the learner corpus would be target classes for other learner corpus-related applications, such as <nationality> for native language identification, we do not use them in this study.

    2.2 Features in the learner corpus

    We explore various automatic metrics that aim to describe the characteristics of the learner corpus, and we find relevant features for the classification tasks. Such characteristics are represented in terms of complexity, fluency, and accuracy features. These features can be used for learner corpus-related applications such as automated assessment and language proficiency classification. All metrics described here should be measured and extracted automatically from the corpus. Therefore, they are evaluated without any human intervention to assess writing quality and classify language proficiency automatically.

    2.2.1 Complexity features

    Complexity features use quantitative measures such as the number of words and sentences in the text with their numbers and mean lengths. The length of the written text is considered as an important feature in the learner corpus. Most previous work on proficiency classification focused on the number of words (Ortega Reference Ortega2003; Vajjala and Loo Reference Vajjala and Loo2013; Alfter et al. Reference Alfter, Bizzoni, Agebjörn, Volodina and Pilán2016). Since many official writing tests for proficiency levels define the number of words for each level, the quantitative measures of text in the learner corpus become the most obvious feature for learner corpus applications.

    We use a part-of-speech (POS) tagging system for Korean morphological analysis to count the number of morphemes instead of eojeols (a blank-separated word unit in Korean). The POS tagger can attribute POS tag information while performing the segmentation task for the word in Korean. For example, the following sentence in (1b) is morphologically analyzed and segmented in (1c). Although the number of tokens differs based on basic units such as eojeols and morpheme, we can deal with compound words in which these units may appear with or without a blank space, in which case we can tokenize Korean words into morphemes to obtain a consistent number of tokens for compound words regardless of the blanks. For example, for two identical but differently segmented compound nouns hakseubja kopeoseu and hakseubjakopeoseu (“a learner corpus”)—both of which are correct and grammatical—the number of morphemes can be homogeneously counted as two using the proposed counting scheme. This scheme performs counting based on what the compound word or phrase semantically represents instead of its surface segmentation, which can be different. Therefore, this scheme counts both as two tokens (as for hakseubja kopeoseu) instead of one token (as for hakseubjakopeoseu).

    1. (1)

      1. a. hajiman bili ssi-hago naoko ssi-neun modu sajingi-ga eobs-eoss-eoyo. However, Billy Mr.-conj Naoko Ms.-top all camera-nom do_not_have-past-decl. “However, Mr. Billy and Ms. Naoko, both of them do not have a camera.”

      2. b. hajiman bili ssi-hago naoko ssi-neun modu sajingi-ga eobs-eoss-eoyo. (# of tokens by word = 8)

      3. c. hajiman bili ssi -hago naoko ssi -neun modu sajingi -ga eobs -eoss -eoyo. (# of tokens by a morpheme = 13, punctuations excluded)

    A type/token ratio is calculated using $\frac{\text{\# of types}}{\text{\# of tokens}}$ , where the number of types represents the unique number of tokens, and the number of tokens represents the number of morphemes. This ratio can help measure the vocabulary richness of a corpus between 0 and 1. Within this range, 0 and 1 indicate low and high lexical variation, respectively. We use the morphological analysis and POS tagging model described in Park and Tyers (Reference Park and Tyers2019), which can generate POS tagging results, as shown in Figure 2.

    Figure 2.Example of Sejong corpus-style POS tagging analysis: MA{J $|$ G} are for adverbs, NN{P $|$ B $|$ G} for nouns, J{KB $|$ X $|$ KS} for postpositions, E{P $|$ F} for verbal endings, VA for adjectives, and SF for punctuations.

    Complexity features can also measure syntactic complexity in L2 writing (Polio Reference Polio1997; Ortega Reference Ortega2003; Lu Reference Lu2010), whereas first language syntactic complexity measures include Yngve’s depth algorithm (Yngve Reference Yngve1960), Frazier’s local non-terminal numbers (Frazier Reference Frazier1985), and the D-level scale (Rosenberg and Abbeduto Reference Rosenberg and Abbeduto1987; Covington et al. Reference Covington, He, Brown, Naci and Brown2006), we do not consider them in this manuscript for second language learning. A tree structure obtained by constituent parsing can show linguistic discrepancy. For example, if the subject is omitted in the sentence, a tree structure of the parsing result has a vp node as a root. A standard tree has an s node as a root as shown in Figure 3. If the root node is a vp, we may consider it as a syntactic complexity feature. We note that a vp root sentence also may be a grammatically relevant sentence in Korean. We use the phrase-structure models described in Kim and Park (Reference Kim and Park2022), which trained the Sejong treebank for Korean using the Berkeley neural parser (Kitaev, Cao, and Klein Reference Kitaev, Cao and Klein2019) with the pre-training of deep bidirectional transformers (Devlin et al. Reference Devlin, Chang, Lee and Toutanova2019). For syntactic complexity features, we add the distribution of grammatical morphemes such as the number of verbal endings and prepositions.

    Figure 3.Example of phrase-structure analysis.

    2.2.2 Fluency features

    We define fluency as the capability of producing language effortlessly. Fluency is the potential of a language learner to apply their knowledge of grammar to produce intelligible speech and writing. This plays an important role in language production. We differentiate between the language fluency of a learner by observing their level of comfort when using that language and identifying if they can efficiently express themselves verbally and in text. Pauses in production and the length of written text are good indicators of fluency (Towell, Hawkins, and Bazergui Reference Towell, Hawkins and Bazergui1996; Ge, Wei, and Zhou Reference Ge, Wei and Zhou2018; Martindale and Carpuat Reference Martindale and Carpuat2018; Qiu and Park Reference Qiu and Park2019). Previous work defined various metrics for fluency. Two metrics defined in previous work and an additional fluency metric by the unigram language model are given below.

    1. 1. Fluency by Asano, Mizumoto, and Inui (Reference Asano, Mizumoto and Inui2017): $\displaystyle f(h) = \frac{\log P_m(h) - \log P_u(h)}{|h|}$

    2. 2. Fluency by Ge et al. (Reference Ge, Wei and Zhou2018): $\displaystyle f(h) = \frac{1}{1+H(x)}$ where $\displaystyle H(x) = -\frac{\log P_m(h)}{|h|}$

    3. 3. Fluency by the unigram language model: $\displaystyle f(h) = -\frac{\log P_u(h)}{|h|}$

    here $P_m$ represents the probability of the sentences given by the language model, and $P_u$ denotes the unigram probability of the sentences.

    We collect a very large monolingual dataset for Korean, which contains over 9.6 M sentences and 130.6 M eojeols, to create a language model: Korean WikipediaFootnote c (5.3 M sentences and 71.8 M eojeols), the Sejong morphologically analyzed corpus (3.0 M and 40.0 M), and articles from The Hankyoreh daily newspaper during 2016 (1.2 M and 18.6 M, previously presented in Park Reference Park2017). After preprocessing the raw text into morpheme-segmented text using the POS tagging system (Park and Tyers Reference Park and Tyers2019), we create a linearly interpolated trigram model and implement the fluency metrics described in Asano et al. (Reference Asano, Mizumoto and Inui2017) and Ge et al. (Reference Ge, Wei and Zhou2018), and the fluency feature counted by the unigram language model. As indicated in (2), we attach the POS label to the morpheme-segmented lexicon and explicitly include a + symbol for consecutive morphemes. A raw text collection for creating a language model is available at by authors of the manuscript.

    1. (2)

    1. 1.

    2.2.3 Accuracy features

    Thus far, we discussed features that can be extracted automatically from the learner corpus. Now, we define accuracy as a feature in the learner corpus. This feature represents the ability to produce correct sentences using correct grammar and vocabulary. However, such a learner corpus requires linguistic information such as grammatical error categories and error correction (e.g., the NUS learners corpus Dahlmeier, Ng, and Wu Reference Dahlmeier, Ng and Wu2013 or the treebank of learner English Berzak et al. Reference Berzak, Kenney, Spadine, Wang, Lam, Mori, Garza and Katz2016). These errors are annotated based on target expressions that a native speaker would produce given the identical context, and they are used to distinguish non-standardized linguistic expressions in the learner corpus. Figure 4 shows a conceptual example of the annotated sentence described in (3) from the Korean learner. S represents the learner’s sentence, and A represents the error correction annotation. 1 2 indicates the path of the tokens where the correction needs to be introduced. The value R:ADP indicates the type of error. For example, yeseo, a functional morpheme (ADP) at 1 2, should be replaced by eseo according to the annotation.

    1. (3)

      1. a. gohyang yeseo jumal e chingu wa manna seo eseo eoyo. “ø (met) a friend (in the hometown) on weekend.”

      2. b. gohyang eseo jumal e chingu wa manna si eoss eoyo.hometown loc weekend ajt friend cjt meet hon past ind. “ø met a friend in the hometown on weekend.”

    Figure 4.Example of an M2 file for the Korean learner corpus.

    The correct sentence is presented in (3b). This example illustrates functional morpheme errors, which are among the most common errors: specifically, these errors involve postposition and honorific morphemes, which we denote as adpositions (ADP) for functional morphemes using a universal part-of-speech tagset (Petrov, Das, and McDonald Reference Petrov, Das and McDonald2012). Using the error-annotated learner corpus, it is possible to perform a grammatical error correction (GEC) process by automatically detecting and correcting grammatical errors in the text. In accurate years, the consistent increase in the number of foreign language learners, especially learners of Korean, and the demand to facilitate their learning with timely feedback have resulted in GEC becoming increasingly popular and attracting considerable attention in both academia and industry. However, because the learner corpus needs to be in another form, that is, an error-annotated corpus instead of the current version of the corpus because of the lack of the error correction dataset in the learner corpus for Korean L2 writing, a task such as GEC including accuracy features is beyond the scope of this study, and we leave it as future work.

    2.2.4 Summary

    We summarize the list of features, including the bag of morphemes, in Table 2, which also shows examples of feature values for the learner corpus presented in Figure 1, which contains six sentences. We present several quantitative complexity features, such as the mean length of sentence by morpheme, mean length of word by morpheme, and morpheme type versus token ratio. In addition, the table shows statistical complexity features such as the number of sentences, number of paragraphs, and number of tokens using morphemes. We consider the bag of functional morphemes as a morpho-syntactic complexity feature and the number of vp heads as a syntactic complexity feature. We denote both the morpho-syntactic and syntactic complexity features as syntactic complexity features for convenience, so that they are differentiated from quantitative complexity features.

    Table 2. Example of features and teir values for te learner’s writing in Figure 1

    4. Neural automated writing evaluation models

    We propose a state-of-the-art neural Korean AWE model and provide a deeper investigation into each feature proposed in Section 2.2. Our system applies XLM-Roberta to represent word forms as word representations along with the multitask learning (MTL) approach that trains several tasks simultaneously (Hashimoto et al. Reference Hashimoto, Xiong, Tsuruoka and Socher2017; Lim et al. Reference Lim, Lee, Carbonell and Poibeau2020). The details of the XLM-Roberta feature representation method and our MTL approach is depicted in Figure 7.

    Figure 7.System structure of the proposed deep learning model. Three linguistic features are applied: syntactic complexity, fluency, and quantitative complexity, in addition to the sequence of token representations. Each token is transformed into a vector representation based on XLM-RoBERTa.

    4.1 Representation of words

    Machine learning (ML)-based grammar checking (Soni and Thakur Reference Soni and Thakur2018) and AWE (Persing, Davis, and Ng Reference Persing, Davis and Ng2010; Taghipour and Ng Reference Taghipour and Ng2016; Yang, Xia, and Zhao Reference Yang, Xia and Zhao2019) have been proposed and widely used in accurate years because of their outstanding performance. The main idea behind ML-based AWE is applying deep learning techniques for automated essay scoring. To compute the score of writing in terms of machine learning, the system has to learn from a training dataset T that comprises a pair of essays $x_i$ and scores $y_i$ , where $(x_{i},y_{i})\in T$ . In the deep learning-based AWE such as in Yang et al. (Reference Yang, Xia and Zhao2019), the sequence of words from the essay $x_i$ is represented as a sequence of vector representations (i.e., word embeddings). Therefore, the essay $x_i$ is composed of m words such that $x = (w_{i,1}, \cdots , w_{i,m})$ , and the system creates a set of sequences of word embeddings $e^{w}_{i,1}, \cdots, e^{w}_{i,m}$ . This vector representation of a word $e^{w}_{i,j}$ is trained to capture syntactic and semantic meanings of a word in a sentence (Pennington, Socher, and Manning Reference Pennington, Socher and Manning2014). We apply a bidirectional encoder representations from transformers (BERT)-like word representation method that is trained using a masked language model (MLM). Many MLM pre-learning methods such as BERT (Devlin et al. Reference Devlin, Chang, Lee and Toutanova2019) perform training by replacing certain input words with [MASK] and restoring them to the original token by training a deep neural network. For example, let the input text be I have no clue; then, the system selects tokens randomly and replaces them as I have [MASK] clue. This process makes the system predict the masked word based on its surrounding words. During training, the system may struggle to learn the best parameters by comparing its prediction and the masked word.

    BERT is a pre-trained word representation model that is trained with large quantities of Wikipedia text as input and over 110 million parameters. RoBERTa (Liu et al. Reference Liu, Ott, Goyal, Du, Joshi, Chen, Levy, Lewis, Zettlemoyer and Stoyanov2019) is an extended version of BERT, which consumes 270 million parameters and a bigger input dataset, and XLM-RoBERTa (Conneau et al. Reference Conneau, Khandelwal, Goyal, Chaudhary, Wenzek, Guzmán, Grave, Ott, Zettlemoyer and Stoyanov2020) is a multilingual model of Roberta trained in 100 different languages. These pre-trained models are effective when transferred to a downstream NLP task because they capture a deep contextual representation of words. In this study, we apply the multilingual XLM-Roberta model to transform the Korean text into a sequence of word representations as

    where $E^{(w)}_i$ is a matrix that denotes a set of vector representation of words, and it comprises k subwords as $E^{(w)}_i=(e^{{(w)}}_{i,1}, \cdots , e^{(w)}_{i,k})$ . This is because XML-Roberta tokenizes a word into several subwords to handle character-level subword information.

    For example, the word joyful turns into two subwords, joy and ful using XLM-Roberta; therefore, the number of words m in an essay is always equal to or smaller than the number of XLM-Roberta representations k. We implement our word representation model using the pre-trained XLM-Roberta provided by Huggingface.Footnote d

    4.2 Representation of linguistic features

    Quantitative complexity, syntactic complexity, and fluency of the learner’s writing are features that are traditionally important to predict essay scores to assess writing, and they can be transformed into vector representations in ML applications using a simple linear transformation method. First, we concatenate the features in Section 2.2 for each quantitative complexity $t_i^{(q)}$ , syntactic complexity $t_i^{(s)}$ , and fluency $t_i^{(f)}$ . Then, we transform each concatenated output based on a linear model with an activation function Relu as

    where $e_i^{(q)}$ , $e_i^{(s)}$ , and $e_i^{(f)}$ denote a vector representation of each feature, G and U represent learnable parameters, and b indicates a bias. We concatenate the representation of features with the vector representation of words. Finally, we unify the representation between the word and the linguistic features as

    where k denotes the number of words in the learner’s writing. The proposed unified representation is commonly used with BERT-like models. For example, Prakash and Madabushi (Reference Prakash and Madabushi2020) designed an enhanced version of contextual representation based on count-based features (BERT with a term frequency), and Xue et al. (Reference Xue, Zhou, Ma, Ruan, Zhang and He2019) investigated the effect of relational features with BERT for the Chinese NER task. To combine pairs of features, a simple concatenation method was applied. However, the concatenation method may not be the best method in our case because our model uses diverse features simultaneously. To investigate this issue, we applied an attention-based method to form unified representations.

    4.4 Prediction of a proficiency level and a score

    Our final goal is to build a system that can automatically measure the proficiency level and the score of a learner’s writing. We use a linear classifier to measure the proficiency level of the essay and use another linear regressor for scoring.

    z denotes an index number of levels where level = {Level 1, …, Level 6}, and $P^{(c)}$ and $P^{(r)}$ are learnable parameters. The classification result $\hat{y}^{(c)}_{i}$ is computed by the selection of the maximum value of $e^{(c)}_{i,z}$ . During the training phase, our system learns by backpropagation of the prediction errors over the entire training dataset T. Because we train two different classification and regression tasks, we use the individual CrossEntropy objective function for predicting the proficiency level and the MSE function for assigning the score of the learner’s writing.

    where $(x_{i},y_{i})\in T$ denotes an element from the training set T, $y_i$ denotes a set of gold labels ( $y^{level}_i$ , $y^{score}_i$ ), and $\hat{y}_i$ represents a set of predicted results.

    5. Results of neural AWE models and discussion

    5.1 Experiment setup

    As presented in Section 4, we evaluate the scores using 5-fold cross-validation with the proposed regression loss to assess writing quality and the prediction accuracy for its proficiency level. Table 4 lists our hyperparameter settings. We apply 768 dimensions for parameters U and Q in (4) and set 400 dimensions for P and D in (10). We run through 80% of the training dataset during the learning phase using an epoch with a batch size of 6 randomly selected sentences. The remaining 20% is used as the test dataset. We report the best performance on the test dataset within 100 epochs over five times for the 5-fold cross-validation.

    5.2 Experiment results

    Table 5 summarizes our results on how we use different linguistic information to Boost AWE results using XLM-RoBERTa. The linguistic features are syntactic complexity features (S), fluency features (F), quantitative features (Q), and self-attention mechanism (A). To investigate the effect of LMs on AWE performance, we compare results between multilingual BERT (M) and XLM-RoBERTa (X). Besides word representation methods, we also evaluate performance that is solely based on linguistic features without the pre-trained language model. For the models without self-attention, we applied a weighted average of the BERT word representations and linguistic features as $c_i^{(wl)} =$ $\frac{1}{(k+3)}$ ( $e_{i}^{(q)}$ + $e_{i}^{(s)}$ + $e_{i}^{(f)}$ + $\sum^{k}_{j=1}{e^{(w)}_{j}}$ ). Note that the dimension of linguistic features is identical to that of the BERT embedding.

    Table 5. Experiment results

    Overall observations. XLM-RoBERTa and syntactic complexity features outperform other experimental settings for in terms of predicting both the proficiency level and the score. The features described in Section 2.2 only narrowly impact the overall results, and linguistic features without the pre-trained language model result in a severely limited performance.

    Effect of different BERT-like pre-trained language models. The model based on XLM-Roberta naturally outperforms the multilingual BERT system, wherein the former was empirically evaluated for result gains including the trade-offs between positive transfer and capacity dilution (Conneau et al. Reference Conneau, Khandelwal, Goyal, Chaudhary, Wenzek, Guzmán, Grave, Ott, Zettlemoyer and Stoyanov2020).

    Effect of linguistic features for AWE We observed a meaningful improvement in the results when using linguistic features compared to that between only XLM-RoBERTa and XLM-RoBERTA and all other features, as listed in Table 5. Among the three different linguistic features, syntactic complexity is found to be the most impactful factor in both assessing the proficiency level and the score. Furthermore, we found that quantitative complexity features have a positive effect on our empirical experiment; however, fluency features lead to performance degradation of about -0.1 points.

    Effect of self-attention. In practice, there are no result gains from using self-attention: A -0.02 accuracy for predicting a proficiency level (a negative result) and -0.48 MSE for assigning a score (a positive result) were observed. This may be attributed to the multi-head self-attention, which computes several attentions simultaneously (Vaswani et al. Reference Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser and Polosukhin2017), being already applied in the XLM-RoBERTa model; therefore, our attention representation is relatively less effective than expected.

    5.3 Analysis

    In the previous section, we showed the performance of our model using different feature selection scenarios. Among the proposed features, syntactic complexity features are relatively more important than other features. However, these observations are based on empirical experiments, and thus, one cannot explain why the neural model makes such a decision. To gain a better understanding of the decision making process of the system, we conduct additional experiments to visualize the attention score added on the top of feature representations. The visualization of the attention score is the most powerful explainable AI (XAI) method where the results of the solution are understood by humans (Park et al. Reference Park, Hendricks, Akata, Schiele, Darrell and Rohrbach2016).

    During the training, the attention score of the i-th learner’s writing— $a^{(wl)}_i$ in (8)—is computed as a probability distribution where $\sum^{k+3}_{j=1}{a^{(wl)}_{i,j}} = 1$ , k denotes the number of subtokens, and three different types of linguistic features—syntactic complexity, quantitative complexity, and fluency features—are proposed. Intuitively, the attention score, therefore, represents the importance assigned by our system to each linguistic feature and the word to yield results for predicting a proficiency level and for assigning a score in a learner’s writing.

    Attention results on all features. Visualization results in Figure 8 show the attention score on the learner’s writing number 1. In the figure, the darker the color, the more attention points of the element are assigned. Figure 8a shows the result of applying three different linguistic features as well as words. We find two interesting observations in Figure 8a compared to those in Figure 8b, where there is attention only with words. First, we observe that the system focuses on [S-COMPLEXITY] (syntactic complexity features). This result is in line with the result reported in Table 5, where the accuracy of our system was improved by 0.69 points when syntactic complexity features were introduced. Second, the system lacks interest in focusing on misspelled words. In this figure, there are several misspelled words such as (ibun) instead of (ibeon, ‘this time’), (jejudu) instead of (jejudo, ‘Jeju Island’); (gapi) instead of (gati, ‘together’); (bipingbab) instead of (bibimbab); and (jacheonggeo) instead of (jajeongeo, ‘bicycle’). Since we do not use accuracy features provided by human annotation, our system can be considered to be sound for the following reasons: (1) The attention mechanism focuses on the proposed linguistic features based on automatic metrics, and (2) a pre-trained large language model can be associated with more proper words instead of spelling errors to yield classification and predicting results.

    Figure 8.Visualization of the attention score proposed in Eq (8).

    Attention results on only words. As reported, our system tends to focus on syntactic complexity features when all linguistic features are available. Then, what happens if the system can only see words? Figure 8b presents the results when we apply only words as the (X) + (A) model in Table 5. We found that the higher attention score is assigned to verbs such as (gabnida, “be going”). However, the distribution of attention scores on words varies based on the input dataset. Therefore, it is difficult to find a specific word or an expression that can directly affect the score of the learner’s writing.

    Attention results on only linguistic features. Table 5 shows that our system predicts a score and a proficiency level of the learner’s writing only with the proposed linguistic features. We are interested in linguistic features that are the most important. Figure 9 presents attention scores of the (A) + (S) + (F) + (Q) model in Table 5 for three sample instances in the dataset. This model does not have any word information, that is, it is without the pre-trained language model. By observing the graph on Essay Number 1 and Essay Number 2, the syntactic complexity is found to be the most significant feature. For 82.7% of essays in the test dataset, the mean attention score of syntactic complexity features is more than 0.8 out of 1. However, we also observe that the quantitative complexity is a more crucial feature for decision-making for some essays such as Essay Number 273. We assume that the attention mechanism attempts to capture quantitative complexity features if it fails to utilize syntactic complexity features. However, in any case, the fluency weight does not exceed 6.7% or more of its attention score. Thus, we can assume that fluency is relatively the least important property for AWE when the system have other complexity information.

    Figure 9.Visualization of the attention scores proposed in (8): [S-COMPLEXITY], [FLUENCY], and [Q-COMPLEXITY] for syntactic complexity, fluency, and qualitative complexity features, respectively.

    The most frequent and important words based on proficiency level. In most Korean textbooks, polite verbal ending (yo) is introduced first because it is the most commonly used ending in everyday context. Then, deferential ending (seubnida) is introduced in the upper beginner level, followed by plain ending (da) in the intermediate level. Accordingly, Figure 10 shows the distributions of verbal endings based on learners’ proficiency levels.

    Figure 10.The usage of verbal endings based on its proficiency levels.

    Discussion of the usage of Korean monolingual BERT. Table 5 shows that XLM-RoBERTa outperforms the multilingual BERT. However, the proposed multilingual BERT and the XLM-RoBERTa models are designed for multilingual purposes. There are several publicly available Korean monolingual BERT models, such as KLUE-RoBERTa,Footnote e KoBERT,Footnote f DistilBERT,Footnote g and KoELECTRA.Footnote h Because these models have been trained with different amounts of training data, their parameters also vary. We additionally investigate the performance of Korean AWE using these monolingual BERT models for following reasons. First, we are interested in whether monolingual Korean BERT models perform better than multilingual BERTs. Second, we must determine the importance of the different hyperparameters in the monolingual BERT models, as well as the optimally cost-effective BERT model size. Table 6 provides data from the ablation study on multilingual and Korean monolingual BERT models. Overall, we did not observe performance improvement by using the monolingual BERTs. Instead, we observed that the model size is more important for monolingual BERT models when comparing KoBERT and DistilKoBERT. One interesting result of the experiment is that comparing KoELECTRA small-V2 and small-V3 shows almost identical results, even with different sizes of training data. Among the monolingual models, KLUE-RoBERTa (Park et al. Reference Park, Moon, Kim, Cho, Han, Park, Song, Kim, Song, Oh, Lee, Oh, Lyu, Jeong, Lee, Seo, Lee, Kim, Lee, Jang, Do, Kim, Lim, Lee, Park, Shin, Kim, Park, Oh, Ha and Cho2021) showed the best performance regardless of their model sizes.

    Table 6. Result comparison using different Korean monolingual BERTs

    Feature comparison with previous work. We compare our linguistic features with others previously proposed and utilized. Most previous work focused on complexity features by our criteria such as statistical features (e.g., length and n-gram)) or style-based features (e.g., part-of-speech labels, sentence structure, and other lexical patterns) (Ramesh and Sanampudi Reference Ramesh and Sanampudi2021). There are also content-based features (e.g., similarities between sentences and prompt overlapping), in which the similarity metric is introduced: for example, Sakaguchi, Heilman, and Madnani (Reference Sakaguchi, Heilman and Madnani2015) used BLEU, Word2vec similarity and WordNet similarity for their reference-based approach, and Dong and Zhang (Reference Dong and Zhang2016) counted the number of words and their synonyms in the essay appearing in the prompt. Due to the availability of spell checker for English, spelling, punctuation, and capitalization errors could also be utilized as accuracy features (Persing and Ng Reference Persing and Ng2013; Sakaguchi et al. Reference Sakaguchi, Heilman and Madnani2015; Dong and Zhang Reference Dong and Zhang2016; Cummins, Zhang, and Briscoe Reference Cummins, Zhang and Briscoe2016; Dong, Zhang, and Yang Reference Dong, Zhang and Yang2017). Table 7 shows a summary of handcrafted features in previous work. We used more detailed quantitative measures (token ratio; length of morphemes, words, and sentences for lexical diversity) and linguistic features by POS tagging and syntactic parsing. We also introduced fluency measures, which no previous work has considered. As we mentioned, in future work we are planning to include a grammar error correction system where we can obtain accuracy features beyond simple spelling errors.

    Table 7. Features utilized in previous work

    6. Conclusion

    In this paper, we explored several types of linguistic features in the learner corpus: quantitative complexity, syntactic complexity, and fluency. These features can be used for learner corpus-related applications that make use of machine learning techniques in addition to pre-trained language models for the neural system.

    We used various metrics that were automatically measured for these features. Therefore, these metrics could be evaluated without any human intervention to assess the proficiency and holistic score of writing automatically. The proposed neural-based state-of-the-art system applied the transformer-based multilingual masked language model and XLM-RoBERTa. In addition, based on the proposed attention mechanism score, we observed how the proposed linguistic features benefit AWE in a complementary manner for neural systems, and we analyzed which sequence of words and expression can be focused on in the neural system.

    Because our AWE system could provide a reliable holistic score while simultaneously detecting students’ proficiency levels, it could offer potential solutions for Korean language instructors who might be struggling with the workload. Furthermore, it can be used as a resource for grading student essays in large classes or placement tests that need to be graded accurately and promptly. Furthermore, the AWE system can benefit Korean language learners in their writing practice. Learners can use the AWE system to self-grade their essays before submission and learn how their scores change as they change vocabulary, syntactic structure, etc. in their writing.

    Although the proposed neural AWE engine can judge the grammaticality of the learner’s writing using linguistic features and a pre-trained neural language model, the current AWE tool has several limitations. One is that it does not “read” students’ essays. That is, the program can detect syntactic complexity and fluency, but does not make judgment on its content whether it is written according to the given writing topic. Similarity between the content and the syllabu can be estimated by defining the distance between words in the content and the concept of the topic. While previous work has proposed content-based features to calculate similarities with the prompt or reference text (Sakaguchi et al. Reference Sakaguchi, Heilman and Madnani2015; Dong and Zhang Reference Dong and Zhang2016), we have left this for future work. Another limitation is that our approach can possibly show biased performance on limited subjects that are included in the training data set. However, we observed that this issue can be mitigated by utilizing the pre-trained neural language model. Lastly, the current model does not provide specific error feedback to students. Although learners could check their scores and proficiency level with the AWE tool, they cannot check their errors, thus making it hard for them to learn from their errors.

    Given that adding error types to the learner corpus has been presented for multiple grammatical (either morphological or syntactic) levels and for several languages (Ramos et al. Reference Ramos, Wanner, Vincze, del Bosque, Veiga, Suárez and González2010; Boyd Reference Boyd2010; Han et al. Reference Han, Tetreault, Lee and Ha2010; Seo et al. Reference Seo, Lee, Lee, Kweon and Kim2012; Dickinson and Ledbetter Reference Dickinson and Ledbetter2012), our next goal is to add error annotations in the Korean learner corpus to broaden the usage of our AWE system. As the current NLP systems used for feature extraction are developed for the standard Korean language, it is expected that the automatic processing system may produce errors. This error-annotated learner corpus can lead to grammatical error correction (GEC) as a preprocessing step for learner corpus applications. We hope that the additional GEC task will Boost learner corpus applications. It is important that the writing be relevant to the given subject, which is an aspect we cannot deal with using the proposed system. To the best of the authors’ knowledge, this has not been presented in previous literature on leaner corpus applications, and we will consider this problem for future work.

    Wed, 06 Jul 2022 12:00:00 -0500 en text/html
    Killexams : MBA in Project Management Online

    Register By: August 20 Classes Start: August 22

    MBA in Project Management Program Overview

    Meet the growing demand for project leaders and couple your MBA with a project management concentration with the Master of Business Administration in Project Management from Southern New Hampshire University. Learn what it takes to plan, monitor, measure and adapt a project from start to finish, and earn an MBA that fits right into your life.

    A Project Manager's job is to keep projects and people on track, and the field of project management is growing as more companies move to project team-based business models. Our MBA is all about understanding and optimizing the functions of a business. The project management MBA builds a strong foundation of management skills, and you can apply these skills to leadership roles across a variety of industries, including construction, healthcare, IT development, manufacturing and more.

    Students who take QSO-645: Project Management for Project Management Professional (PMP)® Certification course as part of their concentration can satisfy the educational requirement of the PMP exam. This industry-recognized credential demonstrates proven project management skills and could help boost career growth and earning potential.

    Learn how to:

    • Analyze quantitative and qualitative data to inform project management decision-making
    • Develop and foster adaptable strategies for various projects
    • Learn to continually Boost organizations and their practices
    • Lead and collaborate with a variety of key stakeholders
    • Cultivate globally aware and culturally responsive teams and organizations
    • Create and implement plans that articulate organizational culture, align with ethical and legal standards, and promote sustainable business practices
    • Demonstrate knowledge in project management that builds upon the core competencies of business administration

    SNHU’s MBA in Project Management is one of the most affordable MBAs in the nation and can be completed in just over a year.

    Career Outlook

    With an MBA in Project Management online from SNHU, you can develop the skills and experience you need to capitalize on the growing demand for qualified project managers.

    PMI® expects project management jobs to grow by more than 31% through 2027, creating a total of 22 million new project management jobs.1 Earning potential for project management workers is also strong – particularly for workers with the PMP certification. A 2020 PMI survey found that PMP-certified workers earned 22% more than those without certification.2

    The project management MBA offers a unique mix of project management skills and broad-based business knowledge that can help you stand out in this growing field.

    Gina Cravedi with text Gina Cravedi“This degree will not only prepare you to carry a project management certification but it provides you the business acumen to put those project skills to work with any industry and any project model environment,” said Gina Cravedi '18, SNHU’s director of marketing operations, an MBA graduate and certified Project Management Professional (PMP).

    Project managers can work in a variety of industries, including:

    • Construction or engineering
    • Healthcare or government agencies
    • Information technology or manufacturing
    • Food service and hospitality
    • Music and entertainment

    Throughout these fields, project managers play an important role in the process of moving projects, organizations and entire industries forward. Supply chain management, for example, relies on the expertise of project managers to run its processes smoothly and maintain availability of essential goods and services.

    A project management MBA can teach you the in-demand skills needed to succeed in one of these critical project management jobs:

    • Program or project manager: Project managers oversee a project from start to finish. They make sure the scope and goals of the project are on track. Project managers typically work closely with company leadership and may be supported in their roles by other project management positions.
    • Project risk manager: Before any project begins, a project risk manager is tasked with analyzing market and operational risks. Risk managers create and communicate risk mitigation policies and processes for a project’s workers.
    • Project cost estimator: Project cost estimators gather and analyze data to estimate the amount of time, money, materials and labor needed for a project. They work to ensure a project is completed on time and within budget.
    • Project procurement manager: Once a budget is set, a project procurement manager communicates with vendors to source supplies, equipment and service contracts. Procurement managers seek the most cost-effective and quality products for the project at hand.
    • Project quality manager: Maintaining project quality from start to finish is the job of a project quality manager. Quality managers monitor the performance and outcome of a project and identify any areas of quality improvement needed.

    Job growth and earning potential for project management careers will vary depending on the career you pursue with your project management MBA.

    Construction managers, for example, earned a median salary of $95,260 in May 2019. Jobs for construction managers are projected to grow 8% through 2029. General and operations management jobs are projected to grow 6% through 2029. The median annual wage for these positions was reportedly $100,780 in 2019.3

    Your project management MBA can also help you prepare for a career as an operations research analyst, using data to drive better business decisions. Jobs in this field are projected to grow 25% through 2029 with a reported median salary of $84,810 in 2019.3

    Not sure you want to work as a project manager? The skills gained in a project management MBA can help you develop key leadership and career skills that enhance any business management position.

    Dara Edge with the text Dara EdgeEarning an MBA in Project Management gave Dara Edge '15 new tools to support her career in social media. As a social media community manager for SNHU, Edge manages engagement on the university’s social media channels and works with teams from across the organization to analyze community engagement data.

    Edge said her MBA program helped develop the strong critical analysis and communication skills needed for this role.

    “You have the ability to use the degree in so many different ways,” said Edge. “Whether you want to work in the project management field, work in management, or if you want to learn how to manage projects in general. You’ll always be able to use the skills and knowledge that you’ll learn in the program.”

    Courses & Curriculum

    The MBA in Project Management online combines theory with practical application. You can graduate with a set of tools that complement today's tech-intensive workplace. In the updated curriculum, you'll engage in scenario-base learning opportunities, allowing you to complete activities and individually graded group work based on solving real-world business problems. This type of learning offers hands-on learning experience in your online classroom that mimics real-world work settings and challenges.

    Taught by professors with many years of business experience, your courses will focus on how to lead a project from start to finish – smoothly. You’ll learn how to define the scope of a project, develop a project timeline, and identify costs and resources.

    Project management learning will be supported by the MBA core curriculum, which focuses on all aspects of business leadership, including:

    • Building Business Leaders
    • Applied Business Statistics
    • Leading People and Organizations
    • Optimizing Brands
    • Leading Organizational Change
    • And more

    Your project management degree courses will focus on the tools, processes and strategies used to successfully hit the goals of any big project.

    You’ll learn how factors like scope, time, cost, quality, risk, resources and communication impact a project. You can apply this learning to real-world case studies to gain key decision-making experience. And you’ll get hands-on practice using manual and technology-based tools to start, plan and control projects.

    If you’re interested in seeking the PMP certification, you have the option to take QSO-645: Project Management for PMP Certification as part of your MBA program. In this course, you'll explore the professional and social responsibilities of project management. You can also get a deeper understanding of the tools and techniques you can use to plan and manage projects.

    This course satisfies the education requirement of 35 hours needed to take the PMP exam. It does not certain certification or passage of the certification exam, but does get you closer to earning this key credential. You must meet all other PMP requirements, including work experience hours, in order to sit for the exam.

    No matter what your goals are, an MBA in Project Management offers key leadership and career skills you can use to be successful as a project manager or business leader. These skills include:

    • Communication. Communicate effectively between internal team members, clients and vendors.
    • Critical thinking. Know how to ask questions, solve problems and make decisions.
    • Leadership. Be an active leader and coach for members of your project team to keep projects running smoothly.
    • Organization. Plan and monitor project timelines to keep projects on track.

    Students with non-business academic backgrounds may be required to take foundation courses. As an add-on to your degree with minimal additional courses required, MBA students can also pursue a graduate certificate beyond the standard degree program, including a project management graduate certificate. This allows you to list another significant credential on your resume with minimal additional coursework.

    Don't have a business background? No problem. Our MBA is accessible to everyone. Interested students must have a conferred undergraduate degree for acceptance, but it can be in any field. Those without an undergraduate degree in business or a related field may be asked to complete up to 2 foundation courses to get started. These foundations cover essential business skill sets and can be used to satisfy elective requirements for the general-track MBA. With foundations, the maximum length of your online MBA would be 36 credits.

    Attend full time or part time. Students in the MBA have the option to enroll full time (at 2 classes per term) or part time (with 1 class per term). Full-time students should be able to complete the program in about 1 year, while part-time students could finish in about 2 years. Our SNHU students are busy, often juggling jobs, family and other obligations, so you may want to work with your academic advisor to identify the course plan that works for you. The good news is, you can switch from full time to part time and back again as often as you want.

    In accordance with SNHU’s relationship with the Project Management Institute (PMI) and the ability to offer Project Management Professional (PMP)® test content, SNHU instructors completed the PMI®’s Authorized Training Partner Train the Trainer – PMP® test Prep Program. This program equips SNHU faculty with the authority to deliver PMP® test prep and training content to PMI’s quality standards for the revised exam, which went into effect in January 2021. This designation is essential to allowing SNHU to offer PMP® preparatory content though QSO-645: Project Management for PMP Certification, which also offers students the 35 hours of project management education required to sit for the exam. Students who choose to pursue their PMP® certification may find that this industry-recognized credential offers proven project management skills and could help boost career growth and earning potential.

    Tuition & Fees

    Tuition rates for SNHU's online degree programs are among the lowest in the nation. We offer a 25% tuition discount for U.S. service members, both full and part time, and the spouses of those on active duty.

    Online Graduate Programs Per Course Per Credit Hour Annual Cost for 15 credits 
    Degree/Certificates $1,881 $627 $9,405 
    (U.S. service members, both full and part time, and the spouses of those on active duty)*
    $1,410 $470 $7,050 

    Tuition rates are subject to change and are reviewed annually.
    *Note: students receiving this rate are not eligible for additional discounts.

    Additional Costs:
    $150 Graduation Fee, Course Materials ($ varies by course)

    Licensure and Certification Disclosures

    SNHU has provided additional information for programs that educationally prepare students for professional licensure or certification. Learn more about what that means for your program on our licensure and certification disclosure page.

    The Project Management Professional (PMP) is a registered mark of the Project Management Institute, Inc.

    The PMI Authorized Training Partner seal is a mark of the Project Management Institute, Inc.

    Thu, 13 Aug 2020 19:12:00 -0500 en text/html
    Killexams : Admissions Policies

    Admission to our program is very competitive. Because each student receives substantial faculty attention, we enroll only about 25 new students each year in the whole program. We may enroll only one or two new students in a given program of study.

    For this coming Fall, we expect to recruit in all our active programs: Accounting; Accounting Information Systems; Economics; Finance; Information Technology; International Business; Marketing; Operations Research, Organization Management; and Supply Chain Management.

    Although most classes in the program are offered on Rutgers’ Newark campus, students must also be prepared to take some classes on the New Brunswick campus.

    Because students work so closely with faculty in the doctoral program, we consider not only the applicant’s qualification but also the match between the applicants and the faculty available to mentor them. We sometimes suspend admission for a particular program of study when it already has a substantial number of students. For additional insights into our admissions process, see Frequently Asked Questions.

    Fri, 27 May 2022 12:02:00 -0500 en text/html
    Killexams : Admissions Frequently Asked Questions

    Here you will find answers to common questions that we receive about the admissions process. If your question is not answered below, please feel free to contact us.

    You may also be interested in general FAQs about the program itself.

    View Program FAQs

    Application Process

    When does the application open?

    The application for the 2022-23 academic year opens on September 1, 2022.

    When is the application deadline?

    Recommended submission deadline: December 15
    Final deadline: March 15

    For the best chance of admission, ALL application materials should be submitted before the recommended submission deadline. We will review all applications received before the recommended deadline and begin sending out admissions decisions within 6-8 weeks. Applications received between the recommended deadline and the final deadline may not receive full consideration if the program is full.

    Can I apply for spring enrollment in MSAI?

    Currently, we only accept fall enrollment in each academic year. Our curriculum was created as an intensive, 15-month time frame, which allows for collaboration between members of the cohort. We do not accept winter, spring or summer applications at this time.

    Does every MSAI applicant have an admissions interview?

    Not all MSAI applicants will be invited to interview. Only applicants offered final admission consideration will be contacted for an admissions interview. International applicants are interviewed via Skype.

    Does MSAI offer deferment or conditional enrollment?

    MSAI does not offer formal deferment or conditional enrollment.

    Is admission into MSAI rolling?

    No. Applications for admission are reviewed following each deadline. Admission decisions will be made available approximately 6 to 8 weeks following the deadline date.

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    Application Materials

    Is the GRE or GMAT required to apply to the MSAI program?

    We do not require GRE scores, however we do recommend that applicants provide them. Applicants can submit an unofficial GRE score report during the application process; these score reports may be uploaded directly to the online application system. We will accept downloaded or scanned PDF copies or a screenshot of your unofficial score report. You may access your unofficial GRE score report from your account on the ETS website. The program will not accept GMAT, LSAT, MCAT or any other standardized test score in place of the GRE.

    I took the GRE a while ago. Can i still use it to apply?

    Official test scores must be taken no more than five years before the intended quarter of entry and must come directly from ETS. During the application process you will be asked to scan a copy of your score report. This uploaded version is not considered "official" and will only be used for preliminary review purposes. Should you gain admission, you will be required to submit your official scores from ETS.

    What is the average GRE score and GPA for students admitted to MSAI?

    Average scores for the current MSAI cohort is 319 and 3.6 (out of 4.0)

    What is the ETS code for the GRE?

    If you have already submitted your GRE score via the online application system, you do not need to send an official score report during the application process. However, if you choose to send official scores as well, please use the ETS code 2601 for the GRE. If you submit your score using the "Northwestern University" code, we will not receive your score at our office.

    Do I need to provide an official transcript?

    Northwestern applicants are required to upload transcripts (scanned copies will be sufficient) for each university they have attended. Admitted students who plan to matriculate will be asked to provide official, university-sealed transcripts. Please do not send official transcripts to the program office ahead of learning your admission decision.

    If I am admitted, will I need to pay a deposit to secure my seat in the program?

    Yes. There will be a $750 deposit required to confirm your intent to enroll. The deposit will be applied towards your first tuition bill. Instructions to make the deposit will be included in your admission materials.

    How can I prepare for the GRE?

    To help you prepare, you may access ETS General Test Preparation Materials here.

    When should I send official documents such as transcripts and test scores?

    Official documents should only be provided to the MSAI program following an offer of admission. During the application process, for review purposes only, unofficial documents are scanned and uploaded to the applicant's online form.

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    Student Background

    Can I apply to the MSAI program if I don’t have a degree in Computer Science?

    The academic requirement (CS degree or related) is meant to be a guideline. Someone who has been working as a developer for a substantial amount of time will probably have those skills as well. We consider applicants from a wide range of academic disciplines and interests; applicants from all backgrounds are encouraged to apply. Please note that candidates are expected to have a strong quantitative and CS background before entering the program.

    I work full time. Is there a part-time or online option available?

    The MSAI program is designed as a rigorous full-time, on-campus program that can only be completed in person. Additionally, classes are held during the day and not offered in the evening.

    What do I need to know/what skills should I have to be successful in the program if admitted?

    Students should already know how to program and have working knowledge of the complexity associated with different algorithmic approaches.  Programming skills are absolutely essential. Successful applicants will either possess programming experience at the time of applying (two years as developers), or be actively engaged in learning to program (e.g., enrolled in a Udacity course in full stack programming). Additionally, a solid understanding of complexity is important.

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    International Candidates

    Are TOEFL/IELTS scores required upon applying to MSAI?

    Applicants to the MSAI program whose primary language is not English must demonstrate language proficiency in one of the following three ways:

    First, provide either a TOEFL or IELTS score. The test must be taken no more than two years before intended enrollment. For the TOEFL, applicants must score 600 or higher on the paper based test, or 100 or higher on the Internet based test. The MSAI program will accept the TOEFL "MyBest Scores". Our institutional code for the TOEFL is C038. For the IELTS exam, applicants must receive a score of 7.0 or higher. Do not send official English proficiency test scores before receiving an admission decision.

    Second, provide transcripts verifying an undergraduate degree from an accredited four-year institution or equivalent, where the language of instruction is English.

    Third, provide transcripts verifying a graduate degree from an accredited institution where the language of instruction is English.

    Can international students enroll in the MSAI program?

    Yes. International students admitted into the MSAI program will receive F-1 visa sponsorship through the U.S. Department of Homeland Security (DHS) to attend Northwestern University.

    I am an international student who attended a 3- year university. Will my 3-year undergraduate degree be accepted?

    Yes. We understand that many international universities operate on a 3-year educational timeline and we will accept 3-year bachelor’s degree from any accredited institution.

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    Scholarships and Funding

    Is there any funding offered through the program?

    The MSAI program does not directly provide any fellowships, research assistantships, teaching assistantships or scholarships. Students who enroll in the MSAI program will be eligible for financial aid (generally student loans) through the Evanston Office of Graduate Financial Aid. Questions regarding financial aid for the MSAI program should be directed to the Evanston Office of Graduate Financial Aid at More information can also be found on their website:

    Are students able to get assistantships through the MSAI program?

    Students are able to investigate assistantship opportunities across campus, however, this is not a feature of the MSAI program. Students will need to identify campus opportunities on their own.

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    Additional Questions

    Is MSAI considered a STEM program?

    MSAI is part of the McCormick School of Engineering and Applied Science and is therefore considered a STEM program. MSAI does qualify international students for OPT extension.

    I have more questions about MSAI or about my application. Whom do I contact?

    Prospective students are invited to email MSAI with questions. You may also call us during our office hours (Monday–Friday, 10 a.m.–4 p.m. CT) at 847-491-7399.

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