... Database: UNF One Search – Advance Search –Limited to Full Text and Peer Reviewed Why selected: I selected this study because I thought it might teach me something about the students I will be teaching in the near future. Knowing what traits or factors are likely to predict success is critically important for teachers, researchers and those people who design curriculum. I also noticed the date of the study and thought it might have something new to add to the body of research that had already been done. There were many studies that caught my attention and frankly, I had to stop searching and reading abstracts because I found many studies to have relevance to Adult TESOL students. Reference: Ro, E & Ryu, J (2013). Investigating Predictors of Nonacademic ESOL Learners’ L2 Literacy Ability. Journal of Research and Practice for Adult Literacy, Secondary, and Basic Education, Volume 2, Number 2, Summer 2013, 82-100. 2. What was the background for the research study? That is, what previous knowledge did the author describe as a foundation for the study in the “review of related literature”? The researchers provided substantial information about the background for the study and the previous knowledge and research that had been performed. A compelling piece of the background information was the increase in foreign-born individuals living in the United States from 31 million in 2003 to 38 million in 2010, a 22.58% increase. The need for English as a Second Language (ESL)...
Words: 1313 - Pages: 6
...too is well supported by research (Tasiemski, Kennedy, Gardner, & Taylor 2005). Our culture also highly values intelligence as a trait because of social reasons and because high levels of intelligence contribute to our ability to accomplish our goals. For these reasons intelligence is also instrumental in an individual’s satisfaction with life (Koydemir, Simsek, Schutz, &Tipandjan 2013). While each of these factors’ effects on Life Satisfaction is well documented in the research the relative strength of each in relation to one another has not been quantified. This paper will first attempt to independently confirm the previous results and answer the following question: Is Art Participation, Sport Participation, or Intelligence the best predictor for Overall Life Satisfaction? 2 Intelligence, Art, and Sport Participation on Life...
Words: 1966 - Pages: 8
...sure. A pretreatment variable can come into the picture in two different ways: either it affects the outcome regardless of the treatment method, acts as a prognostic variable; or it responds differently to different treatment models, as a prescriptive variable. The effects of all potential predictors are examined in this study under five major domains which have different seperate variables inside. In their data analytic strategy, the researchers looked at the interaction between predictor and treatment. In results, they found that chronicity, age and intelligence are prognostic variables whereas marital status, unemployment status and number of life events experienced are prescriptive variables. The difference between treatment methods came out in prescriptive ones: cognitive therapy was more efficacious than medication for those who experience many events, are nonmarried and unemployed. Each of these variables’ effects were shown significant. The first thought come my mind while reading the procedure was whether researchers chose to look the effects of variables seperately, or they were just interested in the interaction. I think there can be some interaction among predictor variables which was omitted in order not to confuse the situation more....
Words: 431 - Pages: 2
...Now for the sake of argument, suppose this is back in the 1800s - before long distance or radio communication. You are the operator of a junction and you hear a train coming. You have no idea which way it will go. You stop the train to ask the captain which direction he wants. And then you set the switch appropriately. Trains are heavy and have a lot of inertia. So they take forever to start up and slow down. Is there a better way? You guess which direction the train will go! If you guessed right, it continues on. If you guessed wrong, the captain will stop, back up, and yell at you to flip the switch. Then it can restart down the other path. If you guess right every time, the train will never have to stop. If you guess wrong too often, the train will spend a lot of time stopping, backing up, and restarting. Consider an if-statement: At the processor level, it is a branch instruction: You are a processor and you see a branch. You have no idea which way it will go. What do you do? You halt execution and wait until the previous instructions are complete. Then you continue down the correct path. Modern processors are complicated and have long pipelines. So they take forever to "warm up" and "slow down". Is there a better way? You guess which direction the branch will go! If you guessed right, you continue executing. If you guessed wrong, you need to flush the pipeline and roll back to the branch. Then you can restart down the other path. If you...
Words: 780 - Pages: 4
...PROJECT: STUDY BASED ON SIMPLESCALAR CS202, SPRING 2011 Project Study Based on SimpleScalar Problem 1: Cache Simulation and Associativity Descriptions Configurations: • least-recently-used (LRU) replacement policy • 128 to 2048 sets • 1-way to 4-way associativity • 16-byte cache lines Command Line: ./sim-cheetah -R lru -a 7 -b 11 -n 2 -l 4 go.ss 2 8 go.in The Simulation under Ubuntu: PAGE 1 / 8 COMPUTER ORGANIZATION AND ARCHITECTURE PROJECT: STUDY BASED ON SIMPLESCALAR CS202, SPRING 2011 Results • simulation statistics sim_num_insn sim_num_refs sim_elapsed_time sim_inst_rate 31394965 # total number of instructions executed 8154766 # total number of loads and stores executed 2 # total simulation time in seconds 15697482.5000 # simulation speed (in insts/sec) Addresses processed: 8155568 Line size: 16 bytes • Miss Ratios Associativity & No. of sets 1 2 3 4 128 256 512 1024 2048 0.18586 0.12974 0.09492 0.05787 0.03718 0.094562 0.062298 0.043441 0.025081 0.014161 0.065089 0.043251 0.030266 0.017734 0.007808 0.051197 0.034149 0.024275 0.012934 0.004801 Analysis Based on the data above, I draw two diagrams for the convenience of comparision. 1 0.2 0.186 2 3 4 0.15 0.13 0.1 0.095 0.095 0.062 0.043 0.034 0.043 0.03 0.024 0.058 0.025 0.018 0.013 0.037 0.014 0.008 0.005 0.05 0 0.065 0.051 128 256 512 1024 2048 PAGE 2 / 8 COMPUTER ORGANIZATION AND...
Words: 1699 - Pages: 7
...Discriminant analysis The discriminant analysis model involves linear combinations of the following form D=b0+b1X1+b2X2+…………………+bkXk The weights b’s are estimated so that the groups differ as much as possible on the values of the discriminant function. Xi is the predictor or independent variable. The data we have used is bankloan.sav. Since the dependent variable is “previously defaulted” which has nominal values as 0 for ‘NO’ and 1 for ‘YES’, we need to use discriminant analysis. The independent variables or the predictors that is used are “Age in years”, “Level of education”, Years with current employees”, House hold incomes”, “Debt to income ratio”, “Credit card debt”, “Years at current address” and “other debts”. The total number of data available for us is 800. The tables shown below is the discriminant analysis that is done on this data Analysis Case Processing Summary | Unweighted Cases | N | Percent | Valid | 700 | 82.4 | Excluded | Missing or out-of-range group codes | 150 | 17.6 | | At least one missing discriminating variable | 0 | .0 | | Both missing or out-of-range group codes and at least one missing discriminating variable | 0 | .0 | | Total | 150 | 17.6 | Total | 850 | 100.0 | Group Statistics | Previously defaulted | Mean | Std. Deviation | Valid N (listwise) | | | | Unweighted | Weighted | No | Age in years | 35.5145 | 7.70774 | 517 | 517.000 | | Years with current employer | 9.5087 | 6.66374 | 517 | 517.000...
Words: 1614 - Pages: 7
...3.0 Analysis and Findings 3.1 Group Means: These are computed for each variables for each groups (Default & Regular basis). In group statistics status –1 is Default &status-2 is Regular. Group Statistics |Status | Variables |Mean |Std. Deviation | |1 |Loan |601000.00 |424013.89 | | |Dependents |1.10 |1.45 | | |Y-P-J |7.30 |4.64 | | |Salary |63681.80 |57749.64 | | |Living |.30 |.48 | | |Savings |37855.80 |42409.82 | | |Cash |743600.00 |1563365.19 | | |Networth |10227583.90 |14289699.78 | | |ACT |1.20 |.42 | | |N-EMT |54.00 |8.49 | | |EMT |16113.59 |10515.55 | | |Interest(%) |18.97 |.03 | | |Guar. |.60 |.52 | |2 |Loan |1072000.00 |1136403.20 | | |Dependents...
Words: 865 - Pages: 4
...DATABASE ASSIGNEMENT ONLINE STORE DATABSE TABLE NAME * STUDENT/CUSTOMER * ENROLLMENT * COURSES * CATEGORY * ORDERS * ORDER DETAILS * PAYMENT * PRODUCT * SHIPPERS * SUPPLIER/COMPANY TABLES ATTRIBUTES STUDENT/CUSTOMER STUDENT table contain attribute such as: * STUDENT ID-PRIMARY KEY * FIRST NAME * LAST NAME * ADDRESS * POSTAL CODE * CITY * STATE * COUNTRY * PHONE * EMAIL * PASSWORD * CREDIT CARD * CREDIT CARD TYPE ID * BILLING ADDRESS * SHIP ADDRESS * DATE ENTERED ENROLLMENT ENROLLMENT table contain attribute such as: * STU ID-PRIMARY KEY * COURSE ID-PRIMARY KEY * SEMESTER-PRIMARY KEY * INTAKE COURSES COURSE table contain attribute such as: * COURSE ID-PRIMARY KEY * COURSE NAME * FACULTY CATEGORY CATEGORY table contain attribute such as: * CATEGORY ID-PRIMARY KEY * CATEGORY NAME * DESCRIPTION * PICTURE * ACTIVE ORDERS ORDERS table contain attribute such as: * ORDER ID * STUDENT ID * PAYMENT ID * SHIPPER ID * ORDER NUMBER * ORDER DATE * SHIPDATE * REQUIRED DATE * SALE TAX * TIME STAMP * TRANSACTION STATUS * FULFILED * DELETED * PAID * PAYMENT DATE ORDER DETAILS ORDERDATAILS table contain attribute such as: * ORDER IDs-PRIMARY KEY * PRODUCT IDs * ORDER NUMBER * PRICE * QUANTITY * DISCOUNT * TOTAL * IDSKU *...
Words: 289 - Pages: 2
.... Introduction top next gema actions are enough for everyday usage but occasionally you may need more power to define a complex transformation. You might, also, want to use the superior matching capabilities of gema to drive your program. GeL is a Lua 5 library that: allows the execution of Lua function (and hence of a C function) in a gema action provides the powerful text matching mechanisms offered by gema as a set of Lua functions it has been tested with Lua 5.0.2. Refer to the detailed documentation to build gel togheter with gema, . 1.1 Status GeL is not as mature as gema or Lua, please report any bugs you may find using SourceForge bug tracking subsystem (you don't need to log in as a SourceForge user) or sending an e-mail to Remo Dentato. 1.2 Licence GeL is released under the same licence of Gema. No specific reference needs to be made to GeL as long as the due reference to Gema and Lua have been made. 2. Usage prev top next 2.1 Gema like Gel may be used exactly as if it was gema, any valid gema script should work when executed by gel: gel [gemaopts] [-f rules.gema] [-l script.lua] [infile [outfile]] . The only differences are: The @lua{} function is available to execute lua scripts in actions Text enclosed between "![" and "!]" is considered lua code and is immediately executed Rules specfied with '-f' are loaded, the lua script specified with '-l' is executed (possibily defining lua function...
Words: 1294 - Pages: 6
...experience. My seminar topic was about examining the imagery that appeared in the play Hamlet. A skill I developed was using technological equipment to present a presentation.Each group had to pick a topic that was in their interest to research online for information. The requirements started with presenting the information in front of the class informing the audience what imagery means and discuss using concrete examples from the play. This presentation gave me the opportunity to work with PowerPoint and Excel and then to present my information. I believe that in order to communicate effectively with the audience you have to have a well prepared presentation that will capture their attention so that you can present your ideas clearly. A skill that I had trouble was to be able to work as a team. The imagery seminar was a great opportunity for me to develop my team work skills because we were put in to pairs to complete the seminar. Each member in the group had specific responsibilities to the team in order for the seminar to be successful. Being in the group to work on the seminar was a great experience for me because I was able to see where my strong and weak points are in group work. I found that it was easier for me to communicate my ideas with Rachna than to the whole class. A big part of group work is good communication between Rachna and I. Communication is also a big part of my learning experience because I will need to be able to get my ideas out by communicating well...
Words: 585 - Pages: 3
...Inequalities Thomas Cunningham MAT221: Introduction to Algebra (GSO13360) Instructor: Justin Hazel Inequalities In this assignment I will be showing you how you can find out how to use Inequalities to find out how much you may or may not be overweight. You can use my equations and the examples of my work to find out exactly what category you might fall in. In this equation we are asked to use our own height to find out exactly if I might have a longer life span than the usual person. Before we start remember that W=my weight, and then H=my height. W=178.2 and then H=67 In this first compound inequality, I had to take the equivalent inequality and replace it with the BMI formula. My height and weight has been added in to replace the current H^. The next steps for this compound inequality I had to multiply all the terms by the denominator. There was some cancellation done towards the first phase of the problem. I then all three terms where divided by 703 to isolate W. People’s height of 67 might be different life span of those whom might weigh between 108lbs and 128lbs. 17<BMI<=22 17<703W/67^<22 17<703W/4489<22 17(4489) <703W<22(4489) 76313<703W<8978 76313/703<8978/703 108<W<128 In order to solve this second inequality, what I need to do is solve for W and place the values of W. The very first thing that happens is I multiply all the terms by H^ in order to take out...
Words: 542 - Pages: 3
...to be successful in terms of assistance in these particular areas. Matrix organizations are kinds of structures that a categorized flexible and responsive and are characterized by employees having more than one supervisor and in order for them to perform at their highest level of productivity, they must have some area of expertise that is disciplined. The project team happens to become a creation of several highly imaginatively intelligent groups of people that are formed to collaborate with each other and strive at creating more diverse ideas, or putting a different take on some of the many already formed ideas and the opinions of those more diverse ideas. The collegial model works flawlessly when each member is able to be independent, but equally share management responsibilities or work out a specific rotation that meets each individuals peer or colleagues needs. The need or want for freedom or independence in the aforementioned paragraph has little merit when they must come together to satisfy matters that affect the entire group....
Words: 270 - Pages: 2
...the initial stage of a team. In this part a team gets formed and the members of the team get through the icebreaking process. Forming is a stage in which group members try to assess ground rules applying to a task and to group interaction (Bartol et al., 2005, p.476). In the first tutorial of organisation and management, all the team members were unfamiliar and unknown to each other. There was diversity because students of many nations and cultures came together. Everyone was confused about what to do. There was no one who wanted to be the first spokesperson. In this situation team members were all divided into some little groups to complete the first tutorial task. As every team member were unknown to each other, one of the team members spoke out and discussed the tutorial task. After that everyone started to contribute and gave ideas, with that the group was working very effectively. Members completed the task and then submitted it to their tutor. In the first tutorial the team members were totally unclear about the team goals. Individuals did not know what their role in the team is. Some students were really nervous as they were not able to cope up with the other team members. It was a negative site of the team, as the team was not able to work effectively. After selecting the team leader the team was divided into sub-groups like- Marketing, Public Relation, Finance and IT. All the students...
Words: 252 - Pages: 2
...Lab 5.1 The Condition | True or False | attAverage >= verizonAverage | True | tmobileAverage == 868 | True | verizonAverage < sprintAverage | False | sprintAverage != attAverage | True | Lab 5.2 The Condition | Expected Output | If tmobileAverage > 800 AND verizonAverage> 800 ThenDisplay “Both have average downloadrates over 800”ElseDisplay “One or both of the averagesare less”End If | Both average download Rates over 800 | If sprintAverage == 800 ThenDisplay “Sprints download rate is 800”End If | No display | If attAverage >= 1300 OR tmobileAverage>=900Display “Select either carrier”End If | Select either carrier | Lab 5.3 Main Module() //Declare variables on the next 3 lines Declare Integer testScore = 0 Declare String category = “ “ Declare String interview = “ “ //Make Module calls and pass necessary variables on the next 4 lines Call getScore(testScore) Call employCat(testScore, category) Call interviewPoss(testScore, interview) Call displayInfo(testScore, category, interview) End Main Module getScore(Integer Ref testScore) //Ask user to test score Display “Please enter test score” Input testScore End Module Module employCat(Integer testScore, String ref category) //Determine what employment category they are in based on their test score //Similar to if the score is less than 60, then category is “No” //Otherwise, if score is less than 70, then category is “Maybe” //…and so on if testScore => 85 then Set...
Words: 288 - Pages: 2
...PDC Dull Grading System Inner 1 Outer 2 Dull Char 3 Location 4 Bearing Seals 5 Gauge 6 Other Dull Char 7 Reason Pulled 8 1: Inner Cutting Structure 2: Outer Cutting Structure 3: Primary Dull Characteristic 4: Location of Primary Characteristic 5: Bearing Seals (XXX for PDC. Only used for roller cone) 6: Amount Under Gauge 7: Other / Secondary Dull Characteristic 8: Reason Bit Was Pulled Inner & Outer Cutting Structure • • Rating for both inner and outer cutting structure is given in a scale of 1 to 8. A rating of 1 states that 1/8 of the cutter’s diamond table has been worn. A rating of 8 would mean that there is no effective diamond table left. An average rating for both the inner and outer cutting structure is entered for the first 2 positions of the dull grading code. • Inner & Outer Cutting Structure Outer Inner The inner cutting structure consists of the inner 2/3 of the bit diameter whereas the outer consists of the final 1/3. A general rule of thumb is the inner number represents the cutters inside the nose and the outer represents those outside. Inner & Outer Cutting Structure Rating: Wear 8 7 6 5 4 3 2 1 When dull grading pure wear, the 1 through 8 rating system can be followed exactly. However, in many cases, a wear flat less than 1/8 of the diamond table can be called a 1. Inner & Outer Cutting Structure Chipping Spalling Delam 2 3 8 When dull grading cutters with characteristics other than pure wear, take into...
Words: 857 - Pages: 4
