Student identification number

The Alaska Department of Education and Early Development alerted students and parents this week that an external hard drive containing more than 89,000 students personal information had been stolen from the department s headquarters in Juneau. In addition to test scores, the Department of Education and Early Development says the stolen hard drive contains a slew of other information, including those test-taking students names, dates of birth, student identification numbers, school and district information, gender, race/ethnicity, disability status and grade level. 

For example, the student Joe Kirtland, who attended Syracuse University and graduated in the year 1999 with a loan of 350, would have the following identification number KIRTJSU 9900350. 

This documentation may include a certificate and an appropriate wallet-sized laminated card with a photograph of the student and the above information. When such course certificate cards are used, the individual identification number for the training certificate should be shown on the card. 

Educational information includes all the data maintained by schools and by other parties acting for the educator. Information collected by a school about a student includes personal information, such as a school-generated identification number, social security number, picture, or list of personal physical descriptors that would make it easy to identify a student. Other information collected may include family information with the name and address of the student, parent or guardian, emergency contact information, date of birth, number of siblings, and date and place of birth. 

The analysis of rank data, what is generally called nonparametric statistical analysis, is an exact parallel of the more traditional (and familiar) parametric methods. There are methods for the single comparison case (just as Student s t-test is used) and for the multiple comparison case (just as analysis of variance is used) with appropriate post hoc tests for exact identification of the significance with a set of groups. Four tests are presented for evaluating statistical significance in rank data the Wilcoxon Rank Sum Test, distribution-free multiple comparisons, Mann-Whitney U Test, and the Kruskall-Wallis nonparametric analysis of variance. For each of these tests, tables of distribution values for the evaluations of results can be found in any of a number of reference volumes (Gad, 1998). 

There are a number of anomalies in the long form, whose exposition has caused the spilling of much ink. One of Mendeleev s greatest triumphs was his prediction of a new element ( eka-silicon ) between Si and Sn in his Table. Mendeleev had the audacity to predict some chemical properties of this new element, and his prophecies were substantially fulfilled a few years later by the isolation of germanium and a preliminary exploration of its chemistry. These predictions were made simply by interpolation between Si and Sn. Chemists, and chemistry students, have come to expect that the chemical properties within a Group follow monotonic trends properties can be predicted by interpolation and extrapolation. Experimental observations which do not fit such simple trends lead to the identification of anomalies . At one extreme, there may be a tendency to sweep such anomalies discreetly under the carpet, or even to question the validity of the data at the other extreme, strenuous efforts are made to account for anomalies by means of elaborate and sometimes fanciful theorising. 

Another true story.) A student who was checking into her lab desk found an unlabeled sample from a previous student. She was asked to identify the sample. She did an IR spectrum and declared, It looks like an alkane. But it seemed too reactive to be an alkane, so she did a GC-MS. The mass spectrum is shown next. Identify the compound as far as you can, and state what part of your identification is uncertain. Propose fragments corresponding to the numbered peaks. 

Throughout the laboratory course outlined in the previous chapters, the typical reactions of a number of important classes of compounds have been illustrated by experiments. These reactions are made use of in the identification of organic compounds. Practice in such identifications is of great educational value, as it requires continuous thought on the part of the student, is an excellent review of many facts which have been learned, and has a practical significance. 

Directions for a large number of preparations are also given. These serve to illustrate the more important synthetic methods and the different kinds of laboratory technique with which the student should become acquainted. In connection with the directions for the preparation of typical compounds, experiments are given which illustrate the properties of the compounds made. These experiments include in each case a study of the reactions of the substance which are of particular value in the identification of the characteristic group present. 

The author has consulted all the well-known texts on laboratory work in organic chemistry in the preparation of the book. In writing the directions for the preparation of compounds on a small scale, valuable help was obtained from S. P. Mulliken s "The Identification of Pure Organic Compounds." A number of experiments on fats, carbohydrates, and proteins have been adapted, with the permission of the author, from a laboratory manual in descriptive organic chemistry prepared for the use of students of household economics, by Professor Alice F. Blood, of Simmons College. The author wishes to express his thanks for the courtesy shown in granting permission to make use of this material. 

The identification task. There was no statistically significant difference in the mean number of items recognized by the two groups on the first computer task. Students who saw only the specific instruction (i.e., concrete examples) performed slightly better than those who saw only the abstract instruction (i.e., the general descriptions of the situations), with both groups identifying correctly about one half of the items (MSI = 5.7 and MAI = 5.2, t < 1). 

Consider first whether the results of the second experiment are statistically different from those of the first. A simple t test confirms that the value 5.45, the mean of the identification task in Experiment II, would not arise by chance if the true mean value were actually the 6.27 previously observed in Experiment I (t = -3.36, df= 41, p <. 05). Similarly, one must reject the hypothesis that the observed value of 7.7 for the number of nodes recalled does not differ significantly from 13.5 (t = -11.69, df = 37, p <. 01). It is important to point out here that these tests are not as rigorous as they would be for a true comparison in which we randomly assigned students to either experiment. We must take into account the possibility that the students in the two experiments differed on some factors other than the experimental conditions. However, it is not easy to discern any systematic differences. Both groups of students were drawn from the same population under identical conditions, and both groups participated in the same room with the same equipment. They did meet with different interviewers, but that difference should influence only the recall of... 

We can draw a number of conclusions about the importance of both types of knowledge. First, although many students show a preference for one or the other type of knowledge, it seems evident that students learn more in the presence of both types of information than when only one or the other is employed. This seems to be the case even if the students cannot explicitly recall the additional information. One inference we can make is that it is important to introduce the abstract information very early in instruction so that it enhances what the students understand of the examples. A second inference is that the first example encountered by the student is very important and is likely to be remembered for a long time. Recall that in the first experiment, most of the example information that students related came from the initial example of the instruction. And in the second experiment in the absence of example instruction, many students nevertheless relied on the first example they encountered - which turned out to be the items of the identification task. Careful selection of the initial examples may turn out to be a critical factor in any instruction. 

ID2 is the number of correct responses made by a student to a second identification task, which required selection of a spatial display rather than the situation name. The tasks that generate ID1 and ID2 are identical. In the former, students respond with the situation names. For ID2, students respond by selecting either the situation icons or the labeled boxes shown in Figure 9.3. A set of 10 standard problems, none of which had been seen previously, were presented in random order to each student, for a possible ID2 score of 0-10. 

To evaluate the conjunction of planning and identification knowledge, SPS asks the student to identify first the situation that corresponds to the primary question asked in a problem and then to recognize the (unstated) secondary question and its associated situation. The exercise that assesses their understanding requires two menu choices of the student, one for the overall situation and one for the embedded, or secondary, one. Each item thus contributes 0-2 points, one for the correct primary question and one for the correct secondary question. The planning-identification score is the total number of correct identifications made on all items in this exercise. 

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