Description: Assessment of student performance is a measure by which the degree of achievement of established desired goals is determined. educational results every student. Typically, such assessments are carried out by teachers within the disciplines they teach. When effectively assessing student performance, a variety of methods are used with a focus on the desired learning outcomes: disciplinary knowledge, personal, interpersonal competencies, the ability to create products and systems (see Standard 2). Such methods include written and oral examinations and tests, test sections, drawing up performance charts, keeping journals and portfolios for each student, self-monitoring and students’ opinions about the classes being conducted.
Rationality: If we prioritize the personal, interpersonal competencies of students, their ability to create products and systems, if we establish them as indicators of the effectiveness of education and taken into account when drawing up curricula and educational assignments, then we need to develop effective methods assessment of these skills. It is necessary to develop your own assessment criteria for each of the designated educational outcomes. For example, the effectiveness of mastering disciplinary knowledge can be assessed during oral and written examinations and tests, but the ability to design and create products and systems is better assessed when performing practical work. The use of a variety of methods for assessing student performance helps to obtain reliable and complete information about student performance. Thus, the degree to which each student achieves the desired learning outcomes will be determined with maximum accuracy.
Data:
· assessment methods directly depend on the established CDIO learning outcomes;
· successful application of selected assessment methods;
· high percentage of teachers using appropriate assessment methods;
· determination of the degree to which each student achieves the desired learning outcome, based on reliable and complete data.
Standard 12 – CDIO Program Evaluation
A system by which the entire program is assessed according to the listed twelve standards for students and teachers
and other key participants for the purpose of continuous improvement of the educational process.
Description: Program evaluation refers to the compliance of the entire program with established success indicators. The assessment must be carried out in accordance with the twelve approved CDIO standards. Collecting statistical data on the success of the program can be done by assessing the success of an individual course, obtaining advice from members of the teaching staff, conducting surveys before and after the program, analyzing reports from external auditors, and conducting surveys of graduates and employers over time, after completion of training. This information may be collected regularly by faculty, students, program administrators, alumni, or any other key stakeholders. All these statistics together make it possible to make an overall assessment of the program and contribute to its further improvement and development.
Rationality: The main objective of conducting a program evaluation is to evaluate its effectiveness and the extent to which it has achieved its goals. Statistical evaluation data collected to produce a global assessment is also necessary for continuous program improvement. For example, if at the end of the program the majority of students believe that they were unable to achieve some of the desired results, then the program can be revised, the reasons why the results were not achieved are identified and eliminated. In addition, many accreditation and auditing agencies often require that program success statistics be collected systematically.
First level : The results of student learning indicate that they have acquired some basic knowledge of basic issues in the discipline. The mistakes and inaccuracies made show that students have not mastered the necessary system of knowledge in the discipline.
Second level : The achieved level of assessment of learning outcomes shows that students have the necessary system of knowledge and master some skills in the discipline. Students are able to understand and interpret the information they have mastered, which is the basis for the successful formation of skills and abilities for solving practice-oriented problems.
Third level : Students demonstrated results at the level of conscious mastery of educational material and educational abilities, skills and methods of activity in the discipline. Students are able to analyze, compare and justify the choice of methods for solving tasks in practice-oriented situations.
Fourth level : The achieved level of assessment of student learning outcomes in the discipline is the basis for the formation of general cultural and professional competencies that meet the requirements of the Federal State Educational Standard. Students are able to use information from various sources to successfully research and find solutions in non-standard practice-oriented situations.
Grading scale
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Characteristics of levels of competence development |
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Levels |
Manifestations |
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Minimum |
The student has the necessary knowledge system and has some skills |
The student is able to understand and interpret the acquired information, which is the basis for the successful formation of skills and abilities for solving practice-oriented problems |
|
Base |
The student demonstrates results at the level of conscious mastery of educational material and educational abilities, skills and methods of activity |
The student is able to analyze, compare and justify the choice of methods for solving tasks in practice-oriented situations |
|
Advanced |
The achieved level is the basis for the formation of general cultural and professional competencies that meet the requirements of the Federal State Educational Standard. |
The student is able to use information from various sources to successfully research and find solutions in non-standard practice-oriented situations |
Level of development of knowledge, skills and abilities
The level of development of knowledge, skills and abilities in the discipline is assessed in the form of a point mark:
"Great" deserves a student who has demonstrated a comprehensive, systematic and deep knowledge of the educational program material, the ability to freely perform tasks provided by the program, who has mastered the basic and is familiar with additional literature recommended by the program. As a rule, an “excellent” grade is given to students who have mastered the interconnection of the basic concepts of the discipline in their meaning for the acquired profession, who have demonstrated Creative skills in understanding, presentation and use of educational material.
"Fine" deserves a student who has demonstrated complete knowledge of the educational program material, successfully completes the tasks provided in the program, and has mastered the basic literature recommended in the program. As a rule, a “good” grade is given to students who have demonstrated the systematic nature of knowledge in the discipline and are capable of independently replenishing and updating it in the course of further education. academic work and professional activities.
"Satisfactorily" deserves a student who has demonstrated knowledge of the basic educational program material to the extent necessary for further study and future work in the specialty, copes with the tasks provided for by the program, and is familiar with the basic literature recommended by the program. As a rule, a “satisfactory” grade is given to students who made errors in their answers on the exam and when completing exam tasks, but who have the necessary knowledge to correct them under the guidance of a teacher.
"Unsatisfactory" awarded to a student who has discovered gaps in the knowledge of the basic educational material, who has made fundamental errors in completing the tasks provided for in the program. As a rule, an “unsatisfactory” grade is given to students who cannot continue their studies or begin professional activities after graduation without additional classes in the relevant discipline.
Grade "passed" awarded to a student who has thoroughly mastered the provided program material; answered all questions correctly and with reason, giving examples; has demonstrated deep, systematized knowledge, masters reasoning techniques and compares material from different sources: connects theory with practice, other topics of the course, and other subjects studied; completed the practical task without errors.
A prerequisite for the grade given is correct speech at a fast or moderate pace. An additional condition for receiving a “pass” grade may be good success in completing independent and test work, and systematic active work in seminar classes.
Grade "not accepted" Awarded to a student who failed 50% of the questions and tasks on the ticket and made significant mistakes in answering other questions. Cannot answer additional questions proposed by the teacher. The student does not have a holistic idea of the relationships, components, and stages of cultural development. The quality of oral and written speech is assessed, as in the case of a positive assessment.
Fokina L. D.
Postgraduate student, senior lecturer
Baikal State University of Economics and Law,
Yakutsk
e - mail : foxlydim @ mail . ru
METHODS FOR ASSESSING STUDENTS' COMPETENCIES
HIGHER PROFESSIONAL EDUCATION
Abstract: This article discusses the main existing methods for assessing competencies and identifies the problems of implementing a competency-based approach.
Keywords Keywords: assessment of student competencies, level of competencies formation, federal state educational standard.
Fokina L. D.
Post-graduated, senior lecturer
Baikal national university of economics and law,
Yakutsk
e-mail:
METHODS OF ESTIMATION SKILLS OF STUDENTS IN HIGHER VOCATIONAL EDUCATION
Abstract: This article describes the main existing methods for estimating skills, the problems implementing competence – based approach.
Keywords: estimation of student’s competencies, level to build skills, state educational and professional standards,
In connection with the transition to a two-level education system, federal state educational standards put forward new requirements for students and graduates as a whole. If earlier assessment took place during testing of knowledge, skills and abilities (KUS), now it is necessary to assess competencies, both general and professional, i.e. In addition to theoretical knowledge, students must demonstrate the application of their skills in a given situation.
In the new educational standards of the third generation, the concept of competence comes to the fore as the concept of developing not only knowledge, skills and abilities, but also the development of abilities for their application. Competencies are understood as a set of professional, social, and personal characteristics that determine the ability to effectively perform activities in a certain area, confidently using one’s knowledge and skills.
To determine the level of formation of competencies of a student who has undergone appropriate training, the following methods and approaches have currently been developed.
This method consists in the fact that all educational material is divided into logically completed modules (blocks), after studying each one a certain number of points is assigned. Modular – rating system allows you to evaluate the individual capabilities of students: activity, originality in finding solutions, determination, etc. Points are made up of compulsory types of work: laboratory, practical, individual homework, independent work, tests, as well as additional ones of the student’s choice - this includes participation in olympiads , at conferences, in a student’s research work, etc. When working on a modular rating system, it is possible to evaluate students without exams and tests.
With the introduction of a competency-based approach, a modular rating system is used to evaluate educational competencies students, carrying out continuous monitoring of mastery educational material and increasing the objectivity of teachers' assessment of the quality of students' academic work.
Case - method.
Its name comes from English word“case” - folder, briefcase, at the same time can be translated as a method of specific situations, a method of situational analysis. The method consists in the teacher using situations, problems, the purpose of analysis, which is the knowledge acquired as a result of active and creative work. Students independently find a solution to a problem by comparing factors (different points of view), put forward different hypotheses, draw conclusions and conclusions. For example: The distribution of trading firms by monthly turnover is characterized by the following data:
Trade turnover, million rublesUp to 5
5 – 10
10 – 15
15 – 20
20 – 25
25 or more
Total
Number of firms
100
Determine: the average monthly turnover per company, the modal and median value of monthly turnover, draw conclusions about the nature of this distribution.
Thus, students learn to solve situational problems that are close to reality.
Portfolio method.
A portfolio is a set of individual educational achievements of students. They may contain the results of control tests, certificates of participation in olympiads, conferences, as well as the most significant works and reviews of them. At the same time, it is important that the student himself chooses and decides what exactly will be included in his portfolio, that is, he develops the skills to evaluate his own achievements.
Method of developing cooperation.
The purpose of this method is to combine efforts to solve a given task or problem. If in the above methods the emphasis is mainly on individual qualities student, his achievements and ability to behave in different situations, then with developing cooperation the formulated tasks cannot be solved alone, therefore collective thinking is necessary, with the distribution of internal roles in the group.
Basic techniques this method training are:
Individual, then pair, group, collective setting of goals;
Collective planning of educational work;
Collective implementation of the plan;
Designing models of educational material;
Designing a plan for your own activities; independent selection of information and educational material;
Game forms of organizing the learning process.
This method is also called the collective way of learning or the democratic system of learning according to ability, the author of which isV.K.Dyachenko. According to this method, students are divided into groups of 5-8 people. Creative groups can be permanent or temporary. After each group proposes its solution to the problem, a discussion begins with the whole group to identify the truly correct solution. Using this method in practice, students learn to work in a team, learn the ability to listen, and draw conclusions.
Standardized pedagogical tests.
A big step has been taken in this direction. Currently, testing is used not only as a check of the educational module of the program, but also at a more advanced level. With the introduction of a competency-based approach, testing is carried out to determine the quality of training and education in the entire university, an example is federal testing (FEPO).
New theory tests are based on mathematical models that provide the most objective testing results.
Basic mathematical models:
Birnbaum two-parameter model;
Birnbaum's three-parameter model;
Rasch model
Wheres
– level of preparedness of the test participant 
,
t
– level of difficulty of the test taskt
,
- the probability of correctly completing the task.
In practice, they are more often used
Since the Rasch model describes the probability of a subject's success as a function of one parameter, it is sometimes called a one-parameter model.
Currently, problems arise in the implementation of the competency-based approach and the creation of a system for assessing the competencies of higher education students, which are determined by the following factors:
The majority of the teaching staff does not want to change anything; they work, as they say, “the old fashioned way”; they do not strive to master innovative teaching technologies, accompanied by a modular organization educational process(rating system, loans);
Absent one system competency assessment;
There is no general methodological support (programs, educational and methodological complexes, etc.);
There is no interaction between universities and employers (there is no single graduate model).
Assessing the level of mastery of students’ and graduates’ competencies requires the creation of a new innovative technology assessing the totality of knowledge acquired by students and the social and personal characteristics that form their competencies. In the context of developing innovative approaches to the design of assessment tools for monitoring the quality of competencies of university graduates, a number of researchers [4] proposes to formulate methodological basis this design and build a general model for comparative assessment of the quality of training. This model may include the following structural components: assessment objects and their subject areas; assessment bases (quality standards - systems of requirements); evaluation criteria (as signs of the degree of compliance with established requirements, norms, standards); subjects of assessment (students, teachers, experts of various commissions); assessment tools and technologies (procedures).
Bibliography:
Bidenko V.I. Competence-based approach to the design of state educational standards for higher professional education (methodological and methodological issues): Toolkit. – M.: Research Center problems of quality of training of specialists, 2005. – 114 p.
Federal State Projects educational standard higher professional education. [Electronic resource]. Access mode:http://mon. gov. ru/ pro/ fgos/ vpo/
Karavaeva E. V., Bogoslovsky V. A.,KharitonovD.V.Principles for assessing the level of mastery of competencies in higher education programs in accordance with the requirements of the new generation Federal State Educational Standard.Vestnik ChelyabinskState University. 2009. No. 18 (156) Philosophy. Sociology. Culturology. Vol. 12. pp. 155–162.
Afanasyeva T. P., Methodological recommendations for the development and implementation of higher education educational programs based on the activity-competency approach, focused on the Federal State Educational Standard of the third generation / T. P. Afanasyeva, E. V. Karavaeva, A. Sh. Kanukoeva, V. S. Lazarev, T. V. Nemova. M.: Moscow State University Publishing House, 2007. 96 p..
Selevko G.K.Modern educational technologies. - M.: Public education, 1998.
Neiman Yu.M., Khlebnikov V.A. Introduction to the theory of modeling and parameterization of pedagogical tests / Yu.M. Neiman, V.A. Khlebnikov - Moscow, 2000. - 168 p. from table and il.
Bulletin of KhNADU, vol. 68, 2015
UDC 519.237.8
FORECASTING STUDENTS' ACHIEVEMENT BASED ON CLUSTER ANALYSIS METHODS
V.A. Shevchenko, associate professor, candidate of technical sciences,
Kharkov National Automobile and Highway University
Annotation. A methodology for predicting student performance based on cluster analysis methods is proposed. The results of the experiment are presented, confirming the effectiveness of the developed methodology for predicting academic performance.
Key words: forecasting, academic performance, cluster analysis, source data matrix, distance matrix.
PREDICTING STUDENTS' SUCCESS BASED ON METHODS
CLUSTER ANALYSIS
IN. Shevchenko, associate professor, candidate of technical sciences,
Kharkiv National Automobile and Highway University
Abstract. A methodology for predicting the success of students based on cluster analysis methods has been proposed. The results of the experiment were presented, which confirm the effectiveness of the developed methodology for predicting success.
Key words: forecasting, success, cluster analysis, output data matrix, output matrix.
PROGNOSTICATION OF STUDENTS PROGRESS ON THE BASIS OF CLUSTER
ANALYSIS METHODS
V. Shevchenko, Asso^ Prof., Ph. D. (Eng.),
Kharkiv National Automobile and Highway University
Abstract. The method of prognostication of students progress on the basis of methods of cluster analysis has been offered. The results of the experiment confirming the effectiveness of the developed method of prognostication have been given.
Key words: prognostication, progress, cluster analysis, matrix of initial data, matrix of distances.
Introduction
Currently, there are hundreds of forecasting methods. Types of mathematical forecasting methods: correlation analysis, regression analysis, cluster analysis, factor analysis, etc.
Analysis of publications
The essence of forecasting in the field of education was considered by B.S. Gershun-
skiy, V.I. Zagvyazinsky, A.F. Juror, R.V. Mayer et al.
During the analysis of publications, it was concluded that cluster analysis methods are most suitable for reliably predicting student performance, since cluster analysis allows the division of objects not according to one parameter, but according to a whole set of characteristics. In addition, cluster analysis allows one to consider a variety of initial data of almost arbitrary nature.
Bulletin of KhNADU, vol. 68, 2015
Goal and problem statement
Based on the results of the analysis in the field of pedagogical forecasting, the following goals were set:
1. Develop a procedure for predicting student performance based on cluster analysis methods.
2. To test the effectiveness of the developed forecasting procedure, conduct an experiment to compare the actual and predicted student performance.
Choosing a cluster analysis method for predicting student performance
To solve the problem posed - developing a procedure for predicting student performance from a variety of clustering algorithms, the most suitable, in our opinion, is the McKean k-means algorithm, in which the user himself must specify the required number of finite clusters, denoted k. The classification principle is as follows:
k observations are selected or assigned to be the primary centers of the clusters;
The remaining observations are assigned to the nearest specified cluster centers;
The current coordinates of the primary cluster centers are replaced with cluster averages;
The previous two steps are repeated until changes in the coordinates of cluster centers become minimal.
However, McKean's algorithm assumes that cluster centers are selected from the existing data set for clustering. To solve the problem, this approach is not acceptable, since there may be groups of students with different academic performance; for example, groups where there are no poor students, or, conversely, no excellent students, or many three students. If you select cluster centers from the data of each student group, then for each group the distribution of students into clusters depending on their academic performance will be different, and it may happen that a student with good academic performance ends up in a cluster of poor academic performance and vice versa. It is necessary to determine such cluster centers, the values of which do not depend on the set of classified data and ensure
They distribute students into clusters in accordance with existing performance parameters: up to 60 points - poor, from 60 to 75 points - satisfactory, from 75 to 90 points - good, over 90 points - excellent.
In addition, according to the McKean algorithm, after adding any data to the cluster, it is necessary to recalculate the cluster center. In this case, the value of the cluster center will change, which will also lead to distortion of the clustering results.
Therefore, it is advisable to apply the McKean k-means method to solve the problem posed after some modification.
Modification of McKean's A-means method
We modify the McKean algorithm based on the following assumptions:
1. When solving the problem, it is necessary to set such cluster centers that represent the average values of each parameter for each class.
2. The specified centers must remain unchanged throughout the clustering procedure.
Formulation of the clustering problem
A set of objects X is known, representing data on the performance of n students, consisting of m features: X = X i, X 2,..., Xm). The set of objects X is described by the set of measurement vectors X j, j = 1, m. It is required to divide sample X into four typological groups characterizing student performance: “excellent”, “good”, “satisfactory” and “poor”. Therefore, we set the number of clusters k = 4.
Clustering procedure
1. Let's set the matrix of the initial data in accordance with formula (1), where Xj - jth parameter i-th object, m - number of parameters
Bulletin of KhNADU, vol. 68, 2015
ditch; n - number of students (clustering objects)
X11 X12. .. X1 j . 1 s
X21 X22. .. X2 j . ..X2m
Xi1 Xi 2 . .. Xj . ..Xm. (1)
Xn1 Xn2 . ..Xnj. ..Xnm
2. Let's assign primary cluster centers. To do this, for each cluster we define the reference values of the parameters as averaged data for each typological group of students, obtained by modeling the process of developing competencies among students. The reference values will be used as the centers of future clusters, around which the closest objects according to the values of the selected parameters are grouped. The reference values of clustering parameters are given in Table. 1.
where ztj is the normalized value of the j-th parameter of the /-th object; Xj - initial value of the j-th parameter of the i-th object; Xj is the average value of the j-th parameter for all objects; 4. For normalized data, construct a distance matrix D (3) " 0 d1,2 ... d1, n d1,n+4 d 2,1 0... d2,n d 2,n+4 D = dn,1 dn,2 . .. 0 dn,n+4 . (3) dn+4,1 dn+4,2 . .. dn+4,n 0 We calculate the distances between objects using the Euclidean metric (4) Table 1 Standards for forecasting Typologist. groups Initial knowledge Knowledge on the topic Number of passes Class 5 85 95 0 Class 4 75 85 0 Class 3 60 70 0 Class 2 40 40 2 Objects that are similar in their parameters are collected around the standards. The objects of clustering in this problem are students, and the parameters are factors whose values can be assessed at the initial moment of studying the discipline: Level of initial knowledge of students; The level of competencies developed by students on the first topic of the discipline; The number of absences from classes by students at the time of making the forecast. where d/j is the distance between the i-th and j-th objects; m - number of clustering features; zik - normalized value of the i-th object by k-th feature; Zjk is the normalized value of the j-th object according to the k-th attribute. 5. From the distance matrix, select the reference distance matrix, which is a matrix of distances (5) from each object to the reference data d1,n+1 d1,n+2 d1,n+3 d1,n+4 d 2,n+1 d 2,n+2 d 2,n+3 d 2,n+4 di,n+1 di,n+2 di,n+3 di,n+4 , (5) dn,n+1 dn,n+2 dn,n+3 dn,n+4 3. Since the selected characteristics have different units of measurement, we normalize the original data together with the standards added to them according to formula (2) i = 1, n + 4, j = 1, m where MEt is the reference distance matrix. 6. In the reference matrix, we determine the minimum value of the distance, the number of the object and cluster standard that are located at this minimum distance. 7. We will assign the selected object to the corresponding cluster. Bulletin of KhNADU, vol. 68, 2015 8. From the source data matrix and the reference distance matrix, we remove data about the object that was assigned to the cluster. We repeat steps 6 - 8 until all objects are separated into clusters. The developed procedure for predicting student performance is implemented in the form of a macro in VBA. Description and results of the experiment To test the effectiveness of the method of forming individual trajectories for independent work based on cluster analysis for organizing the individualization of independent work of a flow of students, an experiment was conducted with students of three groups (61 students in total) of the road construction faculty of KhNADU, studying computer science in the autumn semester. Three factors were used as initial data: the initial level of knowledge of students (assessed at the beginning of the first lesson), the knowledge acquired by students in the lesson on the first topic (assessed in the first laboratory work), and the number of absences from classes (the experiment was carried out in the second lesson). Based on these initial data, a forecast of academic performance was compiled for each student in the discipline “Informatics”. At the end of studying the discipline, the students’ predicted scores were compared with the scores that the students received in the computer science test. Comparative experimental data are given in table. 2 and in Fig. 1. Table 2 Credit and forecast data Excellent Good Satisfactory ОХОІГЦ Total Credit score 3 11 40 7 61 Predictive score 2 13 38 8 61 Rice. 1. Comparative chart of credits and forecast data Conclusion The results of the experiment showed that the predicted student performance differs from the actual one by no more than 3.3%. Therefore, the procedure based on the modified McKean's ^-means method is effective and can be used to predict student performance. Literature 1. Gershunsky B.S. Prognostic methods dy in pedagogy / B.S. Gershunsky. -K.: Vishcha School, 1979. - 240 p. 2. Zagvyazinsky V.I. Pedagogical pre- vision / V.I. Zagvyazinsky. - M.: Knowledge, 1987. - 77 p. 3. Juror A.F. Forecasting as function of a teacher (from a future teacher to a professional): monograph / A. F. Jury. - Chelyabinsk: Education, 2006. - 306 p. tov by cluster analysis method / R.V. Mayer // Problems of educational physical experiment: collection. scientific and method. works - 1998. - Issue. 5. - pp. 12-19. 5. Shevchenko V.A. Construction concept models of knowledge acquisition by students in the discipline “Informatics” / V. A. Shevchenko // Bulletin of KhNADU: collection. scientific tr. - 2012. - Issue. 56. - Reviewer: V.V. Bondarenko, professor, Ph.D., KhNADU.