Педагогические науки/2.Проблемы подготовки специалистов.
k.t.n Mikhaylov K.M.
Kherson national technical university, Ukraine
The generalized algorithm
of choice individual trajectory of training
The
education is a major kind of human activity. The essential increase of
knowledge derivates a plenty of roughly developing specialties. The mastering
by modern specialties assumes drawing up and performance of the personal plan
of preparation. The educational plan on a specialist includes obligatory
disciplines both disciplines at the choice of high school and students can be
submitted as a model [1], [2], consisting from varied blocks. In work [3] one
of possible ways of the choice of a training trajectory is submitted. It is
necessary to note, that the choice of a trajectory is not limited for the sake
of a set of disciplines, it can also be carried out within the limits of
investigated disciplines by a choice of separate kinds of works in view of
their complexity and quantity [4]. As the practice shows, for successful
planning and control of training the application of means of automation is also
necessary.
Statement
of the task.
Using
results of researches [1] - [4], it is necessary to develop the generalized
algorithm for process of drawing up and control of performance of the
individual trajectory of training, both at a level of the educational plan, and
at a level of separate discipline. All assumptions are concerned to formation
of the educational plans for high schools of Ukraine [1].
General
part.
The
generalized algorithm of formation of the educational plan consists of the
following steps [1].
Step 1. Development of the structural
- logic circuit of preparation (bachelor or magister).
As for
development of the structural - logic circuit, it is important to break all set
of disciplines concerned with the educational plan on three subsets related to
the given discipline
|
|
(1) |
so as D1 - subset of
disciplines, studying of which is possible up to the given discipline;
D2
- subset of disciplines, studying which is possible in one semester with the
given discipline;
D3 - subset of disciplines, should be studied only after
study of the given discipline.
For the
first semester priority the disciplines, where subset D1 =Æ, in last semester
- discipline, where D3 =Æ will be.
Step 2. Distribution of disciplines
with control form "Examination" in semester. As the quantity of
examinations in a semester can not exceed five, at presence of eight semester in
the plan, it can be presented no more than forty examinations by preparation of
the bachelors (all the standards are taken as an example for high schools of
Ukraine).
Step 3. Distribution of the stayed
normative disciplines of blocks on semester shows that the general week loading
is equal to 54 hours (if the week loading will be reduced, the quantity of
educational weeks will exceed 140).
Step 4. At for addition of discipline
from the stayed blocks on semester, it states that the general week loading is
equal to 54 hours.
Step 5. A variation of total amount
of disciplines within the limits of the block in view of semester for
performance of equality of loading of a semester which equals 54 hours.
Step 6. Having considered the
thematic plan of a rate consisting from n to those, it is necessary to accept
the decision on inclusion or not inclusion of a theme in the individual plan of
preparation (if the given theme is not obligatory) [2].
The
initial plan includes disciplines P, consisting from followed topics T:
|
Pf={Tf1, Tf2, …, Tfr}, |
(2) |
so as f - serial number of discipline in the
educational plan;
r –
quantity by that concerned by the rate.
Result
of a choice by that of a rate:
|
Pfk={Tfw1,
Tfw2, …, Tfwk}, |
(3) |
where fwk – embodies
the quantity of the chosen themes of a rate, and fn>fwk.
For
each chosen theme the duration of study from an allowable interval is defined:
|
Tfw1 |
[t1min..
t1max], |
|
Tfw2 |
[t2min..
t2max], |
|
Tfw3 |
[t3min..
t3max], |
|
..
|
|
|
Tfwk |
[twkmin..
twkmax]. |
As for
the quantity, duration and forms of the control, they can be defined: as self-checking,
testing, control works, final control.
General
time of control measures:
|
|
(4) |
so as tki - time i of a kind of the
control;
n –
quantity concerned by the rate.
Total
time of training:
|
|
(5) |
and Tfwi
Î Pfk.
Step 7. For development of a
trajectory of performance of a laboratory practical work is created and the
following algorithm is applied [3].
1. The obligatory quantity of
carried out laboratory works is also defined.
2. For each work depending on a
degree, complexity where the weight factor is defined.
3. The level of preparation of the
student is defined according to observed the purpose of the recommendation of a
level of complexity of chosen laboratory works.
4. The individual preferences of
the student can be defined.
5. The order of performance of
works (consecutive, parallel, any) is observed.
6. The intermediate and final
levels of control of a practical work performance both estimation of result of
performance and completeness of the achieved purposes is carried out.
For performance of the specified algorithm the criteria of a choice of
laboratory work in dependence of a level of preparation of the students and their
individual needs to form the practical skills on educational discipline are
offered.
Criterion
1. A level of preparation for performance of the practical work.
Before
realization of a laboratory practical work the estimative levels of preparation
of the student are possible by testing on the disciplines investigated earlier (control
of residual knowledge), which are previous for investigated discipline,
analysis of a final estimation on them.
On the
basis of the carried out work each student can be referred to one of three groups:
- insufficiently
prepared student;
- student with a
sufficient level of preparation;
- student with an
excellent level preparation.
Depending
on the specified class preparations to the student offer levels of complexity
of a practical work.
Criterion
2. A significance value of work during preparation of the expert.
The teacher
estimates the developed laboratory works from the point of view of importance
for purchasing of skills ensuring performance of functional duties of the
future expert in a scale [0,1; 1]. The more in detail given approach is stated
in work [2].
Criterion
3. Individual preferences of the student.
The
given question is considered in work in detail [3]. The procedure of definition
of individual preferences is reduced to the answers to test questions with
their subsequent analysis. As a result of performance of the specified
procedure the weight desire factors of performance of laboratory work are
defined.
Step 8. An estimation of quality of
performance of a laboratory practical work.
The chosen
trajectory of performance of a laboratory practical work at once is necessary
for estimating from the point of view achievable of the purposes of
investigated discipline.
1. For each laboratory work the
method of expert estimations defines weight factor, where ai, j - coordinate in
space of a laboratory practical work.
2. For a laboratory practical work the scale of conformity to estimations
is defined: "perfectly" - S5, it is "good" - S4, is
"satisfactory" - S3.
3. For all laboratory works including in a trajectory, the sum of weight
factors is defined:
|
|
(6) |
so as pi,j =1, if the laboratory work is
included in the trajectory, and pi,j =0 otherwise.
4. The
interval is defined to which belongs Smax.
5.
After realization of a laboratory practical work for each work the level е ё of
performance - Ri,j is defined(determined). Application of the
several approaches in this case is possible. For example, the work is
protected, (Ri,j=1), or is not protected (Ri,j =0). Or
other approach: Ri,j [0,1].
6.
Final estimation of performance of the practical work:
|
|
(7) |
It is
necessary to note, that 
.
7. On a
scale (see item 2), is defined an accessory Sitog to an estimated
interval and the estimation for the laboratory practical work is exposed as
well.
Step 9. The control of presence of
repetitions in investigated disciplines.
The
further perfection of the generated individual trajectory consists in the
analysis of disciplines, which are included in the educational plan at a level
by that.
Let's
consider the ideally generated educational plan. In such plan for any two
disciplines Di = {Ti (1), Ti (2).. Ti
(Ni)} and Dj = {Tj (1), Tj (2).., Tj
(Nj)} the conditions are carried out:
Di(1)
Ç Dj(1)=Æ;
Di(1)
Ç Dj(2)=Æ;
….
Di(Ni)
Ç Dj(Nj)=Æ.
Previous
for investigated discipline, analysis of a final estimation on them.
However
for the really designed educational plan there will be disciplines, for which
Di(А) Ç Dj(B)=Tk.
Three
strategies of modernization of the educational plan are possible.
Strategy
1. To remove the specified theme Tk from discipline Di.
Strategy
2. To remove the specified theme Tk from discipline Dj.
Strategy
3. To generate new discipline from two, and:
Dk= Di(Ni) È Dj(Nj)-
Tk.
The
conclusion. The offered approach will allow to generate an individual
trajectory of training of the student both at a level of the educational plan,
and at the level of separate discipline, that provides high flexibility and
comfort at development of a trade.
THE LITERATURE:
1. Михайлов К.М. Моделирование учебного плана
подготовки бакалавров по специальности экономическая кибернетика //Вестник
ХГТУ. – 2003. - №2(18). – С. 468-471.
2. Михайлов К.М. Моделирование процесса выбора
программы курсовой подготовки методом анализа иерархий //Вестник ХГТУ. – 2005.
-№1(21). –С.568-572.
3. Ефимов О.Н., Михайлов К.М. Способ выбора траектории
обучения студента//Вестник ХНТУ. – 2007. -№ 4(27). – С.561–564.
4. Михайлов К.М., Михайлова В.В. Критерии выбора траектории
обучения студента при выполнении лабораторного практикума//Вестник ХГТУ. –
2006. -№1(24). – С. 464–466.
Mikhaylov K.M.
The generalized algorithm of choice individual
trajectory of training
The algorithm is
offered which allows to individualize a trajectory of training. The features of
algorithm consist in formation of such trajectory, both at a level of the
educational plan, and at the level of separate discipline. The described
approach allows to take into account individual features of the person, which
is trained.
Mikhaylov Konstantin Mikhaylovitsh - k.t.n,
senior lecturer, doctorant of faculty of information technologies of the Kherson
national technical university.
Scientific
interests: modeling of educational processes and structures and progressive
information technologies.