Semion Sheraizin, Prof., Dr.of Sc.  Ph.D, Computer Science Dept.,

College of Management, e-mail: semion@cs.colman.ac.il

Model of an instruction process

In this paper we analyze the student instruction process and consider the ways for the process efficiency increase.

We use a model of an instruction process as an information transmission (Fig.1) from an instructor (sender) to a student (recipient) by taking into account both an instructor and a student pre-existing knowledge [1], thesauruses TI and TS accordingly [2].

The knowledge data sources include all information related to an instruction course. The semantic coding of the knowledge data depends on an instructor thesaurus and its preliminary knowledge regarding a student thesaurus TS or thesauruses TSS of students into group. The instructor knowledge about TS or TSS provides a data choice (semantic coding) for an instruction course. A cognitive message shape accomplished by syntactic coding also depends on TS/TSS and an applied communication channel.

By using the various communication channels, e.g. lectures, textbooks, students receive the messages. It is obvious that a really received knowledge depends on student thesaurus and finally on a student’s cognition.

Instruction process analysis

We used the described model for an investigation of instruction process properties. The received results can be presented by the following propositions.

P r o p o s i t i o n 1.   A semantic coding of knowledge data is defined by both a training system purpose and goals. The coding is accomplished by an instructor and includes a knowledge data choice.

P r o p o s i t i o n 2.  Message after semantic coding is characterized by redundancy value which is increased for a group of students with different thesaurus.

P r o p o s i t i o n 3.  Message source efficiency depends on a message form which defines a student thesaurus completion.

Below we will continue a consideration of the propositions particular for some training course. The diagram on Fig.2 presents a knowledge data transformation during an instruction process.

An existing knowledge data IB about some theme is transformed into a training knowledge data IK by the semantic coding executing an information choice. At present the general semantic coding algorithm is unknown. An instructor selects a knowledge data that provides a needed information volume for training taking into account the training goals. So after a semantic coding there is a limited knowledge data IK < IB (Fig.2, quadrant 1).

A syntactic coder shapes message from the selected knowledge data IK. The message data IS must be equal a selected knowledge data IK (Fig.2, quadrant 2). The message shape takes into account a student’s thesaurus, its cognition and used communication channel (Fig.1). A matching at a message form with a student’s thesaurus provides a student more effective both a reception of a knowledge data and a thesaurus completion that increases a student’s cognition [3]. The matching uses a redundant data RD. So after syntactic coding a message data is characterized by redundancy coefficient MR which value depends on a student’s thesaurus and increases for group of students with different thesaurus.

 MR = 1- IK / ( IK+RD )                                            ( 1 )

The redundancy results in a growth of a lecture time, book volume etc. But it is necessary take into account that a student’s thesaurus is also increased during a training period that is presented on Fig.2 (quadrant 3, nonlinear response 2).Therefore the redundant data RD can be also diminished.

At present many types of communication channels are used in training process: oral lectures, video and audio lecture courses, textbooks, etc. The called syntactic coding performs a message shape in according with used communication channel.

A student message reception is a last and highly important part of an instruction process. For ideal technical communication channel without error a received message data is equal a transmitted data (Fig.2, response 1). A received data for training communication channel depends more on student cognition and a matching a student thesaurus with a message form. The presented reception responses 2 and 3 (Fig.2) are related to middle and low thesaurus levels accordingly. The nonlinear response 2 shows that a thesaurus completion during an instruction process enhances a received data.

Now we can define the efficiency EI of an instruction process by the following expression:

 EI = IR / (IB + RD) = f(IS) / (IB + RD)              (2)

In according with the expression ( 2 ) the following operations specify the efficiency of an instruction process: a selection of a knowledge data IK, a shape of a knowledge data message IS with the redundancy RD and a completion of a student thesaurus that increases a student cognition and a received knowledge data IR= f (IS ). The responses 1’…3’ (Fig.2, quadrant 4) present the efficiency of an instruction process.

C o n c l u s i o n

1. A predefined student’s thesaurus and cognition are basis parameters for an instruction process efficiency enhancement.


2. A matching a message (a knowledge data shape) with a student thesaurus increases a thesaurus completion and a student’s cognition.


3. Message redundant data efficiency depends on a matching of the data with a student’s thesaurus and used communication channel.

 R e f e r e n c e s

1.       Bransford John D., Brown Ann L., Cocking Rodney R. How People Learn: Brain, Mind, Experience, and School, The National Academies Press, 346p, 1999.

2.       Sheraizin Semion M. Theory Fundamentals, Research and Development of Adaptive Image Processing, Dr. of Sc. Thesis, Leningrad El. Comm. Institute, 395p, 1989.