JPS59148085A - Learning apparatus - Google Patents

Abstract

(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed description of the invention] The present invention relates to improvements in learning machines.

Conventionally, this type of information medium has had problems such as the complexity of manufacturing and handling of the learning information medium and the high cost compared to the learning effect.

The present invention has been made in order to solve the above-mentioned problems of the conventional methods, and is equipped with a correctness/incorrect determination function that allows questions to be filled in during learning and information on answers and messages in the event of an error to be obtained from the printed matter. The aim is to provide a learning machine with a high level of learning ability.

An embodiment of the present invention will be described below with reference to the drawings.

FIG. 1 is an overall configuration diagram showing an embodiment of the present invention,
In the figure, 1 is a barcode reader pen, and 2 is a question paper (hereinafter referred to as drill paper). For example, a question asking for the formula for the area of a triangle is printed in a pictorial form, and an optical bar is also printed in the lower left corner. Code 3 is printed. In this barcode 3, for example, digital data such as an answer number of a question or a formula for the area of a triangle is written by converting an ASCII code or the like. 4 is the main body of the learning machine 11 A stand 5 on which the above-mentioned drill paper 2 is placed, a display 6 equipped with a liquid crystal display and configured to be able to display, for example, several digits of alphabets and numbers, a speaker 7 for audio output, and an answer number. (1,2
, 3, 4) and correct/incorrect (○, ×) answerer key 8 and power switch 9.

Note that Tp is an output terminal connected to the barcode reader pen 1 described above.

FIG. 2 is a circuit configuration diagram showing an embodiment of the present invention,
In the figure, 1a is a light emitting source, lb and ld are light receiving elements that receive a light beam from the light source 1a via a splitter lc and photoelectrically convert it, and a light receiving element that focuses the light beam on the barcode 3. These objective lenses 1a to 1d constitute the barcode reader pen 1.

Reference numeral 10 denotes a demodulator, which is configured to return the electric signal received after being photoelectrically converted by the light receiving element lb to the original digital data. 11 is a 1-chip microcomputer (
(hereinafter referred to as a microcomputer) inputs the digital data obtained by the demodulator 10, that is, the digital data of the barcode 3 and temporarily stores it in the memory, and also receives input from the answerer 8, This surface input is compared and determined, and the output is sent to the LS 112 with built-in speech synthesis phoneme data memory and equipped with an A-D conversion function.

Reference numeral 13 denotes an impedance matching transformer for outputting the audio output A-D converted by the LS 112 to the speaker 7. Reference numeral 14 denotes a liquid crystal driver IC, which is configured to drive and control the liquid crystal of the display 6 to display correct answer data of several digits consisting of alphabets and numbers in response to commands from the microcomputer 11 (described later).

Based on the above configuration, the operation of one embodiment of the present invention will be explained with reference to FIG. 3. In addition, FIG. 3 is a program chart showing the operation of one embodiment of the present invention.
) to (S9) indicate each step of the flowchart.

First, set the drill paper 2 on the stand 5 of the learning machine main body 4, and turn on the power switch 9 to start (Sl
). Next, the barcode reader pen 1 reads digital data from the barcode 3 printed on the drill paper 2 (S2), and sends the output signal to the demodulator 10. Next, the microcomputer 11 receives the barcode data demodulated by the demodulator 10, and if it is a correct answer, for example, NO, it waits for the input of the answer key 8 and when the input data arrives (S3 ), compare the above barcode data and the input data using a comparison command (S4) (S5), and if they match, for example, Bi is correct! "Use the data for utterance J (86), and if there is a mismatch, ["wrong °'' utterance -1 (S
7) Data for speech synthesis phoneme data memory built-in LS
112. Therefore, in the audio LS 112, D-
The A-converted audio output is outputted from the speaker 7 through an impedance matching transformer 13 for driving the speaker 7. On the other hand, when the above answer is incorrect, the microcomputer 11 transfers the "wrong utterance" data to the voice LS1.
At the same time, the digital data of the barcode 3, for example, "triangle official digital data" is transmitted to the microcomputer [1] by the correct data display command (S8).
As shown in Figure 2, "(axb)/2" appears on the display 6.
will be displayed as . And “Exjitsu) J (S9)
This leads to the next step.

As explained above, according to the present invention, the sound generation means, the character pattern conversion means, the temporary storage means, and the character display means are driven and controlled by the output from the optical digital data reading means, thereby facilitating learning. The software can now process conventional printed paper and correct answer information using barcodes, allowing the following revolutionary effects to be achieved.

(1) Since the drill paper is made of paper, the questions can be written with pictorial graphs or the like by printing, making it inexpensive and easy to manufacture.

(2) Answers and messages in case of errors Correct answer information can be printed as barcodes in the same way as in item (1) above, and can also be displayed on a liquid crystal display.

(3) It is possible to immediately check whether an answer to a question is correct or incorrect using human voice, etc.

(4) You can learn by placing drill paper on top of the main unit, making it extremely easy to operate.

(5) With the above items (1) to (4), it is possible to obtain a learning means that can be easily provided with software that can be applied to all subjects, and that has extremely high psychological learning effects.

[Brief explanation of drawings]

FIG. 1 is an overall configuration diagram showing one embodiment of the present invention, FIG. 2 is a circuit diagram of the main part shown in FIG. 1, and FIG. 3 is a program flow chart explaining the operation of one embodiment of the present invention. 1... Bar code)" IJ-Daven, 2... Trill paper, 3... Optical bar code, 4... Main body, 6... Display, 7... Speaker, 8... ...Human power key for answering, 10... Demodulator, 11... 1-chip microcomputer, 12... LSI with built-in speech synthesis phoneme data memory, 13... Transformer for impedance matching, 14... Liquid crystal driver IC0 Note that in the drawings, the same reference numerals indicate the same or equivalent parts. Agent Shin Kuzuno - Mu 2 Figure 499 - Thief 3 Figure

Claims (3)

[Claims] (1) It has 9 optical digital data reading means, and the content of the questions is pictures and letters, etc., and the correct answers and other information are printed on paper with digital data to be read by the 9 data reading means. What is claimed is: 1. A learning machine comprising: determining means for determining whether an answer input is correct or incorrect; and announcing means for displaying the determination result. (2) The learning machine according to claim 1, wherein the display means is a sound generation means that generates sound by driving a voice synthesis integrated circuit using the digital data output from the determination means. . (3) The display means is a character display means capable of displaying characters by having means for temporarily storing the digital data and means for converting the digital data into a character pattern. Learning machine described in section.