JPS5967571A - Electronic learning machine - 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 Technical Field The present invention relates to an 811Vi electronic learning machine VC, and particularly to an electronic learning machine equipped with a test function.

Prior Art Various types of electronic learning machines have been proposed in the past, but language learning machines suitable for information retrieval functions are particularly effective.

For example, if we show the functional specifications of the device in a learning mode using a language learning machine, in the initial stage of learning, it is used for operations that focus on simply memorizing, and then in the stage of testing memory, each input answer is correct or not. There is something that displays the response. If necessary, the correct answer can be displayed immediately and the correct answer can be memorized again. As the learning progresses further, the device's built-in test function can be used to conduct a series of tests to help the learner recognize calligraphy dust. However, this means of recognizing penmanship level, as is the case with other electronic learning machines, was simply to display points based on the number of correct answers and the number of misunderstandings.

Purpose The present invention has been made in view of the shortcomings in the prior art described above, and its objective is to perform a pre-learning, complete analysis and evaluation process on the set of stored question and answer information. By adding information indicating various attributes, analyzing and evaluating the test results from various aspects based on these attributes, and providing a means of outputting comments that identify weaknesses in penmanship and provide guidelines for future learning methods, it is possible to improve learning efficiency. Our goal is to provide a complete range of electronic learning machines that can further improve effectiveness.

Description of examples DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the invention will be described in detail below with reference to the drawings.

FIG. 1 is a block diagram showing the configuration of a kanji learning machine used in an embodiment of the electronic learning machine according to the present invention.

In the figure, 1 is a keyboard comprising kana keys, function keys, etc., 2 is a central processing unit (hereinafter referred to as CPU), and 3 is a display unit, and a printer or the like may also be used. Furthermore, 4 is a memory consisting of a ROM that stores programs and dictionary information, and a RAM that is used for various other purposes.

FIG. 2 is an explanatory diagram for explaining dictionary information stored in the ROM. In the figure, A stores the local address attached to the row of the information group, and B stores the kana information of the onyomi or kunyomi of the kanji. Furthermore, in C, extract the kanji pattern from the font memory ff
? :, the kanji code for me is stored. Attribute information associated with this set of kanji and kana information with the reading I11 is stored in DG, and in DK, the level indicating the difficulty of the kanji is stored. This shows that the larger the value, the more difficult it is. Next, E is the number of strokes, F
is a code to identify whether it is On-yomi or Kun-yomi,
G stores the number of shinsho kana, respectively.

In the kanji learning machine having the above-mentioned configuration, the process of asking and answering test questions, analyzing and evaluating test results, and outputting comments will be explained below with reference to the flowchart in FIG. 3.

When the test start key is pressed, the first step is 7°l.
In this step, the number of questions CT is set to, for example, 100, and the counter WRONG for the number of incorrect answers is initialized to zero. Step 2
The path included in steps 6 to 6 is a routine that repeats displaying questions and inputting answers. In step 2, the kanji code C is indexed according to the local address A extracted by the random extraction routine (shown in the figure), and the kanji character ``Kimon'', for example, is displayed on the display section 3 as a question. At the same time, the number of questions CT is updated by 1. In step 3, the answer is entered from the keyboard 1.

In the embodiment, by inputting the ``Finkei kana whisper aka'' for the kanji ``Anmei'' that has been asked, and then pressing the separator key, the number K of the input ``Furishikana'' is counted. Next, if the user inputs ``Okukana Tori 1 Rui'' and presses the input completion key, the flow exits to Y in step 4, and proceeds to step 5, where it is determined whether the input is correct or not. If the answer is correct, the process advances to step 6 and the above-described flow is repeated until a predetermined number of questions are completed. Further, if there is a wrong answer during the question, the flow skips N in step 5 and proceeds to step 18. In step 18, the counter WRONG for the number of incorrect answers is updated by +1, and information indicating the attributes necessary for analyzing the test results is added.
The contents of the data are examined and the results are cumulatively stored in the respective analysis areas. For example, if you make a mistake in the level 1 kanji Rokumei, the level 1 error area is updated by +1, all error areas with 8 strokes are updated by +1, and the 9 error areas with 1111 reading are updated by +1. Stella 7'' 19 compares the input number K of furigana with the number of furigana stored in area G. If the comparison is equal, proceed directly to step 6, and if not equal, go to step 20 to change the furigana input. After updating the numerical error area by +1, proceed to step 7''6. If the above flow is repeated for 1 '00 problems, CT will be completed in step 6.
If the determination of =O is satisfied, proceed to step f7.

The following is a routine for analyzing and commenting on test results, and in step 7, the total number of questions and the number of incorrect answers WRON are calculated.
The difference with G is formed, and a score messeno indicating, for example, the correct answer rate is displayed. In step °8, it is determined whether the number of incorrect answers WRONG exceeds 10 or not. If it is less than 10, proceed to Stella 7'''17 and display the entire Total Message.Total Message displays the contents of each error area cumulatively updated in steps 18 and 19 item by item and displays the error message to the learner. It gives an indicator of the tendency.Also, if the number of incorrect answers WRONG exceeds 10, proceed to Stella 7°9 and judge whether there are more than 30 errors of the same level among the incorrect answers. For example, the process proceeds to step 10, where a comment message ``Level 4 No Sai Gakushi Yuoyousu'' pointing out that there are many errors in Level 4 kanji is displayed. If there is no more, proceed to step 11.
It is determined whether the incorrect answer rate for kanji with 15 strokes or more exceeds 60%. If the number of strokes is exceeded, a comment message will be displayed pointing out that further study is required for kanji with a large number of strokes. If the number is not exceeded, the process proceeds to step 13, where it is determined whether or not the number of errors 9 in on-yomi or kun-yomi among the incorrect answers is 80% or more.

If this is the case, the process proceeds to step 14, where a tone and a comment message of -11 is displayed, and if it is not, the process proceeds to step 15, where it is determined whether or not the number of errors in the number of kanji characters is 80% or more. If it is above, display the furigana review message,
If not, the accumulated content of each error area is displayed item by item in step 17, and a total messeno is displayed to give the learner a detailed index of the error tendency.

With the above explanation, all messages such as scores and comments are displayed in the display section 3.
Although it has been described that the data is output to a computer, it may also be output to a printer or as a graphical representation of a Zorotsuta. Also,
The answer is not necessarily the correct answer in the set of question and answer information stored in advance. For example, if you set up a link with incorrect answers that are likely to occur, you can output a more useful review message.

This type of comment output means can be applied not only to kanji learning machines but to all electronic learning machines, and it is possible to output appropriate comments by analyzing the attributes of fine-grained classifications suitable for the type and purpose of learning. It is.

Effects As described above, according to the present invention, it is possible to analyze and evaluate test results from various aspects using information indicating various attributes added to the stored question and answer information set. Furthermore, by outputting comments based on this analysis and evaluation, learners can immediately know in which areas their own studies have weaknesses, and at the same time, they can also determine which areas they should focus their efforts on in the future. Since it is possible to obtain guidelines, it helps quick and appropriate self-evaluation, and has the effect of further improving learning efficiency and effectiveness.

[Brief explanation of drawings]

Fig. 1 is a block diagram showing the configuration of a kanji learning machine according to an embodiment of the present invention, Fig. 2 is an explanatory diagram for explaining information stored in a ROM, and Fig. 3 is a kanji learning machine of an embodiment. 3 is a flowchart showing the control flow of the machine. In the figure, 1...keyboard, 2...central processing unit, 3...display section, 4...memory.

Claims (1)

[Claims] Analysis and evaluation means that adds information indicating at least one attribute for analyzing and evaluating learning to a set of stored question and answer information in advance, and analyzes and evaluates a test result*' based on the information indicating this attribute. and an un-review output means for outputting a review of learning based on the output information of the analysis and evaluation means.