CN113077368A - Personalized online education system based on smart phone - Google Patents

Personalized online education system based on smart phone Download PDF

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CN113077368A
CN113077368A CN202110453144.2A CN202110453144A CN113077368A CN 113077368 A CN113077368 A CN 113077368A CN 202110453144 A CN202110453144 A CN 202110453144A CN 113077368 A CN113077368 A CN 113077368A
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students
test
questions
knowledge
test questions
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郑修楷
曾宪文
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Shanghai Dianji University
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Abstract

The invention discloses a personalized online education system based on a smart phone, which comprises: the current learning condition detection unit adopts at least three sets of test paper in the same class to evaluate the learning condition of the student; the knowledge ability improving unit collects and summarizes knowledge points related to the test questions with higher error rate in the current learning state detecting unit and provides guidance of the related knowledge points and test question practice for students in a targeted manner; the knowledge point quality inspection unit adds part of new test questions appearing due to changes of the examinees to the system regularly and quantitatively, including explanation of the new questions and interpretation of new question types, and simultaneously removes questions with low quality, overlarge difficulty or lack of practical significance. A targeted learning scheme is provided for different students, so that the learning efficiency of the students is improved, meanwhile, the students are prevented from being involved in the question sea tactics, and the time is saved.

Description

Personalized online education system based on smart phone
Technical Field
The invention relates to the technical field of online education, in particular to a personalized online education system based on a smart phone.
Background
In the prior art, the simian tutoring is taken as an example, only auxiliary materials aiming at the stage learning are provided, and a specific learning scheme is not adaptively adjusted according to the learning condition of each student.
Specifically, the prior art has the following problems:
firstly, students only have problems with individual knowledge points or certain sections of courses, while the prior method mostly is used for the whole explanation practice of the staged courses, and is not different from the study in schools for the students, so that the secondary study of familiar knowledge is easily caused, the knowledge points needing to be perfected are not obviously supplemented and enhanced, and a large amount of time is wasted;
secondly, the problem of embarrassment that students still cannot solve the difficult problem when the difficult problem does not exist is caused by lack of specific learning plan guidance on different mastery degrees of different students facing different knowledge points and the difference of learning abilities of the students;
third, the learning situation of the student cannot be supervised in real time, and whether the current problem is solved cannot be determined.
Therefore, the prior art can not detect and search the weak points of the knowledge of students periodically, has certain limitations, low working efficiency and no pertinence.
Disclosure of Invention
The invention aims to provide a personalized online education system based on a smart phone, which provides a targeted learning scheme for different students, improves the learning efficiency of the students, avoids the students from falling into the theme and sea tactics and saves time.
The purpose of the invention is realized as follows: personalized online education system based on smart phone, comprising:
the current learning condition detection unit adopts at least three sets of test paper in the same class to evaluate the learning condition of the student;
the knowledge ability improving unit collects and summarizes knowledge points related to the test questions with higher error rate in the current learning state detecting unit and provides guidance of the related knowledge points and test question practice for students in a targeted manner;
the knowledge point quality inspection unit adds part of new test questions appearing due to changes of the examinees to the system regularly and quantitatively, including explanation of the new questions and interpretation of new question types, and simultaneously removes questions with low quality, overlarge difficulty or lack of practical significance.
Further, in the knowledge point quality inspection unit, new test questions and previous test questions are randomly distributed and included in the flow pool in a ratio of 1:100, and the quality of the test questions is inspected according to the answer states of students.
Further, in the above-mentioned knowledge point quality inspection unit, if there is a test question that the student can answer in a very short time and the accuracy of the test question in the system is higher than the basic threshold, the test question is determined as a low-quality test question that is too difficult to answer, and then the test question is deleted from the flow pool.
Further, in the above-described knowledge point quality inspection unit, the basic threshold of the accuracy of the test questions that students can answer in only a very short time is 95%.
Further, in the above-mentioned knowledge point quality inspection unit, if the test question appears such that most students cannot answer smoothly after spending a lot of time and the accuracy is lower than the basic threshold, the test question is determined to be a low quality test question which is difficult to answer and is deleted.
Further, in the above-described knowledge point quality inspection unit, the basic threshold value of the accuracy of the questions that the student cannot solve smoothly after spending a large amount of time is 30%.
Further, in the knowledge point quality inspection unit, only test questions with difficulty between too small and too large are retained according to the answer accuracy of students.
Further, the system further comprises:
the system comprises a login module, a student and a management module, wherein the student inputs personal information in the login module to log in the system, and selects ages and subjects after logging in;
the detection module is used for judging whether the students do test questions of related courses within preset time, if not, the current learning state detection unit is called, and if so, the knowledge ability improvement unit is called;
the practice time judging module is used for judging whether the practice time of the student reaches or exceeds the preset time, if so, calling the current learning condition detecting unit to evaluate the learning condition of the student again, and if not, entering a link of selecting course detection;
when entering a link of selecting course detection, if yes, returning and calling the current learning condition detection unit, and if not, continuing to call the knowledge capability improving unit to consolidate and strengthen the exercises.
Further, the system includes a smart phone for running all units and modules in the entire system.
The invention has the beneficial effects that:
1. the pertinence is strong, a targeted learning scheme can be provided for different students, and the learning efficiency of the students is improved;
2. the knowledge points with higher error rate can be extracted for targeted tutoring, the test questions which are too simple or complex can be rejected, the learning efficiency of students is improved, meanwhile, the students are prevented from being trapped in the thematic sea tactics, and the time is saved.
Drawings
FIG. 1 is a schematic diagram of the system operation of the present invention.
Fig. 2 is a flow chart illustrating the examination of the quality of test questions according to the answering states of students.
Detailed Description
The invention will be further illustrated with reference to the accompanying figures 1-2 and specific examples.
The embodiment provides a personalized online education system based on a smart phone, which comprises the smart phone and further comprises the following components in part by weight:
the current learning condition detection unit adopts at least three sets of test paper in the same class to evaluate the learning condition of students, automatically counts the error distribution condition of the students after the students finish the test paper, and then evaluates the learning condition of the students;
the knowledge ability improving unit collects and summarizes knowledge points related to the test questions with higher error rate in the current learning state detecting unit and provides guidance of the related knowledge points and test question practice for students in a targeted manner;
the knowledge point quality inspection unit adds part of new test questions appearing due to changes of the examinees to the system regularly and quantitatively, including explanation of the new questions and interpretation of new question types, and simultaneously removes questions with low quality, overlarge difficulty or lack of practical significance.
When the current learning state detection unit operates, after the detection of the learning state of the student course is completed, the distribution of knowledge points related to wrong questions of the student is firstly obtained through statistics, and according to the occurrence frequency and the importance degree of the wrong knowledge points, a learning scheme is provided in a targeted manner by using a user-based collaborative filtering algorithm, and the learning scheme comprises knowledge point explanation videos, test question strengthening exercises and the like.
The collaborative filtering algorithm based on the user mainly comprises two steps:
A) finding a student set similar to the interest of the target student;
B) finding out the target students in the student set of the step A, wherein the target students search for more clicks or have higher error rate (which can be understood as interest points of target users), and the target students are recommended with no test questions or explanation videos heard by the target students.
The key point of the step (A) is to calculate the interest similarity of two students (searching, clicking preference or knowledge content with higher error rate). Here, the collaborative filtering algorithm mainly calculates the similarity of interest using the similarity of behaviors. Give student u and student v, let NuShow that student u has had a positive feedback test question, explain video set, order NvA set of questions and explanation videos for which the student v has had positive feedback. Then, we can simply calculate the interest similarity of u and v by the following Jaccard formula or by the following cosine formula:
Wuv＝∣Nu∩Nv∣/∣Nu∪Nv∣；
therefore, the knowledge points are analyzed on one hand by utilizing a student-based collaborative filtering algorithm, and corresponding courses and test questions are recommended according to the knowledge points; on the other hand, students with the same error are associated, and the rest error test questions are found out as target students to be checked to see whether similar problems occur.
As shown in fig. 2, in the knowledge point quality inspection unit, new test questions and previous test questions are randomly distributed in a ratio of 1:100 and are included in a flow pool, and the quality of the test questions is inspected according to the answer states of students.
In the above-mentioned knowledge point quality inspection unit, if there is a test question that the student can answer in a very short time, and the accuracy of the test question in the system is higher than the basic threshold value of 95%, the test question is determined to be a low-quality test question that is too difficult to answer, and then the test question is deleted from the flow pool.
In the above-mentioned quality inspection unit for knowledge points, if the test questions appear such that most students cannot answer smoothly after consuming a lot of time and the accuracy is lower than the basic threshold value of 30%, the test questions are determined to be low-quality test questions with excessive difficulty and are deleted.
In the knowledge point quality inspection unit, only test questions with difficulty between over-small and over-large are reserved according to the answer accuracy of students, so that the test questions and the course quality are matched with the current ability of target students.
As shown in fig. 1, the system further comprises, running on the smartphone:
the system comprises a login module, a student and a management module, wherein the student inputs personal information in the login module to log in the system, and selects ages and subjects after logging in;
the detection module is used for judging whether the students do test questions of related courses within 20 days, if not, the current learning state detection unit is called, and if so, the knowledge ability improvement unit is called;
and the exercise time judging module is used for judging whether the exercise time of the student reaches or exceeds 20 days, if so, calling the current learning condition detecting unit to evaluate the learning condition of the student again, and if not, entering a link of selecting course detection.
When entering a link of selecting course detection, if yes, returning and calling the current learning condition detection unit, and if not, continuing to call the knowledge capability improving unit to consolidate and strengthen the exercises.
While the preferred embodiments of the present invention have been described, those skilled in the art will appreciate that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (9)

1. Personalized online education system based on smart phone, characterized by comprising:
the current learning condition detection unit adopts at least three sets of test paper in the same class to evaluate the learning condition of the student;
the knowledge ability improving unit collects and summarizes knowledge points related to the test questions with higher error rate in the current learning state detecting unit and provides guidance of the related knowledge points and test question practice for students in a targeted manner;
the knowledge point quality inspection unit adds part of new test questions appearing due to changes of the examinees to the system regularly and quantitatively, including explanation of the new questions and interpretation of new question types, and simultaneously removes questions with low quality, overlarge difficulty or lack of practical significance.
2. The smartphone-based personalized online education system of claim 1, characterized in that: in the knowledge point quality inspection unit, new test questions and previous test questions are randomly distributed and contained in a flow pool in a ratio of 1:100, and the quality of the test questions is inspected according to the answer states of students.
3. The smartphone-based personalized online education system of claim 2, characterized in that: in the above-mentioned knowledge point quality inspection unit, if there is a test question that the student can answer in a very short time, and the accuracy of the test question in the system is higher than the basic threshold, the test question is determined to be a low-quality test question that is too difficult to answer, and then the test question is deleted from the flow pool.
4. The smartphone-based personalized online education system of claim 3, characterized in that: in the above-described knowledge point quality inspection unit, the basic threshold of the accuracy of the test questions that students can answer in only a very short time is 95%.
5. The smartphone-based personalized online education system of claim 3, characterized in that: in the above-mentioned quality inspection unit for knowledge points, if the test questions appear so that most students cannot answer smoothly after consuming a lot of time and the accuracy is lower than the basic threshold, the test questions are determined to be low-quality test questions with excessive difficulty and are deleted.
6. The smartphone-based personalized online education system of claim 5, characterized in that: in the above-described knowledge point quality inspection unit, the basic threshold of the accuracy of the test questions that the students cannot solve smoothly after consuming a large amount of time is 30%.
7. The smartphone-based personalized online education system of claim 5, characterized in that: in the knowledge point quality inspection unit, only the test questions with the difficulty between over-small and over-large are reserved according to the answer accuracy of students.
8. A smartphone-based personalized online education system according to any one of claims 1-7, further comprising:
the system comprises a login module, a student and a management module, wherein the student inputs personal information in the login module to log in the system, and selects ages and subjects after logging in;
the detection module is used for judging whether the students do test questions of related courses within preset time, if not, the current learning state detection unit is called, and if so, the knowledge ability improvement unit is called;
the practice time judging module is used for judging whether the practice time of the student reaches or exceeds the preset time, if so, calling the current learning condition detecting unit to evaluate the learning condition of the student again, and if not, entering a link of selecting course detection;
when entering a link of selecting course detection, if yes, returning and calling the current learning condition detection unit, and if not, continuing to call the knowledge capability improving unit to consolidate and strengthen the exercises.
9. The system for personalized online education based on smart phone according to claim 8, wherein the system includes a smart phone for operating all units and modules in the whole system.
CN202110453144.2A 2021-04-26 2021-04-26 Personalized online education system based on smart phone Pending CN113077368A (en)

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CN105355111A (en) * 2015-12-02 2016-02-24 华中师范大学 After-class reinforced learning system based on learning situation analysis
CN109949638A (en) * 2019-04-22 2019-06-28 软通智慧科技有限公司 Acquisition of knowledge degree determines method, apparatus, terminal and medium
CN110648042A (en) * 2019-07-26 2020-01-03 浙江迪安证鉴检测技术有限公司 Quality cycle evaluation method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050196732A1 (en) * 2001-09-26 2005-09-08 Scientific Learning Corporation Method and apparatus for automated training of language learning skills
CN105355111A (en) * 2015-12-02 2016-02-24 华中师范大学 After-class reinforced learning system based on learning situation analysis
CN109949638A (en) * 2019-04-22 2019-06-28 软通智慧科技有限公司 Acquisition of knowledge degree determines method, apparatus, terminal and medium
CN110648042A (en) * 2019-07-26 2020-01-03 浙江迪安证鉴检测技术有限公司 Quality cycle evaluation method

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