Internet of Things has the ability to revolutionize computer-based teaching and assess the quality of teaching at the same time. The assessment of teaching efficiency is hampered by two issues: the evaluation index method (EIS) is insufficient, and the assessment framework is incapable of dealing with complicated fuzzy indexes. To address these issues, the theory of fuzzy stratification from IoT perspective is presented for the first time in this paper. This theory is based on the related theory of fuzzy assessment by measuring the teaching quality evaluation index. Initially, theoretical analysis and model measurement were merged to build a better multiangle EIS for teaching quality. To manage fuzzy indexes, a teaching quality assessment model was developed using both quantitative and qualitative studies. The suggested EIS and fuzzy assessment model can effectively evaluate the standard of teaching in schools, colleges, universities, and institutes, according to implementation results. This qualitative assessment approach is empirical and rational, and it strongly promotes the quality enhancement of educational effectiveness, based on our experimental and simulation results.

Teaching evaluation is an important part of the entire teaching process. It is based on a certain teaching aspects and syllabus requirements of each subject to investigate, evaluate, and appraise teaching activities, so as to make value judgments on the quality and effect of teaching. Therefore, it is an indispensable part of the teaching process. It is an important means for in-depth education reform and improvement of teaching quality. However, the current teaching evaluation methods still remain at the level of relying on personal experience and focusing on qualitative analysis. First of all, the evaluation of teaching quality often involves multiple factors (indicators), and it must be comprehensively evaluated based on these indicators, not just from one or a few. Secondly, in comprehensive evaluation, the method of scoring each individual indicator is usually used, and then weighted average is applied. In fact, in the comprehensive evaluation problems encountered in practice, the single-factor evaluation [

Computer comprehensive evaluation is a very effective multifactor decision-making method. It is widely used in software engineering and soft science research, for example, software development cost estimation, design scheme selection, software product quality evaluation, teaching or other work quality assessments, etc. All these approaches can use the computer comprehensive assessment law [

This article will apply the relevant theories of fuzzy mathematics, combined with the multilevel indicator system of teaching quality, and propose a comprehensive computer evaluation algorithm for teaching quality. Along with that, we will discuss the role of Internet of Things in the field of education. As Internet of Things (IoT) technology has the potential to transform education at all levels, including school, college, and university, similarly, the Internet of Things (IoT) has a direct and indirect effect on learning by facilitating overall work and improving educational efficiency. In simple words, it has a broad impact on the teaching and learning process. That is why the evaluation area of education requires serious attention, and Internet of Things (IoT) is well suited for application in this field. Finally, the experimental results and testing of the proposed scheme are relatively analyzed. It is estimated that the planned scheme will support the teachers in the accurate teaching evaluation.

The rest of this paper is organized as follows. In Section

The use of the Internet of Things (IoT) in the educational field has provided a fantastic way to communicate and educate kids. The IoT has a major impact on the learning field. The traditional teaching practice as well as the structure of education institutions has been significantly improved with the use of IoT [

The Internet of Things (IoT) appears to be changing education in terms of teaching and learning, school administration, research and education, school buildings, and so on [

In administration, IoT may be used to simplify everyday operations that school administration would perform in order to free up further space and time for educational processes. While such IoT applications do not have a specific impact on learning, they can help prepare the school atmosphere for learning events and also save time that otherwise would have been wasted on everyday tasks. Mobile sensors, for instance, may be used to track equipment and relieve teachers of the responsibility of taking attendance and submitting to the management company on a daily basis [

The number of planned IoT frameworks at the instructional level is closely relevant to the construction problems, but this period at the person level. This includes the use of smart technology and other IoT-based participant tracking systems. For example, the authors of [

Computer comprehensive evaluation is a complex recursive calculation process. Not only can there be any limited number of evaluation objects, but each evaluation object can contain a variety of characteristics. Therefore, computer comprehensive evaluation must take into account all aspects and repeat it to get a more ideal result.

The basic algorithm of computer comprehensive evaluation is as follows.

Determine the evaluation object set

Determine the object to be evaluated according to actual needs, and construct the entire object into a set, and then use fuzzy mathematical analysis to determine the set of evaluation factors and the set of comments:

Object set:

Factor set:

Comment set:

Among them,

_{i} is the first level index (composite index). Since each _{i} contains _{i} evaluation indexes, there are

Establish a factor weight distribution matrix

The “weight value” determines whether it is reasonable and effective, which has a great impact on the computer comprehensive evaluation results.

Make a single-level comprehensive evaluation of _{i} evaluation indicators in each subfactor set _{i}.

For example, _{i} is _{i}, the evaluation matrix _{i} can be obtained, which is,

Among them,

The so-called single-level comprehensive evaluation of the subfactor set _{i} is actually to calculate the single-factor comprehensive evaluation matrix

This means after implementing _{i} transformation on the input matrix (array) _{i}, the output matrix (array) _{i} can be obtained. Obviously, when _{i} and _{i} are known, compound operations can be performed:

Theoretically speaking, the above compound expressions have infinitely many kinds of calculation models. However, in the actual application process, the “weighted average” comprehensive evaluation calculation model is the most effective, because it balances all the evaluation factors according to the “weight value,” which are applicable to situations where overall indicators are required.

In the “weighted average” comprehensive evaluation operation model, the weighted average operator is denoted as

After comprehensively evaluating the _{i} of

Perform matrix compound operation to calculate the comprehensive evaluation result (comprehensive evaluation value)

Suppose the weight value distribution matrix of

According to the abovementioned two-level computer comprehensive evaluation principle (Steps

Sort the

At this point, the entire computer comprehensive evaluation work is announced. Obviously, for teaching quality evaluation, the larger the evaluation value, the higher the teaching quality level, and the smaller the evaluation value, the lower the teaching quality level.

In order to facilitate computer implementation, the following is a flowchart (simplified diagram) of the computer comprehensive evaluation algorithm. This flowchart of Figure

Flow chart of computer comprehensive evaluation (level 2).

Users can choose any program design according to their needs. Language compiles computer comprehensive evaluation program.

Teaching quality is a fuzzy concept. When people form a concept in their minds, it has a certain connotation and extension. The entire set of objects that conform to this concept is the extension of this concept. For teaching quality, it is both there is no clear extension and the connotation is quite complicated. For its evaluation, generally only a few representative indicators can be selected for assessment.

Considering that the teaching process is complex composed of many factors, the quality of teaching is often reflected from different aspects. Therefore, when evaluating teaching, it is necessary to carry out many aspects of the teaching process from different perspectives and different aspects. It is necessary to evaluate the final teaching effect, but also pay attention to the role played by each link in the teaching process and treat them as a whole. When applying fuzzy mathematics to analyze practical problems, the first step is to establish a hierarchy in the substructure diagram, the highest level is the general goal, the middle level is the criteria to be followed when making decision analysis, and the lowest level is the evaluation index level. Generally speaking, the indicators reflecting the quality of teaching can be summarized into the following 5 aspects:

Teacher preparation: this indicator reflects the overall level of teacher preparation, such as teaching purpose, teaching preparation, and homework design.

Textbook processing: this indicator reflects the ability of teachers to control textbooks and teach students in accordance with their aptitude. Such as the scientific nature of knowledge transfer, the processing of key and difficult points, and the cultivation of student abilities.

Implementation of teaching methods: it reflects whether the structure and arrangement of classroom teaching are reasonable, whether the selection and combination of teaching methods are appropriate, and whether the cooperation between teachers and students is tacitly compatible, such as the overall design of the classroom, teaching bilateral activities, the combination of teaching methods, and the use of teaching methods.

Teaching quality: it reflects the degree of influence of teachers’ teaching quality on teaching quality, including teaching attitude, teaching language, blackboard writing, and demonstration.

Teaching effect: it reflects the final result of the teaching process, including the classroom teaching atmosphere, the situation of completing the teaching plan, the situation of students mastering knowledge, and the improvement of students’ thinking ability.

The above five types of indicators constitute an indicator system that reflects the level of teaching quality, which can be expressed as a hierarchical analysis diagram. In actual application, the indicators should be independent and inclusive of each other.

The comprehensive evaluation indicator system has multiple indicators and multiple levels, and each indicator has different effects on the indicators of the previous layer. In actual comprehensive evaluation, different weight values are often used to indicate the size of its effect. But the determination of the weight value is often subjective factors. In order to make the weight value of each indicator reflect the objective reality as much as possible, the method of 0∼4 score in Table

0∼4 score table.

Index | _{1},_{2}, …, _{n} | Score | Proportion |
---|---|---|---|

_{1} | _{1} | _{1}/_{1} | |

_{2} | _{2} | _{1}/_{1} | |

… | … | … | |

_{n} | _{n} | _{n}/_{n} | |

_{i} | 1.000 |

The specific steps are as follows.

Draw up an expert score sheet and ask each expert to fill it out separately.

Scoring method: compare

There are ^{th} expert’s scoring value for the ^{th} index is

Calculate the total score

Calculate the weight value of each indicator

According to the above method, fill in the form through the scores of the experts, and calculate the importance weight of each level index relative to the upper level index as shown in Table

The importance weight for each level index relative to the upper level index.

Index | Weight | Index | Weight |
---|---|---|---|

_{1}: teachers prepare lessons | 0.19 | _{7}: overall classroom design | 0.27 |

_{2}: textbook processing | 0.16 | _{8}: bilateral teaching activities | 0.22 |

_{3}: teaching method implementation | 0.20 | _{9}: teaching methods | 0.26 |

_{4}: teaching quality | 0.19 | _{10}: application of teaching means | 0.25 |

_{5}: teaching effect | 0.26 | 1.00 | |

1.00 | _{11}: teaching attitude | 0.38 | |

_{1}: teaching purpose | 0.35 | _{12}: teaching language | 0.32 |

_{2}: teaching preparation | 0.41 | _{13}: blackboard writing and demonstration | 0.30 |

_{3}: job design | 0.24 | ∑ | 1.00 |

∑ | 1.00 | _{14}: classroom atmosphere | 0.20 |

_{4}: the science of knowledge transfer | 0.37 | _{15}: completion of teaching plan | 0.23 |

_{5}: treatment of key and difficult points | 0.32 | _{16}: students’ knowledge | 0.27 |

_{6}: the cultivation of students’ ability | 0.31 | _{17}: the improvement of students' thinking ability | 0.30 |

1.00 | 1.00 |

Suppose the object set to be evaluated is

_{i}) of each base index _{j}. According to the theory of fuzzy mathematics, the membership function can be defined as the following evaluation model:

The evaluation of the composite index is the multilevel computer comprehensive evaluation. Let

The comprehensive evaluation of the overall goal is a multilevel and two-level computer comprehensive evaluation, which is a comprehensive evaluation based on a composite index evaluation.

Through the above Steps _{z}, , _{L} to measure the teaching quality can be obtained

As for the application of the abovementioned comprehensive computer evaluation algorithm for teaching quality, four teachers from different teaching and research sections of a technical secondary school are selected as the evaluation objects. According to the evaluation index system of teaching quality, from teacher preparation, textbook processing, teaching method implementation, teaching quality, and teaching effect 5 subobjectives, we comprehensively evaluate the teaching quality of 4 teachers:

The members of the teaching quality evaluation team scored the 4 teachers one by one according to 17 base indicators and then calculated the average score for each base indicator as the score value of the base indicator. Score one by one, and calculate the average score for each base indicator as the score value of the base indicator. Substitute the score value into the membership function to calculate the degree of membership, which is the evaluation result of the base index.

According to the importance weight of the base index and the composite index evaluation mathematical model, the evaluation results of all composite indicators can be calculated. For example, the calculation of the composite indicator Ui of teacher

Evaluation results of composite indicators.

Composite index | Evaluation value | JIA | YI | BING | DING |
---|---|---|---|---|---|

_{1} | _{1} | 0.7416 | 0.7074 | 0.6736 | 0.8266 |

_{2} | _{2} | 0.5272 | 0.4208 | 0.5230 | 0.5672 |

_{3} | _{3} | 0.6150 | 0.6006 | 0.6660 | 0.6500 |

_{4} | _{4} | 0.5700 | 0.4656 | 0.6528 | 0.5928 |

_{5} | _{5} | 0.6526 | 0.6246 | 0.5782 | 0.6188 |

In the same way, all the composite index evaluation values of each teacher can be calculated, as shown in Table

According to the composite index importance weight and the overall objective comprehensive evaluation mathematical model, the comprehensive evaluation results of the four teachers’ teaching quality are calculated (as shown in Table

Comprehensive evaluation results.

Teacher | JIA | YI | BING | DING |
---|---|---|---|---|

Comprehensive evaluation value | 0.6263 | 0.5727 | 0.6192 | 0.6513 |

For example, we have the overall goal

In the same way, the comprehensive evaluation value

From the final evaluation results, it can better reflect the actual teaching quality level of the four teachers, achieve the expected evaluation goals, and achieve satisfactory results.

We proposed an infrastructure that enables a first evaluation of the learners’ approach [

Fuzzy logic.

As seen in the diagram above, the instructor first asks for an integration of the optimal estimated algorithm for solving a problem. Following that, a number of software parameters that influence its architecture will be determined. As a result, we have an example of the ideal estimated algorithm. Then, by each parameter, fuzzy numbers will be generated in the following way: each fuzzy set will begin as a default triangular function centered on the estimated algorithm’s metric value; the teacher may easily change the fuzzy set suggesting the following:

The solution’s highest values that the educator finds low

The bare min of validity

The highest possible score for accuracy

The solution’s min value that the educator finds high

The experimental results of our proposed system can be divided into the following stages.

This paper will conduct performance testing of related models after choosing the model method and design. Developers of network teaching methods have increasingly developed many common course models for various educational needs in recent times. Traditional personal mentoring programmes, student-centered personal mentoring classes, personalized online education classes, knowledge-first personal mentoring classes, explorative personal mentoring classes, and conceptual personal mentoring classes are some of the new trajectory structures available. This article suggests an Internet of Things evaluation algorithm for Computer Teaching Quality Model which is a combination of wireless sensor networks and fuzzy comprehensive evaluation algorithms to address the aforementioned issues. On this foundation, we will choose teaching quality diagnostic situations for performance monitoring based on relevant regulations.

As compared to other models, the design has obtained strong experimental performance, as seen in Figure

Judgment achievement rate of the teaching quality diagnosis system.

The following is the SVM teaching quality assessment method focused on AHP-ISD:

The evaluation result of SVM exercise outcomes when

SVM exercise outcomes when

This paper applies the related theories of fuzzy mathematics and uses the analytic hierarchy process to put forward a new idea of comprehensive evaluation of teaching quality by computer, so as to make a preliminary exploration of the transformation of teaching quality evaluation from qualitative analysis to quantitative analysis. We have addressed different issues of teaching quality and designed a model to solve these issues. We have the theoretical analysis and model measurement to build a better multiangle EIS for teaching quality. To manage fuzzy indexes, a teaching quality assessment model was developed using both quantitative and qualitative studies. Practical results show that the use of teaching quality computer synthesis as the evaluation method can efficiently achieve the expected evaluation goal and obtain satisfactory results. For future we have planned to increase the efficiency of teaching quality evaluation. Furthermore, we have planned to use different AI techniques to improve the teaching quality and to create smart teacher technique which will increase teaching quality.

The data used to support the findings of this study are included within the article.

The author declares that there are no conflicts of interest.