1. bookAHEAD OF PRINT
Detalles de la revista
License
Formato
Revista
eISSN
2444-8656
Primera edición
01 Jan 2016
Calendario de la edición
2 veces al año
Idiomas
Inglés
Acceso abierto

Design of college education evaluation based on accompanying data acquisition and mathematical analysis

Publicado en línea: 15 Jul 2022
Volumen & Edición: AHEAD OF PRINT
Páginas: -
Recibido: 24 Feb 2022
Aceptado: 19 Apr 2022
Detalles de la revista
License
Formato
Revista
eISSN
2444-8656
Primera edición
01 Jan 2016
Calendario de la edición
2 veces al año
Idiomas
Inglés
Introduction

Colleges and universities are an important place for higher education. The level and quality of education and teaching are important indicators to measure an university. To improve the quality of education has become the primary task of college. It is the basic form adopted by many colleges and universities to continuously evaluate their education and teaching of the school, and use the evaluation results to improve teaching management and service, so as to promote the improvement of teaching quality[1]. The basic requirement of teaching evaluation is to evaluate and judge the teaching process and teaching effect according to the teaching content and objectives, and serve the teaching decision-making. It is necessary to formulate a relatively complete evaluation system, involving the evaluation content, standards, methods, indicators, application of results and the relationship between various links[2].

Although the teaching evaluation rules has been established for a long time, its implementation is affected by various subjective and objective factors. The scientificity and practicability of the evaluation results need to be improved for the following reasons:

Firstly, the unequal relationship between evaluation subjects. Teachers and students are the core subjects of evaluation, but in most cases, the relationship between teaching and learning is unequal, and the teacher led teaching method occupies the main form. Although students participate in the whole teaching process, their evaluation to teachers and teaching process can best reflect the teaching level and effect in theory. However, student's evaluation is often affected by his personal factors (such as learning attitude, course nature, examination results, personal relationship between teachers and himself, teacher management methods, etc.), and the evaluation is uncertainly objective and fair[3]. In addition, there are similar situations among teaching managers, decision makers, supervisors and teachers.

Secondly, the main form of evaluation is result evaluation. Most teaching evaluation is to evaluate the teaching effect with the examination results after finishing courses or examination. This is called result evaluation. Although the result evaluation also reflects the teaching level to a certain extent, it can not provide effective information during teaching. At the same time, only the observation and assessment results can not completely reflect the real state and teaching quality of teaching and learning, which is particularly obvious in the courses related to practice and art, such as music, sports, dance, painting and so on [4].

Thirdly, The evaluation indicators are not comprehensive enough and the evaluation feedback delay. Most colleges and universities use information systems to assist teaching and collect relevant data through the systems, the collected data can be used as the basis for teaching evaluation. However, many real-time data cannot be obtained through similar information systems, such as students’ interest and learning attitude, classroom teaching activity, teachers’ clarity and accuracy of classroom expression, which need to be collected by questionnaires lately, Thus, it is affected by human subjective factors. Moreover, students’ extracurricular learning behavior also belongs to an important part of the teaching process, but it can not be perceived by the system, resulting in one-sided evaluation of students’ learning process [5].

With the popularization of Internet of things, face recognition, 5G, boundary computing and other technologies, it is possible to collect and analyze people's data on campus insensitively. In order to build an objective and reasonable teaching evaluation model in colleges and universities, we proposes a teaching evaluation model based on accompanying data acquisition and mathematical analysis, which collects the data generated and associated with daily teaching behavior in real time, and analyzes and displays it with the help of big data tools. It is hoped to improve the evaluation method of higher education by using big data intelligent technology

Whole process data acquisition

In order to obtain as much teaching related data as possible, it is necessary to summarize some columns of data such as classroom teaching, teaching management, teaching service, before and after class, teaching design and assessment scheme, especially the relevant data that may be generated in each segment of the teaching process. The data acquisition is insensitively and no affection to normal teaching activities.

collection of structured data

In Colleges and universities, there are dozens or even hundreds of commonly used information systems, such as educational administration management, student management, personnel management, online learning, etc. These systems usually store structured data, perform independent functions respectively, and are integrated and shared through task management and data exchange. This kind of data is usually to obtain relatively easier. According to the functional categories, these information systems can be divided into teaching, scientific research, management, service and so on. Due to the overlapping and interleaving relationship of data between systems, in order to avoid data conflict, it is necessary to define authoritative data sources, which are called metadata [6]. Table 1 is for various data and metadata relationships.

metadata definition

Classification Data Content Metadata source
management data subset Organization Personnel system
financial Financial system
Logistics service Agile Logistics
Land and property Asset management system, experimental management system
Instrument and equipment
major information Educational administration system
Class information
Course information
Faculty data subset Staff basic information Personnel system
Course information Educational administration system
Scientific research information Scientific research management system
Tutor information Student management system
Teaching management data subset Teaching plan Educational administration system
Curriculum information
Online course resources E-learning
Student course selection Educational administration system
Classroom information
Experimental course
Laboratory Experimental management system
Teaching evaluation Teaching evaluation system
Student management data subset Student status Educational administration system
Student registration
Score
Admissions Enrollment system
Freshman enrollment Freshman enrollment system
Graduate departure School leaving system
Student payment Financial system
Aid financially Student management system
Accommodation
Reward and punishment
Students’ mental health
Student employment Student employment system

The metadata is united and shared through data bus and data exchange center. After extraction, verification and transform, the basic data from the data source enters the sharing data center to form a metadata table. The standardized data can be pushed back to each information system in the form of synchronization, association and sharing, so as to realize the standardization and accuracy of the data of the information system. The logical relationship is shown in Figure 1.

Figure 1

diagram of data exchange

insensitively collection of unstructured data

Another kind of data exists not in the form of records and tables, but in file or raw format, which is called unstructured data or semi-structured data. The common file formats include pictures, audio, video, long text log, etc. Because of its large capacity of unstructured data files, in order to reduce the storage space and facilitate later retrieval and analysis, it is necessary to extract their key information (such as index, description and feature sets) and store them structurally [7].

Let's Take classroom teaching as an example. During class in the smart classroom, the camera will capture the class state. The smart camera with boundary calculation will execute portrait recognition, then face comparison and face recognition. So as to record the students participating in the class, complete the class check-in function, and save the students’ class state image. The camera will take a complete picture of the class in the classroom. The camera with zoom and pan tilt function will also track and snapshoot the teacher's activities, and automatically synthesize teaching materials in combination with projection and other equipment, so as to facilitate students’ on-demand review after class. Voice recognition system converts the teaching content and interactive dialogue into words. In physical education or dance class, the camera not only can finish face recognition function, but also has the functions of posture capture and behavior recognition, which is convenient to judge whether the students’ movements are standardized. These data are collected in real time automatically.

Many machine's logs are in the form of messages, and its text is very long and different in length. For example, the log of study state in e-learning system records online contents and learning situations of students. Therefore, it is necessary to analyze, decompose and convert the whole record into structured records for storage.

Figure 2

Unstructured datas / semi-structured datas collection

acquisition of other types of data

The data types described above can be automatically collected through machines and information systems, while some data can not be obtained through non perceptual way, which requires the cooperation of users. For example, the results of the questionnaire or the data related to personal information need to be authorized or submitted by oneself. When we evaluating the physical condition of students, students are required to submit their own physiological data, or collect them through medical instruments and Internet of things devices on the premise that students know and authorize [8]. In the physical education courses of many colleges and universities, IOT or GPS are used for positioning to complete the measurement and assessment of students’ running and daily exercise, and students are required to actively submit data during exercise [9].

data storage

In order to facilitate data analysis, it is necessary to store the data obtained from each channel orderly. Table 2 shows the subjects involved in teaching evaluation and their relationship. We can see that there are several pairs of subjects that need cross evaluation, including mutual evaluation between teachers and students, students and teachers.

Evaluation Subject

Evaluated Subject Student Teacher Supervise Management Tools Environment
Subject
Student -
Teacher -
Supervise -
Management -

We need to design a corresponding analysis topic, package and sort out the data related to the topic, and store the data according to the column storage rules. The evaluation of teaching process is involving the topics of students, teachers, courses and teaching environment. Teaching management evaluation is involving the topics of specialty, training plan, personnel and teachers. The evaluation of teaching conditions is involving assets, teaching environment, teaching tools and themes. The massive educational data can be storaged, calculated and processed by multi-source heterogeneous data module in Hadoop big data platform. The platform consists of data source, data collection, cleaning and integration, distributed data storage, data analysis, visualization and other modules. In terms of data storage, HDFS is used to store unstructured data, HBase and hive are used to store structured data, and Kafka and redis are used to cache data that are often processed and need rapid response. In terms of parallel computing, MapReduce is used for large-scale data set computing, and spark technology is used to realize streaming data processing and memory computing [10]. The design framework of campus data acquisition and analysis platform is shown in Figure 3.

Figure 3

Campus data acquisition and analysis platform

Establishment of evaluation model
design of evaluation indicators

In order to evaluate education and teaching reasonably, we need to design the evaluation indicators based on the existing data, and get the evaluation results through data analysis and calculation. The design of evaluation indicators should meet the requirements of teaching objectives and correctly reflect the teaching quality and level, it should also feed back the existing problems. Each evaluation index has data support and correct calculation method. The quantitative results can be easily obtained through the evaluation model.

We take the evaluation of teachers’ classroom teaching ability as an example to introduce the analysis and application of accompanying data. Table 3 shows the indicators designed for the evaluation of College Teachers’ classroom teaching ability.

evaluation indicators of teacher's classroom teaching ability

Primary Indicator Secondary Indicator
Content of Courses The teaching content is correct without scientific errors
Specification for words, symbols, units and formulas
The knowledge content is complete and in line with the teaching objectives
Clear knowledge structure and logical level
The key points and difficulties are prominent and Enlightening
Teaching Method Adopt heuristic, inquiry and case teaching methods
Interactive teaching
Students-centered teaching
The exercises are rich and the number of questions is appropriate
Teaching orientation is accurate and adapt to students’ cognitive level
Teaching Attitude Adequate preparation and abundant teaching materials
Clear expression and standard writing
Words and deeds are appropriate and energetic
Observe the time and finish classes on time
Teaching Skills Skilled use of teaching tools, good human-computer interaction
The courseware is fine and artistic
Classroom organization and management
The teaching scene design is creative, vivid and interesting
Teaching Effectiveness Students are active in class and have a high attendance rate
Good classroom order
Students have a high degree of knowledge acquisition
Students’ participation is high and the classroom atmosphere is active
Chosen of the key indicators

Because there are many data sources and a large collection range, the data dimensions that can be used to support the evaluation indicators are large too. Especially there may be a certain relationship between various data sets. In order to reduce the calculating cost, we need to find out the key indicators and key data dimensions in a large amount of data.

We assume that the collected data meet the normal distribution, and use a vector X to represent a set of data to be calculated. Each type of data can be regarded as the dimension of a vector. For example, X = (χ1, χ2, χ3χm) is used to evaluate the quality of teachers’ classroom teaching, where χ1 indicates the compliance of teaching content, χ2 indicates the standardization of writing, χ3 indicates content integrity, and so on. It is definition: H=M2i=1m(xix¯)2,M=k=1iak(xm+1kxk) {\rm{H}} = {{{M^2}} \over {\sum\nolimits_{i = 1}^m {{{\left({{x_i} - \bar x} \right)}^2}}}},\,{\rm{M}} = \sum\limits_{k = 1}^i {{a_k}\left({{x_{m + 1 - k}} - {x_k}} \right)}

When the data sample is large enough, it can be proved that limH=1 \mathop {\lim}\limits_\infty \,H = 1 , that means the assumed normal distribution is valid [11].

Principal component analysis can be used to choose indicators and data. The principle is to execute orthogonal verification on the vectors so as to find out the uncorrelated vectors, which are called the main components [12]. This chosen vectors have a major impact on the evaluation results. Assuming that there are n evaluation data and dimensions of each indicator is m, the sample matrix is: X=[x11x1mxn1xnm] {\rm{X}} = \left[{\matrix{{{x_{11}}} & \ldots & {{x_{1m}}} \cr \vdots & \ddots & \vdots \cr {{x_{n1}}} & \ldots & {{x_{nm}}} \cr}} \right]

There is defination: Xi*=XiE(Xi)varXi,i=1,2,,p X_i^* = {{{X_i} - E\left({{X_i}} \right)} \over {\sqrt {{var}{X_i}}}},\,\,i = 1,2, \ldots,p

Where E(Xi) is the mean value of the data i, varXi is its standard deviation.

The departure variance A, covariance S and positive correlation matrix F of the sample matrix are respectively: A=(aij)=j=1n(xjx¯)(xjx¯) {\rm{A}} = \left({{a_{ij}}} \right) = \sum\limits_{j = 1}^n {\left({{x_j} - \bar x} \right){{\left({{x_j} - \bar x} \right)}^{'}}} S=1n1A=(sij) {\rm{S}} = {1 \over {n - 1}}A = \left({{s_{ij}}} \right) F=(rij),rij=aijaiiajj=sijsiisjj {\rm{F}} = \left({{r_{ij}}} \right),{r_{ij}} = {{{a_{ij}}} \over {\sqrt {{a_{ii}}{a_{jj}}}}} = {{{s_{ij}}} \over {\sqrt {{s_{ii}}{s_{jj}}}}}

The principal component can be obtained by solving the eigenvalue and eigenvector of correlation matrix F. F is actually a sparse matrix. Its characteristic root is defined as λ={λ1,λ1,λm},λ1λ2λm0 \lambda = \left\{{{\lambda_1},{\lambda_1}, \ldots {\lambda_m}} \right\},\,{\lambda_1} \ge {\lambda_2} \ge \ldots {\lambda_m} \ge 0

The corresponding eigenvectors are: vi=(v1i,v2i,vmi),i=1,2,m {v_i} = \left({{v_{1i}},\,{v_{2i}}, \ldots {v_{mi}}} \right)',\,\,i = 1,2, \ldots m

The contribution degree αi=λi/j=1mλj {\alpha_i} = {\lambda_i}/\sum\limits_{j = 1}^m {{\lambda_j}} is defined to illustrate the importance of this data in the evaluation indicator. When the value 1pαk \sum\limits_1^p {{\alpha_k}} is greater than or equal to the specific gravity threshold ρ(0<ρ≤ 1), it can be considered that the top P datas calculated are enough to constitute the evaluation indicator. If ρ= 0.95, to take the evaluation indicator composed of the top P datas would be 95% reliability.

weight coefficient calculation of evaluation indicator

After choosing the evaluation indicators and the datas required, we need to assign weight coefficient to the indicators. Because the evaluation data includes not only the data collected by machines and equipment, but also the mutual scores of various evaluation subjects, the subjective evaluation often has a certain priori. Therefore, combined with subjective and objective evaluation, information entropy is used to calculate the weight coefficient. On the basis of maintaining good subjective orientation, the weight coefficient of each indicator is corrected with objective data [13].

Define m is number of evaluation indicators, and there are n evaluation parameters corresponding to each indicator. Define the judgment matrix: X=(xij),i=1,2,3m,j=1,2,3n {\rm{X}} = \left({{x_{ij}}} \right),\,{\rm{i}} = 1,2,3 \ldots {\rm{m}},\,{\rm{j}} = 1,2,3 \ldots {\rm{n}}

Standardize the matrix: xij=xij/xmax {x_{ij}} = {x_{ij}}/{x_{max}}

xmax is the maximum value of the matrix element.

The information entropy of Matrix is Hj=ki=1mpijlnpij,pij=xiji=1mxij,k=1lnm {H_j} = - k\sum\limits_{i = 1}^m {{p_{ij}}\ln {p_{ij}},{p_{ij}} = {{{x_{ij}}} \over {\sum\nolimits_{i = 1}^m {{x_{ij}}}}},\,k = {1 \over {\ln \,m}}}

The weight coefficient of evaluation indicator j is: αj=1Hjj=1n1Hj,0αj1,1nαj=1 {\alpha_j} = {{1 - {H_j}} \over {\sum\nolimits_{j = 1}^n {1 - {H_j}}}},\,0 \le {\alpha_j} \le 1,\,\sum\nolimits_1^n {{\alpha_j} = 1}

Conclusion

Teaching evaluation is a very important segment in the teaching work of colleges and universities. It is a scientific evaluation and judgment to the teaching process and teaching effect. Reasonable evaluation methods and scientific evaluation models are conducive to promoting the improvement of teaching quality. Using the evaluation mathematical model based on accompany data analysis of the new generation of information technology such as Internet of things and big data, the traditional evaluation method is changed from result, lag and subjectivity to process, real-time and comprehensive, and the evaluation results are more objective and credible.

Figure 1

diagram of data exchange
diagram of data exchange

Figure 2

Unstructured datas / semi-structured datas collection
Unstructured datas / semi-structured datas collection

Figure 3

Campus data acquisition and analysis platform
Campus data acquisition and analysis platform

metadata definition

Classification Data Content Metadata source
management data subset Organization Personnel system
financial Financial system
Logistics service Agile Logistics
Land and property Asset management system, experimental management system
Instrument and equipment
major information Educational administration system
Class information
Course information
Faculty data subset Staff basic information Personnel system
Course information Educational administration system
Scientific research information Scientific research management system
Tutor information Student management system
Teaching management data subset Teaching plan Educational administration system
Curriculum information
Online course resources E-learning
Student course selection Educational administration system
Classroom information
Experimental course
Laboratory Experimental management system
Teaching evaluation Teaching evaluation system
Student management data subset Student status Educational administration system
Student registration
Score
Admissions Enrollment system
Freshman enrollment Freshman enrollment system
Graduate departure School leaving system
Student payment Financial system
Aid financially Student management system
Accommodation
Reward and punishment
Students’ mental health
Student employment Student employment system

Evaluation Subject

Evaluated Subject Student Teacher Supervise Management Tools Environment
Subject
Student -
Teacher -
Supervise -
Management -

evaluation indicators of teacher's classroom teaching ability

Primary Indicator Secondary Indicator
Content of Courses The teaching content is correct without scientific errors
Specification for words, symbols, units and formulas
The knowledge content is complete and in line with the teaching objectives
Clear knowledge structure and logical level
The key points and difficulties are prominent and Enlightening
Teaching Method Adopt heuristic, inquiry and case teaching methods
Interactive teaching
Students-centered teaching
The exercises are rich and the number of questions is appropriate
Teaching orientation is accurate and adapt to students’ cognitive level
Teaching Attitude Adequate preparation and abundant teaching materials
Clear expression and standard writing
Words and deeds are appropriate and energetic
Observe the time and finish classes on time
Teaching Skills Skilled use of teaching tools, good human-computer interaction
The courseware is fine and artistic
Classroom organization and management
The teaching scene design is creative, vivid and interesting
Teaching Effectiveness Students are active in class and have a high attendance rate
Good classroom order
Students have a high degree of knowledge acquisition
Students’ participation is high and the classroom atmosphere is active

ZHANG Guan-hua. A Preliminary Study of Teaching Evaluation for Higher Education. EDUCATION TEACHING FORUM, 2020; 7(28): 354–355 ZHANGGuan-hua A Preliminary Study of Teaching Evaluation for Higher Education EDUCATION TEACHING FORUM 2020 7 28 354 355 Search in Google Scholar

Zhou Huatao, Lu Jie, Huang Yulan, Chen Yu. Research on key Technology of Classroom Teaching Evaluation Based on Artificial Intelligence. Journal of Physics: Conference Series, 2021;v 1757, n 1 ZhouHuatao LuJie HuangYulan ChenYu Research on key Technology of Classroom Teaching Evaluation Based on Artificial Intelligence Journal of Physics: Conference Series 2021 1757 1 10.1088/1742-6596/1757/1/012014 Search in Google Scholar

Gordon Nikolas, Alam Omar. The Role of Race and Gender in Teaching Evaluation of Computer Science Professors: A Large Scale Analysis on Rate My Professor Data. SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, p 980–986, March 3, 2021, SIGCSE 2021 NikolasGordon OmarAlam The Role of Race and Gender in Teaching Evaluation of Computer Science Professors: A Large Scale Analysis on Rate My Professor Data SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education 980 986 March 3, 2021 SIGCSE 2021 10.1145/3408877.3432369 Search in Google Scholar

XIA Junmei, YU Ling, LEI Yun. Application of SOFIT in Aerobics Class Teaching Evaluation in Colleges and Universities, 2021; 11(11): 113–115 XIAJunmei YULing LEIYun Application of SOFIT in Aerobics Class Teaching Evaluation in Colleges and Universities 2021 11 11 113 115 Search in Google Scholar

Hang Guo. The Design of Teaching Evaluation and Organization System Based on the Internet of Things Using Fuzzy Comprehensive Evaluation Software. Mobile Information Systems, 2021; Volume 2021 GuoHang The Design of Teaching Evaluation and Organization System Based on the Internet of Things Using Fuzzy Comprehensive Evaluation Software Mobile Information Systems 2021 2021 10.1155/2021/2285937 Search in Google Scholar

Kostakis Panagiotis, Kargas Antonios. Big-data management: A driver for digital transformation? Information (Switzerland), 2021; 12(10): 1–14 PanagiotisKostakis AntoniosKargas Big-data management: A driver for digital transformation? Information (Switzerland) 2021 12 10 1 14 10.3390/info12100411 Search in Google Scholar

Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo. An experimental survey on big data frameworks. Future Generation Computer Systems, 2018; 86(9): 546–564 InoubliWissem AridhiSabeur MezniHaithem MaddouriMondher NguifoEngelbert Mephu An experimental survey on big data frameworks Future Generation Computer Systems 2018 86 9 546 564 10.1016/j.future.2018.04.032 Search in Google Scholar

DOU Li, CHEN Hua-wei, QIAN Cheng. Study on the Values and Models of “Smart Physical Education Classroom” in Colleges and Universities. Sports Culture Guide, 2018(11): 136–146 DOULi CHENHua-wei QIANCheng Study on the Values and Models of “Smart Physical Education Classroom” in Colleges and Universities Sports Culture Guide 2018 11 136 146 Search in Google Scholar

XIE Jia-hui, LIN, Meng, WANG Yan. Research on Wearable Devices in College Physical Education under the Background of Big Data. Sports Science Research, 2018; 22(1): 84–88 XIEJia-hui LINMeng WANGYan Research on Wearable Devices in College Physical Education under the Background of Big Data Sports Science Research 2018 22 1 84 88 Search in Google Scholar

Sridharan K., Komarasamy G., Daniel Madan Raja, S. Hadoop framework for efficient sentiment classification using trees. IET Networks, 2020, 9(5): 223–228 SridharanK. KomarasamyG. Daniel Madan RajaS. Hadoop framework for efficient sentiment classification using trees IET Networks 2020 9 5 223 228 10.1049/iet-net.2019.0208 Search in Google Scholar

Umek Lan, Tomaževič Nina., Aristovnik Aleksander, Keržič Damijana. Predictors of student performance in a blended-learning environment: An empirical investigation. Proceedings of the International Conference on E-Learning, EL 2017 - Part of the Multi Conference on Computer Science and Information Systems 2017, p 113–120, 2017 UmekLan TomaževičNina. AristovnikAleksander KeržičDamijana Predictors of student performance in a blended-learning environment: An empirical investigation Proceedings of the International Conference on E-Learning, EL 2017 - Part of the Multi Conference on Computer Science and Information Systems 2017 113 120 2017 Search in Google Scholar

Verma Chaman, Stoffova Veronika, Illes Zoltan, Tanwa, Sudeep, Kumar Neeraj. Machine Learning-Based Student's Native Place Identification for Real-Time. IEEE Access, 2020(8): 130840–130854 ChamanVerma VeronikaStoffova ZoltanIlles TanwaSudeep NeerajKumar Machine Learning-Based Student's Native Place Identification for Real-Time IEEE Access 2020 8 130840 130854 10.1109/ACCESS.2020.3008830 Search in Google Scholar

Guo Junq, Bai Ludi, Yu Zehui, Zhao Ziyun, Wan Boxin. An AI-application-oriented in-class teaching evaluation model by using statistical modeling and ensemble learning. Sensors (Switzerland), 2021; 21(1): 1–28 JunqGuo LudiBai ZehuiYu ZiyunZhao BoxinWan An AI-application-oriented in-class teaching evaluation model by using statistical modeling and ensemble learning Sensors (Switzerland) 2021 21 1 1 28 Search in Google Scholar

Artículos recomendados de Trend MD

Planifique su conferencia remota con Sciendo