Online and offline physical education quality assessment based on mobile edge computing

Ji Xu

doi:10.1515/nleng-2022-0330

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Online and offline physical education quality assessment based on mobile edge computing

Ji Xu

Published/Copyright: July 12, 2024

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From the journal Nonlinear Engineering Volume 13 Issue 1

Abstract

Every institution in China has undergone reform and piloting based on its unique situation to enhance the quality of instruction as a result of the ongoing advancement of the times and the educational idea in the information age. The effectiveness of education is increased by hybrid teaching, which combines offline and online approaches. It empowered educators and motivates learners to take responsibility for their learning. Classes in physical education (PE) that place a strong emphasis on training and technical performance are especially well-suited for hybrid instruction. This mode improves teaching abilities and supports the long-term growth of education. As a result, we offer an online and offline technique for assessing the quality of PE instruction that is relied on mobile edge computing (MEC). With the use of the target, index, weight, and standard assessments, this study builds a quality evaluation index (QEI) approach for PE that combines online and offline techniques. Assessing the corresponding significance of every index, factor, and cluster analysis compresses index elements. The weights of the QEIs for integrated instruction are determined using MEC. The efficacy of combined PE is assessed using the fuzzy logic-based comprehensive assessment methodology. According to the analytical model, this strategy increases teaching quality while reducing costs and mistakes and improves assessment effectiveness. The experimental results proved that the proposed model has provided an accuracy of 95.98%.

Keywords: Practice of PE; MEC; online and offline teaching; QEI; fuzzy logic-based comprehensive assessment

1 Introduction

The purpose of physical education (PE) is to encourage kids to be physically active, happy, and healthy overall. It is an essential part of school curricula. Traditional PE teaching strategies do have certain drawbacks, though, including fewer possibilities for participation, a lack of individualized instruction, and restricted access to learning materials. Recent technological developments have created new opportunities for enhancing instruction and student learning. It has become clear that combining hybrid mobile edge computing (MEC) technologies is a viable way to raise the standard of instruction [1]. A thorough method for assessing the effectiveness and efficiency of instruction through the use of MEC technology is the hybrid MEC-based PE teaching quality assessment method. This evaluation approach makes use of the benefits of mobile devices to improve the teaching process, including the utilization of interactive content, audio, and video [2]. Students can access educational materials at any time and from any location thanks to the online teaching method. Students can learn at their own pace and get a customized learning experience with the use of online tools including video tutorials, online tests, and interactive games.

Students can access instructional resources even in places with spotty internet availability thanks to the offline mode of instruction. Students can keep learning even when they are offline by downloading educational content to their devices [3]. There are many benefits of using MEC technology when teaching PE. MEC enables quicker data processing and analysis, giving students immediate feedback. To track students’ movement, heart rate, and other health-related data, wearable gear and sensors can be combined with MEC technology. Students’ development can be monitored over time by using these data to develop individualized learning strategies for them [4]. To meet the changing demands of the students, the evaluation approach integrates both hybrid teaching methods. Several indicators, including student engagement, performance, and feedback, are used in the evaluation process. A thorough evaluation of the learning experiences of the students is possible thanks to the hybrid teaching modes. Student engagement is essential for a successful PE class.

The evaluation procedure measures how engaged students are in both traditional and online learning settings. Using interactive online tools like games and quizzes can help students stay interested in their studies and make the learning experience more enjoyable [5]. Another significant factor considered in the evaluation procedure is student performance. MEC technology can be used to measure and monitor student performance and give them feedback in real-time. The feedback received can be utilized to pinpoint areas that need work and create individualized lesson plans for each student. Li et al. [6], during the transmission of an 8-min medical video titled “Navigation to the Uterine Horn, Exchange of the Horn, and Re-Anastomosis,” the window-based Rate Control Algorithm (w-RCA) was proposed to optimize the medical quality of service (m-QoS) in the MEC-based healthcare by considering the network parameters, such as peak-to-mean ratio, Standard deviation, delay, and jitter [7]. Effective teaching requires a lot of feedback. The evaluation process takes into account instructor and student comments. Teachers can provide feedback through observations and evaluations, and students can do so through online tests and questionnaires. Students’ learning experiences can be improved by using this feedback to raise teaching standards [8]. Figure 1 explains the flow of Cloud edge.

Figure 1

Cloud edge flow.

Although the lead to the onset has so far produced extremely positive study findings, the effectiveness of evaluating the quality of instruction is diminished since the simplification of assessment indexes was not taken into account, increasing evaluation costs and errors. This work design offers an assorted teaching quality evaluation technique based on MEC in conjunction with MEC. The following is the direction of this study.

The four pillars of this methodology for gauging the success of hybrid learning are as follows: assessment objective, evaluation index, index weight, and assessment standard. Factor analysis and cluster analysis can be used to simplify the evaluation index items; the hybrid demonstrates an understood quality evaluation of PE is rounded out by establishing weights of various teaching quality evaluation index (QEI) in tandem with cutting-edge computing and fuzzy logic-based comprehensive assessment. The simulation findings show, finally, that the proposed technique can successfully deal with issues with current strategies, reduce evaluation costs and assessment inaccuracy, and provide a firm foundation for hybrid PE training that integrates hybrid distribution methods.

2 Related work

In the study by Abrar et al. [9], edge computing technology was used to manage the systems that enable the distribution and accessibility of the produced material, whether or not the internet is involved. Technology based on artificial intelligence is used to collect and present the course materials. Trinh et al. [10] aimed to improve the quality of English instruction offered in contemporary educational institutions to encourage the development of better English translation skills. This study also examines the use of English translations and MTI (Master of Translation and Interpretation) course in analyzing the development of translation skills. Chen et al. [11] addressed the difficult problem of the allocation of resources for the Collaborative MEC network, and a Resource allocation framework (RAF) was proposed. The center of RAF is a multi-task deep learning method for the allocation of resources based on network states and job parameters, including edge server and device processing power, communication channel quality, resource consumption, service latency requirements, etc. Cui et al. [12] proposed a technique for offloading autonomous driving services utilizing edge computing. Using an offline scheduling method and an ODA scheduling scheme, the scheduling sequence in this method can be modified while it is active.

Feng et al. [13] created a MEC system that uses a blockchain and takes into account the trust level of nodes, the calculation rate of the computing paradigm, and the problem of maximizing transaction throughput were analyzed. Yang et al. [14] discussed the development and deployment of the wireless network online learning system (WNOLS). Using EC and NDV technology, WNOLS enables many users to use their own digital devices simultaneously without interfering with one another. In the area of wireless network education, WNOLS uses online learning and PBL system for offering efficient online education. Xie et al. [15] looked at the adaptive adjustment of the initial congestion window, or the problem of maximizing flow completion time while minimizing congestion, in MEC to tackle the issue, they suggest utilizing an adaptive online decision technique that applies deep reinforcement learning to find the optimal policy. Wang and Xang [16] examined the texts of short messages created by students while learning online in order to more fully comprehend the impact of instructional technology on improving their capacity and skills of acquiring English efficiently. This will help us to direct teachers’ techniques for instruction, make effective classroom adjustments, optimize the teaching environment, and enhance their academic, practical, and innovative abilities. The w-RCA was suggested by Sodhro et al. [5] in order to enhance the m-QoS in MEC-based healthcare.

3 Methodology

3.1 Development of a hybrid online/offline teaching QEI system in PE

3.1.1 Evaluation criteria

According to theoretical concept and principles, the evaluation target the first link in building and having a framework in place to guide and serve as a foundation for several assessment indicators. The goal of teaching assessment is to make sure that instructors are following established standards for instruction delivery in schools and that pupil learning results are being properly managed. Despite paying attention to the predetermined goals, when examining the educational objectives in the classroom, we should give certain non-predetermined goals priority. Evaluations should start with the fundamental standards of the education sector and consider individual variances as well. Performance goals must be developed to prevent the detrimental effects of predetermined uniform targets on development. One must first ascertain whether the teacher prioritizes the “generating goals” in addition to other considerations before determining whether the instructor has developed suitable, distinct, and clear instructional targets for each PE class. Each and every educational setting is different, evolving, and individual. Certain information presented to students may motivate them to act in ways that their instructors did not intend or could not have foreseen. These “side effects” of classroom instruction constitute a “functional purpose” that significantly influences students’ growth.

3.2 Evaluation index

3.2.1 Evaluation of index structure

According to the assessment’s overarching goal, the assessment index is a way of structurally breaking down the assessment object from several angles, indicating the major factors that determine how some of the evaluation object’s most important features manifest and behave. The meaning of each index in this group should be independent of the others, and there should be no cross, for it to be in its ideal state. At the same time, the complete assessment object can be fully reflected by the connotation of each index. Similar to this, a set of subordinate indexes dissected by all of them at the same level should meet these two requirements. Suitable to the difficulties and fuzziness of the assessment object of the level of hybrid varying teaching, the decomposition and content of the index cannot, of course, totally satisfy these standards, but we should attempt to get as close as possible. The index structure is shown in Figure 2.

Figure 2

Evaluation of index structure.

3.2.2 Multi-index comprehensive evaluation technique

A multi-index comprehensive evaluation is a process of merging non-dimensional relative evaluation values with various statistical indicators that reflect various facets and aspects of the thing being evaluated to build an established significance for the thing’s overall evaluation. Based on one index, it is possible to compare the evaluated things to one another. A multi-vector sorting issue and a dimensional field are both addressed by a multi-index thorough assessment. Each evaluation index is comparable to a separate dimension that is utilized to assess and scale the evaluated items, and the assessed object is analogous to numerous points in the multi-dimensional space.

3.3 Index weight

3.3.1 Index weight idea

In statistics, the idea of weight is fundamental. The weight of the connected index is determined by the index weight, which describes the relative value of each index in the current assessment system and provides the relevant value. The fundamental form that a weight amount takes is a structurally related number, and it is most frequently expressed as a proportional relative number. Usually, there are two requirements.

The weight W i is in the range of 0–1, 0 < W i < 1.

The total weight of each index is 1.

(1) ∑ j = 1 m X j = 1 .

3.3.2 List of procedures for calculating index weight

Choosing the index weight is one of the hard tasks in developing an evaluation index system. The weight value that is chosen has a direct bearing on the results of a multi-index thorough evaluation. A modification in weight value could affect the status of each index, and the valuation guide’s purpose is clear.

(2) B ji = ∑ l = 1 m B ji ( l ) , j , i = 1 , 2 , … , n ,

(3) ∑ B j = ∑ i = 1 m B j ,

(4) c = ∑ B max − ∑ B min b max − b min ,

(5) b j = ∑ B j − ∑ B min c + 0.1 ∑ B max − ∑ B j c .

There is a number of metrics that can be used to evaluate how effective PE programs are received both in person and online: High levels of teaching zeal, meticulous research, and a sense of duty; sophisticated teaching material, proper arrangement, and obviously important points; accurate instructional expression, clear reasoning, and suitable use of the real-life environment to select examples; adaptable and diverse teaching strategies; skill in modifying the learning environment; a broad knowledge base, the incorporation of interdisciplinary theory and knowledge, improved research and teaching techniques, the promotion of student learning engagement and plan, observe the development of student’s abilities in all areas, and instruction that is also individualized for each student’s aptitude.

(6) x j = v j ∑ j = 1 m v j b j .

3.3.3 Evaluation of the Quality of PE using MEC

The various standards of quality, accountability, and efficiency that are used to categorize the various assessment techniques for instructors’ teaching quality are available. The minimum requirements for instructors to carry out various roles are outlined in the quality standard. The efficiency standard takes into account the effectiveness and efficiency of the teacher’s instruction, whereas the responsibility standard primarily considers the teacher’s work process, tasks, and attitude. The objective laws and ideology of science serve as the foundation for the scientific standard, which requires that the laws and principles governing the educational task itself be followed by evaluation activities. It also measures the value of the educational work’s process, method, and outcome on the basis of its scientific rigor. The specialization, duration, and complexity of teaching labor are its distinguishing traits. Another hazy idea is educational excellence. The creation of evaluation criteria is a multifaceted and expertly done task because of the numerous aspects that affect the quality and the interconnections that exist between those factors. To develop evaluation standards that meet the requirements of the legislation, have widespread acceptance among educators, and can be easily implemented in the classroom, we should merge the teaching traits of university professors. This will encourage teachers to improve their own performance, accurately represent the real teaching level of teachers precisely and objectively, and encourage students to learn effectively and achieve positive learning outcomes.

(7) q = t q 2 t w 2 ,

(8) α = l l − k 1 − ∑ j = 1 l q 2 t w 2 x j .

A test’s validity refers to how well it can measure the material you wish to assess, as well as other measuring methods. Validity and reliability have a close relationship, and good reliability is a prerequisite for high validity. The correctness and generalizability of the design compared to the developed and assessment methods are referred to as the validity of the evaluation index system. It investigates the connection between the anticipated outcome and the scope of the assessment index system. The validity is high if the two are close and consistent; on the other hand, if there is a large difference between the two, the accuracy is a little down or level ineffectual. The degree of authority is closely correlated with the precision of the index system, in-depth comprehension of the index connotations, clarity of the assessment purposes, and comprehension of the real circumstance. The precise operational procedure is as follows: In order to create the extra weight of each index to be more sensible, analytical hierarchy process is utilized to establish the weighting of each grade based on the student evaluation index system. The importance of each index at each stage in the student assessment of instruction will be discussed by ten university-affiliated specialists in supervision. The relative weights of each index are calculated and reported.

The following is the evaluation matrix for how well teachers teach as determined by students:

(9) E 1 = 1 1 2 1 3 2 3 2 1 1 2 1 2 3 2 1 4 3 2 2 1 4 1 .

The following is the judgment matrix used by students to assess the teaching style:

(10) E 11 = 1 1 2 2 3 2 1 1 3 2 1 2 1 .

The following is the judgment matrix used by students to assess how well teachers are doing:

(11) E 12 = 1 2 3 2 1 2 1 4 5 2 3 5 4 1 .

The following is the judgment matrix used to assess students’ teaching aptitude:

(12) E 13 = 1 2 3 1 2 1 3 4 5 3 2 1 4 5 2 1 3 1 2 3 2 1 2 5 4 1 4 5 1 2 1 .

The following is the judgment matrix used by students to assess the impact of instruction:

(13) E 14 = 1 1 2 1 2 1 5 2 1 2 5 1 .

The next stage is to create an assessment index set and comment set after figuring out how much weight each indicator should receive in the hybrid handling of a variety of quality assessment of PE. Following that, a multi-level fuzzy thorough assessment is finished. The definite steps to follow are

(14) V = { E 11 , E 12 , E 13 , E 14 } .

To build the set of comments, different grades are employed to stand for the results of teachers’ teaching evaluation in the representation. Although teachers with diverse career titles and majors have diverse teaching experiences and approaches, this is carried out to avoid the automated assessment of various qualifications and the same standards.

(15) U = { Excellent , good , qualified , unqualified } ,

(16) B = V · U α .

The oversight specialists are asked to deliver a more professional presentation of the peer speaker indexes based on the peer lecture index approach to gain more significant weight. The judgment matrix is created using the majority principle to be more accurate, and the MATLAB software then analyzes the judgment matrix uniformly. The results are shown below, and the following indicates a hazy evaluation of peer listening. It is significant to make the assessment index up and create the evaluation set. There is no necessity to consider the specified index weight; a second-level FCLA; an Initial-level FCLA of peer listening.

Table 1

Comparison of the evolution of the cost

Number of test tasks	Cost of evaluation
Number of test tasks	Proposed method	mSETQ	ICES	CEQ
3	0.256	0.265	0.273	0.289
6	0.286	0.298	0.299	0.310
9	0.298	0.309	0.311	0.320
12	0.323	0.331	0.338	0.345
15	0.354	0.365	0.369	0.375
18	0.368	0.375	0.379	0.386
21	0.397	0.403	0.410	0.415
24	0.421	0.436	0.445	0.456
27	0.453	0.485	0.496	0.510
30	0.489	0.502	0.512	0.546

4 Result and discussion

A fuzzy comprehensive assessment model was constructed using 600 samples out of a total of 1,000 samples. The remaining 400 samples were used for testing. The results of a hybrid assessment of physical learning education excellence are used to calculate the test index. Modified system for evaluation of teaching qualities (mSETQ), international credential evaluation service (ICES), and course experience questionnaire (CEQ) are three examples of literary comparisons. The three traditional approaches for assessing teaching quality are contrasted with the proposed hybrid MEC-based method to assess teaching quality. The cost evolution is shown in the Table 1 and Figure 3.

Figure 3

Cost evolution.

The next modeling experimental test was used to compare the instructional quality evaluation mistakes of the methods given in mSETQ, ICES, and CEQ to further confirm the efficacy of the suggested approach. Figure 4 and Table 2 displays the results of a particular experimental comparison.

Figure 4

Analytical error rate (%).

To additionally confirm the efficiency of the suggested strategy, the education excellence assessment times of previous literature [17–19] are compared and reviewed. This is done to further confirm the effectiveness of the advised course of action. Figure 5 and Table 3 represent the comparison results of evolution time.

Figure 5

Evolution time (s).

Using the approach suggested in previous studies [17,18], to do a numerical comparison evaluation of pupil sports performance evaluation, the aim of this study is to examine the education effect from the standpoint of students. This is approved to confirm the effectiveness of the approach described in this article once more. The test results, which may be statistically deduced from the assessment results, are shown in Table 4.

Table 2

Analytical error rate vs no. of tasks

No. of task	Analytical error rate (%)
No. of task	mSETQ	ICES	CEQ	Proposed method
30	0.05	0.08	0.09	0.03
60	0.06	0.07	0.06	0.02
90	0.08	0.096	0.08	0.04
120	0.07	0.088	0.05	0.035
150	0.091	0.1	0.07	0.021
180	0.1	0.11	0.056	0.039

Figure 6 and (Table 5) depicts the accuracy for existing mSETQ, ICES, and CEQ are 88.41%, 86.94%, and 91.56% respectively and our proposed approach has 95.98%. It shows that our proposed method is more accurate.

Figure 6

Accuracy.

Table 3

Time vs no. of tasks

No. of tasks	Time (s)
No. of tasks	mSETQ	ICES	CEQ	Proposed method
30	20	25	15	10
60	35	40	30	17
90	49	58	45	23
120	63	72	67.8	31
150	75	90	89.9	39

Table 4

Assessments of student achievement based on statistics

Evaluation items	mSETQ	ICES	The proposed method
Attendance at work	65	90	90
Testing before lectures	72	85	89
Classroom conversation	78	87	86
After a test in class	85	80	94
Final draft	84	86	95

Table 5

Accuracy (%)

	Accuracy (%)
MSETQ	88.41
ICES	86.94
CEQ	91.56
Proposed method	95.98

5 Conclusion

This article proposed hybrid great effectiveness achievement assessment method based on MEC to address a number of issues with the traditional hybrid great effectiveness assessment techniques of PE. The proposed approach combined qualitative and quantitative research techniques, and it has been used with some success to investigate various elements of hybrid teaching quality assessment at various educational institutions. There is a need for additional research and growth in both theory and practice, as there are many unanswered questions and problems that need to be addressed. Regrettably, due to space, research time, knowledge, and skill limitations, it cannot address all aspects of trying to teach university faculty members to evaluate student learning effectively. Recently, people from all walks of life have voiced growing concern about it. Future research will concentrate on assessing the higher education system as a whole for its quality.

Funding information: The authors state no funding involved.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Conflict of interest: The authors state no conflict of interest.

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Received: 2023-05-24

Revised: 2023-09-10

Accepted: 2023-12-01

Published Online: 2024-07-12

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/nleng-2022-0330

Keywords for this article

Practice of PE; MEC; online and offline teaching; QEI; fuzzy logic-based comprehensive assessment

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