Relationships between Strength and Step Frequency with Fatigue Index in Repeated Sprint Ability

Force−velocity profile (FVP) and repeated-sprint ability (RSA) tests are indicators of physical capacities in most team sport players. The purpose of this study was to examine the stride kinematics during a repeated-sprint ability (RSA) test and to analyze the relationship between Bosco’s force−velocity profile (FVP) and RSA performance in elite female field hockey players. Thirteen elite-female players performed both RSA (six 30 m maximal sprints) and jumping (CMJ weighted and body weight) tests. Sprinting time fatigue indexes during a 30 m RSA test were correlated with step frequency fatigue indexes (r > 0.7; p < 0.01). CMJ50 showed a large relationship with sprint time fatigue indexes. FV50 showed a very large relationship with sprint time fatigue indexes (r > 0.7; p < 0.01), and a large relationship with the step frequency fatigue indexes (r > 0.5; p < 0.05). This study highlighted two possible ways to improve fatigue indexes in RSA, with the aim of maximizing the distances covered at high-intensities during the matches: (a) strength training and (b) focusing on step frequency during speed training.

Introduction
Most team sports are characterized by an intermittent activity where periods of short high-intensity efforts are interchanged with periods of active and passive recovery [1][2][3][4][5][6][7][8]. The total sprint distance covered by players in these disciplines is an important performance factor, as noted in field hockey [1], Australian Football [2], and soccer [3]. Therefore, repeated-sprint ability (RSA) tests have largely been used as an important intermittent sport performance index [4,5], and to track the effectiveness of training programs [6]. Moreover, the ability to perform repeated sprint bouts with a short recovery time between them has been reported to be relevant for field hockey performance and is worth being evaluated and trained [7,8].
From a physiological perspective, RSA is a complex quality that is essential in order to obtain maximal sprint speed and, at the same time, to achieve the oxidative capacity needed for phosphocreatine recovery and hydrogen buffering [9]. Several physical capacities play a determinant role in RSA. For example, lower-limb strength and power provide acceleration and maximal speed during the first repetitions of multiple sprints [10]. This highlights the important of a well-developed neuromuscular system, which for allows a better activation of the motor units [11]. On the other hand, stride kinematics is also a performance determinant in sprinting ability. Specifically, it has been demonstrated that a higher step frequency through a shorter support time can optimize 60 m sprints [12], and that training at a faster step cadence may improve running economy in female distance runners [13]. Additionally, a previous study reported changes in stride kinematics during RSA tests performed on a treadmill, with the step frequency decreasing during the five repetitions of 5 s sprints [14]. This finding has also been described to occur during RSA field tests [15]. However, there is no further information about the evolution of stride kinematics during RSA field tests.
The strength and power values of athletes are critical indicators of sprinting performance, which, at the same time, is the base of RSA [16]. One of the best indicators of lower limb neuromuscular fatigue is CMJ performance, which has been related to sprint ability [17,18] and has been employed within the Bosco Index to identify the force−velocity characteristics of athletes [19,20]. CMJ performance has been used as a tool to individualize training loads during sprinting sessions, providing information about the mechanical and physiological response of athletes, instead of employing a standard fixed number of sprints for all players [21]. In addition, it has been recently reported that CMJ performance is related to specific actions in intermittent-like sports such as field hockey [22]. Nevertheless, no information about the role of force−velocity profile (FVP) on RSA performance has been previously examined.
Therefore, the aim of this study was to examine the stride kinematics during an RSA test, and to analyze the relationship between Bosco s FVP and RSA performance in elite female field hockey players.

Materials and Methods
Thirteen elite-female field hockey players (age 24.9 ± 5.6 years, body height 1.67 ± 0.04 m, and body weight 58.7 ± 3.7 kg) participated in this study. The study was carried out in the ninth week of the 2018/2019 season. The study was approved by the Local University Ethics Committee, and participants were fully informed about the protocol of the study and gave their informed consent according to the Declaration of Helsinki. The team staff was involved in the design and supervision of the study, so the research protocol was part of the regular training routine. All test sessions were conducted at the same time of day and during the physical training part of the session that preceded the field hockey training. Specifically, the jumping tests were performed in Tuesday's session (regular strength session), whereas the RSA test was performed in Thursday's session (regular speed session), both with their respective warm-ups ( Figure 1).
during RSA tests performed on a treadmill, with the step frequency decreasing during the five repetitions of 5 s sprints [14]. This finding has also been described to occur during RSA field tests [15]. However, there is no further information about the evolution of stride kinematics during RSA field tests.
The strength and power values of athletes are critical indicators of sprinting performance, which, at the same time, is the base of RSA [16]. One of the best indicators of lower limb neuromuscular fatigue is CMJ performance, which has been related to sprint ability [17,18] and has been employed within the Bosco Index to identify the force−velocity characteristics of athletes [19,20]. CMJ performance has been used as a tool to individualize training loads during sprinting sessions, providing information about the mechanical and physiological response of athletes, instead of employing a standard fixed number of sprints for all players [21]. In addition, it has been recently reported that CMJ performance is related to specific actions in intermittent-like sports such as field hockey [22]. Nevertheless, no information about the role of force−velocity profile (FVP) on RSA performance has been previously examined.
Therefore, the aim of this study was to examine the stride kinematics during an RSA test, and to analyze the relationship between Bosco´s FVP and RSA performance in elite female field hockey players.

Materials and Methods
Thirteen elite-female field hockey players (age 24.9 ± 5.6 years, body height 1.67 ± 0.04 m, and body weight 58.7 ± 3.7 kg) participated in this study. The study was carried out in the ninth week of the 2018/2019 season. The study was approved by the Local University Ethics Committee, and participants were fully informed about the protocol of the study and gave their informed consent according to the Declaration of Helsinki. The team staff was involved in the design and supervision of the study, so the research protocol was part of the regular training routine. All test sessions were conducted at the same time of day and during the physical training part of the session that preceded the field hockey training. Specifically, the jumping tests were performed in Tuesday's session (regular strength session), whereas the RSA test was performed in Thursday's session (regular speed session), both with their respective warm-ups ( Figure 1).

RSA Test
Before testing, all the participants performed a regular, specific, and standardized 15 min warm-up comprising 3 min of jogging and displacements using different movement and orientation patterns, 2 min of dynamic light stretching, 3 min of basic muscular activation, 2 min of plyometrics, 3 min of running drills, and 2 min of short distance accelerations. The RSA protocol consisted of six 30 m maximal sprints interspersed with 30 s of active recovery periods, where players decelerated during 10 m and jogged 40 m to position themselves for a new start [23]. The sprint times were measured using electronic photocells (Microgate, Bolzano, Italy), which were adjusted according to the height of the players and were placed at 0, 10, 20, and 30 m. Each sprint was initiated from a standing position with their foot 1 m behind the first timing gate. One fixed video camera, EX-ZR800 (Casio Computer Co., Tokio, Japan), located in a lateral view and operating at 60 Hz (shutter speed: 1/1000, 1920×1080 px), recorded the 30 m, similar to previous studies [24,25]. The step frequency (SF = number of steps/time of the number of steps) and step length (SL = sprint distance/sprint time/step frequency) were determined using a video analysis of the test. The RSA performance was assessed using five scores for each variable (sprint time, step frequency, and step length): the best, mean, and worst of the six repetitions, and two fatigue indexes, calculated as a percentage decrement from the best value (Fmean = 100 − (mean/best*100); Fworst = 100 − (worst/best*100)) [26].

Jumping Test
Before the test, all the participants performed a regular, specific, and standardized 10 min warm-up comprising 2 min of general activation, 2 min of light active stretching, 3 min of basic bodyweight muscular activation (10 repetitions of lunges, squats, hip thrusts, and single leg dead lifts), and 3 min of explosive activation (six repetitions of squat jumps, CMJ, and drop jumps). The CMJ and a CMJ50 (CMJ with external loads equivalent to 50% of the players bodyweight) tests were performed on a contact platform (Chronojump-BoscoSystem, Barcelona, Spain). Jumping height was determined based on the flight time using Chronojump software (Chronojump-BoscoSystem, Barcelona, Spain). The best of three attempts was selected for each jump. As the players held down a bar on their shoulders during the CMJ50, they were also instructed to hold down a plastic bar during the CMJ to mimic the jumping execution. Participants were instructed to start from a standing position; perform a rapid flexion-extension of the legs with a minimum pause between the eccentric and concentric phase of the muscle contraction; bend their knees to a freely chosen angle; perform a maximal jump keeping their body vertical throughout the jump, avoiding undue lateral and frontal movements; and to land with knees fully extended [27,28]. The FV50 was calculated using Bosco s Index (FVP50 = CMJ50/CMJ*100) [19,20].

Statistical Analysis
All of the results are expressed as the mean and standard deviation (SD). A repeated measures analysis of variance was used to compare the sprint times, step frequency, and step length across the number of repetitions (1 to 6) and section (0-10 m, 10-20 m, and 20-30 m. Post hoc tests were used to determine the statistical effects (p < 0.05) between factors using Bonferroni corrections, and were interpreted using effect sizes (ηp2) with 0.2, 0.5, and 0.8 threshold values for small, medium, and large effects [29]. Pearson correlation coefficients were used to relate the PFV parameters with the RSA kinematic parameters, with 0.1, 0.3, 0.5, 0.7, and 0.9 being the threshold values that represented small, moderate, large, very large, and nearly perfect correlations, respectively [30]. Statistical analyses were performed using the IBM Statistical Package for Social Sciences Statistics, version 22.0 (IBM Inc., Armonk, NY, USA).

Repetitions and Sections Analysis
The evolution during the six repetitions of the RSA 30-m sprint test ( Figure 2) showed medium differences between repetitions in sprint time (F 3.65 = 19.73, p < 0.001, η2 = 0.62) and step frequency (F 2.70 = 24.40, p < 0.001, η2 = 0.67), and trivial differences in step length (F 4.57 = 2.88, p = 0.026, η2 = 0.19). Pair-wise comparisons in the sprint time and step frequency revealed multiple statistical differences, whereas, for the step length, differences were only found between the last and the fifth repetition (p = 0.03). When comparing the first and the last repetition (Table 1), the sprint time increased by 4.1% (p < 0.001), whereas step frequency decreased by 4.0% (p < 0.001) and the step length remained constant.

Repetitions and Sections Analysis
The evolution during the six repetitions of the RSA 30-m sprint test ( Figure 2) showed medium differences between repetitions in sprint time (F3.65 = 19.73, p < 0.001, η2 = 0.62) and step frequency (F2.70 = 24.40, p < 0.001, η2 = 0.67), and trivial differences in step length (F4.57 = 2.88, p = 0.026, η2 = 0.19). Pair-wise comparisons in the sprint time and step frequency revealed multiple statistical differences, whereas, for the step length, differences were only found between the last and the fifth repetition (p = 0.03). When comparing the first and the last repetition (Table 1), the sprint time increased by 4.1% (p < 0.001), whereas step frequency decreased by 4.0% (p < 0.001) and the step length remained constant.  Sprint Time The

Sprint Time, Step Frequency, and Step Length
The correlation analysis (Table 2) within each variable (sprint time, step frequency, and step length) showed nearly perfect (r > 0.9; p < 0.001) relationships between best, mean, and worst 30 m times. In addition, FImean and FIworst had nearly perfect (r > 0.9; p < 0.001) or very large (r > 0.7; p < 0.001) relationships within each variable (sprint time, step frequency, and step length). However, no relationship was shown between the fatigue indexes with the best, mean, or worst 30 m times in each variable (sprint time, step frequency, and step length). Table 2. Relationships between the best, mean, worst, FImean, and FIworst parameters within the step kinematics (sprint time, step frequency, and step length) during the RSA test in elite female field hockey players.

Variables Sprint Time
Step Frequency Step The correlational analysis between the sprint time, step frequency, and step length ( Table 3) showed very large relationships for both the fatigue index between sprint time and step frequency (r > 0.7; p < 0.01), and for best, mean, and worst between frequency and step length (r < −0.7; p < 0.01). No relationships were found between step length and sprint time.

Sprint Time, Step Frequency, and Step Length
The correlation analysis (Table 2) within each variable (sprint time, step frequency, and step length) showed nearly perfect (r > 0.9; p < 0.001) relationships between best, mean, and worst 30 m times. In addition, FImean and FIworst had nearly perfect (r > 0.9; p < 0.001) or very large (r > 0.7; p < 0.001) relationships within each variable (sprint time, step frequency, and step length). However, no relationship was shown between the fatigue indexes with the best, mean, or worst 30 m times in each variable (sprint time, step frequency, and step length). Table 2. Relationships between the best, mean, worst, FImean, and FIworst parameters within the step kinematics (sprint time, step frequency, and step length) during the RSA test in elite female field hockey players. Bold: Different from the first repetition at p < 0.05.

Variables Sprint Time
Step Frequency Step Length The correlational analysis between the sprint time, step frequency, and step length ( Table 3) showed very large relationships for both the fatigue index between sprint time and step frequency (r > 0.7; p < 0.01), and for best, mean, and worst between frequency and step length (r < −0.7; p < 0.01). No relationships were found between step length and sprint time. Table 3. Relationships between step frequency parameters (best, mean, worst, FImean, and FIworst) with sprint time and step length parameters (best, mean, worst, FImean, and FIworst) during the RSA test in elite female field hockey players. Bold: Different from the first repetition at p < 0.05.

Sprint Time
Step Length Step

Jumping Test and Relationships with RSA Performance
The CMJ parameters (29.8 ± 3.7 cm) showed a very large relationship with the best, mean, and worst sprint time, and a large relationship with the best, mean, and worst step length (Figure 4a). Additionally, the CMJ50 parameters (15.06 ± 3.1 cm) showed a large relationship with the mean, worst, FImean, and FIworst sprint time (Figure 4b). Finally, the FV50 parameters (52.2 ± 7.2) showed a very large relationship with the FImean and FIworst sprint time, and a large relationship with the FImean and FIworst step frequency (Figure 4c).

Discussion
Despite the reported importance of RSA on intermittent-like sport disciplines, the role of RSA performance determinants such as running kinematics and the force−velocity profile are still unclear. Therefore, the aim of the present research was to examine the stride kinematics during an RSA test, and to analyze the relationships between Bosco FVP and RSA performance in elite female field hockey players. Fatigue indexes during a 30 m RSA test were correlated to CMJ50, FVP50 values and fatigue index of step frequency, but not to CMJ.

Repetitions and Sections Analysis during RSA Test
The 30 m sprinting times obtained by elite female field hockey players during the present study (average values below 5 s in all six repetitions) were faster than those recorded in female football players (greater than 5 s in seven repetitions) [31]. This confirms the level of the participants in the present research, and ensures that the test was performed under conditions of maximum intensity. Sprint times increased progressively across all repetitions of the RSA test ( Figure 2 and Table 1), and significant differences were found between the first sprint and the last four repetitions, in line with previous studies [23,31]. In relation to the step kinematics, the step frequency values (average team value equal or greater than 4Hz in the six repetitions) were higher than those recorded in female football players (equal or lower than 4 Hz in seven repetitions) performed under very similar conditions [31]. However, the step length values were similar (average team value between 1.50 and 1.55 m in all repetitions) to those measured in female football players [31]. The differences found between the first and last repetition were similar to those reported in step kinematics during five 5 s treadmill sprints with 25 s of recovery between them [14].
Step length did not seem to be an important parameter in the RSA test in view of the scarce differences (Table 1), as only small variations were observed between the fifth and sixth repetition (Figure 2). However, the step frequency showed a large decrease from the second repetition onwards, which might suggest that the increment in the total sprinting time could be associated with a decrease in the step frequency, whereas the step length remained stable [31]. This finding could encourage coaches to control step frequency as a method to optimize sprint endurance rather than focusing on a greater step length. In this line, maintaining short ground contact times [12,17,31] could help athletes to increase their step frequency. Another interesting aspect that emerges from the current analysis is how athletes manage fatigue during RSA tests. Differences in step kinematics between repetitions indicate that the athletes presented a better technical execution during the initial repetitions, but there is a fatigue effect from the fourth to the sixth repetition. Consequently, training sets of up to three repetitions could be prescribed to optimize sprint time and step frequency values. On the other hand, training sets of more than three repetitions could be prescribed to improve endurance. These ideas to organize the number of repetitions during training are consistent with previous suggestions in the literature [7].
Within the 30 m sprint distance, the step frequency presented small differences between the three 10 m sections (Figure 3), as previously observed in other investigations [14,31,32]. Elite athletes may have an optimum control of their step frequency at a different range of velocities and step lengths, as there were greater differences in these variables in the three 10-m sections (η2 = 0.99). Furthermore, the development of step frequency fatigue would be reflected in efforts between 10 and 30 m.

Sprint Time, Step Frequency and Step Length
Based on the correlations obtained in Table 1, it could be observed that fatigue indexes provide further information than examining the best, average, and worst parameters in isolation, indicating the importance and usefulness of calculating fatigue indexes as RSA performance indicators [33]. Furthermore, the fatigue indexes were not correlated to the best, average, or worst times in any of the step kinematic variables. However, as the sprint time fatigue indexes were only correlated with the step frequency fatigue indexes and not with the step length fatigue indexes, we recommend establishing strategies that will allow athletes to focus and improve frequency control in fatigue situations, in addition to improving top speed [12,17,32,34]. This aspect could be a very useful alternative with younger athletes (U15, U17, and U19) due to their greater fatigue shown in RSA [35].

Jump Test and Relationship with RSA
The correlation found between CMJ and the best, mean, and worst sprinting times is in agreement with other studies that have assessed the efficacy of explosive strength training for the improvement of acceleration and maximum speed in basketball, Australian football, and rugby [17,18,36]. Likewise, previous studies have also reported improvements in maximal velocity, but not in RSA [37], after explosive strength training in young elite male soccer players [11]. For this reason, the most used method to improve RSA fatigue indexes is repeated sprint training itself [11]. Jumping activities with body weight is the load that optimize power output [38], explaining a large part of the performance in the first repetitions [10], even in young players [39].
Based on previous studies, the average CMJ50 jump height obtained in the present research could be associated with a load of 70% of the 1RM in full squat [40]. This value would be classified within a high-load resistance training protocol [41], being sufficient stimulus to maximize adaptations in muscle strength [42]. Positive effects of strength training with greater loads (3x8x75% 1RM) have been found on the RSA fatigue index in futsal players [43]. Similarly, the results of the current investigation with elite female hockey players suggest that training should focus on strength training (CMJ50 and FV50) to obtain improvements in RSA fatigue rates. As the maximal strength (F0) is more compromised than the maximal sprint velocity (V0) over the course of a season in male football players [44], it seems interesting to focus on strength training as a way to improve intra-and inter-session endurance. Moreover, previous studies suggest that the ability to produce a horizontal force at low speed (F0), instead of the maximum speed (V0), is the variable that is altered both before and after the return to sport after an injury [45]. For this reason, it could be assumed that better CMJ50 values can be related not only to better performance due to their relationship with fatigue indices, but also to a lower risk of injury.
According to the results obtained with the methodology used in the present study, it seems that the use of a load of 50% of the total bodyweight provides relevant information for sports performance, due to the correlation between the CMJ50 and FV50 with the RSA variables, specifically with the parameters that are affected by fatigue in the sprint time and even the step frequency.

Conclusions
In summary, kinematic analysis showed that sprinting time fatigue indexes during 30 m RSA test were scarcely correlated with step frequency fatigue indexes. In addition, the PFV analysis showed that the CMJ50 and FVP50 values were correlated to sprint time and step frequency fatigue indexes during the 30 m RSA test.

Practical Applications
Based on the results obtained in the present investigation, we could highlight two ways to improve fatigue indexes in RSA, with the aim of maximising distances covered at high-intensities during the matches: (a) strength training and (b) focusing on step frequency during speed training. Both strategies have in common that they focus on the neural system, presenting a lower risk of sustaining injuries and a lower interference in specific game training than the traditional approach to RSA training. Additionally, these strategies suppose practical training alternatives that require low economic and temporary training and evaluation costs.
Specifically, the use of CMJ50 to assess the maximum strength levels and the FV50 to describe the force−velocity profile of players is proposed. It is suggested that a 50% bodyweight load is the minimum reference value when performing squat exercises with and without jumping. Furthermore, it is easy for athletes to understand the need to obtain FV50 values greater than 50, although a value close to 60 would be expected as an indicator of a balance in the force−velocity profile [19].
In terms of step frequency training, it could be recommended to reduce step lengths by means of agility ladders, hurdles, or other types of materials that focus the attention on step frequency. In this way, focusing on the stride frequency may avoid increased risk of injury situations (as they demand a lower peak velocity and a smaller ROM in hamstrings). Additionally, the use of plyometric exercises with short contact times, proper body alignment and timing with the arms could also be recommended to master step frequency. It is also worth highlighting the psychological implication (strengthening attention span, avoiding monotony, and decreased perception of effort) of focusing the athlete on specific technical aspects during physical training sessions.

Limitations of the Study
The originality of this study relies on being the first study to examine an elite sample of team sports players in their habitual training environment, analyzing the relationship between the force−velocity profile (and not only the CMJ performance) and the kinematic running variables during an RSA activity. Nevertheless, there are some limitations that must be acknowledged. First, the sample of the study is relatively small, so it would be necessary to carry out more studies in larger samples to corroborate the results obtained in the present investigation. Second, this is a descriptive study where the relationships observed do not neccesarily represent causality. Third, only two training loads for the CMJ test were employed, instead of the four loads method extensively reported in the literature. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and was approved by the Ethics Committee of Universidad Francisco de Vitoria (35/2020).


Improvement of English Teaching Model Based on Computer Network

The increase in the number of colleges and universities has brought great pressure to college English teaching. How to improve the teaching efficiency of English mode is an urgent problem to be solved. This article mainly studies the improvement of English teaching mode based on computer image assistance. In the preexperiment test, a unified English final test paper was selected, and the scores of 40 students were entered into the computer. The experimental group chose the English teaching mode of computer-assisted teaching. The control group still used traditional teaching methods to learn. At the end of the experiment, the students in the experimental group and the control group were given a posttest of writing in their respective classrooms, and the teachers gave their scores according to the scoring standard of the college entrance examination. SPSS 17.0 software is used to analyze the students’ scores, and the experimental results are obtained by comparing the changes of students’ scores before and after the test. In groups, tasks are done together, but it also helps students develop a correct sense of collective responsibility and fair play. Students can evaluate team members from the aspects of activity enthusiasm and learning participation. Students’ self-assessment can be evaluated objectively by observing their preclass learning level, chapter task completion, class attendance, group cooperation participation, and so on. In terms of learning motivation, the average value of students’ online learning initiative consciousness (3.04) and the average value of learning interest (3.05) are slightly higher than 3. The results show that computer image-assisted teaching can maximize the use of classrooms to improve students’ opportunities to use language and fully reflect students’ role as the main body of practice in English learning.

Introduction
The network is changing the teaching methods of teachers. Nowadays, teachers can teach for thousands of students by themselves. By using the interactivity of the network, teachers and students can interact in time, so as to achieve the similar effect with small class teaching. At the same time, people can enjoy high-quality online courses at home. As long as there is a computer, anyone can receive university education anywhere. When multimedia and Internet are used in English teaching, first of all, students can learn autonomously and individually according to their own interests, needs, tasks, and cognitive styles, thus breaking through the space-time limitation of traditional teaching mode and constructing an unlimited open teaching space.
China's English teaching has been highly valued by schools and families. From primary school to university, English always occupies the position of the core curriculum, but the effect of English teaching is not satisfactory. By using computer-assisted instruction in the first mock exam, we can explore whether this mode can change students' attitude towards learning English and improve their motivation and motivation to participate in the classroom. On this basis, we can improve students' English level and put forward practical teaching suggestions for current English teaching.
Computer image-assisted teaching has greatly promoted the improvement of English teaching mode. Gamage believes that although GTM is a traditional teaching model, it has been widely used in second language teaching, especially in higher education. Although there is no conclusive evidence that GTM was originally built on a specific theoretical framework, its main translation and memory technologies still play a vital role in the process of second language teaching. His research was part of an action research conducted at the Buddhist and Pali University in Sri Lanka, and 60 students were purposefully selected as samples. The essential research adopts a pretest and posttest experimental research design. He used a paired-sample t-test to compare the difference between the mean values: a significant p value (p < 0:005) indicates the effectiveness of GTM in teaching applications. Although his research can be effectively integrated into current teaching practice, its accuracy is insufficient [1]. Moudden focuses on the integration of games in foreign language teaching, aiming to explore the impact of the use of games in the classroom on the process of foreign language learning, students' cognition of game use, students' game skills, students' participation in games, the impact of learning English, and their attitude. The subject of the questionnaire survey was a group of foreign students studying at the University of Letters and Humanities at the University of Meknes More Ismail, Morocco. The sample included 50 male and female participants. Although his research can improve teachers' understanding of the importance of game integration in foreign language teaching, his experimental process is incomplete [2]. Sidash et al.'s research is dedicated to scientifically demonstrating the formation of educational awareness of university educators in the process of English teaching and determining the level of formation of their educational awareness. His research reveals the organizational and methodological conditions for the formation of educational awareness of educators in the process of college English teaching. He defined the effectiveness and optimization criteria (content and activity, motivation and value, and evaluation and reflection) of the formation of educational awareness. Although his research is relatively comprehensive, it lacks innovation [3]. The main purpose of Sharma and Khanal's research is to explore the effectiveness of the teaching methods of English rhetoric. They selected 31 figures of speech as research objects. They adopted a pretest and posttest control group research design to study 120 third-year undergraduate students from five campuses in Makawanpur District, Nepal. They used lecture teaching method and discussion teaching method, respectively, for 35 days of teaching. The SPSS 20th edition paired-sample t -test was used to compare the total scores of the pretest and posttest of each group. Although their research methods are relatively complete, there is a lack of discussion of experimental results [4].
The innovation of this article is to use computer-assisted teaching technology to present the content to be learned to students in a multimodal way so that students can learn effectively through various senses and with more resources, which improves teachers' teaching in class. The efficiency of knowledge promotes the ability of students to absorb knowledge and greatly changes the limitations of traditional teaching models. After the integration of computer network and foreign language teaching, the teaching mode has changed and the traditional teacher-centered mode has been broken. Foreign language teaching not only emphasizes the student-centered teaching mode but also emphasizes the information-based teaching mode of using modern information technology to promote students' autonomous learning.

Computer Image-Assisted Teaching and
English Teaching Mode 2.1. Computer Graphics-Assisted Teaching. The teaching process of English is quite different from that of other subjects, most of the teaching materials used in the current college English courses are mainly competitive English, and most of the contents are coherent dynamic technical movements. In the traditional way of college English teaching, it is mainly for college English teachers to personally demonstrate and explain the relevant skills and techniques. At the same time, this traditional teaching method also seriously limits the play of college English teachers [5,6]. If the current advanced teaching technology is used to synthesize and decompose each technical action, it will be different. Using the combination of CAI courseware production technology and network technology can make the standardization of oral English in English teaching and the effect of unified and standardized teaching will be greatly improved [7]. If you continue to use the traditional and backward English teaching methods of college English teaching, it will be difficult to adapt to the current rapidly updated environment of knowledge and information, and it will be impossible to reconcile the valuable information and new theories in the field of English teaching in English teaching. The method is taught to students in time [8]. As an important part of the education system, college English teaching has not only the commonalities with other disciplines but also its own different characteristics. The Pearson correlation coefficient between two variables is defined as the quotient of the covariance and standard deviation between the two variables [9]: Among them, μ X represents the mean value of variable X, σ X represents the standard deviation of variable X, μ Y represents the mean value of variable Y, and σ Y represents the standard deviation of variable Y [10]. Specifically, the greater the absolute value of the Pearson product-moment correlation coefficient, the better the linear correlation between the subjective scoring results and the objective prediction results. On the contrary, it shows that there is no good linear correlation between the stereo scoring results and the objective prediction results [11].
For a sample with a sample size of N, N original data X and Y are converted into grade data x and y, and the Spearman grade correlation coefficient is expressed as [12] Among them, x represents the average value of rank data x, and y represents the average value of rank data y [13].
Based on the training sample data set to construct the corresponding regression function f ðX k ′Þ of X k ′, the calculation formula is as follows [14]: 2 Journal of Sensors w T is the transposition matrix of the weight vector, b is the offset, and kðX k ′, X l ′Þ is the radial basis kernel function [15].
Test and analyze each stereo image/video in the test sample data set, and predict the corresponding objective prediction evaluation result Q [16].
where t represents the number of stereo image/video pairs contained in the test sample data, that is, (M-Q) pairs. X i represents the combination of the i-th pair of perceptual feature vectors of the stereo image/video content of the test sample data [17].
Suppose there are two spatial scenes A and B each containing n spatial targets. At the same time, considering the similarity and relationship of the targets and also considering the importance of different spatial features to different scenarios, the expression is as follows [18]: Among them, K G is the weight of target similarity; SIMGðA, BÞ is the target similarity of the two scenes; K R is the weight of the relationship similarity; SIMRðA, BÞ is the similarity of the relationship between the two scenes [19].
Among them, K S is the weight of similar target size; SIMSðA, BÞ is the similarity of target size in two scenes; K θ is the weight of similar target direction [20].
From engineering considerations, digital image processing generally includes three levels: image processing, image analysis, and image understanding. Image processing is to "process" the image itself to improve its visual effect or form of expression and lay the foundation for image analysis. The output of image processing is still an image with a new face. Image enhancement, image smoothing, image sharpening, image restoration, image transformation, image coding, image calculation (arithmetic or logical operations on pixels), etc. fall into this category. Image analysis is to detect the target image in the frame and calculate various physical quantities to obtain an objective description of the image. The main content includes image segmentation, image feature (geometric shape feature, boundary description, and texture feature), and extraction. The output of image analysis is a set of data describing the characteristics and properties of the target image. Image understanding is based on image analysis, understanding the content represented by the image, analyzing the relationship between the images, and obtaining an explanation of the objective scene and then using knowledge and experience, using mathematical, physical, virtual, and reasoning methods. Harmony means establish a rigorous or vague deductive mode and associate, think, and infer by the computer, so as to realize the description, understanding and service of the objective world. Image understanding is closely related to pattern recognition, artificial intelligence, and expert systems.
The mathematical expression of gray linear transformation is In the formula, f a and f b are the low and high gray values of the linear transformation section of the input image; g a and g b are the low and high gray values of the linear transformation section of the output image, respectively.
Since the digital image is in discrete form, differential operation is used instead of differential operation, which is expressed as After the hybrid feature construction is completed, the method based on key frames can be used to perform the hybrid feature matching and calculate the camera motionrelated parameters. Specifically, the degree of dispersion of each stable feature line is determined by the number of optimized feature points, the number of stable feature lines, and the length of the feature line: Among them, N i is the number of points after the ith stable characteristic line is discrete.
When performing image forward matching, assuming that the search window of the previous frame image feature I k−1 ðx k−1 , y k−1 Þ is w k−1 ðx k−1 , y k−1 Þ, the expression for calculating the sum of squares of the feature pixel differences of the two frames using T k−1 ðu, vÞ as the matching template is as follows.

Journal of Sensors
After traversing the image feature in the current frame image matching search window, the smallest S k ðu, vÞ is obtained. When the value is less than a certain threshold, the image feature corresponding to the value is used as the candidate matching feature. At the same time, calculate the sum of squared pixel differences S k−1 ðu, vÞ with T k ðu, vÞ as the matching template: The actual structure of the computer network is shown in Figure 1. Computer network technology is a combination of computer processing technology and communication technology. Therefore, from a traditional point of view, the basic structure of a computer network can be divided into two parts: data processing and data communication according to logical functions; that is, it can be divided into two parts: resource subnet and communication subnet. The communication subnet provides network communication functions and completes communication tasks such as data transmission, exchange, control, and transformation among hosts on the entire network, responsible for the data transmission, forwarding and communication processing of the entire network.

English Teaching
Mode. Unremittingly carrying out the evaluation of English teaching quality in higher vocational colleges, on the one hand, can ensure that the leadership of the college and the teaching management department fully understand the English teaching situation and provide new ideas for the school's English reform and innovative development. On the other hand, the positive interaction between teachers and students relies on a good teaching atmosphere. Teachers' continuous innovation of teaching content, teaching methods, teaching attitudes, and active cooperation with students are the prerequisites for the successful completion of the teaching process, and teachers continue to improve in practice. Teaching ability is a necessary guarantee for students to improve their English level in practice. Teaching and learning complement each other and promote each other to achieve the common progress of teachers and students [21,22].
Constructive English teaching mode requires higher vocational teachers to change traditional teaching concepts and grasp the goals of higher vocational English teaching. In English dialogue and communication, teachers communicate and interact with students as equals. They do not impose their opinions, thoughts, and feelings on students, but allow students to truly express their opinions, thoughts, and feelings, so as to obtain progress and construction knowledge structure [23,24]. For example, in oral English teaching activities, teachers can design corresponding ques-tions around the theme and provide some relevant knowledge information to minimize the correction of language errors so that students can express fluently and freely. Only in this way can English teachers understand the students' real ideas, organize classroom activities smoothly, and control the learning progress [25,26]. Computer-aided English teaching mode emphasizes that students are the main body of the learning process and the active builder of knowledge, so it is conducive to students' active exploration, active discovery, and the cultivation of students' foreign language application ability. However, this kind of teaching mode overemphasizes students' learning and often ignores the role of teachers, the emotional communication between teachers and students, and the role of emotional factors in the learning process.
The English teaching mode under the network environment is shown in Figure 2. In the part of personalized courseware generation, personalized courseware is automatically generated from students' personalized database, learning strategy database, knowledge object database, and educational resources distributed on the Internet or generated with the participation of teachers or indirectly generated by students and stored in the personalized courseware database. This is a learner-centered learning environment. When a learner enters the learning system, the personalized information collection module and the evaluation tracker first collect the students' personalized information through some strategies and then form a new learning record according to the learners' learning needs, the learners' previous learning situation (from the student database), and their learning process. Then, the course controller will present the relevant learning content to the learner from the personalized courseware library according to the new learning records and add the new learning situation to the student library. The course controller will also add a class of representative personalized courseware formed in the process of students' learning to the personalized courseware library.

Improvement Experiment of English
Teaching Mode 3.1. Subjects. This experiment randomly selected 40 sophomore students, including 10 boys and 30 girls. Divide them into two groups evenly and record them as the experimental group and the control group, with 20 students in each group. Their English proficiency is almost the same, and there is no significant difference.

Computer Network Teaching Environment.
The database server and page server under the three-tier architecture run on the same computer at the same time. During the development process, the client browser is used for testing. The hardware environment of the system development environment is as follows: (1)

Teaching Experiment.
In the preexperiment test, a unified English final test paper was selected, and the scores of 40 students were entered into the computer. After that, they will be taught in different ways in groups. The experimental group chose the English teaching mode of computer network-assisted teaching. In the teaching process, the QQ class group commonly used by students was selected as the teaching platform for online communication and learning. QQ can be used in mobile phones and computer tablet light devices and can be used by teachers to transmit instructional videos, documents, pictures, PPT, etc. conveniently. Students can download videos and other files in the group for self-learning before class. Teachers and students can communicate on course learning in the QQ group. The timeliness of QQ facilitates the communication and effectively solves the student's self-learning platform before class. The control group still used traditional teaching methods to learn. After the experiment, the students in the experimental group and the control group were tested after writing in their respective classrooms, and the teachers gave their scores based on the college entrance examination scoring standards. Use SPSS 17.0 software to analyze the students' scores, and compare the changes of the students' pretest and posttest scores to get the experimental results.

Interview.
Among the students surveyed, 5-10 students were randomly selected for interviews. Before and after questionnaire surveys and student interviews with teachers and students, interviews were conducted with 1-2 teachers in each school on the use of multimedia in English teaching. The main content of the interviews between students and teachers is carried out around the open questions in the questionnaire.

Teaching Evaluation.
In order to achieve objective evaluation as far as possible, teachers pay attention observing students' state in each class and give objective evaluation through students' submission of homework, test scores of chapters, participation in group activities, and so on. Therefore, in this stage of teaching activities, teachers' timely and corresponding encouragement is very important for students, which will mobilize students' learning initiative to a certain extent. The development of group cooperative learning helps students learn to help each other and have win-win cooperation. In groups, tasks are done together, but it also helps students develop a correct sense of collective responsibility and fair play. Students can evaluate team members from the aspects of activity enthusiasm and learning participation. Students' self-assessment can be evaluated objectively by observing their preclass learning level, chapter task completion, class attendance, group cooperation participation, and so on. Table 1 shows the overall views of students on the learning resources of the computer network teaching platform. From the average point of view, the average number of network platform resources is 2.88, and its average value is less than 3, which means that students think that the number of online network resources is too small. This shows that students think that online learning content is difficult; the average value of online platform resources for student learning is 3.29, in which the average value is greater than 3, which shows that students think that the use of network platform resources is better for learning. Figure 3 shows the statistical results of the pretest and posttests of the degree to which different models are Under the traditional teaching mode of teaching by teachers, 25% of the students think it is helpful to improve the selflearning ability, and nearly half of the students feel average about improving the self-learning ability. According to the questionnaire data and interview, results show that offline classroom teacher-student interaction is good, and the effect of teacher-student interaction is good. At the same time, the interview shows three problems in the classroom: teachers have a single way of asking questions; the requirements of teachers' tasks are not clear enough; and the offline class hours are less, and the connection degree with online autonomous learning is not enough. Table 2 shows the comparison results of pretest and posttest English learning interests between the control group and the experimental group. It can be seen from the results of the pretest English learning interest that the results of the t-test can be seen, t = 0:457, p = 0:927 > α = 0:05, so it can be considered that there is no significant difference between the two means, that is, the difference in the current learning interest of students in the control class and the experimental class can be ignored. And the average value is   Journal of Sensors small, so it can be considered that before the experiment, the experimental class and the control class have basically the same interest in English learning as a whole, and both show low interest. The results of posttest English learning interest can be seen, t = 2:573, p = 0:006 < α = 0:05, which can be considered that the two means have a significant difference. The description of students' online autonomous learning is shown in Figure 4. In terms of learning motivation, the average of students' online learning initiative consciousness (3.04) and learning interest (3.05) is slightly higher than 3, which indicates that students' English learning motivation is general; in terms of learning methods, the average value of students choosing to solve problems independently (3.69) is greater than 3.5, while the average value of choosing to seek help from others is 2.72. This shows that in the process of online autonomous learning, most students choose to solve problems independently and seldom seek help from others; in terms of learning content, the average value of goal setting (3.46) is greater than 3, and the content absorption (3.15) is greater than 3, which indicates that many students have clear learning objectives and requirements in online autonomous learning and can fully absorb online learning    7 Journal of Sensors content; in the management of learning process, the mean of student time planning (3.29) is greater than 3, and the selfmonitoring average is 3.41 which is greater than 3, which shows that many students can self-monitor and reasonably plan online self-learning time; however, the situation of self-examination is not optimistic, and the mean of selfevaluation (2.71) and self-evaluation (2.94) is less than 3, which indicates that most students do not self-reflect and evaluate online self-study. Figure 5 shows the comparison of the results of the test subjects before and after the test. It can be seen from the figure that after a period of study, the test subjects' English scores have improved. However, the English scores of the experimental class have improved more significantly. With the emergence of the gap between the English performance of the experimental class and the control class, let us clearly realize that computer-assisted teaching is more conducive to improving student performance. The gap between the over-all average score and the excellent rate is constantly increasing, which means that the effect of English differential teaching under the guidance of computer network-assisted teaching on English learning performance is positive.

Results and Discussion
The test results of the two classes were analyzed by SPSS statistical software, and the results are shown in Table 3. The average score of the experimental class was 94.22, and the average score of the control class was 89.97; the highest score of the two classes was 100, but the lowest score of the experimental class was 84, which was higher than the lowest score of the control class of 80. Therefore, compared with the traditional teaching mode, the flipped classroom teaching mode based on micro lesson has better effect on promoting students' performance.
The distribution of scientific research focus of English teachers in different industries is shown in Figure 6. Professional English teachers who are transformed from public English teachers will still focus on language points in their     Journal of Sensors teaching. For scientific research, they mainly study linguistic theories and classroom teaching reforms; professional teachers are part-time teachers who teach industry English.
In the process, more emphasis is placed on the explanation of professional knowledge and not much attention to language points, which has led to the problem of "focusing on professionalism and neglecting language" in many classrooms. The scientific research of these teachers is mainly about classroom teaching reform and curriculum construction; from industry English teachers on the front line of enterprises can integrate theory with practice in teaching and ingeniously integrate language knowledge and professional knowledge to teach students vividly. This part of teachers is the most popular among students.

Conclusions
After the integration of computer networks and foreign language education, the educational model has changed, and the traditional teacher-centered model has been broken. Foreign language education not only emphasizes the student-centered education model but also emphasizes the use of the latest information technology to promote students' independent learning of information. Taking into account the rapid changes in computer technology in the field of computer-assisted English education, teaching methods continue to introduce new knowledge, and the basic purpose of training teachers is to cultivate the ability of English teachers to learn for life. Because the content of the training is briefly explained, the teacher who receives the training can remember the content of the training. In order to enable trained teachers to continuously improve the level of lifelong education, apply the knowledge learned by trained teachers to encourage their own learning. After the integration of computer network and foreign language teaching, the structure of teaching materials has changed into three-dimensional and multimedia teaching materials. However, from the current situation, the three-dimensional form of teaching materials is reflected in the physical concept of the composition of the teaching materials, especially the network teaching content which has become a copy of the paper teaching materials, and the effective composition of the teaching materials should be that the network content is an extension of the paper textbook rather than a copy.

Data Availability
No data were used to support this study.

Conflicts of Interest
The author declares that there are no conflicts of interest regarding the publication of this article.


THE CONCEPT OF TEACHER TRAINING CO-FINANCED 1 BY THE EUROPEAN UNION IN THE TEACHER TRAINING 2 SCHOOL MODEL

of the of at of of co-financing and of the for project co-financed the on the expectations of in the of the pilot studies conducted on a sample of 52 people. recommendations for the organization of the training process for the of the Training as a system of Poland is to ensure an effective system of education and continuous professional development of teachers which will respond to real needs of a school and will allow teachers to improve their professional competences and develop their workshop. The implementation of the Teacher Training School Model in Poland will have a social dimension directly affecting teachers’ and indirectly pupils' key competences. 30 Originality/value Based on the results of the pilot studies, satisfactory conclusions for the needs of teacher education in EU projects were drawn.

Introduction
Professional training of teachers is an integral part of the education system which significantly affects the quality of a teaching process. This is particularly important as teachers' high competences are correlated with students' learning outcomes. Professional development is a process of increasing employee qualifications, the knowledge of which must be constantly supplemented, developed and modernized at the workplace (Wysocka, Hajdukiewicz, 2016, p. 2). Many researchers emphasize that the improvement of employees' workshop will serve to strengthen their competences more if it is of practical nature, based on observation and exchange of experiences among teachers. The research results clearly show that high efficiency is built by a teacher not only through participation in training, but also through cooperation with other teachers and the opportunity to participate in joint projects at school. In Polish schools, however, such activities are rarely undertaken on the school premises (Hernik, Malinowska, Piwowarski, 2013, p. 5).
The priority of educational policy in Poland is to ensure an effective system of education and continuous professional development of teachers which will respond to real needs of a school and will allow teachers to improve their professional competences and develop their workshop. Schools, as well as teacher training institutions, should develop towards learning organizations, and by undergoing permanent evaluation, improve their development (Hajdukiewicz, Wysocka, 2016, p. 7). At the same time, it is important to promote the selfeducation system of teachers (Fila, Matuszczak, Rybińska, 2015, p. 43), and the principals of these institutions should play a key role in education (Fischer, Taylor, 2012, p. 235;Kordziński, 2010, p. 10).
The possibility of co-financing the teacher education system from EU funds allowed the commencement of work on a new model of improving and raising competences by teachers. Work in this area was carried out by the Ministry of National Education in Warsaw as part of the project: "Supporting the creation of training schools" implemented by the Education Development Centre (ORE) in 2016-2018. The developed concept was tested during a 10-month pilot project (in 2017-2018) which allowed the formulation of conclusions and recommendations for the creation of Teacher Training Schools in Poland (www.ore.edu.pl).

Assumptions of the Teacher Training School
The Teacher Training School according to the model is: "a set of planned activities of schools (training schools and cooperating schools) and institutions supporting the learning process of teachers and students (...), "a school where future teachers will experience practical verification of the theory learned during their studies, and working teachers can improve their work techniques in cooperation with other teachers (…), "in the training school, a student under the guidance of a teacher will face educational practice in the areas of education and upbringing, and teachers will develop their workshop and share their ideas with other teachers" (Model szkoły ćwiczeń, 2019, p. 4).
The training school must take into account the cooperation and effective use of specialists and institutional resources from the local education environment: -leading authorities (local governments), -support centers (psychological and pedagogical counseling center, pedagogical library, teacher training center), -universities (teaching schools). Systematic cooperation of the institution with the training school is crucial for its development because the involvement of experts and resources will allow, on the one hand, to build the potential of the training school, and on the other -to popularize its achievements in the local environment (Zasady współpracy szkoły ćwiczeń, 2019, p. 2). The education support system constructed in this way will be consistent with the directions of education policy, conclusions from pedagogical supervision, local education strategies and will respond to the needs of teachers from supported schools. The organization of the support system depends on the involvement of the leading authorities because local governments together with teacher training institutions, pedagogical libraries and psychological and pedagogical counseling centres should initiate cooperation and strategically plan joint activities for the development of local education (Gocłowska, Soćko, 2015, p. 43).
The model training school is to be a place for disseminating innovative activities supporting the development of students' key competences with particular emphasis on teaching foreign languages, mathematics, natural sciences, information and communication technologies and sharing knowledge and skills by teachers. At the same time, the training school should actively cooperate with other schools, support institutions and universities. In the scope of activities supporting the development of students' key competences the principals and teachers of the training school are obliged to promote and implement the use of innovative didactic, organizational and educational solutions in other schools. The training school should also be perceived as an institution implementing the local education strategy in terms of preparing the graduate to function on the labour market. Currently, 42 training schools across Poland are being created from the EU funds. They will carry out tasks in the field of professional development and teacher training and it is also planned to include each of these schools in the support process.

Possibilities of financing Model Teacher Training Schools from the European Union funds
According to the competition regulations, activities that can be financed under the established training school (consisting of at least one and a maximum of four model schools), which will support at least seven cooperating schools are the following: -diagnosis of the needs of supported schools which will be affected by the created training school, -workshops for trainers (teachers) and teaching staff of training schools in the field of methods and forms of didactic work, -conducting activities of the training school, including training and practical development for professionally active teachers in the field of developing key competences for students, practical training of students from pedagogical studies with the use of didactic and methodological materials supporting the professional development of teachers and practical preparation of students within training schools, evaluation of activities undertaken in order to verify the goals and determine the next possible actions. In order to implement the above-mentioned assumptions in training schools for a particular school year, it is necessary for the school to conduct support for teachers of each of the 4 subject areas (foreign languages, mathematics, natural sciences, information and communication

The concept of teacher training on the example of the Teacher Training School Model
As part of the project "Teacher Training School Model in Niemcz and Osielsko", a number of activities have been planned that will lead to the achievement of the project objective: "Improving the functioning of the school support system in the development of key competences and universal skills on the labour market by preparing, supporting and jointly creating a training school by the Agata Mróz Primary School in Niemcz and the Polish Olympians Primary School in Osielsko, thanks to the implementation of comprehensive and complementary tasks: equipping classrooms in the training school (task 1), strengthening the competences of 52 school principals and teachers in the field of methods and forms of didactic work (task 2) and professional training of 62 school principals / teachers of supported schools in the field of conducting effective methods of work, developing key competences and universal skills of students (task 3) in the period from 01/01/2021 to 31/07/2022 ".
One of the tasks planned in the project is to strengthen the competences of school principals and teachers in the field of methods and forms of didactic work as part of workshop training.
Teachers' competences must be strengthened because teachers as trainers of the training school will conduct demonstration lessons for students and teachers of supported schools. Thanks to the implementation of task 1, demonstration lessons will take place in classrooms equipped accordingly to each of the 4 thematic areas: During the pilot studies teachers raised numerous concerns that should be overcome in the project. The most important barriers include: -time barriers -reluctance to participate in direct forms of support due to the significant workload, hence the project should provide a support schedule in consultation with teachers, and most of the methods and forms of activity can be conducted online on the internet platform (www.doskonaleniewsieci.pl), -financial barriers -insufficient financial resources for teachers' participation in professional development, hence the support in the project (training, materials, catering) must be free, -mental barriers -teachers' reluctance to cooperate and share knowledge with other teachers and concerns about the low value of training, hence the integration of the teaching environment is planned, and training organizers must ensure a high level of education corresponding to the diagnosed training needs of teachers.
The expectations regarding the high substantive value of the training are confirmed by the research carried out by House of Skills which shows that the lecturer (trainer) plays a significant role in the training process. He should: -have a charismatic personality (be open, warm, specific, determined, courageous, resourceful, aloof, professional to win the group's trust), -have flexibility and the ability to adapt the content of education to the needs of recipients and their problems, take care of the appropriate pace of training and involvement of participants, -have appropriate substantive knowledge and expert experience to be an authority for the participants.
Potential participants of the training support had the following expectations towards the organization: -short training forms (up to 6h), -post-work training (after 2 p.m.), -small training groups (up to 15 people), -homogeneous groups consisted of teachers who teach similar subjects e.g. mathematicians, natural scientists, linguists.
The teachers' needs in terms of content discussed during trainings indicated the legitimacy of supporting seven issues in the amount of about 6 clock hours in the following topics: -building innovative teaching programmes and lesson plans, -creating one's own interactive teaching materials and making use of available materials on the web, -the use of scientific experiment methods, experience and observations from life in education, -the use of effective learning strategies according to students' sensory preferences, including the latest trends in motivating students to learn, -shaping interpersonal and social skills of students -practical dimension (individual and group exercises, projects, games, simulations), -the use of modern information and communication technologies, including support for modern ICT tools (interactive board, 3D printer, smartphone, tablet, computer clouds), -online safety rules -cyberspace and cyber opportunities as well as ergonomics rules in the use of computer equipment.
According to teachers, the most important attributes (features) of good training are: -teacher trainer -able to establish very good rapport with participants, creating a nice atmosphere, well prepared, being an authority in his field, -training methodology -training conducted with the use of multimedia, activating methods of working with participants (experiments, exercises, discussions), valuable training materials at the end of the training, -training content -training should give an opportunity to share knowledge and experiences with other participants, provide modern knowledge helpful in everyday work, theoretical knowledge should be supported by examples from life, training should encourage reflection and inspire further learning.
The above-mentioned features of good training are consistent with 17 attributes of training diagnosed in literature on the subject (Fazlagić, 2014), hence they may be helpful in preparation of training evaluation tools for teachers.

Summary
Assessment of the effectiveness of the created teacher training school on other schools, with the use of integrated resources of local institutions established to support the work of the training school, will be possible only after the completion of the project. The planned evaluations of 42 training schools established in Poland will allow to verify long-term effects, including the effectiveness, relevance and durability of the changes introduced in the process of professional development and teacher education.
Based on the results of the pilot studies, the following conclusions were formulated for the needs of teacher education in EU projects: -the schedule and hours of support for teachers should be communicated to teachers in advance and adjusted to their preferences which will allow participants to be involved, -training and materials as well as catering during the training should be free of charge for participants which will minimize the costs of teachers' participation in training, -participants should be recruited within homogeneous groups teaching similar subjects which will enable the exchange of experiences and improve the process of sharing practical knowledge among teachers, -the methodology of classes should be based on a short introduction (lecture), individual or group exercises, using case studies and involving participants in discussions and exchanging good practices which will allow to refer to the experiences and knowledge of participants in accordance with the Kolb cycle (experience-reflection-generalizingapplication), -didactic means and tools must be varied, the trainer should use a variety of media as much as possible (flipchart, multimedia presentation, interactive board and computer programmes) which will improve perception and activate participants, -the trainer should establish very good rapport with participants, create a pleasant atmosphere, be an expert and authority in their field which will have a positive impact on teachers' perception of the content of education, -the content of education should be adapted to the diagnosed needs of participants and the training should convey practical knowledge that can be applied in everyday professional work, encourage reflection and inspire further learning.


Effectiveness of a 3-month antifalling program in the mobility, balance confidence, and muscle performance of older adults

This study determined the effectiveness of a 3-month group-based multicomponent exercise program in the mobility, balance confidence, and muscle performance of older adults. A total of 40 participants (mean age=70.60±6.25 years completed pre- and posttest clinical intervention measures of mobility using the Timed Up and Go (TUG) test, balance confidence using the Activities-specific Balance Confidence scale, upper limb strength (handgrip dynamometer), and lower limb function (30-sec chair rise test). Data were analyzed using paired t-test and based on TUG criteria for risk of fall (low- and high-risk groups). Significant improvements were found in all measures (All P<0.05) following the 3-month program. Measures according to the risk of fall categories were also significantly improved (P<0.01), except the left handgrip strength (P>0.05). The low-risk group showed a higher improvement in mobility (14.87% vs. 11.74%), balance confidence (34.21% vs. 26.08%), and lower limb function (96.87% vs. 21.20%) but was not significantly different from the high-risk group (P>0.05). A group-based multicomponent exercise program benefited the physical functions of older adults at low- or high risk of falls.

INTRODUCTION
A fall, which is defined as an event resulting in a person coming to rest inadvertently on the ground or floor or other low level (World Health Organization, 2021), is a major and serious health issue among the older adults. Each year, at least one third of individuals over the age of 65 years living in communities experience falls, and this proportion increases as age advances (Gamage et al., 2019). The study of Motalebi et al. (2018) on community-dwelling older adults in Malaysia revealed that 18.6% of such individuals had a history of falls associated with advanced age, especially those aged 75-84 years and ≥85 years and women. Older adults who experience recurrent falls demonstrate significantly reduced lower extremity strength, dynamic balance, endurance, balance confidence in daily activities, and physical activity levels (Samah et al., 2018). Thus, to prevent fall incidences and optimize their daily functioning, older individuals require effective intervention.
Previous studies suggested that exercise training is the single most effective intervention for improving balance performance and other fall-related physical functions among older adults (Aviles et al., 2019;Halvarsson et al., 2015;Sherrington et al., 2019;Yang et al., 2018). Exercise training for older adults should be adequately long and specifically designed based on individual needs. Longterm training may increase muscle activation and improve coordination through neural adaptation within the central nervous (Krause et al., 2018) or neuromuscular system, which is responsible for coordinating balance performance (Behrens et al., 2017). However, no specific fall prevention program for older adults exists (Shum-way-Cook et al., 2007;Thompson et al., 2019). Thus, to minimize fall risk, development of a multicomponent exercise training program may be the best approach, as such a program combines several types of exercises, such as strengthening, aerobic, balance, and flexibility exercises. The incorporation of strengthening exercises in a balance training program can increase gait velocity and balance confidence in participants after completing a 5-week training routine (Gillespie et al., 2009). Accordingly, strengthening lower extremities may accelerate the movement of fast-twitch and large muscle fibers, thereby enhancing muscle coordination and the neuromuscular system and leading to improved static and dynamic balance (Paterno et al., 2004).
The purpose of this pretest-posttest intervention study is to determine the effectiveness of a 3-month group-based multicomponent exercise program conducted at a senior citizen center in Cheras, Malaysia, in improving balance and muscle functions in community-dwelling older adults. Specifically, the objectives of this study are (a) to examine the effects of the exercise program on mobility, balance confidence, upper limb strength, and lower limb muscle functions and (b) to compare the effects of the exercise program on mobility, balance confidence, and muscle functions between older adults with high and low fall risk.

MATERIALS AND METHODS
This study analyzed data from a pretest-posttest intervention community exercise program conducted at a senior citizen center in Cheras, Malaysia, from November 2019 to February 2020. The center provides a venue for community outreach services for older adults by various government agencies and nongovernment organizations. As this center provides a voluntary community service, the number of participants in the study was based on availability. A total of 40 participants completed the 3-month exercise program. The participants who were included in the analysis were registered at the senior citizen center in Cheras, Malaysia. This study included male and female individuals between the ages of 60 and 83 years who completed the three-month program (2-5 sessions a week). In the implementation of the program, older adults diagnosed with a chronic medical illness, such as neurological impairment or cardiac problems, were restricted from participating in the groupbased training. The center's common practice for older adults with chronic diseases participating in the program is the individual approach. Permission to obtain data was given by the director of the center, and the study protocol was approved by the Research Ethics Committee of a university (approval number: REC/05/2021 [UG/MR/437]).

Demographic data
Demographic data such as age (years), ethnicity (Malay/Chinese/ Indian), gender, employment status (working/retired), medical illness (e.g., hypertension and/or diabetes mellitus), and body mass index (BMI) were obtained from the personal file of each participant.
The exercise program outcomes measured before and after 3 months included mobility (balance performance), balance confidence, and muscle performance (handgrip strength and lower limb functions).

Mobility
Mobility or balance performance was measured using the Timed Up and Go (TUG) test, which aims to determine balance and mobility control during dynamic movement (Sousa and Sampaio, 2005). The TUG test measures the amount of time (in seconds) it takes for a participant to start to stand up from a standard armchair (with a seat height of approximately 46 cm or 18 in and an arm height of 65 cm or 25.60 in); walk a distance of 3 m (approximately 118 in or 10 feet); change direction at the end of the 3-m walk, marked by a cone (turn); head back to the chair; and sit down. After a demonstration by the physiotherapist, the participants were allowed one practice trial. The time it took to complete one attempt was recorded using a second-hand digital watch. The cutoff value for high fall risk is >13 sec (Belza et al., 2006).

Balance confidence
Balance confidence was assessed using the Activities-specific Balance Confidence (ABC) scale. The ABC scale comprises a series of questions assessing subjective fear of falling based on 16 items rated on a scale of 0 to 100, reflecting balance confidence to perform certain activities (Nilsagård et al., 2012). For each question, the participants were required to indicate their level of confidence in doing a task without losing their balance or stability by choosing one percentage point from the 0%-100% scale. If they did not engage in a certain activity, they were instructed to imagine how relaxed they would be if they engaged in the exercise. If they typically used a walking aid or relied on others for support during training, they were asked to indicate their level of confidence in participating in the exercise with the use of such supports. A score of <67% indicated high fall risk. Several studies reported that the ABC scale demonstrates satisfactory validity and reliability for older adults (Liao et al., 2017) and individuals who suffered from a stroke (Tsekoura et al., 2018), while others reported that the ABC scale is the most accurate measure for identifying individuals at risk for multiple falls (Moiz et al., 2017).

Muscle performance
Handgrip strength was measured to represent upper limb muscle strength. A hand dynamometer was used in this study (Lafayette Instrument J00105 Jamar hydraulic hand dynamometer), which is a gold standard tool for handgrip strength assessment, with excellent validity and reliability in clinical and research settings (Guderian et al., 2013). The participants, in a sitting position, were asked to hold the dynamometer in their hand at the second bar, with adducted shoulders, elbow flexed to 90 degrees, and forearm in a neutral position with slight wrist extension. The participants were instructed to use full isometric effort to squeeze the dynamometer and sustain their grip for 2 to 5 sec. The physiotherapist demonstrated the procedure, and the participants performed three trials for each hand, with a 60-sec rest interval between trials. The average of the three trials was recorded as the final score. The cutoff point for handgrip strength is 28 kg for men and 16 kg for women (Yoo et al., 2017).
The 30-sec chair stand test was used to measure lower limb functions (Jones et al., 1999;Nilsagård et al., 2012). For the test, the participants were instructed to sit on a secure straight-backed chair (with a height of approximately 17 in) with their arms folded over their chest. Next, the participants were instructed to rise to their full height then return to a sitting position, and repeat the movement as many times as possible within 30 sec. The participants completed a practice trial of two repetitions following the physiotherapist's demonstration, followed by one 30-sec test trial. The number of stand-sit exercises performed within 30 sec was recorded as the final score.

Exercise program
The training program designed for the participants included multicomponent exercises, that is, a combination of strength, aerobic, balance, and flexibility exercises. Several studies found that multicomponent exercises are safe and effective for older individuals living in a community (Gonçalves et al., 2021;Suzuki et al., 2013) or institution (Justine and Hamid, 2010). Table 1 summa- Cool-down session Duration: 10 min • Range of motion exercises for the neck, shoulder, wrist, trunk, hip, knee, and ankle • Exercise is ended with a breathing exercise rizes the exercise training program implemented for the participants.

Data analysis
Data from the assessment forms were processed and analyzed using IBM SPSS Statistics ver. 20.0 (IBM Co., Armonk, NY, USA). The participants' characteristics were described using frequencies, percentages, means, and standard deviations. The Shapiro-Wilk test indicated that the pre-and posttest data were normally distributed, thus, the paired sample t-test was used to determine the significance differences in the outcomes for the one-group comparison. The 50th percentile (13 sec) of the TUG test score was used as the cutoff point to divide the participants into low and high fall risk categories. The participants who scored <13 sec were classified under the low-risk category, whereas those who scored 13 sec or above were classified under the high-risk category. The comparison within-group for each measure was analyzed using the Wilcoxon sign-ranked test, while, the Mann-Whitney U-test was used to compare differences between groups. The differences in the percentage of changes (pre-and postintervention) were calculated according to the following formula: (posttest mean value-pretest mean value/pretest mean value×100), for each fall risk category and presented in a table. Significance level was set at P<0.05.

RESULTS
A total of 40 participants were included in the analysis. The characteristics of the participants are presented in Table 2. The mean age of the participants was 70.60±6.25 years. The majority of the participants were female, between the ages of 67 and 75 years, had a normal BMI, Chinese, retired, had hypertension, and had high risk for falls. Table 3 shows the results of the pretest and posttest based on the paired t-test. The results revealed a significant improvement in mobility (P<0.001), balance confidence (P<0.001), left (P=0.035), and right (P<0.001) upper limb strength, and lower limb functions (P<0.001) in all the participants. Table 4 reports the ddifferences for within and between groups and the percentage of change of each variable. The low-risk group (vs. high-risk group) showed a higher improvement in mobility

DISCUSSION
Generally, the program was deemed beneficial in improving the physical functions of the participants. The findings of this study may support the need of older adults residing in a community to be involved in activities organized by a senior center as part of the government's efforts to promote active aging in the community.
The findings also provided preliminary data showing that a supervised group-based exercise program was safe and potentially influential, as no adverse events among the participants were reported. A previous study on group-based activity and home-based exercise revealed that 15 of the 17 variables improved after a 12-week intervention and suggested that such types of programs may provide positive feedback and be economical for older adults (Tsekoura et al., 2018). Furthermore, a center-based program could inspire participants effectively through excellent supervision, as instructors' encouragement and input will allow learners to evaluate their development, which may serve as a motivational enhancer (Farrance et al., 2016). Moreover, a group-based program was reported to promote group adhesion and enhance psychological wellbeing and socialization (Kanamori et al., 2015;Mehra et al., 2016).
The data analyzed in this study were obtained from the partici-pants after their completion of the 12-week training program, which is an ideal duration for observing physiological improvements. A previous study conducted for 12 weeks revealed improvements in muscle power, speed, and agility as well as in glucose, insulin, total cholesterol, triglyceride, vitamin D3, and C-reactive protein levels in older patients in long-term healthcare facilities who suffered from a frailty disorder (Ferreira et al., 2018). Another study showed that a 12-week resistance exercise program can positively impact cellular senescence and improve mitochondrial-drying protein deficiency and enzyme-related protein levels (Hooshmand-Moghadam et al., 2020), which may explain improvements in muscle strength and functions. In this current study, 22 participants were categorized as high risk for falls, as their TUG score was >13 sec, whereas 18 participants were categorized as low risk for falls (<13 sec). Hence, further analyses were conducted to compare the improvements between the two fall risk groups.
The analysis showed a significant improvement in mobility of 12.89% (P<0.01) for all the participants following their completion of the program. The findings supported those of previous studies showing that an exercise program consisting of balanced components and different types of exercises, such as strengthening (Hallage et al., 2010;Krause et al., 2018) and step-aerobic exercises, can improve dynamic balance (Hallage et al., 2010;Seguin et al., 2012). Furthermore, exercise prescriptions, as employed in this current program following the recommendation of the American College of Sports Medicine (Nelson et al., 2007), should include strength, balance, aerobic, and flexibility training.
The analysis on mobility based on fall risk showed that improvement was higher in the low-risk group (14.87%) compared with the low-risk group (11.74%). Older adults with low fall risk may have satisfactory mobility, whereas those categorized as high risk Values are presented as mean± standard deviation. The within-group differences were analyzed using the Wilcoxon-signed rank test. The percentage differences in each variable (pre-and postintervention) were calculated according to the following formula: (posttest mean value-pretest mean value/pretest mean value)× 100. The between-group differences were examined using the Mann-Whitney U-test. *P< 0.05, significant difference.
for falls may be frail, as indicated by their low lower limb function score and considerable time to complete the TUG test at the beginning of the program. A previous study emphasized that poor balance in frail older individuals is associated with fear of falling (Kumar et al., 2008), whereas intrinsic factors such as medical illness, medication, balance, and gait problems can also reduce balance performance (Gamage et al., 2019). A previous study found that fear of falling may lead to reduced balance confidence and suggested that people with diminished confidence have deteriorated balance and fear that their balance will be further reduced (Ahn and Kim, 2015). Fear of falling is not necessarily present in older individuals with a history of falls and can be present in older individuals without such a history (Kumar et al., 2008). As hypothesized, after completing the exercise program, the participants showed a significant improvement in their balance confidence signifying their level of fear of falling, as one of the outcome measures in this study, which is consistent with the findings of other studies (Aibar-Almazán et al., 2019;Smulders et al., 2010). Accordingly, people with low balance confidence can avoid falls by restricting their involvement in activities despite having impaired balance or being at risk for falls (Cho et al., 2012). Meanwhile, the percentage of changes in the low and high fall risk groups was 34.21% and 26.08%, respectively, thereby indicating that the low-risk group showed a higher improvement than the high-risk group. This result is similar to that of previous research revealing a considerable improvement in nonfallers compared with fallers in terms of their level of confidence (Moiz et al., 2017).
Grip strength can reflect an individual's overall muscle strength and functional ability (Guderian et al., 2013). Moreover, grip strength is typically used as a diagnostic method for assessing early vulnerability symptoms in aging individuals, as the aging-induced weakening of grip strength may contribute to weak bimanual coordination among older adults (Yang et al., 2018). In this study, following their completion of the program, the participants exhibited a significant improvement in their grip strength for both hands (P<0.05), with a slightly higher percentage for the right hand (5.78%) compared with the left hand (4.51%). A previous study showed a significant improvement in the participants' dominant hand compared with their nondominant hand (Incel et al., 2002;Oppewal et al., 2013;Yoo et al., 2017). We believe that most of the participants in this current study were right-hand dominant. The literature suggested that an often-used general rule indicates that the dominant hand is 10% stronger than the nondominant hand (Incel et al., 2002).
In terms of the current exercise program, the addition of resistance training using elastic bands may have enhanced the participants' upper limb strength (Liao et al., 2017). The use of elastic bands to provide resistance during exercise for older adults, especially those who are frail, is considered safe and practical (Hofmann et al., 2016) and regularly employed in clinical settings (Yasuda et al., 2015). The current study also showed that the high-risk group demonstrated a slight improvement in the left side (6.65%) as well as the low-risk group (2.21%). Meanwhile, in the right side, the high-risk group showed an improvement of 5.85%, whereas the low-risk group exhibited an improvement of approximately 5.77%, which is nearly similar to the result of the high-risk group. Regardless of these results, the high-risk group exhibited better improvement in upper limb strength compared with the low-risk group. This finding is inconsistent with that of a previous report revealing no significant improvement in the handgrip strength of the faller group following intervention (Prata and Scheicher, 2015). Moreover, it was argued that the nondominant hand may have substantial grip strength, thereby showing that neither the dominant nor nondominant hand can be classified as the stronger hand (Oppewal et al., 2013).
Regarding the effect of the exercise program on lower limb functions, our findings showed a significant improvement of 16.06% (P<0.01), which is consistent with the results of a previous study with participants in their 60s, 70s, and 80s (Rikli and Jones, 2013). The current study included a 30-sec chair stand test to determine lower limb functions in terms of endurance and strength related to fall risk. Deteriorated lower limb functions, specifically, poor walking skills, are essential risk factors correlated with falls among older adults (Morita et al., 2005). A previous study showed that lower limb strength is a significant factor for maintaining functions in older adults (Cebolla et al., 2015). In addition, the number of falls increases by 35% to 40% among individuals aged 60 years mainly owing to reduced muscle strength and balance (Hornbrook et al., 1994). A literature also provided evidence showing that the absence of physical activity can lead to the weakening of lower limbs and loss of balance, which are essential precursors of falls among older adults residing in a community (Simpson et al., 2014). The exercise program conducted in this study included exercises for the general strengthening of lower limbs, such as squatting and sit-to-stand exercises and resistance band training, which may have contributed to the considerable improvement in lower limb functions, thereby supporting the recommendation of a previous study (Tsekoura et al., 2018). The effectiveness of elastic bands in resistance training was proven to lead to a significant improvement in lower limb functions, especially leg strength (Motalebi et al., 2018).
In comparing the two fall risk categories, the percentage of changes was higher in the low-risk group (96.87%) compared with the high-risk group (21.20%). This finding is consistent with that of a previous study reporting that fallers had substantially fewer repetitions than nonfallers in the 30-sec chair stand test (Roongbenjawan and Siriphorn, 2020). It was also suggested that adding resistance training to an exercise program can significantly improve the muscle strength of frail older individuals (Phu et al., 2015).
In conclusion, the findings of this study indicated that after 3 months of participation in a group-based exercise program, improvements in mobility, balance confidence, upper limb strength, and lower limb functions were noted in the participants regardless of their level of risk for falls. An exercise program combining multicomponent exercise elements, such as strength, aerobic, balance, and flexibility training, and supervised by a physiotherapist may be safe for community-dwelling older adults.
This study has several limitations. First, the findings cannot be generalized to other programs or populations, as the study was conducted in a center for older adults involving groups and the supervision of a physiotherapist, who could include fun and motivational elements in the program. Second, the male and female participants were placed in the same group for the analysis, which was unavoidable owing to the small number of male participants.