A Pilot Feasibility Study of an Online Youth Tobacco Survey Administration among High School Students

The COVID-19 pandemic restrictions forced many schools to shift to remote or hybrid learning, disrupting surveillance systems such as the New Jersey Youth Tobacco Survey, traditionally administered in schools by paper and pencil. In spring 2021, we conducted a feasibility study among a convenience sample of six public high schools to assess the use of an online survey to allow for remote participation. In each school, 4 to 6 classes were selected randomly, and all students within a sampled class were selected to participate in the survey. A total of 702 students completed surveys. School contacts were asked to provide qualitative feedback about the survey administration. Feedback was generally positive, with a few suggestions for improvement. Approximately 19% of students reported the ever use of e-cigarettes. Among current e-cigarette users, there was a shift in popularity from prefilled or refillable pods or cartridges (23.3%) to disposable e-cigarettes (53.5%). Less than 10% of current e-cigarette users reported using tobacco-flavored e-cigarettes, despite a statewide flavor ban on all other flavors.

Introduction
Tobacco use remains a leading cause of death in the United States, and tobacco use among youth and young adults in any form is considered unsafe [1]. Since 2014, the most commonly used tobacco product among U.S. youths was e-cigarettes [2][3][4]. In 2021, an estimated 2.06 million youths had used e-cigarettes within the past 30 days [4]. Most e-cigarettes contain nicotine, among other harmful substances [1]. Nicotine can harm adolescent brain development, impacting attention, learning, and memory [1]. Addiction to nicotine can lead to the regular use of tobacco products, resulting in long-term exposure to toxic chemicals [5]. Close to 90% of smokers start by the age of 18, highlighting the importance of supporting youth with relevant prevention and cessation efforts [1].
Since 1999, New Jersey has conducted biennial surveillance of tobacco use among New Jersey youth. The New Jersey Youth Tobacco Survey (NJYTS) is an adaptation of the National Youth Tobacco Survey (NYTS) developed by the Centers for Disease Control and Prevention (CDC), consisting of both CDC-recommended core questions and additional questions specific to New Jersey. In the previous iteration, the 2018 NJYTS found that 21.7% of students had used at least one tobacco product in the 30 days preceding that survey [6].
Previous administrations of the NJYTS took place in New Jersey public middle and high schools using a paper-and-pencil survey. Because of the emergency protocols that schools began to follow due to the COVID-19 pandemic, survey data collection could not take place in person and NJYTS could not be conducted as planned in fall 2020. As a result of the ongoing effects of the COVID-19 pandemic during 2021, schools were operating with some students attending in person, some attending remotely (virtually), and some attending in combinations of both (hybrid). In 2019, the National Youth Tobacco Survey was fully administered using an electronic survey version following an electronic pilot survey conducted in 2018, which found that an electronic survey was feasible, was wellaccepted by respondents, and improved efficiency [7,8]. Thus, when COVID-19 disrupted typical school survey procedures, it prompted us to consider transitioning the NJYTS from a paper-and-pencil administration to an electronic survey administration. The 2021 NJYTS was conducted as a feasibility study to evaluate the process of administering a web-based student survey in a convenience sample of six public high schools to prepare to implement a web-based NJYTS in the fall of 2022. The purpose of this study was to assess a reasonable alternative to the previous paper-and-pencil in-person New Jersey Youth Tobacco Survey for future administrations, as well as to present preliminary data regarding tobacco use behavior, knowledge, and attitudes among a small convenience sample of New Jersey high school students. In addition to assessing school contacts' perceptions about electronic survey procedures, the study also presents the latest data on youth tobacco use in New Jersey since 2018.

Materials and Methods
NJYTS typically employs a two-stage cluster design to obtain a representative sample of students in grades 9-12. The first stage of sampling includes a sampling frame that consists of all public high schools enrolling students in grades 9-12. However, for this 2021 feasibility study, our convenience sample consisted of six public high schools of varying enrollment sizes and economic diversity. Two schools were selected with enrollments of less than 1000 students; two schools with enrollments between 1000 and 2000 students; and two schools with enrollments of 2000 or more students. Each school that participated in the 2021 NJYTS received USD 500 for their participation.
Consistent with NJYTS sampling procedures, the second stage of sampling involved selecting classrooms within each school using a sampling frame that consisted of an exhaustive list of classes that either met during a particular period of the day (e.g., 2nd period) or across an academic subject (e.g., English) required of all students. The sampling design ensured that all students had the same probability of selection and that all students had one, and only one, chance of being selected. To accomplish this, all classes were selected using a randomizer. In each participating school, four to six classes were selected at random, and all students within a sampled class were selected to participate in the survey. Data collection occurred between 10 May 2021 and 11 June 2021. A total of 792 students were randomly selected to participate and 702 students completed surveys, for a student response rate of 88.6%.
All six schools reported that every student was given access to an electronic device, so web-based survey access was possible at every school. Survey materials were designed with all three instructional models (in-person, virtual, or hybrid) in mind to support the administration of the web survey. Teachers were asked to read an introductory script and direct students to the online survey link. For those taking the survey remotely, teachers provided students with a unique five-digit classroom access code to log in to the survey. For those in person, teachers distributed sign-in cards with a unique five-digit access code to log in to the survey securely. Students in both situations were routed to a page displaying instructions and their rights as participants, and then were directed to the start of the survey. The survey administration procedures were designed to ensure that data were recorded in an anonymous fashion; no student names or identifiers were collected.
While students were taking the survey, teachers were asked to log in to a separate website and enter the same five-digit classroom access code in order to complete a form documenting class enrollment, returned permission forms, and student absences.
Once survey administration was completed, school contacts were asked to respond to several open-ended questions about the survey procedures. Questions focused on instruc-tional clarity and the reporting of any issues relating to preparation for data collection, the process of data collection, and the completion of the survey.
The 2021 NJYTS included questions about ever use (i.e., any use, even one time) and current use (i.e., use on one or more of the previous 30 days) of cigarettes, cigars, smokeless tobacco (SLT), hookah, snus, electronic cigarettes (e-cigarettes), heated tobacco products, and nicotine pouches, as well as questions that assessed susceptibility to tobacco use, exposure to secondhand smoke, access to tobacco, and product use during the COVID-19 pandemic.
Sample demographics are reported with 95% confidence intervals (CI) to compare distributions among participating students with state enrollment levels. Frequencies and percentages of ever and current use of each tobacco product are reported, as well as e-cigarette use characteristics and perceived change in tobacco product use during the COVID-19 pandemic. All analyses were performed using SAS software version 9.4 (SAS Institute, Cary, NC, USA).

Qualitative Data
Feedback from school contacts was generally positive. Contacts reported that the directions for administering the survey were clearly communicated, and the parental notification process was transparent. Teachers had minimal issues completing the enrollment information and reported that they received the technical support they needed. However, there were a few reported issues. One school reported that the links did not work properly because they were blocked by the school's firewall and suggested that future links should be approved by school IT departments in advance. One teacher distributed permission forms and administered the survey to students in two classes as opposed to the one class that was selected. There was a suggestion to reduce the number of questions in the survey, and extra time was requested for students who required testing accommodations.

Survey Data
The demographics of the sample compared with New Jersey high school enrollments are presented in Table 1. This sample largely comprised Hispanic (37.6%) and Asian (25.4%) students. These groups were overrepresented in our sample when compared to New Jersey high school enrollments (28.7% and 10.5%, respectively). White (21.2%) and Black (8.3%) students were underrepresented in our sample when compared to New Jersey high school enrollments (44.6% and 14.1%, respectively). The prevalence of each product type is presented overall and by gender, race/ethnicity, and grade level in Table 2. The most common tobacco products ever tried were e-cigarettes (18.9%), followed by hookah (7.1%) and cigarettes (6.1%). The prevalence of having tried a cigarette was highest among 12th graders (13.2%). Table 2. Ever use of tobacco products, NJYTS 2021 (n = 702). Current use prevalence for each product is presented overall and by gender, race/ ethnicity, and grade level in Table 3. The product used most frequently in the past 30 days was e-cigarettes (6.1%), followed by hookah (1.4%). E-cigarette use characteristics among students who report current use are presented in Table 4. The most common type of e-cigarette used in the past 30 days was disposables (53.5%). The most common flavor of e-cigarette used in the past 30 days was fruit (48.8%). The most frequently reported source of e-cigarettes in the past 30 days was a friend (37.2%). Among students who reported buying the e-cigarettes themselves in the past 30 days, gas station/convenience stores and vape/tobacco shops were the most frequently reported sources. Figure 1 illustrates the reported product use during the COVID-19 pandemic. Within each product category, a majority of product users reported decreased use during the pandemic. Where e-cigarettes were purchased ab I bought them from another person (a friend, family member, or someone else) 2 (15.4) A gas station or convenience store 5 (38.5) A grocery store 1 (7.7) A drugstore 3 (23.1) A mall or shopping center kiosk/stand 2 (15.4) A vending machine 1 (7.7) On the internet (such as a product website or store website like eBay or Facebook Marketplace), through the mail, or through a delivery service (such as DoorDash or Postmates) 1 (7.7) A vape shop or tobacco shop 5 (38.5) Some other place not listed here 0 (0.0) a Not mutually exclusive. b Among students who indicated they bought e-cigarettes themselves (n = 13).

Discussion
This study explored the feasibility of an online survey platform while examining st dent tobacco use and behavior patterns in a convenience sample of NJ high schools. Ove all, 18.9% of our sample had ever used e-cigarettes and 6.1% currently used e-cigarett

Discussion
This study explored the feasibility of an online survey platform while examining student tobacco use and behavior patterns in a convenience sample of NJ high schools. Overall, 18.9% of our sample had ever used e-cigarettes and 6.1% currently used e-cigarettes. Although not directly comparable, our findings suggest a downward trend in e-cigarette use from 2018, when we found that 28.7% of students had ever used e-cigarettes and 17.8% of students reported the current use of e-cigarettes [6].
There was a notable increase in hookah use, though given the convenience sample, this finding should be interpreted with caution. This observed uptick could partially be explained by our disproportionately Hispanic sample, since the 2018 NJYTS found that ever use and current use of hookah were most prevalent among Hispanic students [6]. However, if genuine, this increase could be an unintended consequence of limited access to flavored e-cigarettes and warrants further study. All non-tobacco-flavored e-cigarettes (especially fruit-flavored) appear more popular than tobacco-flavored. Less than 10% of students who reported current e-cigarette use reported using tobacco-flavored e-cigarettes, which is of interest since flavors other than tobacco should not be available for purchase in New Jersey as of April 2020 [9].
The most commonly used device type was a disposable e-cigarette, which is consistent with findings from the 2021 National Youth Tobacco Survey [4]. Previous administrations reported that the most used device type in 2019 and 2020 was a prefilled pod or cartridge and that disposable e-cigarette use had increased significantly among youths who currently used e-cigarettes [10]. This shift in device type popularity correlates with increases in sales of disposable e-cigarettes nationally [11].
Another notable finding was the reported availability of e-cigarettes. All the students surveyed were under 21 years old and were able to access and purchase e-cigarettes, even though the age of sale has been 21 in New Jersey since November 2017 and nationally since December 2019 [12,13].
Future surveys should note whether the survey is completed in person in the classroom or remotely. The 2021 National Youth Tobacco Study was also fully conducted during the COVID-19 pandemic and found that youths who participated in a school building or classroom reported a higher prevalence of e-cigarette use (15%) than those who participated at home or in some other place (8%) [4,14]. In the current study, students reported an overall decreased use of tobacco products during the COVID-19 pandemic. This could be due to a similar phenomenon observed in the 2021 NYTS; namely, students may have been reluctant to report product use if they were taking the survey from home. Decreased use during the pandemic may have also been due to the lockdowns and inability to purchase products as well as decreased access to peers who could provide them. We anticipate that students will report increased use in the 2022 NJYTS because many COVID-19 restrictions may be relaxed at that time.
Our study was generally successful under difficult circumstances. Overall, the feedback from the schools was positive. Based on school feedback, future web-based school surveys should be mindful of school/district firewalls, simplify any class information forms, have a data collector on site if possible, and use individual sign-in cards to limit issues relating to multiple sign-ins. However, our results are consistent with previous findings that electronic survey administrations in schools are generally well-received and, importantly, can reduce time between data collection and dissemination [8,15,16].
The main limitation of this study was the small convenience sample of New Jersey high schools. This sample is not generalizable to all high schools in New Jersey; it disproportionately comprised Hispanic and Asian students relative to New Jersey high school enrollments. Another limitation of our study was the inability to control survey location (i.e., whether the student was at home or in school when they took the survey), which varied across schools and across classrooms within schools. Future administrations, even if carried out electronically, may be different outside of pandemic remote learning.

Conclusions
The 2021 NJYTS demonstrated the strengths and weaknesses associated with the use of an online platform. Future administrations will use this platform with our findings in mind. Given our small convenience sample, these data cannot be used to determine causality or policy implications. However, the reported student use of and access to flavored e-cigarettes provide evidence that such products remain accessible to youth. Despite a statewide ban on the sale of flavored e-cigarettes, young people face few barriers to finding such products. Continued monitoring of the access to and use of flavored products in young people is critical to evaluate the short-and long-term impacts of flavor policies.


Need Analysis of Developing Digital Teaching Materials Based on MOOCs for Postgraduate Students

This study aims to determine the needs of IAIN Curup postgraduate students for digital teaching materials based on MOOCs. The research sample was 105 postgraduate students from 4 study programs, namely Islamic Education Guidance and Counseling, Islamic Family Law, Islamic Education Management, and Islamic Religious Education. The research instrument used is a student needs questionnaire IAIN Curup postgraduate towards digital teaching materials based on MOOCs. The data analysis technique used is descriptive statistical analysis. The results of this study are digital teaching materials based on MOOCs are urgently needed by postgraduate students of IAIN Curup. This is evidenced by the percentage gain from the results of filling out the needs analysis questionnaire for digital teaching materials based on MOOCs, which is 87.5%, which means that students strongly agree to the development. These results will have an impact on the use of technology in the development of teaching materials.

Introduction
The development of media is now dominated by digital technology. In everyday life, students always interact with digital technology (Lilis, 2019). Indonesia also currently occupies the fifth position as a mobile internet user. The industrial revolution 4.0 which is a revolution that occurs throughout the world provides challenges as well as impacts for the younger generation and also the world of education in Indonesia (Wening & Santosa, 2020). Therefore, the development of science and technology increasingly encourages renewal efforts in the use of technological results in the learning process (Pratita et al., 2021). One of them is the use of digital teaching materials. Digital teaching materials are unlimited and easily accessible learning resources and are part of electronic learning (Alperi, 2019;Faisal et al., 2020). Accessibility of content on digital teaching materials can support the learning process effectively and efficiently (Prastyo et al., 2021). Digital teaching materials can also improve students' ability to think creatively, solve problems, be critical, and be motivated. This can support the achievement of 21st century learning goals (Dewi et al., 2021). The provision of digital teaching materials can stimulate students to learn earlier before class takes place so that students feel confident in presenting answers and expressing their opinions (Wijayanti, 2018). The benefits of digital teaching materials support the statement that learning materials must be well designed to be attractive to students (Erwin et al., 2021).
One form of innovation from digital teaching materials is digital teaching materials based on Massive Open Online Courses (MOOCs). MOOCs are here as a new model of education and learning . MOOCs use the internet to deliver lectures at the world's most prestigious universities and educational institutions, creating a revolution that has thrived since 2012 (Busri et al., 2019;Chapman et al., 2016;Lian et al., 2021;. MOOC is a platform where participants engage in the learning process by sharing course information among themselves through connection and collaboration (Zhussupbekov, 2015). MOOC is an online learning course with unlimited participants and can be accessed openly through the website (Ismail et al., 2018;. MOOCs are built on the idea that "information is everywhere", expanding access to education (Pilli & Admiraal, 2020). MOOCs provide a wider potential for each individual to continuously equip themselves with new knowledge and skills (Rafiq et al., 2019). In principle, MOOCs describe Online Courses with a very large number of open enrollments, which provide not only administrative services but also in terms of content, design, access points, application methods and definition of success .
Based on the description above, it is important to develop digital teaching materials based on MOOCs. However, before doing the development needs analysis needs to be done. according to Utami & Atmojo (2021), analysis of teaching material needs is the main step for developing digital teaching materials.
Therefore, this research will analyze the need for digital teaching materials focused on postgraduate students at IAIN Curup.

Methods
This study aims to determine the needs of IAIN Curup postgraduate students for digital teaching materials based on MOOCs. The research sample was 105 postgraduate students from 4 study programs, namely Islamic Education Guidance and Counseling, Islamic Family Law, Islamic Education Management, and Islamic Religious Education. The research instrument used is a student needs questionnaire IAIN Curup postgraduate towards MOOCs-based digital teaching materials. The data analysis technique used is descriptive statistical analysis. Needs analysis is carried out on the data obtained in the form of percentages. The percentage is obtained based on a modified Likert scale calculation. With a Likert scale, the variables to be measured are translated into variable indicators. Furthermore, these indicators are used as guidelines in compiling items in the form of questions or statements. Instrument items are given a quantitative value as shown in table 1 below: where P is the percentage of the results of the questionnaire analysis, n is the total score of the assessment, and N is the maximum possible score. For the Likert scale, the score interpretation model can be seen in table 1.

Results and Discussion
This study aims to determine the needs of IAIN Curup postgraduate students for digital teaching materials based on MOOCs. IAIN Curup is one of the state universities in Rejang Lebong Regency, Bengkulu Province.

Figure 1. IAIN Curup
The data in this study were obtained from 105 students from 4 postgraduate study programs who filled out a questionnaire on the need for digital teaching materials. Prior to further analysis, the questionnaire data was used to test the validity and reliability of the questionnaire used. The following is a table of validity test results.  The table above shows the results of the calculation of data reliability with 22 statement items using the Cronbach alpha method, a score of 0.969 is obtained.
The value obtained is greater than 0.60, then according to the rules of determining reliability, the questionnaire used in this study is said to be reliable.
After testing the validity and reliability of the questionnaire, data analysis was carried out to determine the needs of IAIN Curup postgraduate students for digital teaching materials based on MOOCs. To find out the percentage of needs development of based digital teaching materials MOOCs can be seen in table 5  There are several previous studies that are relevant to this research, one of which is research conducted by Yuliana & Atmojo (2021). In this study, data was obtained that teachers have not developed digital-based teaching materials to facilitate students' science learning so that the development of digital teaching materials is needed. Then the research conducted byUtami & Atmojo (2021) who obtained data that digital teaching materials were needed in supporting the success of learning and learning in elementary schools, especially science learning materials.

Conclusion
MOOCs-based digital teaching materials are urgently needed by IAIN Curup postgraduate students. This is evidenced by the percentage gain from the results of filling out the needs analysis questionnaire for digital teaching materials based on MOOCs, which is 87.5%, which means that students strongly agree to the development. The results of this study can be used as a basis for further research related to the development of digital teaching materials based on MOOCs for postgraduate students of IAIN Curup.


s2fieldsofstudy: Education Physics

text:
Absence of a COVID-induced academic drop in high-school physics learning

At the start of the COVID 19 pandemic, the majority of secondary instruction in the United States transitioned to an online environment. In many parts of the country, online schooling continued for upwards of two years. Many experts have hypothesized an"academic slide"(a reduction in student learning) following this period of online instruction. We investigated the change in student preparation for introductory college physics in incoming Stanford university students between the Fall term of 2019 to the Fall term of 2021. We did this by looking at the performance on a validated physics diagnostic exam that all Stanford students intending to take a physics course took before enrolling in an introductory physics course. We found no statistically or educationally significant change in scores. Despite many anecdotal faculty reports, at least for this population, the level of student preparation in physics and related math appears to be unchanged.

Introduction:
There have been many reports in the popular media in which university faculty claim that the incoming college students are much less prepared now than they were a few years ago due to the disruptions of COVID-19 on education 1 . We have also heard similar statements from faculty at our institutions. While there are clearly many serious negative consequences from COVID on students of all levels, this is the type of situation where perceptions and evidence are strongly shaped by confirmation bias 2 . This motivated us to try and test these claims. A recent study showed an average reduction in ACT scores in the U.S., but it is unclear what this indicates about college preparation. The reported change was small, and this reduction many have been due to many schools waiving standardized test requirements during the pandemic 3 . We realized that we had data that would allow us to objectively measure the difference in the directly relevant incoming preparation in physics for Stanford undergraduates who completed high school preand post-COVID.
The Stanford undergraduate population is not representative of the population of US college students. The institution is highly selective, with the fraction of applicants who are admitted trending down slowly over the years but remaining essentially constant before and after COVID. While this population represents a small and high performing fraction of the high school population, it is the same fraction before and after COVID, and physics preparation counts very little in the admissions process. This is evident in the very wide distribution of scores shown below on the physics diagnostic exam. Compared to the US university undergraduate population at large, the Stanford population is moderately diverse. So, if there was a reduction in physics and related math learning that was similar across the entire population of graduating high school students in the US, we would expect to see the effect on these Stanford students. That outcome would be consistent with the ACT's reports that drops in performance occur across all groups of students 3 . However, if there was a detrimental impact only on the low but not the high performing students across the US, we would not see it in this study, because our sample only includes the latter.

Methods:
Students who have not yet taken a physics course at Stanford University are required to take an empirically validated physics diagnostic exam 4 to provide them with a recommendation as to which of the different introductory physics course they should take. They typically take this exam the summer before they start college. The diagnostic exam covers a range of topics in physics and mathematics, including vector operations, basic integration, Taylor series approximations, kinematics, static equilibrium, Newton's laws, conservation of energy, angular momentum, and rotational motion. It is strongly predictive of the student scores in the courses. Students were not awarded credit for completing the exam, but they were required to complete it before they could enroll in an introductory physics course.
We examined scores collected in the summer of 2019 (prior to the COVID-19 pandemic) and the summer of 2021 (after most students had experienced two years of remote instruction). We removed those few students from our sample (<1%) who spent less than 10 minutes completing the exam. The median time spent on the assessment was 38 minutes, indicating that most students were making a serious effort. This left 881 students in the 2019 sample and 968 students in the 2021 sample. (The 2021 sample is likely larger because an unusually large number of students admitted for 2020 chose to defer starting college until 2021.) We calculated the mean and standard deviation separately for students who intended to enroll in algebra-based and calculus-based physics, as this provides an objective way to look for possible changes in the top and bottom of the overall distribution separately. Students with better preparation in physics and math look to take the calculus-based course. For each of the two populations (calculus and noncalculus based) we tested for differences in the averages in 2019 compared with 2021 using a ttest. We also calculated Cohen's d between the two years and used histograms to qualitatively investigate the differences in the respective distributions.
The Stanford student population is 28% White, 25% Asian, 18% Hispanic or Latino, 7% Black or African American, 11% Multiracial, 11% International students, and <3% Native American or Pacific Islander. Approximately 33% of students are first generation/low-income studentsstudents with no parent that received a four-year college degree. The interquartile range of admitted students' ACT scores is 32-35.

Results:
The distribution of scores for algebra-based physics is plotted in Fig. 1. In 2019, the average score was 17.3 out of 36 (standard deviation = 6.63 points; N = 214). In 2021, the average score was 17.1 points (s. d. = 6.89 points; N = 212). The difference between these two means as a fraction of the standard deviation is d = -0.027 and is not statistically significant (p = 0.78).
The distribution of scores for calculus-based physics is plotted in Fig. 2. In 2019, the average score was 24.1 out of 36 (standard deviation = 7.44 points; N = 597). In 2021, the average score was 23.6 points (s. d. = 7.31 points; N = 757). The difference between these two means is d = -0.061 and is not statistically significant (p = 0.27).

Discussion:
We observe essentially zero change in scores between 2019 and 2021, the differences in the means are a tiny fraction of the standard deviations and completely consistent with random fluctuations. The standard deviations are also nearly identical between 2019 and 2021.
These results show that, at least in the domain of physics, this group of students showed no detrimental impact on learning due to COVID. In contrast to predictions 5 and many faculty perceptions 1 this suggests that the negative effects of remote instruction on student learning are small, at least for this population. This is an academically high-achieving population overall and may not be representative of the broader U. S. population. Many of these students come from well-resourced high schools and households and may have had the resources to reduce any negative impact of remote learning. However, we know from earlier studies that a substantial fraction of these students are from backgrounds and poorly resourced high schools in which they are not receiving good instruction in physics 6 . However, we do not observe any shift in the score distributions at the lower end, where students from under-resourced schools and households typically score on this assessment. These are the students that are expected to suffer the most educationally from the impacts of covid.
We emphasize that we are only measuring understanding of math and physics, and not any of the other variables that are known to relate to overall student performance and mental health, such as anxiety, motivation, self-efficacy, etc.

Conclusions:
The impact of the pandemic on Stanford students' academic preparation has been smaller than expected or perceived. This suggests that university instructors in introductory physics may not need to reduce the level of their courses as many have suggested.
We do not wish to suggest that the overall impact of the pandemic on students is minor. Many students will have lost parents and relatives, suffered intense social isolation, and feared for their own well-being for an extended period of time. All of these factors are expected to have significant tolls on students' mental health and academic success. There are numerous indications that student mental health has suffered significantly at both our institutions and many others. Our message is only that some anticipated negative impacts of the pandemic may not be as large as expected and more careful analysis than faculty anecdotal impressions is needed.


A Study of Feature Construction Based on Least Squares and RBF Neural Networks in Sports Training Behaviour Prediction

'is paper examines the problem of athletes’ training in sports, exploring the methods and means by which athletes can perform difficult movements in which they normally make minor training errors in order to achieve better competition results and placements. To this end, we test the explanatory and predictive effects of a theoretical model starting with planned behaviour and then use exercise planning, self-efficacy, and support as variables to develop a partial least squares regression model of sports to improve the explanation and prediction of sporting athletes’ intentions and behaviour. An improved RBF network-based method for player behaviour prediction is proposed. On the basis of the RBF analysis, the number of layers and the number of neurons in the hidden layer of the network are adjusted and optimised, respectively, to improve its generalisation and learning abilities, and the athlete behaviour prediction model is given.'e results demonstrate the advantages of the improved algorithm, which in turn provides a more scientific approach to the current basketball training.

Introduction
What if, after countless hours of practice, a sportsperson realises that there is only a 50% chance of successfully completing one of the most difficult manoeuvres of the entire competition and that the athlete might miss the most important and critical moment of the competition? What does the athlete do when faced with these potential threats? Many scholars have suggested adjusting one's breathing to regulate one's emotions, controlling oneself from thinking that the action has a high probability of being wrong, adjusting one's mental state so that he can perform the technique successfully, etc. [1]. However, numerous studies have shown that the use of intentions alone is not a good predictor of behaviour change. Action plans and coping plans are self-adjustment strategies. In many domains, behavioural intentions are supported after planning and execution as a motivation is received. e addition of a theory of planned behaviour appears to be the most appropriate as the theory of planned behaviour lacks sequential structure. It has also been found that when using behavioural interventions, research has not found an effect on behaviour by making implementation intentions, and on the basis of existing research, planning is more important as a mediating variable, and this paper will continue to examine and explore the forecasting effects of planning as an intermediate variable [2].
Currently, most of the research studies in the field of video-based motion human analysis are focused on motion behaviour recognition, but not on motion behaviour prediction. However, in real life, prediction of human movement based on the video is more valuable than behaviour recognition [3]. e public security department can be alerted to the predicted criminal behaviour in time to take corresponding measures. is not only reduces the amount of manpower and material and financial resources spent on security investigations but also provides effective prevention of sudden crime [4]. In the sports sector, a comprehensive set of video-based human movement prediction technologies can be used to accurately capture the game data of some of the best teams during sports training and to create a predictive discriminator for each athlete [5,6]. e contributions of this paper are as follows: We start from the planned behaviour to test the interpretation and prediction effect of the theoretical model and then take the exercise plan, self-efficacy, and support as variables to establish the partial least squares regression model of sports, so as to improve the interpretation and prediction of aerobics athletes' intention and behaviour. We propose a team member behaviour prediction method based on the improved RBF network. e RBF network is analyzed. Based on the RBF analysis, the number of network layers is adjusted and optimized to improve its generalization and learning abilities, and the athlete behaviour prediction model is given. Based on the analysis of the sports competition video, the improved RBF is used to predict the athletes' behaviour, and the prediction results are compared with the traditional prediction results. e results show that our algorithm has advantages.

Related Work
At present, the research of individual sports is mainly reflected in the movement recognition and analysis of athletes [7][8][9] using target detection and tracking technology, the moving target is extracted from the diving video, and the moving target is video synthesized. A motion recognition method based on the human contour curvature scale space template is proposed and applied to the motion recognition of players in tennis competition [10]. e movements of ice hockey and football players are analyzed and studied. e movement direction of players is identified by using the histogram of oriented gradient (HOG) and hidden Markov model (HMM) [11][12][13]. e player's gestures in the lowresolution tennis game video are recognized [14]. By tracking the key points of some body parts of ballet dancers, the dance trajectory is obtained, and the automatic analysis of dance movements is realized [15]. It realizes the recognition of four swimming strokes in the swimming competition video.
In recent years, as research on individual sports analysis has matured so has research on multiplayer team sports [16]. Zhong and Xiao [17] investigated the patterns of passes that help to score goals in football and used multiscale analysis to cluster passes hierarchically and to mine the pattern categories of passes; Chen et al. [18] classified the trajectory and landing point of the ball as special cases in tennis and used an improved Bayesian network to fuse these two features. Li and Zhang [19] proposed a method for two-sided team possession situations, which is based on the ball position and the detection of the interplay, the local minima of the ball motion trajectory velocity of a player's possession start, the local minima of the ball position coordinates, the player's position, and the ball acceleration start point to infer the player's touch point. Li et al. [20] integrated a variety of information on the pitch in the ritual to achieve automatic analysis of football game tactics.

Development of a Regression Model for Sports Training
Simplify the data structure and correlation analysis of bivariate variables, greatly facilitating multivariate data analysis [21]. It uniquely suits to deal with small sample sizes and independent variables with significant multiple correlations between variables. However, the partial least squares regression model can be expressed as the original regression equation for all variables. e partial least squares regression method selects principal components that also include all variables and does not fully address the more severe multivariate correlations, especially in the case of some independent variables and small sample conditions [22]. In this paper, data from sports athletes regarding physical training were used to conduct partial least squares regression modelling in terms of the explanatory and predictive power of three variables-exercise program, selfefficacy, and social support-on exercise intention and behaviour and their role in the intention-to-behaviour process. e subjects of this paper were 10 players of a sports team. e first group of variables was exercise planning, selfefficacy, and social support. In this study, social support consisted of two types, family social support and friend social support, which were not effective predictors of exercise intention. e second group of variables was the training performance indicators of the sport players, which were rotation, bending, and high jump. e raw data are shown in Table 1, and the correlation coefficient matrix is shown in Table 2.
e simple correlation matrix of these six variables shows that an exercise program and self-efficacy are positively correlated; work program and self-efficacy are negatively correlated with social support, and rotation, bending, and high jump are positively correlated. e partial least squares regression model is calculated by presenting two components t 1 and t 2 , which have a crossvalidity of Q 2 2 � −0.1969. e regression equation for the standardised variable y k on component t 1 in this paper is as follows: e resulting partial least squares regression equation for component t 1 is e calculations for r h � (r h1 , r h2 , r h3 ) are e following equation is obtained by reducing the normalized variables y k , x k (k � 1, 2, 3) to y k , x k (k � 1, 2, 3), respectively:

RBF Radial Neural Network Prediction Model
From a structural point of view, the RBF nerve network consists of three layers: an input layer, an implicit layer, and an output layer. e number of nodes in this layer is determined by the actual number of input variables; the number of nodes in the implicit layer is difficult to determine and is determined by the complexity of the problem being described [23]; the output layer is used to output the results of the input and output processing of the system. In the RBF neural network, the transformation from the input space to the hidden space is a nonlinear transformation, and the transformation from the hidden layer to the input layer is a linear transformation. Assuming that the RBF neural network has only one hidden unit [24], the training sample of the RBF neural network can be expressed as X n , d n (n � 1, 2, · · · , N). In this formula, X n � [x n1 , x n2 , · · · , x nM ] T (n � 1, 2, · · · , N) is the sample input; d n (n � 1, 2, · · · , N) is the desired sample output, and the corresponding actual output is Y n (n � 1, 2, · · · , N); the radial basis function φ(X, t i ) is the output of the ith hidden unit, and [t i1 , t i2 , · · · , t im , · · · , t iM ] (i�1,2,···,I) is the centre of the basis function; w i (i � 1, 2, · · · , I) is the connection weight between the ith hidden unit and the output unit. e topology of a specific single-output RBF network is shown in Figure 1.
When the network input training sample is X n , the actual output of the network can be expressed by the following equation: Currently, there are many RBF functions in the field of RBF neural networks, the most common of which are the Gaussian function, the multiple quadratic function, and the thin-slab spline function. e purpose of data normalisation is to transform all data to values in the range [0, 1] to make subsequent calculations more convenient [25]. e data normalisation formula is e network parameters are updated by the following equation: In the above equation, v i � [w i 0 , w i 1 , c i 1 , δ i 1 , · · · , w i K , c i K , σ i K ] T represents the state of the parameter shown for the ith sample after it has entered the network.  1  91  36  50  5  162  60  2  89  37  52  2  110  60  3  93  38  58  12  101  101  4  63  35  62  12  105  37  5  89  35  46  13  155  58  6  82  36  56  4  101  42  7  67  34  60  6  125  40  8  76  31  74  15  200  40  9  54  33  56  17  251  250  10  69  34  50  17 120 38    Computational Intelligence and Neuroscience e calculation is carried out for all hidden node outputs σ k � (o i k1 , · · · , o i kj , · · · , o i kn y ), i � 1 · · · N, k � 1, · · · , K, j � 1, · · · , n y , and ‖o i j, max ‖ � max(‖o i 1,j ‖, ‖o i 2,j ‖, · · · , ‖o i K,j ‖, (j � 1, · · · , n y ). e specific normalized output vector formula is If all r i kj < ε 2 are established, then the kth hidden node can be deleted.

Experimentation and Analysis
In order to verify the effectiveness of the method, we experimented on an Intel Core 2 T6500 PC with 3 G RAM, implemented using VVC++6.0 with the OpenCV vision library programming [26]. To train the FSAMME-based head state classifier, we manually intercepted 800 head images from 100 basketball game videos, 100 images from each class. A typical training sample is shown in Figure 2. In addition, 100 nonhead images were intercepted as a negative example training set, consisting mainly of local stadium background blocks and blocks of other body parts of the players. Figure 3 shows the relationship between the relative size (ratio to sample side length) of the small square area extracted from the training sample and the recognition accuracy. In the training process, we set τ accuracy of the head image detection classifier to 0.9, the learning rate τ rate to 0.1, and τ accuracy of the nonhead image detection classifier to 0.4. We selected another 400 head images as the test set, 80 images of each class, and the experimental results are shown in Table 3.
As can be seen from Table 3, the misjudgements were mainly in the discrimination of two adjacent head poses.
is is mainly due to the low resolution of the head image in the basketball game video, the interference of motion blur and noise, and the similarity of the adjacent head postures themselves. From the experimental results, it can be seen that the covariance descriptor, which incorporates a variety of underlying features, is well adapted to the above situation and achieves satisfactory experimental results, with the average recognition rate of various head poses.
In order to verify that the covariance descriptor can improve the accuracy of head state recognition after fusing the original underlying features, we have selected a subset of feature vectors in the section for comparison experiments, respectively, and the experimental results are colour features, gradient features, texture features, colour-gradient features, colour-texture features, gradient-texture features, and colour-gradient-texture features, as shown in Figure 4.
In addition, we also experimented with the method in this article in the case of partial occlusion of the player's head. Test cases were manually occluded from the top, bottom, left, and right directions, with each direction taking up 10%, 20%, 30%, 40%, and 50% of the total head image area, shown in Figure 5.
As can be seen from the graph, the accuracy of head pattern recognition is generally higher when the left and right occlusion occurs than when the top and bottom occlusion occurs. When the occlusion rate is reached, the lowest upper occlusion recognition rate is also higher than the best right occlusion recognition rate by a close margin. It can be seen that the method in this paper also achieves better results when dealing with partial occlusion.    Computational Intelligence and Neuroscience 5 Finally, the method is compared with two state-of-theart methods for low-resolution cephalosporin recognition. As can be seen from Table 4, this method outperforms the other two methods in terms of average head pattern recognition rate.

Conclusions
e above analysis shows that the RBF neural network has strong generalization and learning abilities in dealing with the prediction of nonlinear problems, and the above construction shows that the algorithm proposed in this paper has more advantages and accuracy in the prediction of sports behaviour, thus proposing a new scientific training method for the scientific training of current sports.

Data Availability
e datasets used during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest
e authors declare that there are no conflicts of interest.   6 Computational Intelligence and Neuroscience


AN ANALYSIS OF THE ENGLISH SPEAKING AND WRITING SKILL LEVEL OF TOUR GUIDES IN TRENGGALEK’S TOURIST VILLAGES

Despite the pivotal role of local tour guides to promote tourist attractions in their region, most of them cannot use English communicatively. There is a need to upskill their English level through a training program. This study aims to evaluate Trenggalek’s local tour guides after joining a series of training on Speaking and Writing in English. This study employs a qualitative design with the test results analysis. Five tour guides were tested through verbal and written assessment through a contextualized framework that fit Indonesians’ characteristics as EFL learners. Their abilities were analyzed through adapted rubrics and scoring criteria. Results show that only a few tour guides successfully upgraded their English skills. However, they showed more diverse Speaking abilities compared to their Writing ability. These findings imply that more focus should be put on the training of Speaking skill, and that more workshops should be presented in series coupled with follow-up activities to get tour guides used to using English.

Despite the pivotal role of local tour guides to promote tourist attractions in their region, most of them cannot use English communicatively. There is a need to upskill their English level through a training program. This study aims to evaluate Trenggalek's local tour guides after joining a series of training on Speaking and Writing in English. This study employs a qualitative design with the test results analysis. Five tour guides were tested through verbal and written assessment through a contextualized framework that fit Indonesians' characteristics as EFL learners. Their abilities were analyzed through adapted rubrics and scoring criteria. Results show that only a few tour guides successfully upgraded their English skills. However, they showed more diverse Speaking abilities compared to their Writing ability. These findings imply that more focus should be put on the training of Speaking skill, and that more workshops should be presented in series coupled with follow-up activities to get tour guides used to using English.

Introduction
Indonesia is a country that has many tourism potentials. Among the potentials are marine tourism (related to beaches, oceans, reefs, and rivers), landscape tourism (related to hills, valleys, mountains, and highlands), and cultural tourism (related to a puppet shows, dances, drama, and live shows). The state income obtained from this sector is also very big which is around 280 trillion rupiahs in 2019, or when the COVID-19 pandemic has not yet hit tourism in Indonesia and the rest of the world. Meanwhile, most of this tourism potential is found in rural areas, this is what then lies behind the development of tourist villages in Indonesia. In other words, tourist villages in Indonesia are being developed to become one of the mainstays in the tourism sector. These tourist villages are spread in various parts of Indonesia.
In addition, the potential of a tourist village also lies in the uniqueness of a village, which includes rural natural scenery, rural souvenirs, homestays, culinary and other peculiarities (Sudibya, 2018). This is in line with the definition of a tourist village, where a tourist village can be defined as a village that 1) Has a combination of attractions, accommodations, and supporting facilities, 2) Has a unique and distinctive charm, and 3) Facilitates tourists to stay and learn about local customs and culture.
In addition, Hadiwijoyo (2012, as quoted by Sudibya, 2018), stated that a tourist village has several criteria, including; 1) Has good accessibility, 2) Has good and attractive tourist objects, 3) Community and village officials support the tourism village program, 4) Security is guaranteed, 5) Availability of accommodation, telecommunications equipment and adequate manpower in the field of tourism, 6) The climate is favourable, for example, cold or cool, and 7) Has a relationship with other tourist objects that are widely known by the public. Meanwhile, Wearing (2001) further stated that in terms of stakeholders to develop tourism including rural tourism, there are three elements involved: the government that acts as a regulator and facilitator, the community as the host or implementer, and the private sector covering the developer or investor. These three elements of stakeholders should go hand in hand for the success of tourism in an area. The development of tourist villages should also carry the theme of communitybased tourism. As explained by Sudibya (2018), community-based tourism or CBT is a tourism area of development model where the development involves maximum community participation and the results will return to the community as well. Therefore, it can be said that in CBT, although the participation of the government and the private sector as developers is still needed, it is the participation of the community that is more needed there.
One of the regencies in Indonesia that is intensifying the development of tourist villages is Trenggalek Regency. Regarding the development of the tourism sector in Trenggalek Regency itself, it has produced several results. One of them is the innovation implemented by the Trenggalek Regency government which succeeded in bringing Trenggalek to win the Entrepreneur Award by Markplus Inc in 2018. However, compared to other districts that received similar awards, the number of tourists in Trenggalek tends to be less (Rahayu, Anantanyu, and Mulyanto, 2019). However, it can be seen that in addition to having promising tourism potential, Trenggalek also has competitive tourism potential marketing.
With such promising tourism potential, as well as support from the government and local communities, it is not surprising that Trenggalek dared to target 100 tourism village developments with the SADEWA (One Hundred Tourist Villages Program) which is targeted to be achieved in 2024. These tourist villages have their specialties. For example, the Tumpuk Tourism Village, which relies on a combination of the beauty of rice fields and mountains as well as village parks, or the Ngulungwetan Tourism Village, which presents the sensation of camping and adventuring on the beach, combined with the beautiful nature of the mountains. The existence of these tourist villages must be supported by several supporting factors, one of which is the availability of a qualified tour guide to guide tourists visiting the tourist villages.

Literature Review Tour Guide and Its Role in the Success of the Tourist Village Program
As emphasized by Mukti (2005) the task of a tour guide or tour guide is complex because he is required to have extensive knowledge and other abilities that support his performance as a tour guide, so that he can provide sufficient information for tourists. The tour guide itself is defined as someone who is paid to accompany or guide visitors or tourists who visit tourism objects. Another definition is someone who works under the auspices of a travel agency or tourism office of the local tourism office (Yoeti, 2000).
According to Naatonis and Bisilisin (2020), a tour guide is someone who directs a tour so that tourists get an experience during the tour. This guiding activity includes providing information about places and tourist attractions visited and leading the tourists. From these definitions, it can be concluded that an object or tourist place cannot be separated from the existence of a tour guide. In addition, as explained by Soraya, Soetarto, and Alfiyah (2021), the skills of a tour guide are needed to boost the number of tourists who come. This is because the skills of a tour guide will leave a good impression on tourists who come to visit which makes the tourists visit again.
For grouping, the types of tour guides, Muhajir (2015) has classified them into several groups, for example, based on where they carry out their duties, namely local guides or city guides. A local guide is a tour guide who guides tourists in a certain place and time. Included in the local guide group are tour guides who are in charge of guiding in museums or other historical places. While a city guide is a tour guide whose job is to guide tourists around an area such as a city while explaining tourist objects or historical places that are passed by the tourist group.
Meanwhile, according to Udoyono (2008), tour guide categories are divided into several types. They are transfer guides (in charge of picking up tourists, for example at airports or ports or other gathering points), walking guides (in charge of guiding tourists on a tour that is taken on foot), local/expert guides (guiding tourists to enjoy certain tourist attractions), common guides (guides who can perform roles both as transfer guides and tours), and driver guides (in charge of delivering tourists to the destination tourist attraction and as a provider of information if needed). For their status, tour guides are divided into payroll guides, part-time/freelance guides, members of guide associations, and government officials.
Tour guides can come from the local community can guide tourism, especially in the context of tourist villages. As it is known, the development of a tourist village should also involve the community completely from the beginning of development to its management; the results will also be intended for residents. This tour guide from the local community can also be a bridge for tourists so they can adjust to the environment, customs and culture that exist in the tourist area. However, it seems that the participation of residents as tour guides has not run optimally. As stated by Egar, Fahmi, Yulianti, and Musarokah (2017) one of the inhibiting factors for the community is the lack of adequate knowledge and skills in guiding tourism. In addition, their English language skills are not yet adequate. This is what makes English language learning programs important to be implemented in the tourism sector, especially for tour guides.
In addition, the tour guides can introduce the potential, uniqueness, beauty, and attractions of local tourist attractions to tourists who come to visit. As revealed by Egar, Fahmi, Yulianti, and Musarokah (2017) a tourist village needs the role of tour guides to take tourists to tourist destinations, as well as to provide the information needed by tourists about the tourist village.

The Importance of Mastering English by Tour Guides
A tour guide is expected to master several abilities, in addition to communication and public speaking skills; he is also required to have foreign language skills, especially English. As stated by Sujaya (2021) the use and application of English need to be carried out by tour guides as a medium of communication, so that foreign tourists can understand what is conveyed by local tour guides. In addition, it is undeniable that English is an international language and the main foreign language in Indonesia. Therefore, tour guides need to be able to communicate using English fluently and fluently. Damayanti (2019) emphasized that there are several roles of English in the world of tourism, namely communicative/interactive roles, integrative roles, lingua-franca roles, relation-fostering roles, economic-business roles, and functional roles. The first role is the communicative/interactive role. In this role, English serves as a medium of communication between tour guides and tourists. The second role is the integrative role. What is meant by integrative role here is a role where English is a unifying language for people from different countries. As an international language, English integrates these people even though they are of different races, countries, and nations. The third role, namely the lingua-franca role, functions as the language of instruction or social language used by people who are in an environment that has a variety of languages. For example, when a foreign tourist visits Turkey and Indonesia, it will be very inconvenient for him to learn both Turkish and Indonesian. Therefore, to solve it, he just needs to learn and use English as a foreign language that can be used in both countries. The fourth role is the relation-fostering role, where English acts as a language that brings people closer to each other. The next role is the -economic-business role, focusing on the role of English as the language used for trade and business between the worlds, including the tourism business. The last role is the functional role where English is used to make it easier for tourists and local people to get certain information related to the destination tourist attraction. With the six roles of English in the world of tourism, it cannot be denied that it is very important for tour guides to master English.
Several efforts have been made to improve the English language skills of tour guides, especially in areas where many foreign tourists come to visit. Sujaya (2021) for example, held English language training for tour guides located in Bali. As a tourist centre for Indonesia or the area in Indonesia that is most frequently visited by tourists, tour guides in Bali will be faced with foreign tourists, most of whom use English as the language of instruction. In addition, Oktavia, Poerwantika, Kunkunrat, and Afriantari (2020) also provided English language training at Wanasari Tourism Village in Bandung. Where the village has a decent potential as a tourist village.
This study will focus on analyzing the speaking and writing skill of the tour guides. Hadah, Maghfiroh, Humairoh, and Ahadah (2020) stated that speaking and writing skill is related to one another. In other words, someone's speaking quality is influenced by his or her writing skill. Goh and Doyle (2014) argued that children's progress in speaking is correlated with their development in writing strength. Therefore, these two skills are inseparable from one another. In addition, tour guides are required to talk a lot. They talk to the tourists that they guide along the way. They are also needed to be able to write the messages that they want to convey to the tourists well. and their messages must be able to be understood. In addition, these two skills belong to the English productive skill strand which often indicates whether someone can master English well.

Mapping English Language Skills Tour Guide
For the sake of the fluency of a foreign language training program, the foreign language skills of a tour guide should be analyzed first, to find out the extent of their English skills, so that the proportion of English language training can be carried out properly along with the provision of training materials based on the level of English of the tour guides, whether enter basic, intermediate, or advanced levels.
The English level of the tour guides needs to be identified by the English language teachers or instructors who also act as researchers in this study, so that the English language skills of the tour guides can be mapped and efforts can be made to increase the level of the tour guide's English skills. This then initiates the following research problem: What are the levels of English speaking and writing skills of the tour guides in tourist villages in Trenggalek Regency?

Research Design
This study employed qualitative design with a result analysis approach. This method was chosen because it fits the study's aims to explain phenomena in the study site (Cohen et al., 2018) to understand the social field from the respondents' perspective (Poedjiastutie, 2020).

Research Participants
The participants of this study were five tour guides in Duren Sari, Desa Sawahan, Kecamatan Watulimo, Trenggalek. These participants were chosen based on purposive sampling to obtain the desired data that fit the research question (Cohen et al., 2018). The inclusive criteria are: 1. Have performed as a tour guide in Duren Sari; 2. Have guided foreign tourists; and 3. Have a comprehensible understanding of Duren Sari as a tourism village based on local products, which is durian. The information gained from participants with these criteria can be used to answer the research question, which is related to local tour guides' ability to promote local values using English.

Research Instruments
The Speaking and Writing assessment rubrics established by Rukmini and Saputri (2017) are used in this study. These rubrics were chosen because of their rigid domains for each skill and clear scoring, which are 10-25 for speaking skills and 1-5 for writing skills. The instruments are summarized in the following table.

Data Collection and Analysis
The data was collected from July to August 2022 with the following steps: 1. The researchers created questions for the tour guides to test their speaking and writing skills. 2. The Speaking test was divided into two tasks. First, the tour guides were required to do a role-play about scenarios that may happen to analyse their ability in handling such problems. Second, they were asked to promote the competitive advantages of their villages and why the tourists should visit. The test took about 30 minutes. 3. In the Writing test, the guides were asked to write a promotion about their villages on their Instagram, and write advertisements based on the pictures given. Following the data collection process, the analysis was carried out. The tour guides' performance on the Speaking and Writing test was scored against the aforementioned rubrics. Following this, the participants were given feedback to improve their skills.

The Speaking Test
The results of five participants were determined through four aspects, namely, pronunciations, grammar, vocabulary, and fluency. The scores of all participants are summarised in the following table. It can be seen from the table that Miss F attained the highest score with 80 out of 100, which means she is a good English speaker. Her pronunciation can be understood clearly despite being influenced by her mother tongue. For grammatical aspects, Miss F made some minor mistakes that did not affect the meaning. This finding follows a previous study by Vold & Brkan (2020) where speakers sometimes use less precise vocabulary, and their diction or word choice should be revised by repeating the sentences.
Meanwhile, Miss O, Mrs U, and Mr R had almost similar speaking scores with only 3-6 point differences. They were quite fluent when speaking but occasionally made some errors in grammar. They sometimes used less appropriate vocabulary that was sometimes not suitable for the context. This finding is in line with Akbari (2016) where such a situation is normal for most speakers, who do not use English as a second language but as a foreign language.
Overall, almost all five tour guides can understand what people were talking about despite some grammatical and pronunciation errors. It can be said that their biggest difficulty was vocabulary, which affected their fluency. This finding lends support to the theory by García Mayo & Hidalgo (2017) where people with more basic vocabulary will likely talk more meaningfully than those without.

The Writing Test
The results of the Writing test were divided into seven domains, named authenticity, agreement, coherency, fluency, grammar, vocabulary, and tidiness. The overall scores from the five participants are depicted in the following table.
P-ISSN 2623-0356 E-ISSN 2654-5586 Different from the Speaking test, the writing test results revealed that three tour guides (Miss O, Mr R, and Miss F) obtained the same score. They got 4 in all seven criteria of assessment with a total score of 28. This is understandable because these three participants performed quite good Englishas observed through the Speaking testwith fluent Speaking ability. This analysis follows a theory by Pérez & Basse (2015) where people with sound Speaking skills likely have advanced written skills, too. This is apparent in this case where Miss O, Mr R, and Miss F could write English well, and their writing could be understood easily. Their writing conveys understandable meanings with the proper use of simple and past tense. This condition lends strong support to the theory of speaking and writing that these skills are intertwined (Asgari & Mustapha, 2011).
Meanwhile, Mrs S got the lowest score with 8. This shows her low ability and how she almost could not write correctly in English. She often did not understand the questions and instructions that she asked for help and assistance from fellow tour guides and researchers. This phenomenon does not concur with the idea of professional tour guides who have to be able to explain their regions in written communication (Nation & Newton, 2009). There is a need to focus on this improvement because written promotion is among the cores of successful tourism village advertisements (Elfiondri et al., 2021).
Mrs U was average with 3 in all criteria. This is indicated by her misunderstanding of the instructions. Her grammatical errors made the meanings somewhat different from what was asked in the questions. This condition is common for average EFL learners because they are struggling with their vocabulary while grappling with the idea of language production (Harmer, 2007).

Conclusion
Our study has found that tour guides in Duren Sari are struggling with their English ability. The findings revealed that not all of them were fluent in speaking and writing skills, although overall they were quite good in those skills. It is shown through their obtained scores that match the "average" and "good" criteria. They were varied in their Speaking ability, while some similarities are shown in their Writing ability, although one or two tour guides showed low English ability. It is suggested that prolonged support activities and follow-ups are conducted to improve their English skills.
In particular, the researchers communicate the test results with the participants as a basis for the next step. This discussion is expected to help the researchers to determine the materials that are better included in the textbook for future reference.