Mind mapping of teachers’ readiness for online teaching and learning: A reflective study of urban and suburban areas

Abstract

Teacher readiness is an important consideration in online teaching and learning (OTL). To achieve successful OTL, teachers should have sufficient hardware, technical skills, and access to the internet. Although many studies have focused on OTL, none has examined teachers’ readiness for student outcomes. To fill these gaps, this study aimed to reflect on the teacher readiness category for student outcomes in the South Sulawesi Province, Indonesia. We examine quantitative closed-ended questionnaires and documents of students’ evaluation reports using descriptive statistics by leveraging the SPSS application. Teachers’ readiness was categorized as (1) very good (43.69%), (2) good (50.70%), (3) moderate (5.14%), or (4) poor or less (0.46%). Moreover, this study demonstrated that student outcomes in urban areas were better than those in suburban areas. This implies that the readiness category of teachers in urban areas is better than that of suburban OTL. The better the teacher readiness category, the higher the students’ online learning outcomes. Therefore, this study suggests that future research should focus on three areas: (1) how willing teachers are to change student outcomes by participating in class experiments; (2) why teachers do OTL; and (3) how competent principals are at doing OTL that works.

Keywords:

• online teaching and learning
• students’ outcome
• TPACK
• urban
• suburban

1. Introduction

Online teaching and learning (OTL) are necessary in light of the rapid growth of technology, particularly after the coronavirus disease (COVID-19) has affected the world (Butarbutar et al., 2023; Cutri et al., 2020). There is no denying the pedagogical impact of the spread of COVID-19 and long-term school lockdowns have pushed the Ministry of Education and Culture of the Republic of Indonesia to make OTL possible (Butarbutar, 2021; Nur et al., 2022). To do so, teachers must prepare for OL through self-belief, high-quality OTL strategies, and institutional support (Scherer et al., 2023a). In addition, Eastin and LaRose (2000) stated that to achieve successful OTL, teachers should have sufficient technical skills, access to hardware necessities, and Internet efficacy.

Previous studies have emphasized that teacher readiness supports the effectiveness of OTL. Baran (2011) reported that evaluating students’ progress by providing ways is important and that teachers need to prepare and support online teachers. He found that teachers waste more time preparing, designing, and structuring online courses than in on-site classrooms. Bran’s investigation did not track students’ progress, which raises bias because they concluded that time-consuming course design and structure preparation were factors in successful online teaching. Furthermore, Baran (2011) sums up a mid-term semester as an approach to ensure that OL can accommodate students’ needs. This implies that teachers have a mature readiness to provide evaluation and feedback to support online success since the course preparation. His research implication is that online teachers make transitions successful for their students. Petko et al. (2018) asserted that the key success of OTL is teacher readiness, beliefs, or self-efficacy. Teachers are supposed to be convinced and have self-efficacy (Tsai et al., 2020) in using technology to effectively reach the learning process in the classroom, and vice versa

However, none of the aforementioned studies have observed teachers’ readiness for student outcomes. We are concerned about how teachers’ readiness affects their outcomes. Our aim was to examine the effectiveness of OTL in terms of student outcomes by providing a final test to evaluate student outcomes after OTL. Seeking students’ outcomes and teacher readiness were claimed to be a new difference in our study.

As Hung (2016) suggested in the Teacher Readiness Online Learning Measure (TROLM) factor structure, another smart thing to do is ensure that the OTL instrument works well for many different groups. It is possible to add more course types and consider more variables such as location (city or suburb), school type, level, type of education, and culture. Similarly, Palloff and Pratt (2013) pointed out that online learning environments present challenges for higher education instructors, including concerns about qualifications, identity maintenance, meeting discipline-related demands, and assessing learning outcomes. Albrahim (2020) highlighted that online learning outcomes are better than those of face-to-face education. Teacher readiness is an important consideration in online learning (Shattuck, 2023). Assessing learning outcomes was a concern of our study; therefore, teachers’ preparedness and student outcomes were highlighted for online learning. To do so, we took samples from 20 schools spread across South Sulawesi Province, Indonesia, with two schools categorized as urban areas and 18 remaining as suburban areas.

In light of our objective, this study examines teacher readiness in urban and suburban areas. Few researchers have explored the OTL in either area. Bach et al. (2006) argued that rural areas and poverty are two barriers to OTL. Lembani et al. (2020) asserted that students in urban areas have significantly different educational experiences than those with poor ICT access in urban, peri-urban, and rural areas. As a result, open distance learning (ODL) offered by institutions to provide materials online can help marginalize communities’ access to education. However, access to ICTs also affects students’ ability to access higher education, highlighting the need for improved ICT access. Additionally, Hu et al. (2022) discovered significant disparities between urban and rural schools in terms of students’ interest in and effectiveness of learning, information technology literacy, and psychological well-being. There is also considerable disparity in the level of teachers’ competency in digital instruction between urban and rural schools. In response, our study is welcome to examine teacher readiness and student outcomes by considering gaps by geographical location.

As a key element of OTL effectiveness as previously mentioned, pre- and post-pandemic teacher readiness is insightful for professional development and policymakers (Cutri et al., 2020). The results of this study might be useful for suburban stakeholders and curriculum developers in arranging teacher readiness and student outcomes. So, this study looks at two types of bias: how ready teachers are for OTL based on where they work, and how that readiness affects how well students do in certain classes (Nenko, 2020) from a larger group of students and a different area (Keramati et al., 2011). In simple terms, this study addressed the following questions:

1. What are teachers‘ readiness categories for OTL in South Sulawesi Province, Indonesia?

2. How does teachers’ readiness for OTL in South Sulawesi Province, Indonesia impact student outcomes?

2. Literature review

2.1. Online teaching and learning

Online learning is an experienced learning-based Internet connection that occurs synchronously and ubiquitously (Singh & Thurman, 2019). Creating a more effective online learning environment requires teaching, cognition, and social presence (West et al., 2023). Satisfactory online learning is achieved when self-efficacy, social presence, and instructors are present in the unstructured content (Lim et al., 2021). Martin et al. (2019) studied online learning using juxtapose facilitation, design, evaluation, and assessment. Bolliger and Martin (2021) designed components of online courses through synchronous, asynchronous, and bichronous systems.

Online teaching and learning involve delivering educational materials and activities via digital platforms, including remote engagement, access to resources, social interaction, and online assignments. Strategies for student engagement include multimedia presentations, lecture videos, interactive assignments, and collaborative online tools.

Online learning allows students to access course materials, communicate with teachers, and complete assignments by using digital tools. They are flexible and suitable for diverse schedules and locations. A supportive community, well-structured courses, clear communication, active engagement, and effective technological use are essential components. Online learning allows students to access course materials, communicate with teachers, and complete assignments by using digital tools. They are flexible and suitable for diverse schedules and locations. A supportive community, well-structured courses, clear communication, active engagement, and effective technological use are essential components.

2.2. Teacher readiness

Online learning allows students to access course materials, communicate with teachers, and complete assignments by using digital tools. They are flexible and suitable for diverse schedules and locations. A supportive community, well-structured courses, clear communication, active engagement, and effective technological use are essential components.

Pertinent to student outcomes, this is a manifestation of teachers’ preparation for OTL (Lynch et al., 2017). They found that teacher readiness had a strong impact on student outcomes; however, this was not separate from that of principal leadership. Martin et al. (2020) found that success and retention are student outcomes that should be achieved at the end of the course. Consequently, it must be oriented at the beginning of the OTL preparation.

As we talked about in the introduction, there is a lot of research on the structure, dimensions, factors, indicators, and perceptions of OTL. In this study, we looked at how ready teachers were by looking at five indicators: (1) digital literacy, (2) self-efficacy, (3) technological pedagogy content knowledge (TPCK), (4) infrastructure, and (5) school management. This indicator considers teachers’ instructional needs for OTL in southern Sulawesi, Indonesia. This can be explained as follows: Readiness is the most important indicator for OTL applications. Educators must modify their teaching methods, techniques, and strategies in online learning settings to engage students soundly through digital platforms (Albrahim, 2020; Budhai, 2021). Teacher readiness refers to the specific knowledge and understanding of online learning interactions (Bergdahl, 2022). Keramati et al. (2011) identified techniques, organizations, and social factors as components of teacher readiness. These three factors are influenced by e-learning outcomes (teacher and student progress, and access). Furthermore, Moore-Adams et al. (2016) implied through their systemic literature that when teachers prepare for online learning, suppose in light of both empirical and evident experiences. In addition, there is adaptation to face-to-face instruction using technological devices to prepare online teachers. Teachers are ready for OTL whether they know their usefulness (Scherer et al., 2023b) to make their teaching process more effective and efficient from preparation up to the evaluation phase.

Pertinent to students’ outcomes, this was a manifestation of teachers’ preparation for OTL (Lynch et al., 2017). They found that teacher readiness had a strong impact on student outcomes; however, this was not separate from principal leadership. Martin et al. (2020) reviewed that success and retention are student outcomes that are supposed to be achieved at the end of the course. Consequently, it must be oriented at the beginning of OTL preparation.

A wealth study of construction, dimensions, factors, indicators, and perceptions of OTL as mentioned in the introduction section; thus, in the current study, we adapted teachers’ readiness indicated through five indicators: (1) digital literacy, (2) self-efficacy, (3) technological pedagogy content knowledge (TPCK) (Howard et al., 2021), (4) infrastructure, and (5) school management. This indicator considers teachers’ instructional needs for OTL in Southern Sulawesi, Indonesia. This can be explained as follows:

2.2.1. Digital literacy

Digital literacy (DL) involves online platforms, searching for resources, assessing the reliability of online sources, and effectively communicating using digital tools (Butarbutar et al., 2021). This involves working knowledge of emails, file uploading/downloading, and website navigation (Polizzi, 2020; Prior et al., 2016). Shopova (2014) described DL as the capacity to find, use, and transfer information wisely via digital technology, applications, or internet networks. Digital intellectual literacy (DIC) refers to one’s capacity to acquire and share important knowledge. Similarly, DL refers to familiarity with the cognitive use of information and communication technology. It focuses on the psychological and socio-emotional aspects of the digital environment and world and is connected to technical proficiency (Blau et al., 2020). DL can be developed through the following principles: (1) how to extract ideas from media sources both explicitly and implicitly; (2) how social factors affect media’s long-term success; and (3) how to share, look for, reshape, and store data well. (4) Duration, or the ability to evaluate and save a tale for later analysis, is another principle (Terrell, 2023).

Another definition of DL is the ability to communicate by integrating digital tools with society or the community in order to entertain and spread new knowledge. Therefore, society must be conscious of the necessity of gathering and evaluating information before sharing it with the public (Butarbutar et al., 2021; Eshet, 2004).

2.2.2. Self-efficacy

Self-efficacy is a psychological term that describes a person’s confidence in their capacity to complete particular tasks or achieve particular objectives (Bandura, 1997; Schunk, 2012). A person with strong self-efficacy is more likely to approach projects with persistence, confidence, and conviction that they can overcome problems. People who feel low in self-efficacy doubt their aptitude and are more likely to give up when faced with difficulties (Kundu, 2020). Self-efficacy is domain-specific, meaning that a person’s belief in their ability can vary across different areas of their life (e.g., academic, athletic, & social). It is influenced by various factors including past experiences, observed successes and failures, verbal persuasion from others, and emotional states (Maddux & Volkmann, 2012).

It is therefore crucial to consider online learning and education (Nurhikmah et al., 2021; Panergayo & Almanza, 2020; Shen et al., 2013). Corry and Stella (2018) highlight that teachers’ self-efficacy in online learning makes it easy to adopt online teaching.

Ross et al. (2001) investigated the impact of teachers’ self-efficacy on students’ outcomes. Their investigation of 387 students who had moved to a new group was affected by teacher efficacy. Its impact includes three skills: basic and advanced computers and computer self-efficacy

2.2.3. Technological pedagogy content knowledge (TPCK)

TPACK seeks to characterize the knowledge and comprehension educators need to incorporate technology successfully into their teaching methods. Mishra and Koehler (2006) developed TPACK. The convergence of three crucial components—technological knowledge (TK), pedagogical knowledge (PK), and content knowledge (CK)—forms the foundation of TPCK. Technological knowledge (TK) is teachers’ understanding of various technologies as well as how they can be used in educational settings. This calls for competence in computer hardware, software, and other educationally pertinent technologies. Pedagogical Knowledge (PK): The understanding of instructional strategies that promote learning and the spread of knowledge is referred to as pedagogical knowledge. This entails understanding how to plan instructional activities, control classroom interactions, and involve students in worthwhile educational experiences (Eichelberger & Leong, 2019; Moore-Adams et al., 2016).

2.2.4 Infrastructure

Occasionally, infrastructure refers to physical and organizational structures, equipment, utility, and systems that facilitate the functioning of a society, organization, or project. It includes a wide range of essential elements required for various activities and the overall operation of a system. Infrastructure can encompass both physical and virtual components, and plays a crucial role in enabling economic, social, and technological development (Fernández et al., 2023). Hence, information technology infrastructure involves the hardware, software, and networks necessary to support digital communication (Churchill, 2020), data storage, and information sharing. It includes data centers, servers, routers, Internet connections, and cloud computing services (Zulu, 2019).

2.2.5. School management

Occasionally, infrastructure refers to physical and organizational structures, equipment, utilities, and systems that facilitate the functioning of a society, organization, or project. It includes a wide range of essential elements required for various activities and the overall operation of a system. Infrastructure can encompass both physical and virtual components, and plays a crucial role in enabling economic, social, and technological development (Fernández et al., 2023). Hence, information technology infrastructure involves the hardware, software, and networks necessary to support digital communication (Churchill, 2020), data storage, and information sharing. It includes data centers, servers, routers, Internet connections, and cloud computing services (Zulu, 2019).

2.3. Urban and suburban areas

Urban areas have a high population density and extensive infrastructure, offering a broader range of amenities and services. They often include cities and towns, which offer a faster pace of life. Suburban areas, on the other hand, have smaller communities, lower population density, and focus on open, natural, and agricultural spaces. They often have poor Internet connections, weather-dependent signalling, digital literacy, technology skills, dependency on others, and frequent use of technology for OTL purposes (Butarbutar et al., 2023; Hart et al., 2005).

3. Research method

3.1. Research design and instrument

This study was designed as an online survey, using a descriptive statistical approach. The chosen online survey considered respondents’ residences in different geographical areas in South Sulawesi, Indonesia. The study used closed-ended questionnaires to gather data. It consists of five indicators: digital literacy and self-efficacy (ten items), technological pedagogy content knowledge (TPCK) (eight items), behavior (five items), infrastructure (six items), and school management (five items).

3.2. Population and sample

The study employed senior high school teachers in the South of Sulawesi, Indonesia, as its population. Of the population of 14.752 (Center of Data Technology Information Ministry of Education and Culture of Republic of Indonesia, 2021), 428 teachers were chosen as the sample. Samples were selected using the proportion technique formulated by Wrenn et al. (2002) as follows:

$n=\frac{Z^2\left(p.q\right)}{{\left(e\right)}^2}$

where,

n is the number of samples.

Z is the value of the normal distribution table for the desired level of confidence (the alpha level for 0.05 is 1.96).

p is the proportion of the population obtained at 50%, and q = l-p.

e is sampling error or desired precision = ±0.05.

3.3. Respondents’ profiles

A total of 428 senior high school (SMA) responded to the survey. Details of their profiles are presented in Charts 1 and 2 as follows: Gender (female = 286; male = 142) and education (undergraduate = 360; post-graduate = 68). As research is ethical, all respondents’ profiles would be hidden and not unconditionally exposed. With regard to the Institutional Review Board (IRB), the study holds the Universitas Negeri Makassar and Dinas Penanaman Modal dan Pelayanan Terpadu Satu Pintu (PTSP) province of South Sulawesi, Indonesia as research permit.

Chart 1. Teacher’s demography range age.

Chart 2. Teachers’ profile based on regency area.

3.4. Technique of collecting data

Online survey questionnaires were created using Google Forms, and their links were distributed through the WhatsApp Group (WAG) of teachers in each district. Each respondent was asked to fill the survey link for one week. At the end of data collection, all teachers responded to a spread sheet for analysis.

3.5. Preliminary study

Before gathering data from South Sulawesi Province, a small-scale instrument trial was conducted, and the results were evaluated to determine the R-count as a benchmark for the validity of each instrument item and to determine the level of reliability (Priddis & Rogers, 2018). To ensure the validity and reliability of the measurement, a preliminary study was conducted with 50 teachers from the Sinjai district. Of the 43 instruments that were produced and deemed credible in the analysis of the teacher readiness level instrument in online learning, nine were found to be invalid. In this case, for the scale, 1 = strongly disagree, 2 = disagree, 3 = agree, and 4 = strongly agree. Cronbach’s alpha coefficient was 0.9; the R coefficient was 0.7, indicating reliability. The instruments used were categorized as follows: (1) digital life and self-efficacy consisted of ten items with a mean score of 3.12, (2) TPACK consisting of eight items with a mean score of 3.02, (3) behavior consisting of five items with a mean score of 2.86, (4) infrastructure consisting of six items with a mean score of 2.82, and (5) school management consisting of five items with a mean score of 2.88.

3.6. Data analysis

This study was conducted using descriptive statistics in Microsoft Excel. There were 34 closed-ended questionnaire items with four options; thus, the minimum score was 34 and the highest score was 136 (Hung et al., 2010). As a result, the interval range was the main decision for categorizing teachers’ readiness for OTL, as shown in Table 1. Subsequently, the data were discussed, interpreted, and compared with students’ outcomes.

Table 1. Total score category

Range	136–34=102
Intervals	102:4=25.5
Poor or Less	34–59.5
Moderate	59.6–85.1
Good	85.1–110.7
Very good	110.8–136

4. Results and discussion

In response to the research question, how are teachers’ readiness for OTL categorized in South Sulawesi, Indonesia? To gain in-depth knowledge and comprehension, the results are presented in Table 2, Figures 1, and 2.

Figure 1. Teacher readiness in regencies of South Sulawesi Province.

Table 2. Teachers readiness indicators

No	Teacher readiness indicators	Items	Mean	Standard Deviation
1	Digital literacy and self-efficacy	1–10	3.12	0.55
2	TPACK	11–18	3.02	0.53
3	Behaviour	19–23	2.86	0.59
4	Infrastructure	24–29	2.82	0.65
5	School management	30–34	2.88	0.60

Figure 1 demonstrates that digital literacy and self-efficacy readiness mean scores are 3.12 or good category, and the only one lower with a 2.95 mean. This means that DL teachers have a good readiness for online learning. In this vein, our results are in line with those of Shopova (2014), who defined DL as not simply the skill of using, searching for, consuming, and transforming digital technology tools appropriately and wisely regarding its purposes. Teachers differ in terms of promotion, advertising, and content learning. Tan (2013) affirmed that teachers’ digital literacy can be facilitated through informal learning, which is used not only for education, but also for amusement (Nurhikmah et al., 2022). The greatest potential is that they are more literal and critically involved in analyzing and evaluating the materials found. It is pertinent to the current study teacher DL item, which said, “I have the ability to evaluate sources of internet information”

Pertinent with TPACK readiness, this indicator was categorized as good (mean = 3.02), and it is evident that their readiness was not only to use technological tools per se but also to content knowledge (CK). Nevertheless, there were nine areas with a mean of less than 3.00 such as Sinjai, Luwu Timur, Bulukumba, Palopo, Wajo, Jeneponto, Enrekang, Takalar, & Pangkep. This means that teachers’ TPACK in these regencies was not as good as that in the others. This implies the need for additional training in the TPACK.

In addition, Mishra & Koehler (2006) stated that learning is more effective when teachers appropriately integrate TPACK into their OTL preparations. Luo and Zou (2022) suggested in their literature review that TPACK was helpful in teachers’ language in the technology integration course and OTL platform. Nevertheless, integrating technology and pedagogy is difficult; therefore, it may be an effective infrastructure. Therefore, Brinkley-Etzkorn (2018) showed that TPACK affects effective faculty training and teaching by integrating the value components. According to Santos and Castro (2021), it is essential to consider students’ information, course values, feedback, and communication methods in order to enhance the effectiveness of OTL through the incorporation of TPACK.

The mean score for teachers’ behavioral readiness for OTL was 2.86. This indicates that teachers’ behavioral readiness for OTL in South Sulawesi Province was not maximized. This finding implies that readiness for OTL should increase. Furthermore, in this study, collaboration between teachers and colleagues to design online learning materials and teachers’ students to solve online projects was shown to be a positive behavior in OTL.

In addition, the positive behavior of the teachers in this study affected their ease of use and familiarity with technology. This behavior correlates with Joosten and Cusatis’s (2020) finding that familiarity with social technology influences the degree or frequency of using computers and mobile device-based tasks. Furthermore, teachers’ positive behavioral readiness in this study was reflected in their creativity during the OTL process. The better the teacher’s behavior, the more creative it is (Ardiningtyas et al., 2023; Butarbutar & Nur, 2022). Finally, positive teachers’ readiness behavior is seen through their willingness to learn new computer programs to support their skills. Thus, positive behavioral readiness for OTL manifests in sustainability rather than in-situ.

In terms of infrastructure readiness, the mean score of 2.82.There are three mature readiness areas in terms of infrastructure (Enrekang, Pinrang, & Soppeng). This is seen through the equal facilitation provided by schools, experienced technicians, funding for maintenance, and training for teachers. In contrast, some teachers are reluctant to transition from conventional face-to-face interactions in the classroom because of the limited infrastructure (Butarbutar et al., 2021). Technology information supporting online learning had the highest score for teacher readiness for OTL, with a mean score of 2.93.

Another readiness is that school-provided video conference tools (Zoom, Cisco Webex Meeting, & Microsoft Teams) warrant consideration. The results of the current study were supported by web-based learning (WBL) and manageable servers (Zulu, 2019).These innovations provided by schools are parallel to the technological development of OL (Fernández et al., 2023). In this vein, teachers perceive that novice teachers need to increase OTL design skills focused on students’ values regarding their need to support professional management (Kidd & Murray, 2022).

Figure 1 shows teachers’ readiness to support OTL through effective school management. It shows four areas with above 3 mean scores above three (Pinrang, Luwu, Bone, & Soppeng). The remaining areas had a mean score of 2.88, and only one area had a mean score of two (Maros). The mean score of 3.02 indicates that the principal and faculty staff is supported and cooperative. School endorsement for OL indicates that the technician item score is the second highest among the principal and administrative staff. This indicates that a three-power person (technician, principal, and administrative staff) was particularly beneficial for teacher readiness in the current study. This is relevant to the usefulness of educational tools and technical skills as indicators of instructors’ readiness for mobile learning (Cheon et al., 2012).

Figure 2. Teacher readiness category in regencies of South Sulawesi province.

Overall, the study notes teacher readiness in South Sulawesi province categorized as follows “Very good was 187 teachers (43.69%); ‘Good was 217 teachers (50.70%)’; Moderate was 22 teachers (5.14%)”, and “Poor or Less was 2 teachers (0.46%)”. Meanwhile, for teachers’ readiness in light of regency areas, of the 20 areas, the three area categories are good (Bantaeng, Sidrap, & Wajo). Only one area (Maros) was considered very good, whereas one teacher from Takalar in a suburban area was categorized as poor.

Aligned with the second research question, how do the readiness categories of urban and suburban teachers’ impact student outcomes? The results of this study demonstrate a comparison between the Makasar and Sinjai regencies. In this study, we chose both regencies by considering the following: (1) the highest mean score for teachers’ readiness indicators; (2) we assumed that Makassar was representative of urban and Sinjai was suburban area representation; (3) the similarity of the subject was Germany; (4) the grade of students was ten (X); and (5) in this study, we did not provide intervention to the class but simply observed students’ outcome final evaluation. Therefore, we examined the impact of teachers’ readiness indicators on student outcomes without interventions or experiments. The comparison is as follows:

Table 3 shows that students’ outcomes (Makassar, M = 57.635 and Sinjai, M = 44.831)

Table 3. Report outcomes of Germany course

Regency	Mean	N	Standard Deviation
Makassar	57.635	318	18.4635
Sinjai	44.831	59	17.7620
Total	55.631	377	18.9150

Table 4 shows that the learning outcomes in Makassar are not normally distributed (less than 0.05). In contrast, Sinjai Regency was distributed normally (more than 0.05). Table 5 shows that sig. less than 0.05, indicating that the data were not distributed homogeneously, which might have caused the variant data population to not be homogenous. For this reason, we examined Brown Forsythe and Welch using the SPSS application, and the results are shown in Table 6.

Table 4. Tests of Normality

	VAR00005	Kolmogorov-Smirnov^a			Shapiro-Wilk
		Statistic	df	Sig	Statistic	Df	Sig.
VAR00004	Makassar_X_Germany	.275	318	.000	.797	318	.000
	Sinjai_X_Germany	.103	59	.186	.975	59	.271
a. Lilliefors Significance Correction

Table 5. Test of homogeneity of variances

		Levene Statistic	df1	df2	Sig.
VAR00004	Based on Mean	422.858	1	375	.000
	Based on Median	95.865	1	375	.000
	Based on Median and with adjusted df	95.865	1	317.312	.000
	Based on trimmed mean	390.606	1	375	.000

Table 6. Robust tests of equality of means

	Statistic^a	df1	df2	Sig.
Welch	25.542	1	82.977	.000
Brown-Forsythe	25.542	1	82.977	.000
a. Asymptotically F distributed.

Table 6 shows that the sig. was less than 0.05, indicating that there were significantly different student outcomes between the Makassar and Sinjai regencies. The students’ outcomes in Makassar were higher than those in Sinjai. In summary, the better the teacher readiness category, the higher the students’ online learning outcomes.

5. Conclusion and implication

The study comes to the conclusion that, first, a lesson learned to be reflected in the current study is that teacher readiness in South Sulawesi province is categorized as follows: “Very good was 187 teachers (43.69%); ‘good was 217 teachers (50.70%)’; moderate was 22 teachers (5.14%)”; and “poor or less was 2 teachers (0.46%)”. This implies that teacher readiness for OTL in the South Sulawesi province category was good post-pandemic COVID-19 until the paper was written. These ideas are similar to those of Miller et al. (2021), who stated that pandemic can be both a challenge and an opportunity for teachers (Butarbutar et al., 2019; Leba et al., 2021; Nurhikmah et al., 2023; Taghizadeh & Basirat, 2022). However, there are still indicators that require improvement, such as the infrastructure and school management. The indicators are not equal between urban and suburban areas, so this requires the government’s in-depth attention to make one regulation in light of the OTL in the South Sulawesi Province and beyond. Parallel to our analysis, teachers aged 26–45 years were the most ready for OTL. Therefore, we claim that the younger the teachers, the more literate the TPACK and the more ready they are for OTL

Second, this study found no significant differences in teacher readiness among 20 regencies in South Sulawesi, Indonesia. Teachers made this assumption by implementing self-directed learning and updating and upgrading information technology without regard to specific geographic areas. Nevertheless, the results of our study are crucial for local governments and stakeholders in keeping with the sustainability of technology skills training for teachers and the creation of comfortable learning conditions for students by region.

Third, pertinent to student outcomes, this indicates that there were significantly different student outcomes between the Makassar and the Sinjai regencies. Students’ outcomes in Makassar were better than those in Sinjai due to teachers’ readiness for Makassar. The better teacher readiness category the higher students’ online learning outcomes. This implies that local governments make OTL regulation-based teachers ready to enhance a comfortable learning atmosphere based on region because students’ learning styles and needs might differ in urban and suburban areas.

Furthermore, owing to the rapid growth of technology, teachers are expected to engage in lifelong learning, training, supervision, and sustainability activities. Therefore, this study suggests that future research should focus on three aspects: (1) how ready teachers are to change student outcomes by actually taking part in class experiments, (2) why teachers want to do OTL and how they can prepare for it, and (3) how competent principals are to help OTL.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the DIPA UNIVERSITAS NEGERI MAKASSAR [551/UN35/HK/2021].