Introduction

With the rapid development of information technology, Chinese online education has become more and more popular for its convenience. According to Erie’s consulting data, Chinese online learners reached 135 million in 2018 and are expected to continue to grow in the next two to three years (Erie 2018). It is the trend of future education. At the same time, more and more instructors are encouraged to teach online (Seirup et al. 2016). The Chinese ministry of education requires teachers to conduct and create their own online courses. Most of these instructors had some classroom teaching experience or some online learning experience, but their online teaching experience is little. They thought teaching online was moving classroom teaching materials into online teaching platforms (Xiao 2018). It is necessary to give them some training to facilitate their online teaching competencies. Online teaching is defined as teaching that “most or all of the content (80% or more) is delivered online” (Allen and Seaman 2008).The current teachers’ training on online teaching mainly focused on the technology competencies (Liu 2015). But it is not enough to improve teachers’ technology competencies for those online instructors. Instructors play essential roles in online education. They are supposed to organize and guide students’ online learning. Barbour et al. (2013) supported that not all online instructors are competent in designing, delivering, and supporting online learning; especially for those beginning online instructors. Davis and Roblyer (2005) found that a good classroom teacher is not necessarily a good online instructor. It is necessary to explore beginning online instructors’ online teaching competencies which can guide instructors to implement successful online teaching and assisted online trainers design targeted training program for the beginning online instructors. Though studies explored instructors’ online teaching competencies, few analyzed instructors’ online teaching competencies from the teaching process perspective. It is still unknown on the level of beginning online instructors’ online teaching competencies in preparing themselves to teach online, selecting appropriate tools, preparing learners to learn online, facilitating online learning, and conducting meaningful appraisals of student learning. Their competencies level on these dimensions can give instructors and teacher trainers some guidance. Thus, it is helpful to explore the beginning online instructors’ competencies in teaching online from the teaching process perspective. The beginning instructor is the instructor with no or less than 3 years’ online teaching experience in this study. Though instructors’ demographic factors are supported to be essential to instructors’ online teaching competencies (Ahmed et al. 2016; Patrick and Yick 2005), as for what the specific effects of these demographic factors on the online teaching competencies need further exploring. Studies supported that online teaching is not directly moving face-to-face courses to online platforms (Allen and Seaman 2013). Thus, instructors’ perceived challenges of moving traditional courses online can reflect the essential aspects of online instructor training.

Activity theory provides an appropriate framework for designing and analyzing needs, tasks, and outcomes for designing learning environment (Jonassen and Rohrer-Murphy 1999). It is supported that a successful learning activity requires the appropriate need, task, and outcome which can give us an explicit guide of conducting teaching activities. Based on the Activity theory and instructors’ online teaching experience, this study constructed a process-based scale of teachers’ online teaching competencies and applied it to Chinese beginning online instructors to further explore the effects of beginning instructors’ demographics on their online teaching competencies and their challenges of moving traditional courses online. It can also assist teacher trainers to design targeted training programs.

Literature review

Study of online teaching competencies

Competency is knowledge, skill, ability and attitude required to effectively perform in an organization (Paquette 2007). In that way, online teaching competencies are knowledge, skills, abilities and attitudes required to implement online teaching effectively. Technological Pedagogical Content Knowledge (TPACK) is a widely accepted theoretical framework for understanding teacher knowledge required for effective technology integration (Harris and Hofer 2011). Every knowledge defined in the TPACK is contextual related factors, which makes it difficult to be learned and applied (Schmidt et al. 2009). For example, in the online teaching environment, the TPACK framework cannot give instructors explicit explanations on how to design and conduct a successful online course. Competencies for Online Teaching Success (COTS) defined effective online teaching competencies including the following six aspects, attitude/philosophy, building a learning community, class administration, faculty workload management, teaching and learning, and technology-use abilities (Bigatel et al. 2012). Visible, Organized, Compassionate, Analytical, Leader-by-example (VOCAL) defined online teaching competencies including the following five aspects, establishing a social presence; designing and organizing plans, presentations, and timing; handling students’ problems; evaluating and improving the system; modeling best online teaching practices (Klein et al. 2004; Savery 2005).

Many studies investigated online teachers’ competencies. Roberts (2018) investigated the instructors at the University of South Africa and found that distance instructors’ technology and instructional design competencies are crucial and future training needs to be developed to support these areas. McAllister and Graham (2016) investigated instructors’ online teaching preparation through a survey inquiring on the capacity of K-12 online teaching endorsements and course descriptions in the United States. They found that more support on the online pedagogy, instructional design, and online field experience are necessary. Richter and Ware (2016) investigated and analyzed nurse educators’ educational technology competencies and efficacy in teaching online in the U.S and found that instructors reported “somewhat competent” to “very competent” in the use of educational technology competencies. González-Sanmamed et al. (2014) investigated the expert instructors in Spain on their perceived essential peripheral online teaching roles besides the central pedagogical roles and proposed that social, evaluator and administrator roles are necessary to be considered in instructors’ professional development. Murphy et al. (2011) surveyed Canadian teachers’ perspectives on asynchronous and synchronous online teaching and found that pedagogy is essential for both asynchronous and synchronous online teaching. Kong et al. (2017) reviewed and discussed the related materials of teacher development on e-Learning and found that Beijing focused on promoting self-directed teacher development on e-learning in K12 education.

Though these online teaching competencies scales can give us some guidance on teachers’ online teaching competencies from the attitude, management, technology, these scales cannot give us a comprehensive understanding of instructors’ online teaching competencies from the online teaching process. For example, instructors’ competencies on preparing, selecting tools, preparing learners, facilitating, and conducting appraisals. Instilling these essential online teaching skills can be helpful for both beginning instructors and practitioners (Stein and Wanstreet 2017).

Demographic factors of online teaching competencies and perceived challenges

Instructors’ demographic factors including the gender, age, educational level, and teaching experience could facilitate or constrain their teaching competencies (Dong et al. 2015). Online teaching competencies in this study represent instructor’s competency level in teaching online. Some studies supported that instructors’ demographic factors had positive effects on their online teaching competencies. For example, Ahmed et al. (2016) supported that male instructors’ perceived higher Technological Pedagogical Content Knowledge (TPACK). Patrick and Yick (2005), as well as Kopp et al. (2012) supported that age and teaching experience has positive effects on instructors’ online teaching competencies. Peechapol et al. (2018) supported that computer experience had positive effects on users’ perceived ease of use and self-efficacy on online learning system. Akiri and Ugborugbo (2009) and Sogillo et al. (2016) found that there is a significant positive correlation between the instructors’ educational level and their teaching competencies. However, some studies proposed that instructors’ demographic factors had negative effects on their online teaching competencies. For example, Whitaker (2015) found that the female instructors scored higher than males on online teaching practice. Klug et al. (2014) proposed that instructors with an older age demonstrated a lower competencies score on promoting students’ lifelong learning. Michaelowa and Wittmann found that instructor with higher educational level showed lower satisfaction with their work which further influenced their teaching (Michaelowa and Wittmann 2007). Though these studies concentrated on the effect of instructors’ age, gender, and teaching experience on their teaching competencies, there is not a consensus on instructors’ demographic factors on their online teaching competencies.

Pang (2016) proposed that a considerable time and effort investment is a big challenge for instructors. Chou and Tsai (2002) supported that the biggest challenge for course designers is rethinking and adapting traditional curriculum development models. Hew and Cheung (2014) proposed that instructors’ perceived challenges reflect their expectations for training. They found that evaluating students’ work, speaking into a vacuum due to the absence of student immediate feedback, the amount of time and money investment, and a lack of student participation are four challenges of teaching MOOCs. Mccown (2010) supported that instructors’ discomfort with technology is a challenge for many instructors. They supported that management, pedagogy, technology are challenges for instructors which is consistent with the online teaching competencies; as for the specific challenges of moving traditional courses online need further exploration.

Activity theory

Activity theory is proposed by Engeström (1999) based on the process concept (Lektorsky 1999). It is supported that mind and activity are intertwined. This is essential for analyzing teaching activities. Activity theory analyzes people’s activities in a specific context from the subject, object, and tool. It is a powerful lens which can be used to analyze and guide instructors’ teaching competencies and philosophy. The subject of an activity is a major role in an activity. The object can be the goal of the activity. The tool can be anything that used in the goal achievement process such as teaching method or teaching tools. Specifically, in the online teaching context, the subject can be the instructor or the educational manager. The object can be improving students’ online learning effectiveness. The tool can be teaching methods or online teaching platforms or resources.

Teaching are activities to improve students mind (Palinscar and Brown 1984). The core idea of Activity theory is consistent with teaching. Therefore, Activity theory is often used in teaching context. For example, Jonassen and Rohrer-Murphy (1999) designed constructivist learning environments based on the Activity theory. Zurita and Nussbaum (2010) designed a conceptual method for computer-supported collaborative learning based on Activity theory. Mercer and Howe (2012) explained the dialogic processes of teaching and learning from the lens of the Activity theory. Few studies applied Activity theory to construct the teaching competencies framework for teaching online.

In summary, though there are some instruments for online instructors’ online teaching competencies, they cannot give us a comprehensive understanding of instructors’ online teaching competencies from the teaching process perspective. Though studies supported that pedagogy, technology, and management are essential competencies for teaching online, few explored instructors’ specific online teaching competencies from the teaching process perspective. Though studies supported that instructors’ demographic information are essential to their online teaching, there is not a consensus on the specific effects on online teaching competencies. Therefore, it is necessary to construct an online teaching competencies instrument from the lens of the Activity theory and investigate instructors’ online teaching competencies. Besides, the effects of instructors’ demographic factors on their online teaching competencies, and instructors perceived challenges of moving traditional courses online were explored.

Purpose and research questions

The aim of this study is to construct the process-oriented online teaching competencies instrument and provide an overview of Chinese beginning online instructors’ competencies and their relationships with demographic factors. To achieve this objective, individual instructor’s characteristics and competencies related to teaching online are considered. The following research questions are explored:

  • RQ1 What is the Chinese beginning online instructors’ competencies level in teaching online?

  • RQ2 What are the effects of instructors’ demographic factors on instructors’ online teaching competencies?

  • RQ3 What are the beginning online instructors’ perceived challenges of moving traditional courses online?

Methods

Instrument development

The questionnaire used in this study was designed by online teaching experts with more than 20 years of online teaching experience (Stein and Wanstreet 2017). Thus, the questionnaire used in this study is stem from practice and followed the Activity theory to guide online teaching practices. It can give teacher trainers practical guidance on online teacher training design. Based on the Activity theory and online teaching experts’ experience, the beginning online teaching competencies are scaled from the following five dimensions: preparing instructors to teach online, selecting appropriate tools, preparing learners to learn online, facilitating online learning, and conducting meaningful appraisals of student learning (as shown in Fig. 1). The performance statements of the five dimensions described the role instructors should play and the underlying knowledge and skills they should possess to carry out a successful online teaching.

Fig. 1
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Beginning Online Instructor Competencies Questionnaire (BOICQ) based on Activity theory

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There were 41 items in the questionnaire in which 5 were about participants’ demographic information including the gender, age, educational level, online learning experience, and online teaching experience. Instructors were asked to report on their competencies in teaching online using a four-Likert scale on 36 items, ranging from “I do not know anything about this topic” (1) to “I have conceptual and experiential knowledge of this competency” (4). The last item of the scale is an open question on the perceived challenges of moving traditional courses into online. There were six possible challenges, including, not clear about the roles of teaching online, difficulty in conducting and organizing an online course, technically difficult, difficulty in preparing students for online learning, difficulty in managing and promoting online learning, difficulty in evaluating students’ learning. The questionnaire can be linked through https://www.wjx.cn/jq/21628630.aspx.

Since it is the first time that the Beginning Online Instructor Competencies Questionnaire (BOICQ) was used in China, it is crucial to test whether the measurement structure of BOICQ was appropriate in this context. As a result, before formal data analysis for answering the research questions, a confirmatory factor analysis (CFA) model was estimated to check if the assumed measurement structure could be generalized and meet the data structure. The CFA was estimated with robusted maximum likelihood (MLR) estimator with lavaan package in R (Rossel 2012). The model fit is evaluated by Chi square statistics, Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR).

A non-significant result with p-value larger than 0.05 in Chi square statistics indicated a good fit. But the Chi square test is sensitive to sample sizes. For other model fit indexes, a model with RMSEA values about 0.08 or less would indicate a fair fit, and values about 0.05 or less would indicate a close fit of the model (Browne and Cudeck 1992). A model with SRMR value less than 0.08 would indicate acceptable fit to the data (Hu and Bentler 1995). Factor loadings and R-squares are also double-checked in considering the item-model fit. Generally, items with factor loadings higher than 0.4 can be defined as valid items and kept in further data analysis processes. Invalid items would be deleted or modified according to modification indexes. The modified model would be compared with the original model in model fit using Akaike information criteria (AIC) and Bayesian information criteria (BIC), lower AIC and BIC values indicate better model fit. The likelihood ratio test could not be used when any item is deleted for the two models do not share the nested relationship.

Participants and data collection

Participants are pre-service instructors majoring in educational technology from four universities in the central and west of China. All participants had either taken online courses or taught online courses by themselves. The Beginning Online Instructor Competencies Questionnaire (BOICQ) was administrated through a link to pre-service teachers in the spring of 2018. No incentive was provided. The aim of the survey was introduced in the beginning of the questionnaire, “It is to investigate pre-service teachers online teaching competencies which can provide guidance for the teacher training”. They participated in this survey in an anonymous way. It takes participants 10–15 min to complete a questionnaire. The questionnaire can be submitted only if all the items were answered. Eighty-nine pre-service instructors participated in this study voluntarily. Participants were asked to provide their demographic information in the first five questions of the questionnaire, including gender, age, educational level, and online teaching and learning experience. The demographics of the participants are shown in Table 1.

Table 1 Demographics of participants
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Data analysis

To answer the first question and know about Chinese beginning online instructors’ teaching competencies, the descriptive analysis is conducted base on the CFA result. As the CFA confirmed the reasonable factor-loading structure, the average of all scores of items that loaded on that factor would be generated while the descriptive statistics are calculated accordingly. Then we can get more information about the distributions of each factor of online instructors’ competencies in China.

To answer the second research question, five multiple regressions using participants’ demographic information to predict their online teaching competency scores are conducted. To reduce the measurement error, the factor scores extracted from the modified CFA models are used as dependent variables, and the group variables—participants’ gender, age, educational level, online teaching experience, and online learning experience—are used as predictors. A backward model-building strategy is used in the multiple regression model-building process. All predictors are entered first, and then non-significant predictors are moved out step by step from the highest p-value reported. The final model would report significant factors, with only at 0.05 alpha level or marginal effect. Considering some of the factor scores may not follow the normal distribution, we apply the bootstrap strategy in determining the significance of parameter coefficients. In the bootstrap process, 10,000 repeats are performed and the final 95% confidence intervals are reported. The regression coefficients and its significance would bring more information about the relationship between demographic information and the beginner’s online teaching competencies.

To answer the third question, all the results of the instructors’ perceived challenges of moving traditional courses online were calculated with a descriptive analysis. Then we can get more information about the beginning online instructors’ perceived challenges of moving traditional courses online.

Results

Measurement model

Results supported that the reliability of the questionnaire was 0.966 (using Cronbach’s alpha). As the CFA model with original-designed measurement model is estimated, the results reported a good model fit with χ2(550) = 610.284, p = 0.038, RMSEA = 0.035, SRMR = 0.077. This result indicated generally the original model with five factors fit the data well. When checking the factor loadings for each item, most items report reasonable factor loadings, but only the first item reported a weaker relationship to the factor score, with r = 0.348 when the latent variable is standardized. The modification indexes indicate no modification activities that can help to increase the model fit significantly. Based on this result, we deleted the first item. After the item is deleted, the model fit reported a reasonable model fit with χ2(517) = 573.419, p = 0.043, RMSEA = 0.035, SRMR = 0.076. Both the AIC and BIC report the modified model fit the data better with AIC decreased from 5441.356 to 5249.696, and BIC decreased from 5640.447 to 5443.810. In the modified model, all items reported factor loadings with values higher than 0.4. Hence, we can conclude after deleting the first item, the measurement model with 5 factors fit the data well. The five-factor scores generated through this model can be used in further regression analysis. The standardized factor loadings are presented in Table 2.

Table 2 Summary of confirmatory factor analysis results
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RQ1: What are the instructors’ competencies in teaching online?

Based on the CFA results, the most items are kept, and only the first item is deleted. The factor scores extracted from the remained 5-dimensional 35 items would be used in further regression analysis. The factor scores are standardized to means equal to zero, standardized deviations equal to 0.5. The descriptive statistics of these factor scores are presented in Table 2. The Shapiro–Wilk test indicated none of these variables follows normal distribution, with W = 0.962, 0.969, 0.958, 0.958, and 0.967, and p = 0.010, 0.034, 0.006, 0.005, and 0.021 for factors 1 to 5 respectively, though both the skewness and kurtosis are all in the reasonable range. Hence all these factor scores deviate from the expected normal distribution, the bootstrapping strategy in the further model-building process is necessary to reduce the bias introduced by assumption violation.

To know the beginning online instructor competencies in China, the statistical analysis of each item of five-factor scores is conducted (as shown in Table 3).

Table 3 Descriptive statistics of online teaching competencies in five dimensions
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The result demonstrates that the mean competency level of each item is not good (mean < 2.50). It is lower than 3 which means most of the instructors have no experiential knowledge of this competency. Cronbach’s coefficient alpha was used to measure the internal consistency of the five dimensions. Results support that the consistencies of all the five dimensions are high enough (alpha > 0.83) which suggests that the questionnaire is reliable. Instructors’ competencies in selecting appropriate tools (M = 2.41, SD = 0.57) and facilitating online learning (M = 2.32, SD = 0.60) were the highest of the five dimensions. Instructors’ competencies in conducting meaningful appraisals of student learning (M = 2.14, SD = 0.63), preparing themselves to teach online (M = 2.15, SD = 0.56), and preparing learners to learn online were the lowest among the five dimensions (M = 2.24, SD = 0.60). Much work should be done to improve online instructors’ competencies in conducting meaningful appraisals of students learning, preparing themselves to teach online, and preparing learners to learn online in China.

RQ2: What are the demographic factors that influence the instructors’ five-dimensional online teaching competencies?

As it is reviewed in the literature review, there is no consensus on the effects of demographic factors, such as gender, age, educational level, and online teaching experience, on instructors’ online teaching competencies. Therefore, it is valuable to further explore what effects of instructors’ gender, age, educational level, and online teaching and learning experience have on their five-dimensional online teaching competencies. The results from the final regression models are presented in Table 4.

Table 4 Summary of final models
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In the full model, no significant difference was found among different gender and age groups when controlling other predictors, in all the five dimensions. When controlling age group, participants’ educational level, online teaching and learning experience, no significant differences were found between two gender groups with unstandardized coefficients. The point estimates of differences between two groups on factor scores are b = − 0.076, − 0.092, − 0.045, − 0.006, and 0.055, and p = 0.473, 0.485, 0.755, 0.963, and 0.676 for five factors respectively. Also, there is no significant difference between participants older than 30 years old and participants younger than 30 on all the five factors of competencies when controlling gender, educational level, online teaching, and learning experience, with unstandardized coefficients. The point estimates of difference between two groups on factor scores are b = 0.051, 0.055, 0.085, 0.005, and − 0.056, and p = 0.630, 0.682, 0.973, 0.963, and 0.673.

Preparing themselves to teach online

In the final model, for teachers’ competencies in preparing themselves to teach online, no significant difference was found among different educational level groups when controlling their online teaching and learning experience.

Teachers with more than one-year online teaching experience reported significantly stronger competencies in this factor than teachers with less online teaching experience when controlling their online learning experience. The semi-partial r2 = 0.341, which indicates that about 34.1% of instructors’ competencies in this competencies dimension can be explained by their online teaching experience only.

Participants’ online learning experience also contributed to the difference in this factor when their online teaching experience is controlled. After bootstrapping, teachers with more than three years online learning experience reported significantly stronger competencies in the same factor when controlling their online teaching experience. The semi-partial r2 = 0.241, which indicates that about 24.1% of this competencies dimension can be explained by their online learning experience only.

Selecting appropriate tools

After the model-building process, for online teachers’ competencies in selecting appropriate tools factor, when controlling participants online teaching and learning experience, participants with a Master’s or higher degree reported a significant higher factor score than participants with lower educational level, with the semi-partial r2 = 0.222, indicating that about 22.2% of teachers’ competencies in selecting appropriate tools can be contributed by their educational level only.

When controlling participants' educational level and online learning experience, a significant difference was also found between teachers with more than three years online teaching experience and teachers with less online teaching experience. The semi-partial r2 = 0.286 indicates that 28.6% of teachers’ competencies in this dimension can be contributed by teachers’ online teaching experience only.

When controlling online teachers’ educational level and their online teaching experience, teachers with more than three years online learning experience reported significantly stronger competencies than teachers with a less online learning experience. The semi-partial r2 = 0.283, which indicates 28.3% of teachers’ competencies in this dimension can be explained by teachers’ online learning experience only.

Preparing learners to learn online

For online teachers’ competencies in preparing learners to learn online, a significant difference was found between participants who have a Master’s or higher degree and those who do not. The semi-partial r2 = 0.241, which indicates 24.1% of instructors’ competencies in preparing learners to learn online can be explained by their educational level only.

When participants’ educational level and online learning experience is controlled, instructors with more than one-year online teaching experience reported significantly stronger competencies than participants with less experience. The semi-partial r2 = 0.273 which indicates that 27.3% of instructors’ competencies in preparing learners to learn online can be explained by their online teaching experience.

Moreover, when both the educational level and online teaching experience is controlled, participants’ online learning experience also predict their competencies in this factor significantly that instructors with more than a 3-year online learning experience. The semi-partial r2 = 0.276 means 27.6% of instructor’ competencies in this dimension is contributed by participants’ online learning experience.

Facilitating online learning

For participants’ competencies in facilitating online learning, a significant difference was also found in different educational levels, online teaching experience, and online learning experience. Participants with a master or higher educational degree reported significantly stronger competencies. The semi-partial r2 = 0.219 means 21.9% of instructors’ competencies in facilitating online learning can be explained by their educational level only. Participants with more than three-year online learning experience reported significantly stronger competencies when controlling both the educational level and online teaching experience. The semi-partial r2 = 0.279 indicates 27.9% of teachers’ competencies in facilitating online learning can be explained by their online learning experience. When both the educational level and online learning experience is controlled, participants with longer online teaching experience (more than one year) also reported significant stronger competencies than participants with less experience. The semi-partial r2 = 0.285 indicates 28.5% of the variance in this factor outcome is contributed by participants’ online teaching experience.

Conducting meaningful appraisals of student learning

Finally, for participants’ competencies in conducting meaningful appraisals of students’ learning, no significant difference was found between different educational level groups of participants. Participants with more than three-year online learning experience reported stronger competencies significantly than participants with the less online learning experience. The semi-partial r2 = 0.220 means about 22.0% of instructors’ competencies in conducting meaningful appraisals of student learning can be explained by their online learning experience only. A significant difference is also found between teachers with 1 or more year(s) online teaching experience and other participants. The semi-partial r2 = 0.220, indicating that about 22.0% of instructors’ competencies in this dimension can be explained by participants online teaching experience only.

RQ3: What are the instructors’ perceived challenges of moving traditional courses online?

This is a multiple- choices question. Participants can choose more than one perceived challenges of moving traditional courses online. Results indicate that 55 (21.8%) instructors perceived managing and facilitating online teaching as a challenge (as shown in Fig. 2). It accounts for the biggest percentage among all the challenges. The second is organizing the online teaching (n = 53, 21.0%). Thirty-eight (15.1%) instructors supported that the difficulty to evaluate students’ learning is a challenge for them. There are 37(10%) instructors supported that they are not sure if they are competent in the roles of the online teaching environment. Thirty-six (14.3%) instructors supported that it is difficult to prepare students for online learning. Thirty-three (13.1%) instructors proposed that technically difficult is one of the challenges to move the traditional course to the online environment. Results indicate that organizing, managing and evaluating are the biggest challenges for teachers to move traditional courses to online.

Fig. 2
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Instructors’ perceived challenges of moving traditional courses online. Note The numbers in this figure are the numbers of instructors who perceived this dimension as a challenge of moving traditional courses online

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Discussion and conclusion

RQ1: What are the instructors’ competencies in teaching online?

The result supported that most of the Chinese beginning online instructors need further training on teaching online. In the preparing themselves to teach online, instructions on developing a syllabus with information specific to an online environment, developing or adapting instructional materials, and setting online office hours are useful for the beginning online instructors. Because most of the participants are majoring in educational technology, their competencies in selecting appropriate tools are relatively higher than the other four dimensions. But the result indicated that instructors need more training on the basic theory of online teaching and learning. In the preparing learners to learn online dimension, instructors need more instructions on creating space and activities to develop a class identity and establishing appropriate communication norms. As for why they are good at facilitating online learning is that the facilitating techniques in an online environment are consistent with traditional teaching which is relatively more familiar for them. Besides, the result implies that instructors need more training on sustaining students’ engagement and addressing learners’ issues and other barriers that detract learners from learning. In the conducting meaningful appraisals of student learning dimension, more instructions on how to create an online evaluation rubric to support students’ high-order thinking and how to give students guidance on assignments and grading criteria are helpful for instructors. It is consistent with the study conducted by González-Sanmamed et al. (2014) in Spain. This consistency indicated that the Chinese instructors' online teaching competencies reflected some common problems of instructors in other cultural backgrounds.

One explanation for the result is that Chinese online teaching was at the starting stage. Systematic evaluation framework on online teaching has not formed. More work should be done in online teaching evaluation for it is the guide of online instructors to organize and implement their online teaching; especially in the following three dimensions, conducting meaningful appraisals of student learning, preparing instructors to teach online, and preparing learners to learn online.

RQ2: What are the demographic factors that influence the instructors’ five-dimensional online teaching competencies?

As for the demographic factors of instructors’ online teaching competencies, their gender and age have no significant influence on their online teaching competencies. However, their educational level, online teaching experience, and online learning experience had significant effects on their competencies in selecting appropriate tools, preparing learners to learn online, and facilitating online learning. Instructors’ teaching and learning experience had a significant influence on their competencies on preparing themselves to teach online and conducting meaningful appraisals of student learning.

Koh et al. (2010) supported that male instructors are more skilled in online teaching. But there is no significant difference between the male and female instructors in this study. One reason for this difference is that instructors in this study have less than three-year online teaching experience, both of the male and female instructors are not very skilled at teaching online.

Besides, there is no significant influence of instructors’ age on their online teaching competencies which is different from the result of Koh et al. (2010) which reported that instructors’ age had a significant correlation with their TPACK competencies. One explanation for this discrepancy is that the teaching experience in this study is controlled when analyzing the effects of the age. However, it may not be controlled in Koh and his colleagues’ study.

There is a significant effect of instructors’ educational level, teaching experience on their online teaching competencies which is supported by Sogillo et al. (2016) and Akiri and Ugborugbo (2009). The regression analysis result indicated that instructors’ educational level contributes to 22.7% competencies on selecting appropriate tools, 27% competencies on preparing learners to learn online, and 23.4% competencies on facilitating online learning. Thus, it is appropriate to take the educational level into account when enrolling online instructors.

Instructors’ teaching and learning experience have contributed to their competencies on all five aspects which indicate that teaching and learning experience is essential for a competent online instructor. This result is supported by Blume et al. (1971) who proposed that instructors teach as they are taught.

In summary, although instructors’ gender and age had no significant influence on their online teaching competencies, their educational level, online teaching experience, and online learning experience had significant influences on their online teaching competencies. Thus, instructors with high educational level (master or higher) and rich online teaching (more than 1 year) and learning experience (more than 3 years) are the best candidates for online teaching.

RQ3: What are the instructors’ perceived challenges of moving traditional courses online?

Most instructors perceived that managing, facilitating, and organizing online teaching, as well as evaluating students for online learning are the biggest challenges to move traditional courses online. It indicated that online teacher training needs more instruction on how to design and organize online teaching, and how to evaluate students’ performance. This is supported by Garrison et al. (2010) who proposed that design and organization lay the foundation for a community of inquiry.

Implications and future directions

There are three innovations in this study. First, the instrument distilled online instructors’ essential skills based on the online teaching experience from the teaching process perspective. It can help instructors and trainers understand the essential skills in teaching online. Furthermore, it gives us a comprehensive understanding of the effects of instructors’ demographic characteristics on their online teaching competencies. Last but not least, it demonstrated the challenges teachers faced when moving traditional courses online.

Because of the low scores in preparing themselves to teach online, it is necessary to facilitate beginning instructors’ competencies in designing and organizing online course which are supported by the result of the perceived challenges. Many Chinese teachers’ professional development focused on teachers’ technology skills (Liu et al. 2015). However, it is not enough to focus only on the technical skills, it will be more effective to guide instructors to know how to design and organize online teaching supported by technology skills (González-Sanmamed et al. 2014).

Given the lowest score on the conducting meaningful appraisals of student learning dimension, it is helpful to give instructors more support on online learning evaluation. There are many methods to carry out effective online learning evaluation including the peer-feedback and technology-supported feedback (Planar and Moya 2016). It is necessary to give instructors more instructions on online learning evaluation.

Instructors score on the preparing learners to learn online is relatively low. Thirty-six instructors proposed that it is difficult to prepare learners for online learning. Thus, it is necessary to give instructors more training on creating space and activities to develop an inquiry community and establishing appropriate communication norms.

More training on facilitating online learning will be helpful for beginning online instructors as it is reported that Chinese teachers tend to organize teacher-centered teaching influenced by Chinese traditional education (Kim 2018). Instructors are not good at facilitating students learning especially in the online environment. Therefore, it is of great importance for Chinese teachers to receive more training on how to facilitate students’ online learning from providing feedback, sustaining engagement, and addressing learner issues and other barriers that detract learners from learning. Thirty-eight instructors supported that it is difficult to give students’ feedback.

Although the instructors’ competencies of selecting appropriate tools were the highest of the five dimensions, it is still less than 2.5. Besides, there are still thirty-three instructors proposed that technically difficult is one of the challenges to move traditional courses online. Results supported that more training on using tools to support collaboration and individual work, the advantages of different online teaching methods, and the learner assessment functions of the online instructional platform is necessary for Chinese online teachers’ professional development. When preparing online teachers, educational level, teaching and learning experience are three factors that should be considered.

This study provided a reliable questionnaire for online teaching competencies. It helped us know more about the Chinese beginning online instructors’ competencies and gave stakeholders some guidance on how to design appropriate training for beginning online instructors. Compared with existing studies, this study explicit the relationships of instructor’s demographic factors, including age, gender, educational level, teaching and learning experience with their online teaching competencies.

Nevertheless, there are some limitations that can be addressed in the future. On the one hand, this study relied on instructors’ self-report surveys. Although instructors perceived online teaching competencies reflect their competencies level, future researchers could provide additional insight by gathering instructors’ online teaching data. On the other hand, the number of participants is limited. Future studies could involve more instructors. Despite its limitations, this study is one of the few studies investigating the Chinese beginning online instructors’ competencies aimed at giving stakeholders a reference for online teacher training.