Introduction

Online learning (OL) is a learning model that is conducted by using technologies and internet connections, involving teachers and students who participate in different settings (Panigrahi et al., 2018). Teachers use mobile devices such as smartphones, laptops, or computers to operate various learning management systems (LMS) such as Edmodo, Google Classroom, and Schoology, as well as video conferencing application such as Zoom to conduct online learning with students (Sadikin & Hamidah, 2020). Social media such as Instagram, WhatsApp, or YouTube can be as they allow students and teachers to communicate at a distance (Kumar & Nanda, 2019).

Online learning has shown to have many benefits when it is implemented in the area of education. For example, Nanayakkara (2007) revealed that the technology used in online learning improves students’ communication rates. More recently, Al-Rahmi et al. (2019) stated that online learning increases both teachers’ and students’ familiarity with more up-to-date technology. Meanwhile, Wong et al. (2020) explained that online learning provides more time and space flexibility, and as well as creating more interest in attractive media, and it also improves digital literacy.

The COVID-19 pandemic boosted the implementation of online learning, encouraging some researchers to study the correlation of learning achievement with psychological aspects such as readiness and motivation. Discussing the relationship between Online Learning Readiness (OLR) and Learning Achievement (LA), Wahyuni and Siagian (2021) showed a relationship between OLR and LA with a significant value of 0.048 <0.05. In the context of EFL, Torun (2020) stated that self-directed learning as an indicator of OLR had the strongest and most positive correlation (β=0.820 and p=0.000) compared to other OLR sub-factors (computer self-efficacy, internet self-efficacy, online self-efficacy, learner control, and motivation toward learning) with learning achievement during the pandemic situation. Some studies also indicated similar results (Hung et al., 2010; Prior et al., 2016; Suri et al., 2016; Winarso, 2016). They emphasized that it is important for an educational institution to prepare students' OLR before implementing OL since it may affect students' ELA.

Motivation (M) is another psychological aspect that is correlated to ELA in OL. Without motivation among the students, there is no “pulse” and there is no life in an OL class (Anjomshoa & Sadighi, 2015). Cocca and Cocca (2019) studied the relationship of affective variables and motivation with ELA in an EFL context. Their study showed that students' achievement was significantly associated with motivational intensity (p=0.001). The study emphasized that motivation is an important factor in keeping students' engagement in English as a Foreign Language (EFL) courses and avoiding burn-out and, consequently, participation drop-out. Similarly, Bimayu et al’s (2020) study showed that motivation was positively correlated with learning achievement. Additionally, Purwanti et al. (2019) stated that there was a significant correlation between motivation and ELA, proven by r-count (0.346) > r-table (0.224) with a 5% level of significance. More specifically, they found that extrinsic motivation did not have any correlation with ELA, while intrinsic motivation had a correlation.

Recent research correlates the students’ OLR and ELA (Kirmizi, 2015; Martin et al., 2020; Walidaini, 2020). Besides, there have been some studies into the association of students' ELA with students’ motivation (Howard et al., 2021; Hoxha & Sumner, 2022; Mauliya et al., 2020; Purwanti et al., 2019; Rosmayanti & Yanuarti, 2019; Su, 2017). However, the studies that investigate the correlation of two psychological factors, such as learning readiness and motivation toward the students’ ELA, are still few. For example, Mariyati et al. (2020) suggested the need for future researchers to investigate the students’ academic achievement as seen from their learning readiness and motivation. Hence, the present study aimed to investigate the correlations between students’ OLR and motivation toward students’ learning achievement as represented by their scores from English skills courses in a higher education online learning context. For these purposes, the research questions are formulated as follows:

1. What is the students’ online learning readiness level?
2. Is there any correlation between online learning readiness and English learning achievement?
3. What is the students’ motivational level in learning English?
4. Is there any correlation between motivation and English learning achievement?
5. Which contributes more to English learning achievement: online learning readiness or motivation?

Method

Research design and setting

The present study employed a correlational design with a quantitative approach. This study was conducted at the English Education Department of a reputable public university which is located in Mataram City, Lombok Island, West Nusa Tenggara, Indonesia.

Participants

The current study included thirty-five first-semester students (Class C) from the English Department at the university during the academic year 2021–2022. The sample of the present study was chosen randomly by lottery with some reasons. First, from the total population consisted of 122 students, they were divided into four classes (A, B, C, and D) without grading their enrollment test results from the most proficient class to the less proficient class. So, all classes had similar level of proficiency. Second, English skills (reading, speaking, and listening) lecturers taught all classes (A, B, C, and D) which allowed all students to experience similar learning procedures. Third, due to feasibility to gather the data, the researchers took only a class which was decided randomly by lottery. With respect to the research context, the participants joined the English language skills classes through the university learning management system (LMS) which was developed using a Moodle platform. All teaching and learning for all English skills were conducted via the LMS and students could access them at anytime.

The participants were chosen based on the following criteria: 1) they took English skills (reading, speaking, and listening) courses during the first semester of the academic year 2021-2022, 2) they took the midterm tests of English skills (reading, speaking, and listening) courses, and 3) they participated in filling out both the OLR questionnaire and the motivation questionnaire. The researchers got an official permission letter from the head of department for conducting the survey and collecting the students’ midterm tests scores from the academic staff in the department where this study was conducted. The participants took part in this study voluntarily after signing the consent forms.

Research instruments

Documentation

A research tools was implemented for data collection, specifically the students’ midterm tests of English skills (reading, speaking, and listening) course scores. The present study documented only three English skills (reading, speaking, and listening) scores since the English Education Department at the university did not provide a writing skills course in the first semester of academic year 2021-2022. Then, the three English scores were tabulated and calculated to find the average score of the English skills course. This average score was considered the students’ ELA score. The quantitative calculation of the ELA score is illustrated in the following formula:

Note: 
ELA Score: Score obtained from the average of reading, speaking, and listening scores
R score: Students’ mid-test score for reading skill
S score: Students’ mid-test score for speaking skill
L score: Students’ mid-test score for listening skill

Questionnaires

The OLR questionnaire was adapted from the Online Learning Readiness Scale (OLRS) developed by Hung et al. (2010) and the motivation questionnaire, Attitude / Motivation Test Battery (AMTB), adapted from Gardner (2004) were administered to the participants. The questionnaire was adapted in several ways. First, only those items from the original questionnaires were selected that met the research objectives of this study which were about scrutinizing the participants’ OLR and English learning motivation. Second, the questionnaire items were adjusted to conform to the context of this research (e.g., OLR item no. 1, “I feel confident in performing the basic functions of online tools (emails, etc.)” was replaced with “university LMS"). Third, `the language of the adapted questionnaire was simplified to avoid participant misunderstandings.

Each questionnaire contained fifteen statements with a five-point Likert scale ranging from strongly disagree to strongly agree. The participants had to decide which of these five points to choose for each of the fifteen statements. The OLR questionnaire included five clusters which aimed to investigate students’ OLR as follows:

• Cluster 1: Computer self-efficacy (Statements 1 to 3)
• Cluster 2: Self-directed learning (Statements 4 to 6)
• Cluster 3: Learner control (in an online context) (Statements 7 to 9)
• Cluster 4: Motivation for learning (in an online context) (Statements 10 to 12)
• Cluster 5: Online communication self-efficacy (Statements 13 to 15)

The motivation questionnaire statements were grouped into four clusters. Each cluster contained some statements as follows:

• Cluster 1: Desire for career and economic enhancement (Statements 1 to 6)
• Cluster 2: Desire to become global citizen (Statements 7 to 9)
• Cluster 3: Desire to communicate with foreigners and learn other cultures (Statements 10 to 12)
• Cluster 4: Desire for self-satisfaction in learning (Statements 13 to 15)

To test the validity and reliability of the instrument, various instruments available in SPSS 23 version were used. The results of the reliability of the instruments are presented in Table 1, and the result of the validity test is presented in Appendix 2.

Table 1: Reliability test results of motivation and OLR questionnaires

Table 1 shows that the Cronbach’s Alpha for Motivation questionnaire was 0.753 and the OLR questionnaire was 0.763. According to Guilford (1956), the instrument is categorized as having high reliability if the Cronbach’s Alpha reliability test is between 0.60-0.80. This means that both the motivation and OLR questionnaires had high reliability. Before carrying out correlation statistical tests, the earlier step that needed to be taken was screening the data to be processed to ensure that each variable was normally distributed (Ghozali, 2011). This step is intended to reduce barriers in subsequent analysis according to analytical techniques that have been planned. Henceforth, the normality test was conducted using the SPSS 23 version with the result as presented in Table 2.

Table 2: Normality test through One-sample Kolmogorov Smirnov Test

It is clear from Table 2 that the sig. value is 0.200 which is above 0.05 (standard sig. value) meaning the data used in this research were normally distributed. Therefore, the statistical calculation to seek correlation between variables was granted.

Data analysis

To answer the research questions, data were analyzed in several ways. First, both the OLR and motivation questionnaire responses from the participants were coded into numbers, from 1 to 5 (1- strongly disagree, 2-disagree, 3-neutral, 4-agree, or 5-strongly agree). Then, the coded data were quantitatively calculated to get the OLR mean, and motivation mean scores. The mean scores would help reveal the students’ OLR level and motivation level.

Second, to analyze the OLR level, the obtained OLR mean score was measured based on the Aydin and Tasci (2005) OLR leveling model. This model suggested that the minimum level of readiness would be reached as the OLR mean score met the 3.4 mean score. Furthermore, the present study adopted the Likert scale questionnaire description level in Rahardjo and Pertiwi (2020) to comprehend the level of students' motivation.

Third, in order to analyze the correlation between variables, the statistical calculation using SPSS 23 version software was done on the numerical data perceived from the OLR and motivation questionnaires. This calculation was conducted to seek the correlation coefficient and significance of OLR-ELA and motivation-ELA as illustrated in Figure 1. In order to know the direction of the variable correlation, the correlation coefficient guideline adopted by Meghanathan (2016) was used. Finally, the comparison of Pearson correlation coefficients between OLR-ELA and motivation-ELA was conducted to search for variable that contributed more to ELA.

Figure 1: Conceptual model of correlations between variables

Results

The current study sought to investigate the correlations between OLR and M towards ELA in higher education. The results would be presented in accordance with the order of the research questions.

The students’ online learning readiness level.

The first research question “What is the students’ online learning readiness level?” was set to know the students’ OLR level according to the OLR questionnaire administration. The results of the questionnaire are presented in Table 3.

Table 3: Students’ OLR

Table 3 shows that thirteen statements achieved a mean score of above 4.00. Then, the overall students’ OLR mean score is 4.29. To analyze the level of students’ OLR, this research adapted the OLR scale introduced by Aydin and Tasci (2005) as presented in Figure 2.

Figure 2: OLR Leveling Model

As shown in Figure 2, the threshold level of readiness is 3.40. Thus, the students' OLR level (4.30) is considered at a “ready” level. It means that 35 first-semester students (class C) at a public university's English Department in the academic year 2021–2022 were prepared to participate in the online teaching and learning process being conducted through the Moodle-based university LMS. However, based on the fifteen statements of the students’ OLR, there were three OLR statements that were categorized as ready with improvement levels. They were students’ eagerness to seek assistance when facing learning problems (4.11), students’ ability to set goals (3.80), and students’ ability to direct their own learning progress (3.77).

Correlation between online learning readiness and English learning achievement

The second research question “Is there any correlation between online learning readiness and English learning achievement?” was designed investigate the correlation between students' OLR and ELA. The statistical calculation employing SPSS 23 was carried out to find the correlation score and its significance value. The result of the statistical calculation is presented in Table 4.

Table 4: Correlation between OLR and ELA

Table 4 shows that the Pearson correlation score between OLR and ELA is 0.647 with a 0.000 significance value. This statistical data rejects H₀ and accept H_a, which means there was a positive correlation between OLR and ELA. The positive correlation indicates that both variables would experience similar directions and numbers, whether they were increasing or decreasing. The linear plots of this correlation are shown in Figure 3.

Figure 3: Linear plots of correlation between OLR and ELA

The students’ motivation level

The third research question “What is the students’ motivation level?” was intended to investigate the level of the students' motivation. Hence, the motivation questionnaire was administered to students, and the result was calculated to search for the overall mean score as presented in Table 5.

Table 5: Students’ motivation level

Table 5 shows that only four motivation questionnaire statements did not reach the mean score of 4.00. The four statements included motivation to get a good position in a job, motivation to broaden their knowledge, internal motivation to get learning happiness, and motivation to learn the target language culture. The other eleven statements all perceived mean scores above 4.00. To analyze the level of students’ motivation level, the present research adopted the Likert scale questionnaire description level in Rahardjo and Pertiwi (2020) as presented in Table 6.

Table 6: Description of motivation level mean score

According to Table 6 and the mean score of the motivation questionnaire, students’ motivation is categorized at a very high level, as the motivation mean is 4.23, or above 4.0. Furthermore, Table 5 states that 11 statements, or 73% of all statements in the motivation questionnaire, their mean scores that are at a very high level too. Nonetheless, there are two statements in regards to getting a good position in a job with a 3.83 mean, and motivation to learn the target language culture with a 3.77 mean that reach a high level of motivation.

Correlation between motivation and English learning achievement

The fourth research question “Is there any correlation between motivation and English learning achievement?” aimed to know the correlation between students' motivation and ELA. The statistical calculation employing SPSS 23 was carried out to find the correlation score and its significance value. The result of the statistical calculation is presented in Table 7.

Table 7: Correlation between motivation and ELA

Table 7 explains that the Pearson correlation score between students’ motivation and ELA is 0.497 with a 0.002 significance value. This statistical data rejects H₀, which means that there was a positive correlation between students' motivation and ELA. The positive correlation shows both variables would go in the same direction when they were increasing or decreasing. The linear plots of this correlation are shown in Figure 4.

Figure 4: Linear plots of correlation between motivation (M) and ELA

Online reading readiness or motivation which contributes more to English learning achievement

The last research question “Which contributes more to English learning achievement: online reading readiness or motivation?” aimed to investigate which predictor contributes more to ELA, the students' OLR, or the students' motivation. To explore the truth, the Pearson correlation scores of OLR and motivation (shown in Table 8) were compared.

Table 8: Correlations of OLR and motivation toward ELA

Table 8 clearly demonstrates that OLR has a stronger correlation with ELA than motivation. The OLR-ELA Pearson correlation score is 0.150 scores higher than the motivation-ELA Pearson correlation score. According to the correlation coefficient guideline adopted by Meghanathan (2016), the OLR-ELA correlation (r=0.647) was at a strong level. In contrast, motivation had a moderate level of correlation with ELA (r=0.497).

Table 9: Correlation coefficient guideline (Meghanathan, 2016)

Discussion

The results as shown below have responded to the research questions. The level of students’ readiness to participate in the online learning was noted as ready with a mean 4.29. The indications of students’ OLR were noted from: 1) their confidence to perform basic functions of university LMS; 2) their confidence in managing the online software for learning; 3) their confidence to use the internet; 4) their eagerness to ask about meeting difficulties; 5) their ability to manage their time; 6) their ability to set up learning goals; 7) their ability to direct their own learning progress; 8) their focus for not being distracted by other online activities; 9) their eagerness to repeat the online instructional materials on the basis of their needs; 10) their openness to new ideas; 11) their motivation to learn; 12) their eagerness to share ideas with others; 13) their ability to use university LMS to effectively communicate with others; 14) their confidence to express themselves using discussion room in university LMS; and 15) their confidence to post questions in university LMS online discussions (Hung et al., 2010).

The students' OLR may be correlated to their ELA since there is an assumption that if the students are ready to join the class, they will be able to devote all their potential to achieving their best in learning (Kirmizi, 2015). Winarso (2016) also strengthens the need for students to be ready before learning the language, as it promotes students' activeness and achievement in learning. Henceforth, the present research results support some previous studies (Hung et al., 2010; Prior et al., 2016; Suri et al., 2016; Torun, 2020; Wahyuni & Siagian, 2021) that state that OLR has a strong correlation (r=0.626 and sig.=0.000) to the students' ELA.

Motivation (M) is another psychological factor that plays a very important role in the success of language learning (Anjomshoa & Sadighi, 2015). In the present study, students showed a very high level of motivation, as indicated by the mean of 11 statements (taken from 15 statements) being above 4.23 in the motivation questionnaire. However, some others were situated at moderate and high levels of motivation. The students' moderate level of motivation was recognized as they did not want to gain broader knowledge through English learning (mean=2.91), and they did not search for happiness in learning English (mean=2.91). Meanwhile, the high level of students’ motivation appeared to be based on students who want to get a good position in the future (mean=3.83) after studying and the students' need to learn English speaking countries' culture (mean=3.77). Furthermore, this factor showed a positive correlation with the students’ ELA in the present study, as shown by a positive correlation coefficient score of 0.497 and a significance score of 0.002 (Kirmizi, 2015; Martin et al., 2020; Walidaini, 2020). According to Bimayu et al. (2020), a positive correlation means that students with high learning motivation have high learning achievement. Conversely, students with low learning motivation have low learning achievement. Moreover, the Bimayu et al. study supports this research's results that students’ learning achievement was positively correlated to motivation, as indicated by a 0.00 significance score and a 0.620 coefficient score. Lastly, the results of the present research show a similar strength of motivation-ELA correlation, and a moderate strength of correlation, as stated by Cocca and Cocca (2019) and Purwanti (2019).

Concerning the correlations between OLR and motivation toward ELA, both correlations showed a similar positive correlation direction as shown in Figure 2 and Figure 3. Furthermore, based on statistical calculation, the OLR-ELA correlation score (r=0.647) showed a stronger correlation than the motivation-ELA correlation (r=0.497). Hence, the analysis of the data confirmed that OLR contributed more to ELA than motivation, which is not in line with the recent study by Mariyati et al. (2020), who found that motivation has a greater role compared to readiness in the learning achievements of elementary school students. Due to these contrastive results, the present research suggests further investigation of the correlation between variables in various contexts.

Conclusion

The present research was aimed at investigating the levels of students’ OLR and motivation, the correlation between OLR and motivation towards ELA, and the predictor that contributed more to ELA. The students exhibited OLR at a ready level, and they had very high motivation to learn at the time of the research implementation. Both OLR and motivation had positive correlations with students' ELA. OLR was strongly correlated with ELA with r=0.647 and sig.=0.000. Motivation with a lower Pearson correlation score (r=0.497, sig.=0.002) showed moderate strength of correlation with ELA. Hence, it was found that in this study OLR contributed more to ELA than motivation. However, a different result was shown by Mariyati et al. (2020) in terms of the strength of the correlation between OLR-ELA and motivation-ELA. They claimed that motivation contributed stronger to ELA than OLR, while in the present research, OLR had stronger correlation with ELA than motivation. Due to these contrastive results, the present research suggests further investigation of the correlation between some psychological factors such as OLR and motivation to ELA in various contexts. Finally, this research implies the urge to consider students' psychological factors to facilitate the learning process and to achieve better academic performance.

While this research has investigated OLR, M, and ELA in higher education in a descriptive quantitative correlational design, some limitations should be taken into consideration. First, because of the limited number of participants and scope of this research, future research needs to discuss similar topics in a wider scope or even at a different level of education. Second, this research examined correlation of only two psychological factors (readiness and motivation) to English learning achievement without considering the gender of participants. Future research should take gender and other psychological factors into consideration.

The present research provides both theoretical and practical benefits to the university where this research was conducted. This research cultivates the theories and understanding to ponder psychological factors in Teaching English as Foreign Language as they may not be the English lecturers’ major concerns. Practically, the findings of this study can be used as a basis for university policy on teaching and learning, which relies on students’ psychological factors (for example, OLR and motivation) and new teaching and learning model (i.e., implementing online learning, to assure the success of TEFL as represented by the students’ ELA).

Acknowledgement

The first author would like to express gratitude to the Indonesia Endowment Fund for Education (LPDP) for supporting this research.

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