The effects of online learning self-efficacy and attitude toward online learning in predicting academic performance: The case of online prospective mathematics teachers

Suphi Önder Bütüner and Serdal Baltacı[*]

doi: https://doi.org/10.18543/tjhe.2214

Received: 4 October 2021
Accepted: 27 June 2023
E-published: November 2023

Abstract: This study aims to discover if Online Learning Self-Efficacy (OLSE) and attitude toward online learning (AOL) significantly predict the academic performance (AP) among Turkish prospective mathematics teachers. Unlike the studies conducted in the literature, online learning self-efficacy and attitude towards online learning as predictor variables were included in the study and both quantitative and qualitative data were collected. The study included 1075 prospective mathematics teachers’ responses in the analysis. The Pearson correlation was employed to determine how strongly OLSE, AOL, and AP are related. Results indicated that OLSE and AOL influenced the level of AP. Also, the multiple regression aimed to predict AP based on OLSE and AOL, and this model explained 44.6% of the variance in AP. The beta weights demonstrated that OLSE and AOL (OLSE β = .36, t(1072) = 9.705, p < .001, and AOL β = .34, t(1072) = 9.176, p < .001) significantly contributed to the model. The results showed that the level of academic performance can be predicted by online learning self-efficacy and attitude toward online learning. In addition, this study revealed the factors that have favorable and adverse effects on the academic performance of prospective mathematics teachers to gain more extensive information. Under the theme of negative factors, there were 7 codes. The results obtained from the study can be a guide for practitioners, policy makers and teachers to take the necessary precautions for the effective execution of the distance education process.

Keywords: Online learning; self-efficacy; attitude; academic performance; online prospective mathematics teachers.

I. Introduction

The 2019–2020 Coronavirus (COVID-19) pandemic, which emerged in the city of Wuhan, the capital of the Hubei province of China, caused vital changes and effects, especially on health at the global level, along with social life, economy, and educational practices. Furthermore, on March 11, 2020, the World Health Organization declared the Coronavirus (COVID-19) pandemic as a global pandemic. The resulting crisis’s effects, particularly on health and also the economy, social life, psychology, and education have still continued.[1] As in, all countries of the world, education and training activities were suspended in Türkiye according to the progression of the case numbers. Exams were postponed, distance education started, lessons continued synchronously or asynchronously, and teachers used homework, online exams, and forum discussions for student evaluation.

Distance education is students’ web-based access to education by means of developing internet technologies and computers.[2] Thanks to communication technologies, distance education is a bridge between teachers and students.[3] With the synchronous and asynchronous model used in the 21^st century owing to computer technologies, students and teachers can carry out education regardless of time and place.[4]^,[5]^,[6]^,[7] Universities took immediate steps to ease crisis caused by the coronavirus pandemic and Universities switched from formal to distance education.[8] Like the rest of the world, Türkiye was unprepared for educational activities amid the COVID-19 pandemic and tried to improve the education and training processes by switching to emergency distance education.

There are different forms of application of distance education, and among these, it is seen that mostly online learning types are applied frequently. In this direction, courses can be conducted as synchronous (simultaneous) and asynchronous (asynchronous) courses within the scope of distance education. In simultaneous education, students and teachers meet at a predetermined time (usually online) and live lessons.[9] In this process, it is tried to create a more active environment for teachers and learners such as in-class interaction and discussion, asking questions instantly and expressing parts that are not understood, and an environment close to face-to-face education is tried to be provided. In asynchronous education, on the other hand, it is the type of education in which teachers and students do not have the opportunity to work simultaneously and students can access the course content (presentation, video, audio recording, etc.) over the internet whenever they want or need it. Communication between participants takes place mainly through e-mail and online forums and is usually moderated by trainers.[10]

Universities in Türkiye used software that provides an online environment during the pandemic process. In this direction, some universities have preferred to use online synchronous methods as distance education methods, some have preferred to use offline asynchronous methods and some have chosen to use mixed methods.[11] In many universities, courses in distance education have been processed through methods such as creating presentation files and sharing course content (articles, ppt, Word, pdf, etc.), uploading lessons to the system with live lectures and video recording, asking instant questions and giving feedback, and sharing homework. In addition, it has been observed that universities use different online methods such as homework, projects, online exams and quizzes within the scope of measurement and evaluation regarding the courses offered.[12]

Educators tried to determine student performance in education and training through assessment and evaluation tools such as homework, online exams, and forum discussions. However, due to the rapid transition to distance education, researchers could not evaluate the adaptation processes of students toward distance education.[13] There may be many variables (perceptions and attitude toward online learning, self-efficacy, readiness for online learning, thought processes toward distance education, and individual innovation) that have an impact on students’ academic performance in this process. Students’ self-efficacy and attitude toward online learning are two of the variables that may affect student performance.

Accordingly, this study investigated to what extent these two variables predict academic performance through multiple regression analysis. In addition, the researcher tried to identify the factors that have favorable and adverse effects on the academic performance of teacher candidates to gain deeper knowledge.

I.1. Self-efficacy toward online learning

Considering that humans are emotional beings, it may not be enough to prepare the physical environment and its factors alone to direct them to the target. Being competent in tasks demands both skills and self-beliefs concerning how well these tasks can be accomplished.[14] Therefore, students’ high self-efficacy for online learning is a significant component in the successful execution of this process. Self-efficacy is people’s belief in their own competence to learn and develop behaviors.[15]^,[16] Schunk[17] defined self-efficacy as individuals’ evaluation of their own skills and capabilities and their ability to transform them into behaviors. Gallagher[18] expressed self-efficacy as evaluating whether people believe that they can carry out their behaviors when necessary. On the other hand, when it comes to the aspects of learning taking place in rather non-traditional environments like online learning, self-efficacy seems to gain more authentic features. In such platforms, self-efficacy consists of five dimensions: These are self-efficacy concerning finishing an online course, using tools in a course management system, establishing interactions with lecturers as well as classmates for social and academic purposes in an online course.¹⁹ Self-efficacy might also be considered as a major factor that determines the readiness of teachers for distance education.[19]^,[20] One of the essential factors affecting prospective teachers’ online learning-teaching competencies is their self-efficacy regarding distance education environments.[21]^,[22] When learners believe they have the capacity to do a task, they may be much keener and more determined for fulfilling this task and exhibit behaviors accordingly.[23] Learners having a substantial level of self-efficacy in learning a subject adapt more easily, work harder, and are more successful in coping with difficulties.[24]^,[25] Similarly, Pajares[26] observed that individuals with high self-efficacy have high success and are happier due to this success. Bandura[27] stated that students having weak self-efficacy have less motivation to learn, meaning that they are less willing to learn and make less effort accordingly. Self-efficacy has a mediating role in students’ academic success in distance education, and success and self-efficacy are positively related.[28]^,[29]^,[30]^,[31] Besides, Tsai, Cha, Marra, and Shen[32] revealed that whoever has a favorable outlook toward online learning and high self-efficacy expects higher grades.

I.2. Attitude toward online learning

Attitudes are the positive or negative feelings of individuals toward any object, person, or subject.[33] There may be many external factors that affect the forming of attitudes. Learners can change their attitudes and acquire new ones with their experiences as a result of their interaction with their environment. Another predictor that can affect student performance in the distance education process is the attitude toward online learning.[34] because learners’ attitude toward new technologies can affect their acceptance of these advancements. In the effective execution of distance education, beyond how advanced its technology is, Liaw, Huang, and Chen[35] highlighted the significance of students having a positive attitude toward online learning. They also stated that students’ positive attitude levels toward online learning affect students’ tendencies toward distance education. From this point of view, student attitude toward online learning can directly relate to their academic performance. In studies conducted on attitudes, students’ attitudes and academic achievements are strongly related.[36]^,[37]^,[38] In addition, the positive attitudes of students toward online learning will facilitate the teaching process of the teacher, and there will be improvements in the success of the students.[39] In their study, Lijie, Zongzhao, and Ying[40] revealed that mathematics attitude has a positive and direct impact on students’ mathematics academic performance. Offir et al.[41] stated that students’ attitude toward online learning are effective in students’ success. Falowo[42] specified that individuals’ negative attitudes toward online learning generally stem from their prejudices. On the other hand, Martinez et al.[43] stated that researchers should conduct more research on attitude toward online learning.

In the literature, there are studies on the attitudes[44]^,[45]^,[46]^,[47] and self-efficacy[48]^,[49] of university students in distance education environments. However, there was no study that investigated whether these two factors are significant predictors of academic performance. It is critical to reveal the extent to which the attitudes and self-efficacy toward online learning predict academic performance and whether they are meaningful predictors in terms of evaluating the functionality of distance education activities that educators use now and will continue using in the future.

In this respect, the first two questions of this study deal with the level and direction of the relationship between the attitude and self-efficacy toward online learning and academic achievement, and the third question investigates if the attitude and self-efficacy toward online learning are significant predictors of academic success.

Apart from the attitude and self-efficacy toward online learning, there may be different variables that predict academic performance. For example, some studies indicated that technological infrastructure is a significant predictor of students’ academic success in the distance education process.[50]^,[51]^,[52]^,[53]^,[54]^,[55] Also, various studies have demonstrated that solving student problems quickly and paying attention to teacher-student interaction in distance education by using a supportive language have a significant impact on success in distance education.[56]^,[57]^,[58]^,[59] Haber and Mills[60] and Bolliger and Wasilik[61] stated that the lack of social interaction between students and their lecturers, the problems experienced by lecturers in the process of preparing course materials for distance education, and examining the development of the students are effective factors in the academic success of the students. In addition, Chao, Saj, and Tessier[62] stated that another important factor affecting the success of students in the distance education process is the richness and quality of learning-teaching materials, as well as the assessment and evaluation process. Irani et al.[63] and Petracchi[64] revealed in their study that students’ perceptions of distance education affect their academic achievement. Also, there are observations that students’ motivation levels are a crucial factor on academic performance in distance education environments.[65]^,[66]^,[67] Upon examining these studies, it is seen that there are variables such as teaching methods, technological infrastructure, student-teacher interaction, and assessment and evaluation processes in online learning that can predict academic performance. In this regard, the fourth problem of this study aimed to determine the factors that positively and negatively affect the academic performance of prospective mathematics teachers in the distance education process.

In a study conducted during the SARS epidemic during the pandemic period, it was determined that distance education was effective in reducing people’s anxiety levels and increasing and increasing knowledge with the use of distance communication ways.[68] Other advantages of distance education can be listed as allowing students to work at their own pace, providing flexible working opportunities independent of time and space, saving time and therefore less cost. Some of the disadvantageous points of distance education are difficulties in providing motivation, lack of face-to-face interaction and social isolation, difficulty in getting instant feedback, a constant need for technology and situations related to accreditation.[69]^,[70]^,[71] Although there are some advantages brought by distance education, it can be seen that not all students are successful in online classes in distance education and the failure rates in distance education courses are 10 to 20 percent higher than traditional face-to-face courses.[72]

People believe that the use of distance education as a complement to formal education in higher education will increase. In this respect, examining whether students’ attitudes and self-efficacy toward online learning are significant predictors of academic performance and determining the factors that have positive and negative effects on academic performance may provide important contributions both for future studies at the institutional level and for studies in the academic field. This can contribute to the more effective planning and execution of the distance education process. Therefore, this study seeks to address the following research questions:

•Question 1: Is there a significant correlation between Online Learning Self-Efficacy (OLSE) and academic performance (AP) among prospective mathematics teachers?

•Question 2: Is there a significant correlation between Attitude toward Online Learning (AOL) and AP among prospective mathematics teachers?

•Question 3: Which of OLSE and AOL is the most effective in predicting AP?

•Question 4: What are the factors that positively and negatively affect the academic performance of prospective mathematics teachers in the distance education process?

II. Method

II.1. Research design

This study was non-experimental correlational research and contained quantitative and qualitative data. The Pearson correlation was employed to explore any significant correlations among OLSE, AOL, and AP. A multiple regression was also conducted to analyze the impact of OLSE and AOL on AP. In addition, the researcher obtained qualitative data by asking the question “What are the factors that positively and negatively affect the academic performance of prospective mathematics teachers in the distance education process?”

II.2. Participants

In the selection of the participants of the study, primarily, the researcher determined universities with faculties of education (76 universities in total) in each of the seven regions of Türkiye. Then, two easily accessible faculties of education (14 in total) in each region were selected. For the required sample size for multiple regression, Stevens[73] stated that there should be 15 participants per predictor, and Tabachnick and Fidell[74] expressed that the required number of participants should be higher than 66 when there are two independent variables. These rules are very pervasive but they oversimplify the issue. In fact, the sample size required will depend on the size of effect that we’re trying to detect (i.e., how strong the relationship is that we’re trying to measure) and how much power we want to detect these effects. The simplest rule of thumb is that the bigger the sample size, the better.[75] The number of participants included in this study is above the benchmark value the literature specifies for each region of Türkiye. A total of 1106 prospective mathematics teachers responded to the web survey questionnaire. Additionally, to gain deeper knowledge within the scope of the study, the researcher obtained the written opinions of 118 volunteer prospective mathematics teachers to identify the factors that have favorable and adverse effects on the academic performance of prospective mathematics teachers in the distance education process. Table 1 contains information about prospective mathematics teachers who filled out the scales and provided written opinions.

II.3. Instruments

II.3.1. The online learning self-efficacy scale (OLSES)

This study employed the Turkish adaptation of the self-efficacy scale for online learning (Appendix 1-Original Form, Appendix 2-Turkish Form) developed by Sun and Rogers. Unlike the scales used in previous studies, the fact that all items in the scale used in this study are positive will prevent the respondents from getting confused.[76] Moreover, compared to 4 and 5 Likert type scales the 6-point Likert type scale used in the study does not have neutral or uncertainty points, hence providing better measurement properties. Literature review revealed that the scales used in previous studies are insufficient to meet one or more of the four different dimensions.[77] For the stated reasons, the Online Learning Self-Efficacy Scale developed by Sun and Rogers was adapted into Turkish and applied to elementary mathematics teacher candidates to determine their levels of self-efficacy for online learning.

In the first stage of the adaptation process, three academics who are experts in the field translated the scale into Turkish. Then, each academic examined the translations of the others and gave their suggestions on the form. In the second stage, two academicians working in the Computer and Instructional Technologies Department and three academics working in the Turkish language teaching department examined the scale items in terms of content validity and suitability for the Turkish culture and made the necessary corrections. In the third stage, the researcher applied the scale to 23 prospective mathematics teachers and asked them to write the incomprehensible and unclear items in the blank section under the scale form. In the fourth stage, both versions of the scale were applied to 128 prospective teachers studying in the English Language Teaching Department and the correlation coefficient between both forms of the scale was calculated as .92 at a high level. At the last stage, a second level confirmatory factor analysis was performed on the scale. Since the absolute value of the skewness values of the items in the scale was less than 3 and the absolute value of the kurtosis values was less than 10, the scale met the necessary normality conditions for the confirmatory factor analysis.[78] Due to the normal distribution of the data, the study used the maximum likelihood estimation method.[79] Muthén and Muthén[80] stated that a sample size of 150 is sufficient, granted that the data are normally distributed and there are no missing data. In this respect, the sample size (1,078 people) was sufficient for confirmatory factor analysis. The second-order factorial structure of the online learning self-efficacy scale consisting of four sub-dimensions and 31 items was tested using the AMOS 24 program. The results of the second-order confirmatory factor analysis of the scale indicated that the factor load values of the items were between .66 and .89, at the desired level. The goodness of fit values obtained as a result of the second-order confirmatory factor analysis (χ2/df = 2.627; RMSEA = .068; SRMR = .063; CFI = .929; TLI = .923; NFI = .890) indicated that the proposed four-factor model is compatible with the data and acceptable.[81] These results signified that the data obtained from the study were compatible with the predicted theoretical structure (four-factor model) of the online learning self-efficacy scale. The final version of the validated OLSES has 31 items, and they load on four factors: Technology use self-efficacy (TU), online learning task self-efficacy (OLT), instructor and peer interaction and communication self-efficacy (IPIC), and self-regulation and motivation efficacy (SRM). The Cronbach’s α values for these factors varied from 0.914 to 0.966 revealing high internal consistency reliability for the OLSES. The scale items were graded as “Strongly Agree” (6 points), “Agree” (5 points), “Partly agree” (4 points), “Partly Disagree” (3 points), “Disagree” (2 points), and “Strongly Disagree” (1 point). The lowest score that one could obtain from the scale was 31, and the highest score 186. All items in the scale were positive, in this regard, there was no reverse scoring, and a high score indicated that the self-efficacy level of the individual who completed the scale is more positive toward online learning. Lin[82] also mentioned this scale in his article.

II.3.2. The attitude toward online learning scale (ATOLS)

This study used the attitude toward online learning scale, for which Kışla[83]^,[84] examined the validity and reliability. The exploratory factor analysis was carried out. The eigenvalues of the scale items gathered under 5 factors greater than 1, and these five factors explained 54% of the variance. Upon examining the factor-item loads, the factor-item loads of all items were above .30 and the exploratory factor analysis was repeated by limiting the number of factors to one. The exploratory factor analysis obtained 35 items with factor-item loadings ranging from 0.30 to 0.74. This factor explained 28% of the total variance. A confirmatory factor analysis was performed to confirm this single factor structure and produced the goodness of fit suggested that the single factor model was compatible with the data and feasible (χ2/df = 2.54; RMSEA = .021; SRMR = .07; CFI = .93; GFI = .90; AGFI = .91). The internal consistency coefficient of the single factor scale consisting of 35 items was 0.89. The scale used a 5-point Likert-type rating in the options for the statements. Accordingly, the scale items number 1, 2, 4, 5, 9, 11, 14, 15, 16, 18, 19, 22, 23, 25, 26, 28, 29, 33, and 34 were scored as “Strongly Agree” (5 points), “Agree” (4 points), “Undecided” (3 points), “Disagree” (2 points), “Strongly Disagree” (1 point), and the remaining items were scored in reverse. While the highest score that one can obtain from the scale is 175, the lowest score is 35. A high score indicates that the individual who completed has a more positive attitude toward online learning. Fidan[85] also used this scale in his study.

II.3.3. Academic performance (AP)

The researcher requested a document (a transcript) showing the courses taken by the participants of this study during the pandemic and their grades received from these courses. The grade point average of the courses each student took during the pandemic period (2019–2020 spring and 2020–2021 fall terms) was included in the analysis as the prospective mathematics teachers’ academic performance. Since the grading format in universities in Türkiye is in the 4 and 100 point system, participants were requested to write the 4-point equivalents of their average scores in the 100 system in the data collection form by using the grade conversion table created by the Council of Higher Education and published on its website.[86] The grade point averages of the prospective teachers were recorded in SPSS as a value between [0–4]. Figure 1 presents an example of the transcript requested from the students.

II.4. Data collection and analysis

This study used a web survey to collect data to measure OLSE and AOL. Both scales contained two control questions (“This is a control question. If you are reading this question, mark the strongly agree option”) each. Accordingly, the researcher excluded the data of 28 prospective mathematics teachers who marked the scale items without reading them from the analysis. In addition, the normality test showed that data from 3 participants had extreme values. Consequently, these data were excluded from the analysis, and this study included the data of 1075 prospective mathematics teachers in the analysis. The quantitative data were gathered between April 1st and 25th in 2021.

Basic descriptive statistics, Pearson correlation and multiple regression were used to analyze quantitative data. The level of confidence for all statistical tests in this study was assumed as an alpha level of .05. Descriptive statistics were employed to express the characteristics of the participants. Pearson correlation was conducted to explore if relations among OLSE, AOL and AP were significant. Afterwards, multiple regression analysis was used to discover if there was a significant impact of OLSE and AOL in predicting AP. The study tried to determine the factors that have favorable and adverse effects on the academic performance of prospective mathematics teachers to gain deeper knowledge. For this purpose, the prospective mathematics teachers received the prompt to “Write down the factors that positively or negatively affect your academic performance in the distance education process.” The written answers of 118 prospective mathematics teachers were reviewed through content analysis. Two different researchers conducted the content analysis, and the consistency index between the coding was high, at 0.94. For the coding in which the researchers could not reach a consensus, a third researcher was consulted, and the majority’s opinion was accepted.

III. Results

III.1. Results for Question 1 and Question 2

A Pearson correlation was used to analyze the association between OLSE and AP. Table 2 illustrated that there was a significant positive correlation between OLSE and AP (r(1075) = .634, p < .01). Also, AP had significant correlation with the four subscales of OLSE (TU: r = .586 p < .01, OLT: r = .545 p < .01, IPIC: r = .562 p < .01, SRM: r = .501 p < .01). A Pearson correlation analysis was conducted between AOL and AP among prospective mathematics teachers, and a significant positive correlation between AOL and AP was found (r(1075) = .630, p < .01).

III.2. Results for Question 3

The study used multiple regression enter method and stepwise method to determine the accuracy of OLSE and AOL on predicting AP. Data were scanned to determine missing data and outliers and to test assumptions. For this, Mahalanobis and Cook’s distance was taken into account. It is recommended that data above 1 for Cook’s distance and data above 13.82, which is the critical value for the Mahalanobis distance, are extreme values and should be excluded from the analysis.[87] There was no Cook distance value greater than 1. However, when the Mahalanobis distances were examined, there were 3 data sets exceeding the critical value of 13.82, these were excluded from the analysis, and the analysis was carried out with 1075 data. Table 3 presents all tolerance levels which were more than .1 and all variance inflation factors (VIF) that were less than 10. Additionally, the Pearson correlation coefficient between the predictor variables r = .693 was found to be less than .70. Thus, it revealed that there was no problem of multicollinearity.[88] The Durbin-Watson value being 1.639, which is a value greater than 1 and less than 3, indicates that there is no autocorrelation in the model. Linearity was then analyzed by creating a scatter plot matrix (Figure 2). The scatter plot of the standardized residuals shows that most of the scores are concentrated in the center (along the 0 point). The residual plot was analyzed to evaluate homoscedasticity.[89] Figure 3 indicates that the errors have a near-normal distribution and the residual plots were not extreme. Therefore, linearity and homoscedasticity will be assumed.

In Table 3, multiple regression demonstrated that the overall model significantly predicted AP (R² = .446, R² adj = .445, F(2,1072) = 431.246, p < .001). This model explained 44.6% of the variance in AP. The beta weights in Table 3 illustrates that the contribution of OLSE and AOL to the model is significant (OLSE β = .36, t(1072) = 9.705, p < .001; and AOL β = .34, t(1072) = 9.176, p < .001).

In Table 4, multiple regression using the stepwise method, represented that the first model with the predictor (OLSE) accounted for 40.2% of the variance in AP and was significantly influential in predicting AP. And as the second model of two predictors added 4.4% of R² change, which, in total, accounted for 44.6% and was significantly influential in predicting the criterion (AP). The result of this study revealed that OLSE and AOL can significantly have an effect on predicting AP.

III.3. Results for Question 4

This study also tried to determine the factors that have favorable and adverse effects on the academic performance of prospective mathematics teachers in order to gain deeper knowledge. To that end, the prospective teachers were directed to “Write down the factors that positively or negatively affect your academic performance in the distance education process.” The researcher conducted content analysis on the written answers of the prospective teachers. Table 5 presents the obtained results.

As a result of the content analysis, 4 codes were obtained under the theme of the factors that positively affect the academic performance of prospective mathematics teachers in the distance education process. These codes are, respectively, “Ease of accessing lecture notes and video recordings of the lecture (f = 77),” “Efficient use of time (f = 84),” “Using different assessment and evaluation techniques (homework, forum, quiz, and performance tasks), (f = 33),” and “Comfort of the working environment (f = 18).” On the other hand, under the theme of negative factors, there were 7 codes. These codes are, respectively, “Technological problems (f = 38),” “The teaching method and teaching tools used (f = 87),” “Instruction time (f = 88),” “Teacher-student interaction (f = 97),” “Assessment and evaluation related problems (f = 91),” “Distractibility (f = 23),” and “Belief in the efficacy of face-to-face education over distance education (f = 32).”

In summary, this study determined that attitude toward online learning and self-efficacy toward online learning are significant predictors of academic performance (R² = .446; p < .001; OLSE β = .36, t(1072) = 9.705, p < .001; and AOL β = .34, t(1072) = 9.176, p < .001), and found that there are other factors that have positive and negative effects on academic performance.

IV. Conclusion and discussion

In this study, it was first investigated whether Online Learning Self-Efficacy (OLSE) and attitude towards online learning (AOL) significantly predicted the academic performance (AP) of Turkish pre-service mathematics teachers. In the next stage, 118 volunteer teacher candidates were asked what factors they thought had a positive or negative effect on their academic success during distance education and were asked to explain these factors. In this study, unlike the studies conducted in the literature,[90]^,[91]^,[92]^,[93]^,[94] online learning self-efficacy and attitude towards online learning as predictor variables were included in the study and both quantitative and qualitative data were collected. The results obtained from the study are important because they reveal the positive and negative factors that affect the academic performance of students in the online learning process, as well as showing whether the variables of online self-efficacy and attitude towards online learning are significant predictors of academic performance. The results obtained from the study can be a guide for practitioners, policy makers and teachers to take the necessary precautions for the effective execution of the distance education process.

This study revealed several critical conclusions with the findings from four research questions. A significant positive relationship between prospective mathematics teachers’ online learning self-efficacy and level of academic performance was found. Therefore, as the prospective mathematics teachers’ self-efficacy toward online learning improves, their academic performance will also improve positively. This result is similar to other studies in the literature.[95]^,[96]^,[97]^,[98]^,[99]^,[100]^,[101] It is concluded that students with low self-efficacy are less likely to make an effort and be successful in subjects they have difficulty with than students with high self-efficacy, and drew attention to the relevance of self-efficacy in student success.[102]

This study showed that there was a significantly positive relationship between prospective mathematics teachers’ attitude toward online learning and level of academic performance. Therefore, as prospective mathematics teachers’ attitudes toward online learning develop positively, their academic performance will also improve. This result is similar to the studies in the literature.[103]^,[104]^,[105] For example, Martinez et al.[106] concluded that student attitude in the distance education process affects academic success. Mohamed and Waheed[107] concluded that if students’ attitudes toward lessons are positive, there is a significant increase in their performance and academic achievement in the learning process.

This study revealed that prospective mathematics teachers’ online learning self-efficacy and attitude toward online learning had a significant influence on their academic performance. The R² and R² change values in Table 4 show that Online Learning Self-Efficacy is more effective in predicting academic performance. The results showed that the level of academic performance can be predicted by online learning self-efficacy and attitude toward online learning. When the studies are examined, it is seen that self-efficacy is one of the most important predictors of academic success.[108]^,[109]^,[110] On the other hand, there were results revealing that another predictor of academic success is attitude.[111]^,[112] However, there were no studies on whether the attitude and self-efficacy toward online learning are significant predictors of academic success. Yet, some studies have revealed the significant effects of motivation and self-efficacy on academic achievement.[113]^,[114]

Therefore, academics should first determine the self-efficacy and attitude levels of prospective mathematics teachers toward online learning. For students who do not have sufficient self-efficacy and attitude, educators can concretize abstract concepts that are difficult to understand. Computer software can assist the concretization process. The distance education process can utilize learner-interface interaction to make learners active in their own learning processes and to participate in the lesson productively. For this, one can obtain support from software that can help share content created by learners and benefit from cooperation.[115] On the other hand, various discussions can be conducted in relation to social media applications (WhatsApp-Telegram-Facebook-Twitter) to ensure learner-instructor and learner-learner interaction. In addition, online educators can increase the motivation of their students by using communication tools such as email, chat room, social networking services, and bulletin boards for online learning.

The findings of the study determined that there are factors that have favorable and adverse effects on the academic performance of prospective mathematics teachers in the distance education process. As a result of the written opinions received from teacher candidates, the factors that have a positive effect on academic performance were coded as “Ease of accessing lecture notes and video recordings of the lecture,” “Efficient use of time,” “Use of different assessment and evaluation techniques (homework, forum, quiz, and performance task),” and “Comfort of the working environment.” The factors that have a negative impact on academic performance were; “Technological problems,” “The teaching method and teaching tools used,” “Instruction time,” “Teacher-student interaction,” “Assessment and evaluation related problems,” “Distractibility,” and “Belief in the efficacy of face-to-face education over distance education.” In addition to these factors, existing studies have highlighted other factors such as technological infrastructure,[116]^,[117]^,[118]^,[119] teacher-student interaction,[120]^,[121] assessment and evaluation processes,[122] and time management and motivation[123]^,[124]^,[125] also affect the academic performance of the students in the distance education process. Reasons such as technical failures in the distance education system, lack of content and material, communication breakdowns, and the emotional reluctance of students negatively affect students’ attitudes toward distance education.[126] According to Fidalgo et al.,[127] many students believe that time management and lack of motivation are major concerns about distance education. Especially after the earthquakes that took place in Türkiye on February 6, 2023 and negatively affected 10 provinces, the decision of distance education was taken again in the universities in Türkiye. It should also be emphasized that students living in the earthquake area are likely to have a lack of concentration, loss of motivation and a source of mental depression.

V. Suggestions and implications

According to the results obtained from the study, when prospective mathematics teachers’ self-efficacy and attitudes toward online learning are positive and high, their academic performance will be congruent. It can be said that the learning-teaching process in distance education requires interactive, rich content practices and course tools that increase the quality of the time they spend. In distance education, the duration of the lessons is shorter than in normal education, but the intense content plays an important role in the individual participation of the students, their following the lesson, their interaction with each other and with the lecturer. For this reason, it is important to organize the course contents, course design, questions, examples and assignments in the course in a way that attracts students’ attention and motivates them. If the learning environments are organized in a student-centered manner in line with the expectations of the teacher candidates, it can be said that the attitudes and self-efficacy of the prospective mathematics teachers towards distance learning can be improved in a positive way.

Additionally, when there are no technological disruptions in the distance education process, when educators use appropriate teaching methods and tools that will make students active in the teaching process and enable them to access information themselves, when they keep interactions such as educator-student, student-student, student-interface at a high level, and when they use process-based appropriate assessment and evaluation tools, educators can contribute to the improvement of students’ academic performance. If the instructors involved in the distance education process take into account the factors that have positive and negative effects on the academic performance of the students and plan their lessons accordingly, this situation can contribute to the effective and efficient execution of the distance education process. In the distance education process, apart from the positive and negative factors revealed in this study, it may be beneficial for the instructors to have regular online meetings with their students and consider the opinions of the students to identify the different factors that may arise and to take the necessary precautions in this direction.

The results obtained from this study are limited to the answers from 1075 and 118 prospective mathematics teachers. In addition, the study reviewed two predictor variables (self-efficacy toward online learning and attitude toward online learning). The research tried to overcome this limitation with the prompt, “Write down the positive and negative factors that affect your academic performance in the distance education process” directed at the prospective mathematics teachers. In the light of the results, it is necessary to reconsider the roles and competencies of distance educators in traditional education according to distance education environments,[128] because educators becoming effective instructors in distance education applications depend on whether they have multidimensional roles and various competencies.[129]

Caution needs to be paid to the generalizability of the results obtained in this study. Students in different countries have different access to technological tools. Self-efficacy levels and attitudes towards online learning of students who do not have their own devices such as computers and tablets at home may differ from those who have these tools. In addition, whether universities in different countries are familiar with the distance education process and their technological infrastructures and the experiences of academicians in this process may differ. Since the participants in this study are prospective mathematics teachers, similar studies can be conducted on prospective teachers from different branches in future research. Future studies can also investigate whether variables other than self-efficacy toward online learning and attitude toward online learning (motivation, satisfaction, academic stress, etc.) are significant predictors of academic performance. Additionally, studies can investigate direct and indirect effects between predictor (motivation, satisfaction, academic stress, self-efficacy, and attitude) and predicted (academic performance) variables through path analysis or structural equation modeling.

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Appendix 1. Factors and items of the Online Learning Self-efficacy Scale (Sun and Rogers, 2020)

Appendix 2. Çevrimiçi öğrenme öz yeterlik ölçeği (Online Learning Self-efficacy Scale-Turkish Form)

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