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Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023,
http://www.tuningjournal.org/
Tuning Journal
for Higher Education
Volume 11, Issue No. 1, November 2023
DOI: https://doi.org/10.18543/tjhe1112023
Educational Journeys in times of uncertainty:
Weathering the storms
ARTICLES
The effects of online learning self-efcacy 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
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197
Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
http://www.tuningjournal.org/
The effects of online learning self-efcacy 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-efcacy 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 inuenced 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) signicantly contributed to the model. The results
showed that the level of academic performance can be predicted by online
learning self-efcacy 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
* Suphi Önder Bütüner (corresponding author, s.onder.butuner@bozok.edu.tr), PhD, is
Associate Professor of Math Education at Faculty of Education, Yozgat Bozok University,
Turkey. His research focus is on teaching mathematical concepts and teacher training.
Serdal Baltacı (serdalbaltaci@gmail.com), PhD, is Associate Professor of Math
Education at the Faculty of Education, Kırşehir Ahi Evran University, Turkey. His research
focus is on teaching mathematical concepts and teacher training.
More information about the authors is available at the end of this article.
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198
Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
teachers to take the necessary precautions for the effective execution of the
distance education process.
Keywords: Online learning; self-efcacy; 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 21st 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
1 World Health Organization, “Advice for the public: Coronavirus disease (COVID-19),”
accessed July 3, 2021, https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
2 Timothy, J Newby, Donald Stepich, James Lehman, James D Russell, and Anne Todd
Leftwich, Educational Technology for Teaching and Learning (New Jersey: Pearson Merrill
Prentice Hall, 2006).
3 Michael, G Moore and William G. Anderson, Handbook of Distance Education
(London: Lawrence Erlbaum Associates, 2003).
4 Margaret Driscoll, Web-based training: Creating E-learning Experiences (San
Francisco: JosseyBass/Pfeiffer, 2002).
5 Allan J Henderson, The E-learning Question and Answer Book: A Survival Guide for
Trainers and Business Managers (New York: Amacom Press, 2003).
6 Dongsong Zhang and Jay F. Nunamaker, “Powering E-learning in the New Millennium:
An Overview of E-learning and Enabling Technology,” Information Systems Frontiers 5, no. 2
(2003): 207-218. https://doi.org/10.1023/A:1022609809036.
7 Anita Rosen, E-Learning 2.0: Proven Practices and Emerging Technologies to Achieve
Real Results (New York: Amacom, 2009).
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Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
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 ofine 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
les 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
8 Virginia Gewin, “Five Tips for Moving Teaching Online as COVID-19 Takes Hold,”
Nature 580, (2020): 295-296. doi: https://doi.org/10.1038/d41586-020-00896-7.
9 Patricia Fidalgo et al., “Students’ Perceptions on Distance Education: A Multinational
Study,” International Journal of Educational Technology in Higher Education 17, (2020):
1-18. https://doi.org/10.1186/s41239-020-00194-2.
10 Lynette Watts, “Synchronous and Asynchronous Communication in Distance
Learning: A Review of the Literature,” Quarterly Review of Distance Education 17, no 1
(2016): 23-32.
11 Ersin Kurnaz and Murat Serçemeli, “A Research on Academicans’ Perspectives on
Distance Education and Distance Accounting Education in the COVID-19 Pandemia Period,”
International Journal of Social Sciences Academy 2, no 3 (2020): 262-288.
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© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
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-efcacy, 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-efcacy 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-efcacy 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-efcacy for online learning is a signicant component
in the successful execution of this process. Self-efcacy is people’s belief
in their own competence to learn and develop behaviors.15,16 Schunk17
12 Council of Higher Education, “COVID-19 Information Note: 1,” accessed April 5,
2020, https://www.yok. gov.tr/Sayfalar/Haberler/2020/.
13 Parvati Iyer, Kalid Aziz, and David M. Ojcius, “Impact of COVID-19 on Dental
Education in the United States,” Journal of Dental Education 84, no. 6 (2020): 718-22. https://
doi.org/10.1002/jdd.12163.
14 Albert Bandura, “Organizational Application of Social Cognitive Theory,” Australian
Journal of Management 13, no. 2 (1988): 275–302. https://doi.org/10.1177/031289628801300210.
15 Albert Bandura, “Social Cognitive Theory: An Agentic Perspective,” Asian Journal of
Social Psychology 2, no. 1 (1999): 21-41. http://doi.org/10.1146/annurev.psych.52.1.1.
16 Jerry L Jinks and Morgan L. Vicky, “Students’ sense of academic efficacy and
achievement in science: A useful new direction for research regarding scientic literacy?,” The
Electronic Journal of Science Education 1, no. 2 (1996): accessed May 1, 2020. http://unr.
edulhomepage/jcannon/jinksmor.htm.
17 Dale H Schunk, Learning Theories: An Educational Perspective (Boston: Pearson, 2009).
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© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
dened self-efcacy as individuals’ evaluation of their own skills and
capabilities and their ability to transform them into behaviors. Gallagher18
expressed self-efcacy 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-efcacy seems to gain more authentic features. In
such platforms, self-efcacy consists of ve dimensions: These are self-
efcacy concerning nishing 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-
efcacy 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-efcacy 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 fullling 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 difculties.24,25 Similarly, Pajares26 observed that individuals
with high self-efficacy have high success and are happier due to this
18 Matthew W Gallagher, “Self-Efcacy.” In Encyclopedia of Human Behavior, edited
by. Vilayanur S. Ramachandran, 314-320. San Diego: Academic Press, 2012.
19 Demei Shen et al., “Unpacking Online Learning Experiences: Online Learning Self-
efcacy and Learning Satisfaction,” The Internet and Higher Education 19 (2013): 10-17.
https://doi.org/10.1016/j.iheduc.2013.04.001.
20 Min-Ling Hung, “Teacher Readiness for Online Learning: Scale Development and
Teacher Perceptions,” Computers & Education 94 (2016): 120-133. https://doi.org/10.1016/j.
compedu.2015.11.012.
21 Chia-Lin Tsai et al., “The Self-Efcacy Questionnaire for Online Learning,” Distance
Education 41, no. 4 (2020): 472-489. https://doi.org/10.1080/01587919.2020.1821604.
22 Stuart Woodcock, Ashley Sisco, and Michelle J Eady, “The Learning Experience:
Training Teachers Using Online Synchronous Environments,” Journal of Educational
Research and Practice 5, no. 1 (2015): 21-34. https://doi.org/10.5590/JERAP.2015.05.1.02.
23 Caroline Sharp, Pocklington Keith, and Weindling Dick, “Study Support and the
Development of Self-regulated Learner,” Educational Research 44, no. 1 (2002): 29- 42.
24 Journal of Physics: Conference Series. “Mathematics self efcacy and mathematics
performance in online learning.” accessed May 1, 2021, https://iopscience.iop.org/
article/10.1088/1742-6596/1882/1/012050.
25 Barry J Zimmerman, “Becoming a Self-Regulated Learner: An Overview,” Theory Into
Practice, 41, no. 2 (2002): 64-70. doi: 10.1207/s15430421tip4102_2.
26 Frank Pajares, “Self-efcacy Beliefs and Mathematical Problem-Solving of Gifted
Students,” Contemporary Educational Psychology 21, no. 4 (1996): 325-344. https://doi.
org/10.1006/ceps.1996.0025.
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© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
success. Bandura27 stated that students having weak self-efcacy have less
motivation to learn, meaning that they are less willing to learn and make
less effort accordingly. Self-efcacy has a mediating role in students’
academic success in distance education, and success and self-efcacy are
positively related.28,29,30,31 Besides, Tsai, Cha, Marra, and Shen32 revealed
that whoever has a favorable outlook toward online learning and high self-
efcacy 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 Chen35 highlighted the
signicance of students having a positive attitude toward online learning.
They also stated that students’ positive attitude levels toward online learning
27 Albert Bandura, Self-efficacy Encyclopedia of Human Behaviour (New York:
Academic Press, 1994).
28 Katrin A Arens, Anne C. Frenzel, and Thomas Goetz, “Self-Concept and Self-Efcacy in
Math: Longitudinal Interrelations and Reciprocal Linkages with Achievement,” The Journal of
Experimental Education 90, no. 3 (2020): 1-19. https://doi.org/10.1080/00220973.2020.1786347.
29 Adeneye A O Awofala, “Correlates of Senior Secondary School Students’ Mathematics
Achievement,” Educatia 21, no. 17 (2019): 15-25. https://doi.org/10.24193/ed21.2019.17.02.
30 Dan Li, “A Review of Self-efcacy of Learners Through Online Learning,” Journal of
Humanities and Education Development 2, no. 6 (2020): 526-533.
31 Bikkar S Randhawa, James E. Beamer, and Ingvar Lundberg, “Role of Mathematics
Self-efcacy in the Structural Model of Mathematics Achievement,” Journal of Educational
Psychology, 85, no. 1 (1993): 41. https://doi.org/10.1037/0022-0663.85.1.41.
32 Chia-Lin Tsai et al., “The Self-Efcacy,” 472-489.
33 Richard E Petty and John T. Cacioppo, Attitudes and Persuasion: Classic and
Contemporary Approaches (New York: Westview Press, 1996).
34 Diana W Sanders and Alison I. Morrison-Shetlar, “Student Attitudes Toward Web-
Enhanced Instruction in an Introductory Biology Course,” Journal of Research on Computing
in Education 33, no. 3 (2001): 251–262. https://doi.org/10.1080/08886504.2001.10782313.
35 Shu-Sheng Liaw, Hsiu-Mei Huang, and Gwo-Dong Chen, “Surveying Instructor and
Learner Attitudes Toward E-learning,” Computers & Education 49, (2007): 1066–1080.
https://doi.org/10.1016/j.compedu.2006.01.001.
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Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
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 Ying40 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. Falowo42
specied 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.
36 Brian R Evans, “Student Attitudes, Conceptions and Achievement in Introductory
Undergraduate College Statistics,” The Mathematics Educator 17, no. 2 (2007): 22-24.
37 Lawsha Mohamed and Hussain Waheed. “Secondary Students’ Attitude Towards
Mathematics in a Selected School of Maldives,” International Journal of Humanities and
Social Science 1, no. 15 (2011): 277-278.
38 Solomon O Ogunniyi, “Resource Utilisation, Teaching Methods, Time Allocation and
Attitude as Correlates of Undergraduates’ Academic Achievement in Cataloguing in Library
Schools in Southern Nigeria.” PhD diss., University of Ibadan, 2015.
39 Sanjaya Mishra and Santosh Panda, “Development and Factor Analysis of an Instrument
to Measure Faculty Attitude Towards E-learning,” Asian Journal of Distance Education 5, no.
1 (2007): 27-33.
40 Zhang Lijie, Mo Zongzhao, Zhou Ying, “The Inuence of Mathematics Attitude on
Academic Achievement: Intermediary Role of Mathematics Learning Engagement,” Jurnal
Cendekia: Jurnal Pendidikan Matematika 4, no. 2 (2020): 460-467. https://doi.org/10.31004/
cendekia.v4i2.253.
41 Baruch Offir et al., “Teacher–Student Interactions and Learning Outcomes in a
Distance Learning Environment,” The Internet and Higher Education 6, no. 1 (2003): 65-75.
https://doi.org/10.1016/S1096-7516(02)00162-8.
42 Rasheed Falowo, “Factors Impeding Implementation of Web-based Distance Larning,”
AACE Journal 15, no. 3 (2007): 315-338.
43 Romero J Sonia Martínez et al., “Attitudes Toward Technology Among Distance Education
Students: Validation of an Explanatory Model,” Online Learning, 24, no. 2 (2020): 59-75.
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© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
In the literature, there are studies on the attitudes44,45,46,47 and self-
efficacy48,49 of university students in distance education environments.
However, there was no study that investigated whether these two factors are
signicant predictors of academic performance. It is critical to reveal the
extent to which the attitudes and self-efcacy 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 rst two questions of this study deal with the level and
direction of the relationship between the attitude and self-efcacy 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-efcacy 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
44 Karen E Brinkley-Etzkorn, “The Effects of Training on Instructor Beliefs About and
Attitudes Toward Online Teaching,” American Journal of Distance Education 34, no. 1
(2019): 1-17. https://doi.org/10.1080/08923647.2020.1692553.
45 Eleni Koustriava and Konstantinos Papadopoulos, “Attitudes of Individuals with
Visual Impairments Towards Distance Education,” Universal Access in the Information
Society 13 (2014): 439–447. https://doi.org/10.1007/s10209-013-0331-2.
46 Shu-Sheng Liaw, Hsiu-Mei Huang, and Gwo-Dong Chen, “Surveying Instructor,”
1066–1080.
47 David Ojo and Felix Kayode Olakulehin, “Attitudes and Perceptions of Students to
Open and Distance Learning in Nigeria,” International Review of Research in Open and
Distance Learning, 7, no. 1 (2006): 1-10. https://doi.org/10.19173/irrodl.v7i1.313.
48 Demei Shen et al., “Unpacking Online,” 10-17.
49 Stuart Woodcock, Ashley Sisco, and Michelle J Eady, “The Learning,” 21-34. https://
doi.org/10.5590/JERAP.2015.05.1.02.
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© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
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 signicant impact on success in distance education.56,57,58,59 Haber
and Mills60 and Bolliger and Wasilik61 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,
50 Pia Ceres, “A Covid Slide’ Could Widen the Digital Divide for Students,” accessed
May 4, 2021, https://www.wired.com/story/schools-digital-divide-remote-learning/.
51 Rachel Gong, “Coping with MCO: Distance learning and the digital divide," accessed
October 15, 2020, https://www.malaymail.com/news/what-you-think/2020/03/27/coping-
with-mcodistance-learning-and-the-digital-divide-rachel-gong/1850758.
52 Brian Hawkins and Diana G. Oblinge, “The Myth About the Digital Divide,” Educause
Review 41, no. 4 (2006): 12–13.
53 Natalie Helbig, Ramón Gil-García, and Erico Ferro, “Understanding the Complexity of
Electronic Government: Implications From the Digital Divide Literature,” Government
Information Quarterly 26, no. 1 (2009): 89–97. https://doi.org/10.1016/j.giq.2008.05.004.
54 Thelma Obiakor and Adeniran Adedeji P, “COVID-19: Impending Situation Threatens
to Deepen Nigeria's Education Crisis,” accessed May 1, 2020, https://www.africaportal.org/
publications/covid-19-impending-situation-threatens-deepen-nigerias-education-crisis/.
55 Yash Sharma, “Massive Open Online Courses (MOOCs) for School Education in
India: Advantages, Challenges and Suggestions for Implementation,” Microcosmos
International Journal of Research 1, no. 2 (2015): 1–5.
56 Jason D Baker, “An Investigation of Relationships Among Instructor Immediacy and
Affective and Cognitive learning in the Online Classroom,” The Internet and Higher Education
7, no. 1 (2004): 1-13. https://doi.org/10.1016/j.iheduc.2003.11.006.
57 Stefan Hrastinski, “The Potential of Synchronous Communication to Enhance
Participation in Online Discussions: A Case Study of Two E-learning Courses,” Information &
Management 45 (2008): 499–506. https://doi.org/10.1016/j.im.2008.07.005.
58 Marie Huff, “A Comparison Study of Live Instruction Versus Interactive Television for
Teaching MSW Students Critical Thinking Skills,” Research on Social Work Practice 10, no.
4 (2000): 400-416. doi: 10.1177/104973150001000402.
59 Stewe Wheeler, “Student Perceptions of Learning Support in Distance Education,”
Quarterly Review of Distance Education 3, no. 4 (2002): 419-429.
60 Jennifer Haber and Michael Mills, “Perceptions of Barriers Concerning Effective
Online Teaching and Policies: Florida Community College Faculty,” Community College
Journal of Research and Practice 32, no.4-6 (2008): 266-283. https://doi.
org/10.1080/10668920701884505.
61 Bolliger, Doris U and Oksana Wasilik, “Factors Inuencing Faculty Satisfaction With
Online Teaching and Learning in Higher Education,” Distance Education 30, no. 1 (2009):
103-16. https://doi.org/10.1080/01587910902845949.
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and Tessier62 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 Petracchi64 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 exible working opportunities independent of time and
space, saving time and therefore less cost. Some of the disadvantageous
points of distance education are difculties in providing motivation, lack
62 Tracy Chao, Tami Saj, and Felicity Tessier, “Establishing a Quality Review for Online
Courses,” Educause Quarterly 3 (2006): 32-39.
63 Tracy Irani et al., “Personality Type and Its Relationship to Distance Education
Students' Course Perceptions and Performance,” Quarterly Review of Distance Education 4,
no. 4 (2003): 445-453.
64 Helen E Petracchi, “Distance Education: What do our Students Tell us?,” Research on
Social Work Practice, 10, no. 3 (2000): 362-376. https://doi.org/10.1177/1049731500010003.
65 Kuan-Chung Chen and Syh-Jong Jang, “Motivation in Online Learning: Testing a
Model of Self-Determination Theory,” Computer in Human Behavior 26, no. 4 (2010): 741-
752. https://doi.org/10.1016/j.chb.2010.01.011.
66 Reinhard Pekrun et al., “Boredom and Academic Achievement: Testing a Model of
Reciprocal Causation,” Journal of Educational Psychology 106, no. 3 (2014): 696-710. https://
doi.org/10.1037/a0036006.
67 Allen Wigeld et al., “Development of Achievement Motivation and Engagement,” In
Handbook of Child Psychology and Developmental Science, edited by. M. E. Lamb, R. M.
Lerner, M. E. Lamb, & R. M. Lerner, 657-700. Hoboken, NJ: Wiley, 2015.
68 Sophia S-C Chan et al., “Improving Older Adults’ Knowledge and Practice of
Preventive Measures Through a Telephone Health Education During the SARS Epidemic in
Hong Kong: a Pilot Study,” International Journal of Nursing Studies 244, no. 7 (2007): 1120-
1127. https://doi.org/10.1016/j.ijnurstu.2006.04.019.
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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 signicant 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 eld. 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 signicant correlation between Online Learning
Self-Efcacy (OLSE) and academic performance (AP) among prospective
mathematics teachers?
• Question 2: Is there a signicant 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?
69 Liesbeth De Paepe, Chang Zhu, and Koen DePryck, “Drop-out, Retention, Satisfaction
and Attainment of Online Learners of Dutch in Adult Education,” International Journal on
E-Learning 17, no. 3 (2018): 303-323.
70 Virginia Gewin, “Five Tips,” 295-296.
71 Agi Horspool and Carsten Lange, “Applying the Scholarship of Teaching and
Learning: Student Perceptions, Behaviours and Success Online and Face-to-Face,” Assessment
& Evaluation in Higher Education, 37, no 1 (2012): 73-88. https://doi.org/10.1080/02602938.
2010.496532.
72 Papia Bawa, “Retention in Online Courses: Exploring Issues and Solutions–A
Literature Review,” Sage Open 6, no. 1 (2016): 1-11. https://doi.org/10.1177/2158244015621777.
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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 signicant 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, Stevens73 stated that there should be 15
participants per predictor, and Tabachnick and Fidell74 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 species 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
73 Junko Stevens, Applied Multivariate Statistics for the Social Sciences (New York:
Routledge Taylor Francis Group, 1996).
74 Barbara G Tabachnick and Linda S. Fidell, Using Multivariate Statistics (Boston:
Allyn and Bacon, 2013).
75 Andy Field, Discovering Statistics Using IBM SPSS Statistics: And Sex and Drugs and
Rock “N” Roll (Los Angeles, London, New Delhi: Sage, 2013).
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prospective mathematics teachers who lled out the scales and provided
written opinions.
Table 1
Information on prospective mathematics teachers who filled
out the scales and provided written opinions
Variables Categories n (number of scales
filled out)
n (number of written
opinions received)
Region Marmara 205 26
Aegean 180 21
Mediterranean 126 11
Black Sea 129 12
Central Anatolia 167 18
Eastern Anatolia 135 13
Southeast Anatolia 164 17
Gender Male 512 53
Female 590 65
School
Level
1. 270 33
2. 298 29
3. 278 28
4. 260 28
Total 1106 118
II.3. Instruments
II.3.1. The online learning self-efcacy scale (OLSES)
This study employed the Turkish adaptation of the self-efcacy 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
76 Richard Netemeyer, William O. Bearden, and Subhash Sharma, Scaling Procedures
Issues and Applications (USA: Sage Publications, 2013).
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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
insufcient to meet one or more of the four different dimensions.77 For the
stated reasons, the Online Learning Self-Efcacy 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 rst stage of the adaptation process, three academics who are
experts in the eld 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 coefcient between both forms of the scale was calculated
as .92 at a high level. At the last stage, a second level conrmatory 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 Muthén80 stated that a sample size of 150 is sufcient,
granted that the data are normally distributed and there are no missing data.
In this respect, the sample size (1,078 people) was sufcient for conrmatory
77 Yan Sun and Reenay Rogers, “Development and Validation of the Online Learning
Self-efcacy Scale (OLSS): A Structural Equation Modeling Approach,” American Journal
of Distance Education 35, no.3 (2021): 184-199. http://doi.org/10.1080/08923647.2020.183
1357.
78 Rex Kline, Principles and Practice of Structural Equation Modeling (New York:
Guilford Publications, 2005).
79 Sait Gürbüz and Faruk Şahin, Research Methods in Social Sciences (Ankara: Seçkin
Publication, 2018).
80 Linda Muthén and Bengt O. Muthén, “How to Use a Monte Carlo Study to Decide on
Sample Size and Determine Power,” Structural Equation Modeling 9, no. 4 (2002): 599–620.
https://doi.org/10.1207/S15328007SEM0904 8.
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factor analysis. The second-order factorial structure of the online learning
self-efcacy scale consisting of four sub-dimensions and 31 items was tested
using the AMOS 24 program. The results of the second-order conrmatory
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 t values
obtained as a result of the second-order conrmatory 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 signied that the data obtained from the study
were compatible with the predicted theoretical structure (four-factor model)
of the online learning self-efcacy scale. The nal version of the validated
OLSES has 31 items, and they load on four factors: Technology use self-
efcacy (TU), online learning task self-efcacy (OLT), instructor and peer
interaction and communication self-efcacy (IPIC), and self-regulation and
motivation efcacy (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-efcacy level of the individual who
completed the scale is more positive toward online learning. Lin82 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
Kışla83,84 examined the validity and reliability. The exploratory factor
analysis was carried out. The eigenvalues of the scale items gathered under 5
81 Barbara Byrne, Structural Equation Modeling with AMOS: Basic Concepts,
Applications, and Programming (New York: Taylor Francis, 2010).
82 Tzung-Jin Lin, “Exploring the Differences in Taiwanese University Students’ Online
Learning Task Value, Goal Orientation, and Self-Efcacy Before and After the COVID-19
Outbreak,” Asia-Pacific Education Researcher 30, no. 3 (2021): 191–203. https://doi.
org/10.1007/s40299-021-00553-1.
83 Kışla Tarık, “University Students' Attitudes Towards Distance Education,” Master
diss., Ege University, 2005.
84 Kışla, Tarık, “Development of a Attitude Scale towards Distance Learning,” Ege
Journal of Education 17, no. 1 (2016): 258-271. https://doi.org/10.12984/eed.01675.
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factors greater than 1, and these ve 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 conrmatory factor analysis was performed to
conrm this single factor structure and produced the goodness of t 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 coefcient 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. Fidan85 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.
85 Mustafa Fidan, “Distance Education Students’ Attitudes Towards Distance Education
and Their Epistemological Beliefs,” Hacettepe University Journal of Education 31, no. 3
(2016): 536-550. https://doi.org/10.16986/HUJE.2016016666.
86 Council of Higher Education, “Correspondence of Grades in the 4-Point System in the
100-Point System,” accessed April 26, 2021,https://www.yok.gov.tr/Documents/Kurumsal/
personel_dairesi/4_luk_sistem_100.pdf.
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Figure 1
A sample document showing the student’s grade point average
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 condence 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 signicant. Afterwards, multiple regression analysis was used to discover
if there was a signicant 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
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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
signicant 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).
Table 2
The correlation between (OLSE, AOL) and AP
N AP p
OLSE
1075
.634 .000**
First factor (Technology use self-efficacy, TU) .586 .000**
Second factor (Online learning task self-efficacy, OLT) .545 .000**
Third factor (Instructor and peer interaction and
communication self-efficacy, IPIC) .562 .000**
Fourth factor (Self-regulation and motivation efficacy,
SRM) .501 .000**
AOL .630 .000**
** Correlation is significant at the 0.01 level (2-tailed).
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
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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 ination factors (VIF) that were less than 10. Additionally, the
Pearson correlation coefcient 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.
Figure 2
Scatter plot
87 Barbara Tabachnick and Linda S. Fidell, Using Multivariate Statistics (Boston: Allyn
and Bacon, 2013).
88 Julie Pallant, The SPSS Survival Manual (London: McGraw-Hill Education, 2013).
89 Andy Field, Discovering Statistics Using IBM SPSS Statistics (London: Sage, 2013).
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Figure 3
Histogram of regression standardized residual and Normal Probability
Plot (PP) of the regression standardized residual
In Table 3, multiple regression demonstrated that the overall model
signicantly 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
signicant (OLSE β = .36, t(1072) = 9.705, p < .001; and AOL β = .34,
t(1072) = 9.176, p < .001).
Table 3
Multiple regression for predicting AP using the enter method
Model Variables B βt Tolerance VIF Durbin-
Watson FR2R2
adj
1
Constant 2.417 63.784
1.639 431.246*** .446 .445OLSE .004 .36 9.705 .371 2.69
AOL .003 .34 9.176 .371 2.69
*** p < .001.
In Table 4, multiple regression using the stepwise method, represented
that the rst model with the predictor (OLSE) accounted for 40.2% of the
variance in AP and was signicantly inuential 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
signicantly have an effect on predicting AP.
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Table 4
Multiple regression for predicting AP using the stepwise method
Model Variables B βt Tolerance VIF F R2R2
change
1Constant 2,316 722.275*** .402 .402
OLSE .007 .634 26.875 1.00 1.00
2
Constant 2.417
431.246*** .446 .044AOL .004 .362 9.705 .371 2.698
OLSE .003 .343 9.176 .371 2.698
*** p < .001.
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),” “Efcient 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
efcacy of face-to-face education over distance education (f = 32).”
In summary, this study determined that attitude toward online learning
and self-efcacy toward online learning are signicant 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.
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Table 5
Factors that positively and negatively affect the general academic performance
of prospective mathematics teachers in the distance education process
Theme Codes Supporting Statement f
The
Positive
Factors
Ease of accessing
lecture notes and
video recordings of
the lecture
P3. Since the lessons we took online were recorded, we were able to watch the
lessons we missed or could not understand again later, allowing us to understand
the points that we were not able to comprehend.
P34. Since the lectures are recorded, I can listen to them repeatedly, which
contributes to my learning.
77
Efficient use of
time
P95. Since we were always at home, I did not have any problems allocating time
for the lessons.
P99. Being at home during this process allowed us to make better use of our day
and spend our time more productively.
P101. Since I was at home during online education, I spent my time more
efficiently. This situation contributed to my academic success, and I found time for
the aspects I wanted to develop individually.
84
Using different
assessment
and evaluation
techniques
(homework,
forum, quiz, and
performance tasks)
P55. Since the exams in the form of homework, I learned more about the course.
P59. The positive side of the distance education process is the assignments,
because when the exams are in the form of homework, I learn the subjects
better and do not forget them easily because I have to study the subjects more
comprehensively.
P88. Taking regular quizzes every week or receiving our exam grades based on
homework prevented us from disconnecting from the class. Thus, assessments and
evaluations were not only result-oriented, but process-oriented.
33
Comfort of
the working
environment
P70. The fact I could study more comfortably at home and spare time for myself
had a positive effect.
P80. I had the comfort of taking exams at home.
18
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Theme Codes Supporting Statement f
The
Negative
Factors
Technological
problems
P41. Due to problems in internet access, my participation in classes was
interrupted from time to time. Unfortunately, I felt disconnected when I could
not attend a few classes.
P66. Since I live in the village, my internet was cut off during some classes and I
could not attend the classes.
P79. Losing connecting to the internet or the constant freezing of the audio and
video while I was listening to the lectures, prevented me from having motivation
for some lessons.
P85. I had concerns that the electricity would go out or the phone-computer
would present errors.
P95. In some cases, problems would occur in the exams during the distance
education process. In the exams on the system, sometimes the system could
remove us from the exam, and sometimes we experienced a loss of time due to
the slowness of the internet. Therefore, it was inevitable to be in a constant state
of stress during the exam.
P102. I had concerns that the electricity would go out or the phone-computer
would present errors.
38
The teaching
method and
teaching tools
used
P29. Some lecturers conducted their lectures using the direct instruction method,
which caused the lecture to be monotone. I lost interest in the lesson.
P71. The instructor’s constant self-explanatory state and the fact that they did not
use sufficient and compelling teaching tools reduced my active participation and
interest in the lesson.
P93. Most lessons were taught through presentations, causing them to be boring
after a certain period.
P111. Most of our teachers only used power point presentations. I wish they had
used other engaging teaching tools. Then I could have been more motivated
toward the lesson.
87
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Theme Codes Supporting Statement f
The
Negative
Factors
Instruction Time
P32. Since the lessons are conducted through technological tools such as the
phone and computer, I can get distracted after a certain period of time in front of
the screen. Therefore, there is not as much activity in the course as in face-to-face
education.
P49. Some lessons lasted too long. Thus, we were in front of the screen for a long
time and this reduced our interest in the lesson.
P61. The long instructing hours and the fact that we lost connection to the course
after a certain period of time adversely affected our academic performance.
88
Teacher-Student
Interaction
P13. Interactive learning in lessons decreased to a minimum. This situation had a
negative impact on our grades.
P25. Since the student-teacher interaction was inadequate compared to the
face-to-face classroom environment, this had a negative impact on my academic
performance.
P45. Both our and our teacher’s cameras were closed, so there was no proper
teacher-student interaction. This situation made us feel as if we were listening to
the radio.
P82. Since I am not in the same environment with the lecturer, I have difficulty
understanding the lessons, and this is reflected in my exams.
P101. Trying to listen to the teachers without even seeing their faces prevented
me from being highly motivated for the lesson.
P103. I did not have the opportunity to do intensive question-and-answer sessions
with our teachers for facts that I was curious about or when I had questions left in
my mind.
97
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Theme Codes Supporting Statement f
The
Negative
Factors
Assessment and
evaluation related
problems
P23. The limited exam times and the problem of the inability to return to a
question when one made a wrong marking caused stress.
P56. In distance education, I think there was too much homework in each course
at the same time. The fact that it took a lot of time and sometimes the homework
being unproductive reduced my motivation.
P59. In the simultaneous multiple-choice exams, which are rarely used for certain
courses, the students were not allowed to return to the items, and the allocated
time was insufficient.
P87. Some courses had a very short exam duration (like 20 questions 19 minutes).
This situation caused me to be unable to answer all of the questions.
P99. We had to be in front of the computer all the time because we were given
a lot of homework. This situation caused me to take a dislike to the lesson and
reduced my interest.
P104. Excessive homework and exams alienated us from the lessons. Even though
we reached the finals week, we continued to do our midterm homework and we
were doing them reluctantly.
91
Distractibility
P39. Since I did not have a room of my own at home, my siblings’ noise and my
parents’ conversations distracted me during lessons.
P77. The small size and crowded nature of our house negatively affected my focus
in lessons.
23
Belief in the
efficacy of face-
to-face education
over distance
education
P22. I think that face-to-face education is more efficient and effective than
distance education. Until now, I always had face-to-face training and people
cannot easily give up their habits.
P44. Distance education definitely cannot replace face-to-face education, because
you can clearly feel the authority of the teacher in face-to-face education.
P90. In face-to-face education, one can establish teacher-student communication
in a healthier manner compared to distance education. Students can participate
more actively in the lesson. Therefore, I cannot say that distance education has
positively affected my academic performance.
32
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IV. Conclusion and discussion
In this study, it was rst investigated whether Online Learning Self-
Efcacy (OLSE) and attitude towards online learning (AOL) signicantly
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-efcacy and attitude towards online learning are signicant
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 ndings from
four research questions. A significant positive relationship between
prospective mathematics teachers’ online learning self-efcacy 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
90 Judy Drennan, Jessica Kennedy, and Anne Pisarski, “Factors Affecting Student
Attitudes Toward Flexible Online Learning in Management Education,” Journal of Educational
Research 98, no. 6 (2005): 331-338. https://doi.org/10.3200/JOER.98.6.331-338.
91 Maria Puzziferro, “Online Technologies Self-efcacy, Self-regulated Learning, and
Experimental Variables as Predictors of Final Grade and Satisfaction in College-Level Online
Courses,” American Journal of Distance Education 22, no 2 (2006): 72-89. https://doi.
org/10.1080/08923640802039024.
92 Mariia Rizun and Artur Strzelecki, “Students’ Acceptance of the COVID-19 Impact on
Shifting Higher Education to Distance Learning in Poland,” International Journal of
Environmental Research and Public Health 17, no 18 (2020): 1-19. https://doi.org/10.3390/
ijerph17186468.
93 Chia-Lin Tsai et al., “The Self-Efcacy,” 472-489.
94 Shem Unger and William Meiran, “Student Attitudes Towards Online Education
During the COVID-19 Viral Outbreak of 2020: Distance Learning in a Time of Social
Distance,” International Journal of Technology in Education and Science 4, no 4 2020: 256-
266. https://doi.org/10.46328/ijtes.v4i4.107.
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studies in the literature.95,96,97,98,99,100,101 It is concluded that students with low
self-efcacy are less likely to make an effort and be successful in subjects
they have difculty with than students with high self-efcacy, and drew
attention to the relevance of self-efcacy in student success.102
This study showed that there was a signicantly 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 Waheed107 concluded that if students’ attitudes toward lessons are
95 Katrin Arens, Anne C. Frenzel, and Thomas Goetz, “Self-Concept and Self-Efcacy in
Math: Longitudinal Interrelations and Reciprocal Linkages with Achievement,” The Journal of
Experimental Education 90, no. 3 (2020): 1-19. https://doi.org/10.1080/00220973.2020.1786347.
96 Adeneye Awofala, “Correlates of Senior,” 15-25.
97 Toni Honicke and Jaclyn Broadbent, “The Inuence of Academic Self-efcacy on
Academic Performance: A Systematic Review,” Educational Research Review 17, (2016): 63-
84. https://doi.org/10.1016/j.edurev.2015.11.002.
98 Dan Li, “A Review of Self-efcacy,” 526-533.
99 Journal of Physics: Conference Series, “Mathematics self efcacy and mathematics
performance in online learning,” accessed May 1, 2021. https://iopscience.iop.org/article/
10.1088/1742-6596/1882/1/012050
100 Chia-Lin Tsai et al., “The Self-Efcacy,” 472-489.
101 Ya-Ling Wang, Jyh-Chong Liang, and Chin-Chung Tsai, “Cross-Cultural Comparisons
of University Students’ Science Learning Self-efficacy: Structural Relationships Among
Factors within Science Learning Self-efcacy,” International Journal of Science Education 40,
no. 6 (2018): 579-594. https://doi.org/10.1080/09500693.2017.1315780.
102 Dale H. Schunk, “Self-efcacy and Education and Instruction,” In Self-Efcacy,
Adaptation, and Adjustment: Theory, Research, and Application, edited by James E. Maddux,
281-303. Plenum Press, 1995.
103 Gwo-Jen Hwang, Po-Han Wu, and Chi-Chang Chen, “An Online Game Approach for
Improving Students’ Learning Performance in Web-based Problem-Solving Activities,”
Computers & Education 59, no. 4 (2012): 1246-1256. https://doi.org/10.1016/j.compedu.2012.
05.009.
104 Linda Khateeb, Sameer Aowad Kassab Shdaifat, and Nidal A. K. Shdaifa,
“Effectiveness of communication techniques in distance education and its impact on learning
outcomes at Jordanian Universities (Northern Province),” International Journal of Higher
Education 10, no. 2 (2021): 74-82. https://doi.org/10.5430/ijhe.v10n2p74.
105 Mengping Tsuei, “Using Synchronous Peer Tutoring System to Promote Elementary
Students’ Learning in Mathematics,” Computers & Education 58, no. 4 (2012): 1171-1182.
https://doi.org/10.1016/j.compedu.2011.11.025.
106 Romero J. Sonia Martínez et al., “Attitudes Toward,” 59-75.
107 Lawsha Mohamed and Hussain Waheed, “Secondary Students’ Attitude,” 277-278.
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positive, there is a signicant increase in their performance and academic
achievement in the learning process.
This study revealed that prospective mathematics teachers’ online learning
self-efcacy and attitude toward online learning had a signicant inuence 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-efcacy 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-
efcacy toward online learning are signicant predictors of academic success.
Yet, some studies have revealed the signicant effects of motivation and self-
efcacy on academic achievement.113,114
Therefore, academics should rst determine the self-efcacy and attitude
levels of prospective mathematics teachers toward online learning. For
students who do not have sufcient self-efcacy and attitude, educators can
concretize abstract concepts that are difficult to understand. Computer
software can assist the concretization process. The distance education
108 David B Feldman and Maximilian Kubota, “Hope, Self-efcacy, Optimism, and
Academic Achievement: Distinguishing Constructs and Levels of Specicity in Predicting
College Grade-point Average,” Learning and Individual Differences 37 (2015): 210-216.
https://doi.org/10.1016/j.lindif.2014.11.022.
109 Meera Komarraju and Dustin Nadler, “Self-Efcacy and Academic Achievement:
Why Do Implicit Beliefs, Goals, and Effort Regulation Matter?,” Learning and Individual
Differences 25, (2013): 67-72. https://doi.org/10.1016/j.lindif.2013.01.005.
110 Antonio Zufanò et al., “Academic Achievement: The Unique Contribution of Self-
efcacy Beliefs in Self-regulated Learning Beyond Intelligence, Personality Traits, and Self-
esteem,” Learning and Individual Differences 23 (2013): 158-162. https://doi.org/10.1016/j.
lindif.2012.07.010.
111 Peter Kpolovie, Andy Igho Joe, and Tracy Okoto, “Academic Achievement Prediction:
Role of Interest in Learning and Attitude Towards School,” International Journal of Humanities
Social Sciences and Education 1, no. 11 (2014): 73-100.
112 Wisdom Owo and Emmanuel F. Ikwut, “Relationship Between Metacognition,
Attitude and Academic Achievement of Secondary School Chemistry Students in Port
Harcourt, Rivers State,” IOSR Journal of Research & Method in Education 5, no. 6 (2015):
6-12. https://doi.org/10.9790/7388-05630612.
113 Edward L Deci and Richard M. Ryan, “Facilitating Optimal Motivation and
Psychological Wellbeing Across Life’s Domains,” Canadian Psychology 49, no. 1 (2008): 14-
23. https://doi.org/10.1037/0708-5591.49.1.14.
114 Meera Komarraju and Dustin Nadler, “Self-Efcacy,” 67-72.
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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 ndings 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,” “Efcient 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 efcacy 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
115 Neelu Sinha, Laila Khreisat, and Kiron Sharma, “Learner-Interface Interaction for
Technology-Enhanced Active Learning,” Innovate: Journal of Online Education 5, no. 3
(2009): 1-9.
116 Pia Ceres, “A Covid Slide’ Could Widen the Digital Divide for Students,” accessed
May 4, 2021, https://www.wired.com/story/schools-digital-divide-remote-learning/.
117 Thelma Obiakor and Adeniran Adedeji P, “COVID-19: Impending Situation
Threatens to Deepen Nigeria's Education Crisis,” accessed May 1, 2020, https://www.
africaportal.org/publications/covid-19-impending-situation-threatens-deepen-nigerias-
education-crisis/.
118 Rachel Gong, “Coping with MCO: Distance learning and the digital divide,” accessed
October 15, 2020, https://www.malaymail.com/news/what-you-think/2020/03/27/coping-
with-mco distance-learning-and-the-digital-divide-rachel-gong/1850758.
119 Yash Sharma, “Massive Open,” 1–5.
120 Doris U Bolliger and Oksana, Wasilik, “Factors Inuencing Faculty Satisfaction With
Online Teaching and Learning in Higher Education,” Distance Education 30, no. 1 (2009):
103-116. https://doi.org/10.1080/01587910902845949.
121 Jennifer Haber and Michael Mills, “Perceptions of Barriers,” 266-283.
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evaluation processes,122 and time management and motivation123,124,125 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-efcacy 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-efcacy 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
122 Tracy Chao, Tami Saj, and Felicity Tessier, “Establishing a Quality,” 32-39.
123 Patricia Fidalgo et al., “Students’ Perceptions,” 1-18.
124 Reinhard Pekrun et al., “Boredom and Academic,” 696-710.
125 Allen Wigeld et al., “Development of achievement,” 657-700.
126 Rasheed Falowo, “Factors Impeding,” 315-338.
127 Patricia Fidalgo et al., “Students’ Perceptions,” 1-18.
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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 efcient 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-efcacy 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-efcacy 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
128 Michael Beaudoin, “The Instructor's Changing Role in Distance Education,” The
American Journal of Distance Education 4, no. 2 (1990): https://doi.org/10.1080/0892364
9009526701.
129 Nada Dabbagh and Brenda Bannan-Ritland, Online learning: Concepts, Strategies,
and Application (Prentice Hall, 2005).
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(motivation, satisfaction, academic stress, etc.) are signicant predictors of
academic performance. Additionally, studies can investigate direct and indirect
effects between predictor (motivation, satisfaction, academic stress, self-
efcacy, and attitude) and predicted (academic performance) variables through
path analysis or structural equation modeling.
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About the authors
SUPHI ÖNDER BÜTÜNER (corresponding author, s.onder.butuner@bozok.edu.tr)
is Associate Professor of Math Education at Faculty of Education, Yozgat Bozok
University (Turkey) where he teaches undergraduate and post-graduate courses.
He worked as a mathematics teacher between 2002 and 2014. He received
master’s degree in 2006 and doctorate degree in 2014. He has been working at
the Faculty of Education at Yozgat Bozok University since 2015. His research
focus is on teaching mathematical concepts and teacher training.
SERDAL BALTACI (serdalbaltaci@gmail.com) is Associate Professor of Math
Education at Faculty of Education, Kırşehir Ahi Evran University (Turkey)
where he teaches undergraduate and post-graduate courses. He received
doctorate degree in 2014. He has been working at the Faculty of Education at
Kırşehir Ahi Evran University since 2007. His research focus is on teaching
mathematical concepts and teacher training.
Appendix 1. Factors and items of the Online Learning Self-efcacy
Scale (Sun and Rogers, 2020)
Factor Items
Technology use
self- efficacy
1) I feel confident in downloading and installing a software
or application from a website.
2) I feel confident in printing a website.
3) I feel confident in downloading (saving) an image from a
website.
4) I feel confident in bookmarking a website.
5) I feel confident in copying a block of text from a web site
and pasting it to a document in a word processor.
6) I feel confident in accessing links to web resources.
7) I feel confident in conducting an Internet search using
one or more keywords
The effects of online learning self-efcacy and attitude toward online learning Bütüner and Baltacı
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Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
Factor Items
Online learning
task self-
efficacy
8) I feel confident in taking an online quiz/test.
9) I feel confident in viewing my grades in the grade book
of the Learning Management System (e.g., BlackBoard).
10) I feel confident in viewing my online course materials in
the Learning Management System (e.g., BlackBoard).
11) I feel confident in submitting course assignments
through the Learning Management System (e.g.,
BlackBoard).
Instructor
and peer
interaction and
communication
selfefficacy
12) I can develop a sense of community through interactions
with other online course participants.
13) I can feel connected to others in my online courses.
14) I can develop a sense of community through interactions
with my online
instructors.
15) I can share my problems with my online classmates so we
know what we are
struggling with and how to solve our problems.
16) I can communicate with my online classmates to find out
how I am doing in my
online classes.
17) I can develop a sense of collaboration through team
work/projects in my online
18) I can gain a sense of belonging in my online courses by
getting to know other
course participants.
Self-regulation
and motivation
efficacy
19) I can make myself feel the need to do an outstanding
job in an online course.
20) I can encourage myself to understand the most difficult
materials presented in an online course
21) I can motivate myself to persist in my online courses
when facing difficulties or setbacks
22) I can motivate myself to explore content related
questions in my online courses
23) Even in the face of technical difficulties, I can motivate
myself to learn the materials presented in an online course.
The effects of online learning self-efcacy and attitude toward online learning Bütüner and Baltacı
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Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
Factor Items
Self-regulation
and motivation
efficacy
24) I can motivate myself to learn online through the belief
that my online courses can broaden my knowledge about
subjects which appeal to me.
25) I can motivate myself to perform well in my online
courses by seeing how these courses can move me closer to
my career goals.
26) I can motivate myself to learn in my online courses
without the presence of instructors to assist me.
27) I can manage study time for my online courses by setting
goals.
28) I can find where I am able to study most efficiently for
my online courses.
29) I can make myself feel the need to utilize a variety of
information sources to explore problems posed in my online
courses.
30) I can work extra problems in my online courses in
addition to the assigned ones in order to master the course
content.
31) I can motivate myself to work hard in my online courses
through the belief that my online courses can help me get a
degree allowing me to get a better salary later
1 point: strongly disagree, 2 point: disagree, 3 point: somewhat disagree, 4 point:
somewhat agree, 5 point: agree, 6 point: strongly agree
The effects of online learning self-efcacy and attitude toward online learning Bütüner and Baltacı
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Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
Appendix 2. Çevrimiçi öğrenme öz yeterlik ölçeği (Online Learning
Self-efcacy Scale-Turkish Form)
Faktör Madde
Teknoloji
Kullanımı öz
yeterliği
1) Bir web sitesinden bir yazılım veya uygulama indirip
yüklerken kendime güvenirim.
2) Bir web sitesinden çıktı alırken kendime güvenirim.
3) Bir web sitesinden bir görsel indirirken (kaydederken)
kendime güvenirim.
4) Bir web sitesini sık kullanılanlara eklerken kendime
güvenirim.
5) Bir web sitesinden bir metni kopyalayıp, bu metni word
belgesine yapıştırmada kendime güvenirim.
6) Web sayfalarının bağlantılarına erişimde kendime güvenirim.
7) Bir ya da birden fazla anahtar kelime kullanarak internette
arama yapmada kendime güvenirim.
Çevrimiçi
öğrenme
görevi
öz-yeterliği
8) Çevrimiçi bir sınava (test, quiz vb.) girmede kendime
güvenirim.
9) Öğrenme Yönetim Sisteminin (örn. Boysis, Moodle, AYDEP,
Proliz vb) notlar kısmından notuma bakmada kendime
güvenirim.
10) Öğrenme Yönetim Sisteminde (örn. Boysis, Moodle,
AYDEP, Proliz vb.) çevrim içi ders materyallerini
görüntülemede kendime güvenirim.
11) Öğrenme Yönetim Sistemi (örn. Boysis, Moodle, AYDEP,
Proliz vb.) aracılığıyla dersin ödevlerini teslim etmede kendime
güvenirim.
Eğitici
ve akran
etkileşimi ve
iletişimi
öz-yeterliği
12) Çevrim içi derslerimde sınıf arkadaşlarımla etkileşimler
yoluyla bir topluluk duygusu geliştirebilirim.
13) Diğer çevrim içi ders katılımcılarıyla iletişim kurabilirim.
14) Çevrim içi derslerimde öğretim elemanlarıyla etkileşimler
yoluyla bir topluluk duygusu geliştirebilirim.
15) Çevrim içi derslerimde sınıf arkadaşlarımla eğitim-
öğretimle ilgili (öğrenme güçlüğü yaşadığım konular,
kavramlar vb) problemlerimi paylaşabilirim.
The effects of online learning self-efcacy and attitude toward online learning Bütüner and Baltacı
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Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
Faktör Madde
Eğitici
ve akran
etkileşimi ve
iletişimi
öz-yeterliği
16) Çevrim içi derslerimde ekip çalışması/projeler aracılığıyla
bir işbirlikli öğrenme ortamı oluşturabilirim.
17) Çevrim içi derslerimde eğitim öğretim ile ilgili (öğrenme
eksiklikleri vb.) ne durumda olduğumu öğrenmek için sınıf
arkadaşlarımla iletişim kurabilirim
18) Çevrim içi derslerimde diğer katılımcıları tanıyarak,
çevrimiçi derslerime aidiyet
duygusu (bir gruba ait olma, mensup olma) kazanabilirim.
Öz
düzenleme ve
motivasyon
öz-yeterliği
19) Çevrim içi derslerde başarılı olmak için gayretli bir şekilde
çalışmam gerektiği hususunda kendimi motive edebilirim.
20) Çevrim içi bir derste sunulan en zor materyalleri bile
anlamak için kendimi cesaretlendirebilirim.
21) Zorluklar veya aksaklıklarla karşılaştığımda çevrim içi
derslerime devam etmede kendimi motive edebilirim.
22) Çevrim içi derslerimde öğretim elemanları tarafından
sorulan soruların cevaplarını bulmak için ilgili kaynaklara
ulaşmada kendimi motive edebilirim.
23) Çevrim içi derslerimde teknik zorluklar ile karşılaşsam bile,
derste sunulan ders içeriklerini öğrenmek için kendimi motive
edebilirim.
24) Çevrim içi derslerimin, ilgimi çeken konular hakkında
bilgimi arttıracağına inandığım için kendimi çevrim içi
öğrenmeye motive edebilirim.
25) Çevrim içi derslerin beni kariyer hedeerime nasıl
yaklaştırabileceğini görerek, çevrim içi derslerimde iyi
performans gösterme konusunda kendimi motive edebilirim.
26) Çevrim içi derslerde hiçbir destek almadan ilgili konuları
öğrenmek için kendimi motive edebilirim.
27) Çevrim içi derslerim için çalışma süresini, kendime hedeer
belirleyerek yönetebilirim.
28) Çevrim içi derslerime verimli şekilde çalışmam konusunda
kendimi motive edebilirim.
The effects of online learning self-efcacy and attitude toward online learning Bütüner and Baltacı
241
Tuning Journal for Higher Education
© University of Deusto • p-ISSN: 2340-8170 • e-ISSN: 2386-3137 • Volume 11, Issue No. 1, November 2023, 197-241 •
doi: https://doi.org/10.18543/tjhe.2214 • http://www.tuningjournal.org/
Faktör Madde
Öz
düzenleme ve
motivasyon
öz-yeterliği
29) Çevrim içi derslerimde ortaya çıkan sorunları (ders ile ilgili
veya teknik sorunlar vb.) çözmek için çeşitli bilgi kaynaklarını
kullanma konusunda kendimi motive edebilirim.
30) Ders içeriğine hâkim olmak için verilen ödevlere ek olarak
çevrim içi derslerimde ekstra problemler üzerine çalışabilirim.
31) Çevrim içi derslerimin, daha iyi bir maaş almamı sağlayacak
bir kariyere ulaşmamda bana yardımcı olabileceği inancıyla,
çevrimiçi derslerimde çok çalışmak için kendimi
motive edebilirim.
1 puan: Kesinlikle Katılmıyorum, 2 puan: Katılmıyorum, 3 puan: Kısmen Katılmıyorum, 4
puan: Kısmen Katılıyorum, 5 puan: Katılıyorum, 6 puan: Kesinlikle Katılıyorum
