The number of educational institutions providing education via distance learning has been increasing recently, especially due to the Covid-2019 pandemic. Advantages of e-learning mediums in terms of time and space provided it to come into the forefront (Nwagwu, 2020). During this session, university students could not participate the lessons physically; instead, they took courses via distance education system in many countries. However, sufficient importance is not given to the individual and technical prerequisites necessary for enabling success and satisfaction in these educational institutions (Pillay, Irving, & Tones, 2007). For university students to be able to benefit from the advantages of online courses that they take via distance learning, they need to possess certain technical and educational skills. In this context, a number of studies have been conducted that investigate the success of and student satisfaction with online courses taken via distance learning (Dikbas Torun, 2020; K.M. Lin, 2011; Ozturk, Ozturk, & Ozen, 2018; Paechter, Maier, & Macher, 2010; Wu, Tennyson, & Hsia, 2010; Zhu, 2012). In its simplest terms, e-learning expresses access, from the desired location and at the desired time, to the material and components required for learning in an online environment (Holmes & Gardner, 2006). As online or internet-based distance learning has become more widespread recently, many expert educators have begun to inquire about the extent to which distance learning students are prepared in order to be successful in this environment (Watkins & Corry, 2005).
In this context, it is important to determine the previous learning experiences of students who have certain expectations from e-learning environments and the degree to which they are prepared for e-learning, so that these environments can be correctly and effectively designed and utilized (Hukle, 2009). For a higher education institution, readiness for new technologies is essential for the desired effects to be observed in areas like productivity and expected benefit (Machado, 2007). When the related literature is examined, there are certain studies investigating students’ readiness for using technology-supported products and the effects of this variable on their behaviors (J.S.C. Lin & Hsieh, 2007). In this study, readiness for e-learning concept was used to refer e-readiness. While organizing learning activities in e-learning processes, in terms of enabling greater flexibility, students should be able to have control over their own learning activities and make their own decisions regarding the scope and depth of content, the type of media accessed, and the time spent on study. In this respect, the student control dimension, and at the same time, student readiness is regarded as an important part of e-learning (Stansfield, McLellan, & Connolly, 2004). Also, examining students’ readiness and awareness regarding e-learning mediums contribute positively to the developmental studies in this area (Dikbas Torun, 2020). Sufficient evidence can be found in the literature stating that the variables mentioned here have an effect on a number of characteristics, principally on achievement (A.R. Artino, 2010; Keramati, Afshari-Mofrad, & Kamrani, 2011; Klein, Noe, & Wang, 2006; Muilenburg & Berge, 2005). However, no academic study can be found which deals with all these variables together.
At University, where this study was carried out, approximately 9000 students register every academic year for campus-based courses conducted via distance learning. Examination of the variables considered to be effective for these students’ satisfaction and academic achievement is an important research subject. Therefore, investigation of the psychological and sociological variables that may affect university students’ success in e-learning environments is important in scientific terms. Pandemic session has already shown the added-value of e-learning systems. In this context, determining the effects of undergraduate and postgraduate students’ readiness for e-learning and self-regulation skills for online environments on their satisfaction and academic achievement has an effective value in terms of offering students a better distance learning environment and supporting student success. Most probably, results of such research studies will be important not only during pandemic session but also after the session.
Readiness for e-learning
Readiness for e-learning is defined by Lopes (2007) as the ability of any organization or individual to benefit from the advantages offered by e-learning. Kaur and Abas (2004) define readiness for e-learning as individuals’ ability to utilize e-learning resources and multimedia technologies with the aim of increasing the quality of learning. Readiness for e-learning expresses the student’s possession of the prerequisite knowledge, skills, and beliefs required for learning in such an environment (Warner, Christie, & Choy, 1998). Similarly, Alem, Plaisent, Zuccaro, and Bernard (2016) stated that e-learners require certain skills and orientations to overcome issues related to e-learning mediums. In other words, readiness for e-learning is, in short, the degree to which an individual or organization possesses the prior knowledge/skills and effective characteristics (attitude, motivation, etc.) required for experiencing e-learning in the most effective way (Yurdugül & Demir, 2017).
Possession of readiness by students supports the advancement of e-learning and increases the quality of interaction in e-learning environments (Hukle, 2009). Therefore, for e-learning applications to be successful, students’ readiness must be assessed prior to the process (So & Swatman, 2006). Carrying out this assessment will make it possible for the aims of designing suitable e-learning strategies and of developing information and communication technology skills to be implemented effectively (Kaur & Abas, 2004). In Valtonen, Kukkonen, Dillon, and Väisänen’s (2009) study, it is emphasized that readiness for e-learning among students without e-learning experience ranged between high, medium, and low levels. From this aspect, it is seen that even if they have no e-learning experience, students’ levels of readiness may vary.
Readiness for e-learning consists of 6 dimensions, namely computer self-efficacy, internet self-efficacy, online communication self-efficacy, self-directed learning, learner control, and motivation toward e-learning (Hung et al., 2010). Among them, computer self-efficacy refers to abilities related to utilizing computers efficiently whereas internet self-efficacy focuses on internet-related tasks (Eastin & LaRose, 2000). Moreover, online communication self-efficacy corresponds to learners’ insistence to continue communicating and sharing knowledge with others through computer-assisted mediums. Next, self-directed learning covers learners’ evaluations about learning requirements. Then, learner control refers to beneficiaries’ management of learning processes as well as controlling their individualized learning needs. Finally, motivation toward e-learning covers learners’ intrinsic and/or extrinsic orientation related to comprehension (Hung et al., 2010).
It is stated in the literature that together with readiness for e-learning, there are relationships among a number of variables such as self-regulation skills for online environments, satisfaction, and academic achievement (A.R. Artino, 2009; M.H. Cho & Kim, 2013; M.H. Cho & Shen, 2013; Dikbas Torun, 2020; Ilgaz & Gülbahar, 2015; Kruger-Ross & Waters, 2013; Kuo, Walker, Schroder, & Belland, 2014; S.S. Liaw & Huang, 2013; Zhu, 2012). For example, C.K. Lim (2001) determined that computer self-efficacy, which is one of the sub-factors of readiness for e-learning, was a predictor of satisfaction in web-based distance learning classes. Similarly, Eastin and LaRose (2000) stressed that computer and internet self-efficacy resulted in both improved performance in technical subjects like downloading documents or online system management, and better performance in solving problems in online learning. Therefore, an increase in computer and internet self-efficacy can be considered to increase students’ satisfaction and achievement. For example, Tsai and Tsai (2003) stated that students with higher levels of internet self-efficacy learned better in a web-based learning task than students with lower levels of internet self-efficacy.
Yakin and Tinmaz (2013) state that readiness for e-learning has a significant effect on users’ adoption of technological innovations. In this regard, students’ possession of adequate readiness is important for design and implementation of e-learning (Hukle, 2009; Ilgaz & Gülbahar, 2015). For this reason, evaluation of students’ readiness as well as their satisfaction with online courses is a necessary process in terms of the success of online learning applications (Gülbahar, 2012; Kaur & Abas, 2004; Ozturk et al., 2018; So & Swatman, 2006). To sum up, the literature clearly states that readiness for e-learning is related to learners’ self-regulation skills, satisfaction, and academic achievement (A.R. Artino, 2009; M.H. Cho & Kim, 2013; M.H. Cho & Shen, 2013; Dikbas Torun, 2020; Ilgaz & Gülbahar, 2015; Kruger-Ross & Waters, 2013; Kuo et al., 2014; S.S. Liaw & Huang, 2013; Zhu, 2012). However, the relationships between these variables are not examined in a comprehensive model in the way that they are in the present study.
Self-regulation focuses on individuals’ responsibility for their own learning, control of their own learning processes, ability to adjust their learning process when necessary, and ability to motivate themselves throughout their learning lives (B.J. Zimmerman, 2011). Students who can self-regulate can take control of their learning processes by developing metacognitive strategies such as planning, being organized and being motivated (E. Yukselturk & Bulut, 2007).
Studies show that self-regulation is critical for determining students’ successful learning experiences in online learning environments (M.H. Cho & Kim, 2013). It is known that students who can self-regulate are successful in setting goals, planning, and monitoring their learning processes and in evaluating these processes. It is expected that these students, who can manage their time and learning resources effectively (Pintrich, 2004; B.J. Zimmerman, 2011), will have successful learning experiences in online lessons by using their self-regulation skills for distance learning.
At the same time, self-regulated learning expresses students’ systematic efforts toward managing their learning processes in order to achieve their goals (Pintrich, 2004; B.J. Zimmerman & Schunk, 2011). Self-regulated learning is generally explained in the context of integration of motivation, emotion, and learning strategies (Abar & Loken, 2010). Regarding motivation, students who possess self-regulation skills are disposed to gain competence by mastering the work that they do (Pintrich, 2004; B.J. Zimmerman, 2011). The conducted studies show that motivation, which is one of the components of self-regulation, and emotion have a significant effect on students’ learning experiences such as achievement, satisfaction, and passing or failing the course (M.H. Cho & Heron, 2015).
In some studies, an attempt has been made to explain the role of motivation in self-regulated learning. Among these, M.H. Cho and Kim (2013) revealed that students’ mastery goal orientations and their interaction in online learning environments were positively correlated with their self-regulation. Moreover, M.H. Cho and Shen (2013) also revealed that in nonsynchronous online learning environments, metacognitive self-regulation was not only correlated with their learning and academic performance but was also positively related to their self-efficacy.
Student satisfaction reflects the way students regard their learning experiences. In his evaluation of online learning quality determined by an online learning consortium, Moore (2005) states that together with learning effectiveness, faculty satisfaction, scalability, and access, student satisfaction is one of the five basic components. Satisfaction is a critical variable that affects a student’s decision to take another online course (Kuo et al., 2014).
While student-student interaction plays an important role in student satisfaction, the quality and currency of student-teacher communication is also asserted to be an important determinant of student satisfaction, as supported by various experimental studies (Croxton, 2014). Among these examples, it is stated that up-to-dateness of instructor feedback affects the general course satisfaction of online undergraduate and graduate students (Walker & Kelly, 2007). S.K. Parahoo, Santally, Rajabalee, and Harvey (2016) revealed that student-student interaction has an important effect on satisfaction. Similar findings have also been revealed by other researchers (Einarson & Matier, 2005; Hollenbeck, Mason, & Song, 2011; Ivankova & Stick, 2007). Finally, certain researchers (e.g., A.R. Artino, 2009) mentioned that learners’ self-regulation skills positively affected their satisfaction with e-learning.
The conducted studies state that levels of readiness for the internet and technology are among the most important factors expressing e-learners’ satisfaction with learning management systems (LMS) (Parnell & Carraher, 2003; R. Watkins, Leigh, & Triner, 2004). Furthermore, learners’ levels of online readiness are a directly effective structure for their success (A.R. Artino, 2009; Dikbas Torun, 2020; Galy, Downey, & Johnson, 2011; Kruger-Ross & Waters, 2013). On the other hand, with a different view, there are also studies stating that internet self-efficacy, which is one of the subdimensions of readiness for e-learning, is not related to or is not a predictor of student satisfaction (Kuo et al., 2014).
Students who have task value and a high level of self-efficacy for learning are likely to be satisfied with online courses. A.R. Artino (2009) revealed that motivation variables such as self-efficacy and task value were positive predictors of course satisfaction but that negative emotional variables like boringness and failure to meet expectations had a negative effect on course satisfaction. In another of his studies, A.R. Artino (2009) stated that motivation variables including self-efficacy and task value explained 43.4% of the variance in course satisfaction in an online learning environment.
In their online learning study, Kuo et al. (2014) found that students’ internet self-efficacy, self-regulated learning, student-student interaction, student-teacher interaction, and student-content interaction were correlated with their satisfaction. S.S. Liaw and Huang (2013) found that satisfaction was related to self-efficacy, anxiety, and interactive learning environments. Consequently, it can be said that these variables are related to readiness for e-learning.
Student achievement emerges in the knowledge, skills, and behaviors acquired by any students in education environments, and this is also realized in their learning outcomes (Demirtas, 2010). On the other hand, it can be considered that in online learning environments developed with technological support, there are a number of factors that will affect student achievement. For example, M.H. Cho and Shen (2013) stated that students’ self-regulation skills affected their academic achievement in online learning environments.
Furthermore, studies show that well-designed e-learning environments have a positive effect on student performance (K.S. Hong, 2002; K.S. Hong, Lai, & Holton, 2003; Johnson, Hornik, & Salas, 2008; S.S. Liaw, Huang, & Chen, 2007. Moreover, Song, Singleton, Hill, and Koh (2004) state that for online learners, course design and time management are important components of successful online learning, while lack of communication and technical problems are complicating factors for students.
The proposed model and hypotheses
To examine the research problem, the relational model in Fig. 1 is suggested. The university students’ readiness for e-learning, online self-regulation skills, satisfaction, and academic achievement expressed in the proposed model are based on studies in the literature.
To examine the research problem, by taking the above theoretical framework and empirical studies into consideration, the proposed research model presented in Fig. 1 includes five (5) hypotheses:
H1: University students’ readiness for e-learning (so their computer self-efficacy, internet self-efficacy, online communication self-efficacy, self-directed learning, learner control, and motivation toward e-learning) positively predicts their self-regulation skills for online environments.
H2: University students’ readiness for e-learning (so their computer self-efficacy, internet self-efficacy, online communication self-efficacy, self-directed learning, learner control, and motivation toward e-learning) positively predicts their satisfaction with online environments.
H3: University students’ readiness for e-learning (so their computer self-efficacy, internet self-efficacy, online communication self-efficacy, self-directed learning, learner control, and motivation toward e-learning) positively predicts their academic achievement in the online courses that they take.
H4: University students’ self-regulation skills for online environments positively predict their satisfaction with online environments.
H5: University students’ satisfaction with online environments positively predicts their academic achievement in the online courses that they take.
The problem statement
The aim of this study is to reveal the relationship of readiness for e-learning in students taking campus-based courses via distance learning with their levels of online self-regulation skills, satisfaction, and academic achievement. With this aim, an attempt is made to find an answer to the following research problem:
What are the relationships among university students’ readiness for e-learning, their online self-regulation skills, satisfaction, and academic achievement?