Abstract
Educational psychologists are concerned with understanding the factors that predict academic performance. Among others, self-regulated learning has been found to significantly predict academic performance. Self-regulated learners play an active role in the construction of knowledge, control, and regulation of their academic work. Currently, self-regulated learning is viewed as a process rather than a learner characteristic. In all the process-oriented models of self-regulating learning, the student’s ability to control their learning is the key. Time spent studying outside classroom as an aspect of self-regulated learning does not seem to have received much attention from educational psychologists. Therefore, the current study was focused on assessing how well organization/self-management may predict academic performance. In particular, the purpose of the study was to predict academic performance using time allocated to study outside the university’s set contact hours. The study was conducted as cross-sectional survey research design. Participants were working university students. Linear regression was run to determine how well hours spent studying predicted module score. The extracted model was good fit for the data, and was able to explain a total variance of 4.6% after adjusting for the effect of the confounding variables. Consistent with the previous literature, the findings of this study show that hours spent studying outside classroom statistically significantly predicts highest module score among university students. The finding of the study suggests that universities might consider allocating resources to the promotion of interventions that enhance self-regulated learning among students.
Introduction
Educational psychologists are concerned with understanding the factors that predict a student’s academic performance. Researchers have documented an array of findings with respect to the predictors of academic success in order to enable educational policy makers and other stakeholders to effectively predict academic achievement and prevent dropout (Mega, Ronconi, & De Ben, 2014). Research on the predictors of academic performance has focused on a variety of predictors, including personality traits, cognitive abilities, social behaviors, motivational factors, self-regulated learning approaches, and learning approaches (Chamorro-Premuzic & Furnham, 2008; Diseth & Martinsen, 2003; Flook, Repetti, & Ullman, 2005; Laidra, Pullmann, & Allik, 2007; Malecki & Elliot, 2002; Mega et al., 2014).
Self-regulated learning is an aspect of learning valued by teachers, parents, and other educational stakeholders. Self-regulation refers to the extent to which learners take active participation in their learning process at the metacognitive, motivational and behavioral levels (Zimmerman, 2008). Self-regulation of cognition and behavior is an essential antecedent of learning and subsequent academic performance (Bouffard, Boisvert, Vezeau, & Larouche, 1995; Pintrich & De Groot, 1990). It is a useful process in the preparation of life-long learners (Roux, 2013). Some more specific components of self-regulated learning are organization/time management, elaboration, self-evaluation, exam preparation strategies, and metacognition (Kitsantas et al., 2008; Mega et al., 2014). Self-directed learning stresses the active role that the learner takes in the learning process (Mega et al., 2014).
Self-regulated learners play an active role in the construction of knowledge and control and regulation of their academic work (Mega et al., 2014). A self-directed student understands the requirements of a task and his or her own needs with respect to learning. They are able to control behaviors that might be detrimental to their academic performance. They are actively involved in the planning, organization, monitoring and evaluation of their learning, through metacognitive strategies (Mega et al., 2014; Pintrich & De Groot, 1990). They tend to set goals, objectives, and standards to guide their study efforts. Their motivation, cognition and behavior are adapted in order to be consistent with the goals, the objectives, and the standards which serve as benchmarks for assessing the progress of the learning efforts.
Individual differences in students’ self-regulation may be associated with academic achievement (Kitsantas, Winsler, & Huie, 2008; Zimmerman, 2008). Pintrich and De Groot (1990) found that self-regulated learning was associated with the students’ efficacy perceptions about their ability to carry out classroom tasks and that such activities were interesting and worth learning. Bouffard et al. (1995) found that self-regulation was the most powerful predictor of academic performance. They defined self-regulation as consisting of three components: cognitive strategies, metacognitive strategies, and motivation (Bouffard et al., 1995). Consistent with these findings, Kitsantas et al. (2008) found that self-regulation statistically significantly contributed to the prediction model for total GPA. They assessed self-regulation through metacognitive and management strategies (Kitsantas et al., 2008).
Some universities have made efforts to enhance the students’ ability to engage in self-directed learning. For example, a South African study found that a large group instructional strategy did not statistically significantly influence self-regulated skills of the learners (Roux, 2013). Lack of improvement was attributed to lack of important aspects of self-regulation, such as time management, and readiness to engage in more practice, which seems to touch on motivation (Roux, 2013). It important to note that Roux did not determine whether students used self—regulated learning strategies, but rather whether there were improvements in the skills used in self-regulated learning. Therefore, this might suggest that the intervention was ineffective, rather than suggesting that students were incapable of adopting self-regulated learning skills and strategies. Unlike Roux (2013) Kintu and Zhu (2015) have reported that university students are able to employ self-regulated learning strategies in their study. In the study conducted in Uganda, they found that students taking blended courses (on campus) tended to apply more self-regulatory strategies than those in the off-campus courses. The behaviors applied were help seeking, self-evaluation, time-management, goal setting, environment structuring, and task strategies. The most common strategies were help seeking behaviours and environment structuring (Kintu & Zhu, 2015).
Currently, self-regulated learning is viewed as a process rather than a learner characteristic (Pilling-Cormick & Garrison, 2007). The aspect of control is at the heart of the models that have been proposed to explain self-directed learning. The models have several variables, including environmental, personal, cognitive, and social variables. For example, Garrison (1997) proposed a three-dimension model that views self-directed learning as a process in which the learners assumes the responsibility and control of their learning process (Pilling-Cormick & Garrison, 2007). The major dimensions of the model are self-management, self-monitoring, and entering and task dimensions. The first dimension, self-management, is related to contextual control of task, with a special focus on the social and behavioral implementation of learning intentions. The scope of the dimension is limited to setting learning goals, and managing learning resources. Management is concerned with external task control, while monitoring and motivation are focused on cognitive and metacognitive aspects (Pilling-Cormick & Garrison, 2007).
The second dimension, self-monitoring, refers to the commitment to construct meaning and occurs at the cognitive and metacognitive levels. The role of the learner in monitoring and controlling learning goes beyond external task control into integrating cognitive aspects into the monitoring and management functions. Motivation, the third dimension, is a significant aspect of self-directed learning. It is intimately involved initiation and sustenance of learning efforts and achievement of cognitive goals. Motivation is believed to significantly determine the kind of cognitive activities that people engage in (Pilling-Cormick & Garrison, 2007).
The current study was focused on assessing how well organization/self-management may predict academic performance. Mega and colleagues defined organization as the “academic time management and involves allocating time for different activities, for example designating particular times through-out the week for the preparation of a particular exam” (p. 122). Organization describes students’ ability to manage and control their classroom academic tasks, especially with respect to time allocations. In this study, the two are used interchangeably. Organizational strategies may enhance active cognitive engagement in learning, resulting in improvements in academic performance (Pintrich & De Groot, 1990). Self-management was assessed through the time that learners allocate to study on their own, other than the time allocated for contact such as lecture times.
The time that students spent studying outside classroom does not appear to have been extensively studied. In this light, the purpose of the study was to predict academic performance using time allocated to study outside the university’s set contact hours. In this light, the study sought to answer this research question: How well does time spent studying predict academic performance among university students? As the research question suggests, there was only one predictor variable (time spent studying) and one outcome variable (academic performance).
Methods
Participants
The study was conducted among 86 university students. Participants said they were also working, in addition to being students. Participants said they work for an average of 7.10 hours (SD = 7.172). The participants missed a mean of 9.15 hours (SD = 11.872) of contact time. Descriptions of the participants are provided in Table 1.
Research Design
The study was conducted as a cross-sectional quantitative research design. There was one predictor variable and one outcome variable. The predictor variable was “hours spent studying” while the outcome variable was “the highest module score” in the previous semester’s examinations.
Instruments
Data for the current study was collected using structured questionnaire (Appendix A). The questionnaire consisted of four items that sought information about participants’ study and work habits, in relation to time management. The tool was found to be valid and reliable. To enhance clarity, some questions gave examples of what key phrases in the questions meant. A sample item from the questionnaire was: “Last term, how many hours-worth of contact time did you miss? e.g. lectures, tutorials, seminars etc.”
Procedures
The study received ethical approval from the ethics committees based at the School of Science, Medicine, and Dentistry. Participants were informed about the study and presented with consent forms and participant information forms. Participants signed the informed consent before they were allowed to participate in the study. The study was completed at the psychology department’s laboratory in the presence of the researcher. After the study, participants were debriefed.
Statistical Analysis
The data was analysed using IBM SPSS Version 20 (Armonk, NY: IBM Corp.). Descriptive statistics were run to obtain means and standard deviations. To generate the model for predicting the value of academic performance based on hours spent on studying, a linear regression was run.
Ethical Considerations
The study met standard ethical considerations for research involving humans. Of particular concern were informed consent, privacy, and confidentiality. Participants were informed about the study, including its purpose, procedures, and any benefits and harms anticipated. There was no harm expected to occur to participants as a consequence of their participation in the study. However, participants were asked to refer to the psychology department in case they need any counseling. Participants were also informed that they were free to refuse to take part in the study. Their decision would not have any negative effect on them. It was also explained to participants that they could withdraw their participation at any given stage of the study, without any negative consequences. Participants were assured that their privacy would be protected. There was no need to provide names in the questions so as to enhance privacy. The information they provided would not be disclosed to unauthorized persons. The data will be kept in a password-protected computer with only the researcher and the supervisor having the authorization. Data will be destroyed in line with university guidelines.
Results
The purpose of the current study was to determine how well time spent studying outside the formal classroom time might predict academic performance. The average of the participants’ highest score with respect to previous semester’s examination was 70.65 (SD = 8.935). Participants reported that they spent a mean of 9.20 (SD = 8.445) hours every week outside the classroom.
The major assumptions of linear regression were not violated. The extracted model had weak degree of correlation between the observed and the predicted performance in the previous semester’s performance (R = .239).The extracted model accounted for 5.7% of the variance in the performance in the highest module, before adjustment for confounding variables. When the effect of the extraneous variables was penalized, the model was able to explain 4.6% of the variance in the performance in the module. Table 1 shows how well the model predicts the highest module score.
ANOVA Table
As shown in Table 1, the model was a good fit for the data, F(1, 84) = 386.652, p = .027. Findings provided in Table 2 show that time spent studying statistically significantly contributed to the model for predicting highest module score (p = .027). Using the information provided in Table 2, the regression equation for predicting performance may be presented as:
Highest module score = 68.328 + .253(hours spent studying outside class time)
Coefficients in the Model
Discussion
Hours that students spend studying outside class time are part of self-regulated learning. They may belong to the organization or self-management component of self-regulated learning (Garrison, 1987). Bouffard et al. (1995) argued that self-regulated learning might be the most powerful predictor of academic performance. These observations were consistent with those of Kitsantas et al. (2008) who found that self-regulated learning significantly added to the model for predicting total GPA. Consistent with previous research (Bouffard et al., 1995; Kitsantas et al., 2008), the findings of this study show that self-regulated learning is a significant predictor of academic performance.
The reason why self-regulated learning predicts learning might be that it might enhance student efficacy in relation to their ability to accomplish academic tasks (Pintrich & De Groot, 1990). In addition, self-regulated learning might enable learners to perceive those academic activities as interesting and worthy their attention (Pintrich & De Groot, 1990). Moreover, it has been observed that self-regulated learning involves active participation in the learning process at the metacognitive, motivational and behavioral levels (Garrison, 1997). The involvement of these different levels of learning might be responsible for the ability of hours spent outside classroom, to predict academic performance.
Conclusion
In conclusion, this study shows that hours spent studying outside class time might be a significant predictor of academic performance. Self-regulated learning activities such hours spend studying outside class time might trigger student’s interest in academic tasks and enhance their efficacy to accomplish those academic tasks. The findings of the study suggest that universities might consider allocating resources to the promotion of interventions that enhance self-regulated learning among students.
References
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