Today’s educational institutions are expected to create learning opportunities independent of time and place, to offer easily accessible learning environments and interpersonal communication opportunities. Accordingly, higher education institutions develop strategies to meet these expectations through teaching strategies, such as e-learning, blended learning, mobile learning, etc., by using teaching technologies. These new technology-based teaching strategies are mainly shaped by decision-makers in education. This study seeks to analyse the individual factors that affect learners’ mode of teaching and learning delivery preferences. In this study, blended and online learning is considered as preferences of learners’ mode of teaching and learning delivery. The individual factors discussed in this research are cognitive learning strategies, e-learning readiness, and motivation. The data were obtained from the pre-service teachers at the end of the academic semester when they experienced online and blended learning. Data were analysed using optimal scaling analysis. The analysis method provides a two-dimensional centroid graph which shows the correlations between the variable categories. According to study findings, there is a correlation between the preferences of the learning environment, and the constructs of self-efficacy, e-learning motivation, and task value. It can be said that the motivational variables are more effective in the learning environment preference. The students with high task value, e-learning motivation, and self-efficacy preferred studying in blended learning environments. Cognitive strategies, self-directed learning, learner control, and test anxiety factors are independent of the learners’ learning delivery preferences.
Self-assessment is an important tool enabling learners at the level of higher education to control and construct their learning processes. To allow for further study, we modified a web-based self-assessment system to provide individuals with the opportunity to test and retest their own learning and receive feedback. This study included 59 students. Following completion of the test, feedback was structured based on a comparison of the student’s performance to the standard performance, their position in the group and their previous performances. Each test deadline had to be waited for determining the positions in the group of students and the delayed feedback were sent to the learners by e-mail. Through this external feedback, learners were able to intervene in their own learning process, thus achieving better future learning prospects and to observe the effectiveness of these intervention though feedback from the next assessment. We defined this process as the self-intervention perception process due to the active participation of the learner. The determination of the structures that affect the meaning and using of the feedback received by the learners were at the forefront. This study aimed to examine the relation between learners’ metacognitive awareness and their self-intervention perceptions and create a learner profile based on this information. Participants also completed Perceived Self-Intervention Scale and the Metacognitive Awareness Inventory. Learners with high levels of metacognitive skills awareness were found to have high levels of perceived self-intervention. Furthermore, knowledge of cognition had indirect effects on the perception of self-intervention, and that the regulation of cognition was the mediator variable.