Online learning can be defined as the job of gaining knowledge and skills through synchronous and asynchronous learning applications written, communicated, active, supported and managed through internet Technologies (Morrison, 2003). These online learning platfomrs are Moodle, Sakai, Blackboard etc. Moodle LMS, which is preferred in more than 193 countries with more than 120 language options that is mostly used in higher education. Beside this, Moodle LMS has a questionnaire, forum, chat, assignment, feedback, dictionary, scorm, database, different components. Therefore, such online environments can provide clues about students 'behavioral patterns and students' interactions with other students, teachers, content or the system (Baker, 2010). On the other hand, students' taking responsibility for learning and using appropriate strategies in online learning environments and this stituation is closely related to self-regulated learning of the students (Jarvela and Jarvenoja, 2011; Schunk, 2012; Sun et al., 2018). According to Zimmerman (2000) examines the self-regulation skill in three cyclical stages as foresight, performance and self-reflection. Foresight Phase; there are task analysis and self-motivation titles, and it is stated that students try to reach their goals by making strategic plans in the learning process in line with their self-efficacy and interests. Performance Phase; self-control and self-observation subheadings. Within the framework of these headings, students learn to learn individually by determining their tasks and strategies throughout the learning process and thus control their performance by constantly observing themselves in the process. Self-Reflection Phase; students evaluate and judge themselves in the process. They reveal their satisfaction status as a result of the learning process and the effect of the process by reacting with their behavior. There are various measurement tools in the literature. Based on the self-regulation model of Zimmerman (2000), Barnard et al. (2008) developed a 5-Likert scale with 24 questions to measure SRL skills in online learning environments. Factors of this scale are; Environment Structuring (ES); Goal Setting (GS); Time Management (TM); Help Seeking (HS); Task Strategies (TS); and Self-Evaluation (SE). On the other hand, Schraw (2010) suggests that data on SRL skills can be obtained from students' thinking aloud or observing students during their active learning, through measurement scales. Beside these, students’ traces (interaction data, log data etc.) that show their background interactions through learning processes without receiving direct data from students. These tracks can be all kinds of data collected from users 'interactions, such as students' clicks on LMS, duration of events, frequency of following events etc. (Greene and Azevedo, 2010; Nesbit et al., 2016; Zhou et al., 2010). In this context, learning analytics are used to related to the real interactions on LMS for students' SRL skills (Li et al., 2020; Viberg, et al., 2018, Xing & Du; 2019). In this context; learning analytics includes processes such as collecting, measuring, analyzing and reporting data on learners and learning contexts in order to understand and improve the learning process in online learning environment (Siemens & Gasevic, 2012). In this analysis process, the data provided by many log records such as when students studying on LMSs are in the system, their use of course content, their posts in the discussion forum or chat room, their scores from online tests, and their status of getting support from teachers or peers can be used (Lung-Guang, 2019; Kim et al., 2018; Sedrakyan et al., 2018). Therefore; this research aims to analyze the relationship between the data based on the students' perceptions of SRL skills and the students’ interaction data in LMS (Moodle) through learning analytics. For this purpose, the research problem;

• What is the relationship between online students' interactions and perceptions in terms of self-regulation skills?

Research Model This research was examined by using the relational data mining method and analysis based on the learning analytics process. Multivariate analysis (correspondence, classification, clustering, etc.) methods were preferred in the study in which the relational analysis method which is handled within the framework of descriptive or predictive models within the context of relational data mining method was used. Participants of the research group consisting of the fourth year students of the Fall semester ICT Department in Education Faculty of the 2019/2020 academic year. Data Collection Tools Interaction Data; in the Moodle system, the database has been added to the system within the framework of events based on interaction types in the log records and self-regulation skills. The location and time information regarding each event performed by learners in online learning environments is stored by Moodle LMS. Online Self-Regulation Scale; developed by Barnard et al. (2009) adapted to Turkish by Kilis and Yıldırım (2018) was used. The sub-dimensions of this scale; Environment Structuring (ES); Goal Setting (GS); Time Management (TM); Help Seeking (HS); Task Strategies (TS); and Self-Evaluation (SE). The scale items are 24-item and five-point Likert type, and Cronbach's alpha coefficients of this scale vary between 0.67 and 0.87, and were found to be 0.95 for the whole scale. Data Collection Process; the Scientific Research Methods course which was given to ICT students studying at the final year in the 2019/2020 Fall semester through the Moodle system, took 12 weeks. Scientific Research Method course will be conducted over the system for one semester, and that the materials related to the course (weekly activities; scorm-compatible contents, forum discussions, chat room, etc.) and the whole course process will be presented through this system to the online students. Data Analysis Correspondence analysis; in this study, in order to reveal the relationship between the data obtained from the online self-regulation scale and the events in the Moodle environment according to their interactions data and self-regulation skills scores, and subjected to correspondence analysis. This analysis which is placing all categories of each variable on the map according to the distance principle, the correct interpretation of the data and the relationships between categories can be easily defined and used as an alternative to the chi-square test (Clausen, 1998).

FINDINGS and CONCLUSIONS Multiple Correspondance Analysis Results of Students' Environment Structuring (ES); Goal Setting (GS); Time Management (TM); Help-Seeking (HS); Task Strategies (TS); and Self-Evaluation (SE) Interaction Behaviors Events based on self-regulation skills interaction data such as course_view, assignment_view, assignment_submission, quiz_view, dictionary_view, dictionary_update as 41 events based on student-content, student-student, student-teacher, student-system interaction types.The scale data based on the interaction data and the perceptions of the students’ self-regulation skills have two categories (Low, High) and the initial matrix values of the relevant data are shown statistically. Accordingly, the events belonging to the Goal Setting variable explain 62.76% of the total change. Beside this result, the other self-regulation skills variables explain percentages of the correspondence analysis; Environment Structuring 86,50%, Task Strategies 45,25 %, Time Management 44,21 %, Help Seeking 51,76%, Self Evaluation 58,61 %. As a result, it was revealed that the percentages of correspondence based on students' perceptions of the scale data and online students’ system interaction data were highly consistent. In summary, self-regulation skills based on interaction data and students’ perceptions are highly consistent. Therefore, it is seen that system interaction data in online learning environments provide considerable data for students' self-regulation skill measurements.

Baker, R. S. J. D. 2010. “Data mining for education”. International Encyclopedia Of Education, 7, 112-118. Barnard, L., Lan, W.Y., To, Y.M., Paton, V.O., & Lai, S.L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12, 1-6 Clausen, S.-E. (1998). Applied Correspondence Analysis: An Introduction. Thousand Oaks: Sage. Greene, J. A., ve Azevedo, R. 2010. “The measurement of learners’ self-regulated cognitive and metacognitive processes while using computer-based learning environments”, Educational Psychologist, 45 (4), 203-209. Järvelä, S., ve Järvenoja, H. 2011. “Socially constructed self-regulated learning and motivation regulation in collaborative learning groups”, Teachers College Record, 113 (2), 350-374. Joksimović, S., Gašević, D., Loughin, T. M., Kovanović, V., & Hatala, M. (2015). Learning at distance: Effects of interaction traces on academic achievement. Computers & Education, 87, 204-217. Kim, D., Yoon, M., Jo, I. H., ve Branch, R. M. 2018. “Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea”, Computers & Education, 127, 233-251. Li, Q., Baker, R., & Warschauer, M. (2020). Using clickstream data to measure, understand, and support self-regulated learning in online courses. The Internet and Higher Education, 45, 100727. Lung-Guang, N. 2019. “Decision-making determinants of students participating in MOOCs: Merging the theory of planned behavior and self-regulated learning model”, Computers & Education, 134, 50-62 Morrison, D. (2003). Using activity theory to design constructivist online learning environments for higher order thinking: A retrospective analysis. Canadian Journal of Learning and Technology/La revue canadienne de l’apprentissage et de la technologie, 29(3). Schunk, D. H. 2012. Learning theories: An educational perspective (6. Basım). Boston, MA: Pearson. Schraw, G. 2010. “Measuring self-regulation in computer-based learning environments”, Educational Psychologist, 45 (4), 258-266. Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., ve Kirschner, P. A. 2018. “Linking learning behavior analytics and learning science concepts: designing a learning analytics dashboard for feedback to support learning regulation”, Computers in Human Behavior. Siemens, G., ve Gasevic, D. 2012. “Guest editorial-Learning and knowledge analytics”, Educational Technology & Society, 15 (3), 1-2. Sun, Z., Xie, K., ve Anderman, L. H. 2018. “The role of self-regulated learning in students' success in flipped undergraduate math courses”, The Internet and Higher Education, 36, 41-53. Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110.