Online learning environments can provide clues about students' behavioral patterns and students' interactions with other students, teachers, content or the system (Baker, 2010). Students take responsibility for learning and using appropriate strategies in online learning environments and require developed self-regulated learning skills (Jarvela and Jarvenoja, 2011; Schunk, 2012; Sun et al., 2018).  Zimmerman (2000) examines the self-regulation skill in three cyclical stages as foresight, performance and self-reflection. With this in mind, the necessity of support systems for students who are alone in the online learning environment is also explained in the literature (Maldonado-Mahauad, 2019). These systems should also be feedback supported systems so that students can get the support directly get through this system. Therefore, the SRL-supported feedback system developed by the researcher provides feedback to improve the self-regulation skills of online students. This system which is Feedback based Srl Management System (FSMs) consists of a learning panel visualized by the reflection of the interaction data calculated within the framework of the active participation of the online students in the weekly activities to the self-regulation skill scores. Beside this, feedbacks for feedforward each student individually presented with feedback panel. 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 via FSM system. In addition to this, students receive explained feedback on their learning activities weekly and they monitor, follow their learning processes in line with these feedbacks. On the other hand, the self-regulation model of Zimmerman (2000), Barnard et al. (2009) 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) suggested that data on SRL skills can be obtained from students' think aloud or observing students during their active learning, through measurement scales. Beside these, students’ traces (interaction data, log data etc.) reflect their background interactions or tracks through learning processes without receiving self-reported data. 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; Zhou et al., 2010). In this context, learning analytics are used to related to the real interactions on LMS for students' SRL skills (Wong & Li, 2020; Viberg, et al., 2018). In this line, this study aims to analyze the relationship between the data based on the students' perceptions of SRL skills and the students’ interaction data in FSMs (Moodle) through learning analytics. For this purpose, the research problem;

• What is the effect of the Feedback-based SRL Management system (FSMs) on the development of online students’ SRL skills?

This research was examined by using the correlational analysis method was used. Participants; of the research group consists of the fourth year students of the Fall semester ICT Department in Education Faculty of the 2019/2020 academic year and they consist of 53 higher education students. Designing of the System process; before the environment design of the research, Goal Setting (GS); Environment Structuring (ES), Time Management (TM) etc. SRL skills and components of Moodle LMS (forum, chat, assignment, etc.) have been associated based on the literature and this integrated system providing feedbacks to the students through learning and feedback panel (FSMsystem). Data Collection Tools Interaction data; in the FSM system, the database has been added to the system within the Zimmerman (2000) framework based on interaction types in the log records and SRL skills. Online Self-Regulation Scale; developed by Barnard et al. (2009) adapted to Turkish by Kilis and Yıldırım (2018) was used. The SRL sub-skills 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 were found to be 0.95 for the whole scale Data Collection Process; this study was conducted in scientific research methods course in the 2019/2020 fall semester through the FSMsystem via 12 weeks. Feedbacks that was presented through panels on missing and completed activities on scorm-compatible contents, forum discussions, chat room, etc. aimed to improve online SRL skills of students. Data Analysis; Spearman Rank Differences correlation test, which is one of the non-parametric test was implemented with feedbacks interaction data and SRL scale data.

Results of Correlational Analysis on SRL skills (Gs, Ts etc.) with Feedbacks Within the framework of the research problem, the correlation between the online students' feedback-based interaction data and the SRL sub-skills and their total scores were examined. As a result of the Spearman Rho Correlation Coefficient analysis, a significant relationship was found between the online students' feedback scores, SRL sub-skills and the total SRL skill score (p<0.01). Although there is no clear classification of correlation coefficients in the literature, Roscoe (1975) stated that between 0 and 0.30 is low, 0.30 and 0.70 is medium, 0.70 and 1.00 is highly correlated. As a result of this study, it can be said that the feedbacks provided to the online students through the FSMsystem has a highly significant effect on the relationship between the students' SRL skill total scores (r=.850, p<0.01). It is seen that the support based feedbacks provided to the online students' self-regulation development was positive effect and interactions based feedbacks significantly correlated with SRL sub-skills and total SRL skills score. Beside, main challenges in the implementation process conducted entirely through distance learning so need to be encourage student participation. Therefore, there is a need for practice that increase participation of online students on the systems. In addition, it is important to increase diversity in sample group and this study should be implemented a much larger sample group. Finally, since student autonomy on online systems is an issue that needs to be taken into account, the individualized and adaptive support systems which based on self regulation skills should be increased.

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 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. Wong, B. T. M., & Li, K. C. (2020). A review of learning analytics intervention in higher education (2011–2018). Journal of Computers in Education, 7(1), 7-28. Mahauad, J. J. M. (2019). Analysis of Students' Self-regulatory Strategies in MOOCs and Their Impact on Academic Performance, Doctoral dissertation, Pontificia Universidad Catolica de Chile (Chile)). Roscoe, J.T. (1975). Fundamental research statistics for the behavioural sciences, (2nd edition). New York: Holt Rinehart & Winston. Schunk, D. H. (2012). Learning theories: An educational perspective (6th Ed.). Boston, MA: Pearson. Schraw, G. (2010). Measuring self-regulation in computer-based learning environments, Educational Psychologist, 45(4), 258-266. 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. Kilis, S., & Yildirim, Z. (2018). Online self-regulation questionnaire: Validity and reliability study of Turkish translation. Cukurova University Faculty of Education Journal, 47(1), 233-245. Zhou, M. X., Wei, F., Liu, S., Song, Y., Pan, S., Qian, W., ... ve Zhang, Q. (2010). Tiara: a visual exploratory text analytic system. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 153-162). ACM. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). San Diego: Academic Press.