Online Learning Environments In Higher Education Research Articles

Promoting self-regulated learning in higher education

The importance of self-regulated learning is a heavily discussed topic in higher education. Existing literature indicates that self-regulated learning practices and strategies are relevant and important factors in student learning outcomes within blended and online contexts (Broadbent & Poon, 2015). According to literature, the self-regulated learner is aware of their strengths and weaknesses. They set goals, monitor their progress through self-reflection and the constant evaluation of their learning approaches, which enables them to adapt their engagement in academic-related tasks (Hawe, Lightfoot & Dixon, 2019). These are key principles of self-regulated learning, which aims to position learners as active agents in the learning process (Winne & Perry, 2000).
 
 Studies have found that students arrive at universities without the skills or practices required for self-regulated learning (Balapumi, von Konsky, Aitken, & McMeekin, 2016). This is a crucial time for students, as they develop new ways of thinking, learning and communicating. Practices and strategies should be introduced to students as they begin their journey into tertiary studies, to ensure they are equipped with the necessary skills that are key to academic success (Lear & Li & Prentice, 2016). To develop students as independent, self-regulating learners has become a valued and desired outcome of higher education institutions, and as such they should offer opportunities to develop these skills as they progress though their studies (Hawe et al., 2019).
 
 Therefore, to help and support students in the development and enhancement of their self-regulated learning skills, the Learn2Learn module, was developed by the Technology-Enabled-Learning (TEL) Team at Western Sydney University, and piloted to students in Autumn session, 2021. The key features and functionalities of the Learn2Learn module include, goal setting and study planning tools, lessons and content pieces informed by literature on self-regulated learning, and the ability for instructors to take a specific lesson and embed it in-line with their instructional materials within the LMS. Since its launch, there has been a consistent growth in usage. In Autumn 2021, there was a total of 427 users and 681 sessions have been initiated. Students are spending an average of approximately 8 minutes per session. In Spring 2021, there was a total of new 321 users, with 536 sessions, and an average of approximately 8 minutes per session.
 
 More recently, three focus group sessions were conducted, consisting of 4 to 6 students. Students’ experiences with the module confirmed its value in helping their self-regulated learning practices, including, setting goals, self-reflection, planning and time management. There was also a considerable number of responses from the students that have indicated that the module could benefit the first-year transition into university.
 
 This presentation will cover the evidence and research that informed the content and design of the module We will provide some insight into the preliminary findings from quantitative and qualitative data analysis and discuss the development process of the module, including an overview of the learner experience (LX) design principles used to guide the design of the module, to help promote student engagement.
 References
 
 Balapumi, R., von Konsky, B. R., Aitken, A., & McMeekin, D. A. (2016). Factors Influencing University Students’ Self-regulation of Learning: An Exploratory Study. In Proceedings of the Australasian Computer Science Week Multiconference (pp. 51–59). New York, NY, USA: ACM. http://doi.org/10.1145/2843043.2843067
 
 Broadbent, J., & Poon, W. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007
 Hawe, E., Lightfoot, U., & Dixon, H. (2019). First-year students working with exemplars: promoting self-efficacy, self-monitoring and self-regulation. Journal of Further and Higher Education, 43(1), 30–44. https://doi.org/10.1080/0309877X.2017.1349894
 Lear, E., Li, L., & Prentice, S. (2016). Developing academic literacy through self-regulated online learning. Student Success, 7(1), 13-23.
 Winne, P. H., & Perry, N. (2000). Chapter 16—Measuring Self-Regulated Learning. In Handbook of Self-Regulation (pp. 531–566). Retrieved from: https://www.researchgate.net/publication/232472158_Measuring_Self-Regulated_Learning

The potential of open learner models to promote active thinking by enhancing self‐regulated learning in online higher education learning environments

AbstractThe main aim of the present study is to assess whether the open learner model (OLM) is capable of promoting students' active thinking by enhancing their self‐regulation in online higher education learning environments. To this aim, we systematically reviewed the literature of the last three decades and found 67 articles, of which only a sample of 15 were considered. Based on the findings, we performed a narrative analysis of the studies concerning technological features of OLMs that cater to the three main aspects concerning self‐regulated learning, namely, cognition, metacognition and motivation. Our analysis of the literature confirmed that these three aspects are all subject to some measure of influence. In mutually interacting, these three components support learners to reach a better understanding of their learning process. Specifically, it seems that mostly all three type of OLMs, inspectable, negotiable, and co‐operative, with simple and complex graphical presentation of their learner models and capacity to colour‐code and compare—alike appear optimal for augmenting cognition, metacognition, and motivation. They seemingly do so through offering a wealth of techniques pertaining to knowledge, difficulties, and misconception visualization. The results presented suggest that OLMs have a positive impact on learners' active thinking regarding their learning process. Practitioner NotesWhat is already known about this topic Several educational environments have been enhanced with open learner models (OLMs) with the aim of supporting learners. OLM seems to be a potential avenue for improving higher education by supporting learners' active thinking about their learning process by enhancing self‐regulated learning. Despite growing interest in OLMs, there has yet to be any effort to systematically review current research studies investigating their potential to promote self‐regulated learning. What this paper adds All type of OLMs, with their varied visualization features, to some extent are useful for promoting cognitive, metacognitive, and motivational components of self‐regulated learning. In mutually interacting, the three components of self‐regulated learning support learners to reach a better understanding of their learning process, and thereby promoting active thinking. Implications for practice and/or policy We suggest that practitioners must first grasp the possibilities for the cultivation of self‐regulated learning contained in the particular features of each OLM, and then add this insight to their own expertise in order to best integrate them for supporting students' active thinking. While a number of specific features of OLMs seem pertinent to cultivating one particular element of self‐regulated learning, there remains the chance that, through the interaction of cognitive, metacognitive, and motivational tactics, these features could in fact make a more holistic and general contribution. This supports learners to move from active‐in‐behaviour to active‐in‐thinking activities in digital learning environments.

Can self-regulated learning strategies improve academic achievement in online higher education learning environments?

Can self-regulated learning strategies improve academic achievement in online higher education learning environments?

Review of Research on Online Learning Environments in Higher Education

A growing number of studies have been carried out to understand learning and teaching activities in online learning environments (OLEs) and to design effective OLEs for meaningful learning in higher education. Although there were a small number of studies to provide research trends in educational technology in regards to research topics, methods, and researchers, more research is necessary to help educators to understand new trends and issues of OLEs in higher education. This study intends to provide an overview of practical and theoretical issues pertaining to OLEs by analyzing literature recently published in peer-reviewed journals. The present study qualitatively analyzed research questions and purposes to identify themes of OLEs in higher education. The OLE research topics were grouped into three major themes: (1) participants, (2) micro-level environments, and (3) macro-level environments. The findings are followed by discussion of an ecological approach as a new theoretical framework to guide future OLE research and practice.

Blended Learning and Sense of Community: A Comparative Analysis with Traditional and Fully Online Graduate Courses

<P>Blended learning is a hybrid of classroom and online learning that includes some of the conveniences of online courses without the complete loss of face-to-face contact. The present study used a causal-comparative design to examine the relationship of sense of community between traditional classroom, blended, and fully online higher education learning environments. Evidence is provided to suggest that blended courses produce a stronger sense of community among students than either traditional or fully online courses.</P> <P><B>Keywords:</B> Blended learning, sense of community, higher education, online learning, computer-mediated communication, faculty training</P>

Steps toward Promoting Cognitive Achievements

The Elementary School Journal Volume 85, Number 5 ? 1985 by The University of Chicago. All rights reserved. 001 3-5984/85/8505-0008$0 1.00 Research on teaching has always been carried out under the assumption that what happens in classrooms influences students' learning. In the heyday of the research guided by the process-product paradigm (Dunkin & Biddle 1974), there were two central determinants of students' learning that teachers could manipulate. One was the teacher's behavior. The second was the