Assessment, development and experimental evaluation of self-regulatory support in online learning

Online learning requires a higher level of self-regulation than face-to-face learning. Learners are likely to differ in their cognitive, metacognitive, affective or motivational resources to meet this demand. Individual differences in self-regulation is one major factor contributing to success or failure in online learning, other factors include characteristics of the online learning environment and the complexity of the learning content itself. Lack of self-regulation is likely to affect learners’ engagement with the course content, may result in sub-optimal learning outcomes, including failure to complete the course. A virtual learning assistant has been designed and developed to support online learners. This research aims at ascertaining the effectiveness of providing adaptive assistance in terms of (a) compensatory and (b) developmental effects. Online learners involved in the empirical part of this study (N = 157) were randomised into one of two experimental conditions. For the intervention group, the online learning assistant provided personalised in-browser notifications. This feature was disabled for the learners in the control condition. Results indicate that the adaptive assistance did not result in noticeable developmental shifts in learners’ self-regulation as assessed via conventional self-report measures. However, learners allocated to the intervention group spent less time online per day in first three weeks of being exposed to the adaptive assistance, reduced their time commitment to entertainment websites during first two weeks, and increased their engagement with educational web resources during the first ten days. In addition to the time-varying effects, these compensatory (behavioural) shifts were moderated by learners’ individual differences in personality. The outcome of this study suggests that the utilisation of a virtual learning assistant that provides adaptive assistance can be effective in compensating for not yet developed self-regulatory skills, and subsequently help facilitating success in learning on short online courses

Utilizing Online Activity Data to Improve Face-to-Face Collaborative Learning in Technology-Enhanced Learning Environments

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2019. 2. Rhee, Wonjong .We live in a flood of information and face more and more complex problems that are difficult to be solved by a single individual. Collaboration with others is necessary to solve these problems. In educational practice, this leads to more attention on collaborative learning. Collaborative learning is a problem-solving process where students learn and work together with other peers to accomplish shared tasks. Through this group-based learning, students can develop collaborative problem-solving skills and improve the core competencies such as communication skills. However, there are many issues for collaborative learning to succeed, especially in a face-to-face learning environment. For example, group formation, the first step to design successful collaborative learning, requires a lot of time and effort. In addition, it is difficult for a small number of instructors to manage a large number of student groups when trying to monitor and support their learning process. These issues can amount hindrance to the effectiveness of face-to-face collaborative learning. The purpose of this dissertation is to enhance the effectiveness of face-to-face collaborative learning with online activity data. First, online activity data is explored to find whether it can capture relevant student characteristics for group formation. If meaningful characteristics can be captured from the data, the entire group formation process can be performed more efficiently because the task can be automated. Second, learning analytics dashboards are implemented to provide adaptive support during a class. The dashboards system would monitor each group's collaboration status by utilizing online activity data that is collected during class in real-time, and provide adaptive feedback according to the status. Lastly, a predictive model is built to detect at-risk groups by utilizing the online activity data. The model is trained based on various features that represent important learning behaviors of a collaboration group. The results reveal that online activity data can be utilized to address some of the issues we have in face-to-face collaborative learning. Student characteristics captured from the online activity data determined important group characteristics that significantly influenced group achievement. This indicates that student groups can be formed efficiently by utilizing the online activity data. In addition, the adaptive support provided by learning analytics dashboards significantly improved group process as well as achievement. Because the data allowed the dashboards system to monitor current learning status, appropriate feedback could be provided accordingly. This led to an improvement of both learning process and outcome. Finally, the predictive model could detect at-risk groups with high accuracy during the class. The random forest algorithm revealed important learning behaviors of a collaboration group that instructors should pay more attention to. The findings indicate that the online activity data can be utilized to address practical issues of face-to-face collaborative learning and to improve the group-based learning where the data is available. Based on the investigation results, this dissertation makes contributions to learning analytics research and face-to-face collaborative learning in technology-enhanced learning environments. First, it can provide a concrete case study and a guide for future research that may take a learning analytics approach and utilize student activity data. Second, it adds a research endeavor to address challenges in face-to-face collaborative learning, which can lead to substantial enhancement of learning in educational practice. Third, it suggests interdisciplinary problem-solving approaches that can be applied to the real classroom context where online activity data is increasingly available with advanced technologies.Abstract i Chapter 1. Introduction １ 1.1. Motivation １ 1.2. Research questions ４ 1.3. Organization ６ Chapter 2. Background ８ 2.1. Learning analytics ８ 2.2. Collaborative learning ２２ 2.3. Technology-enhanced learning environment ２７ Chapter 3. Heterogeneous group formation with online activity data ３５ 3.1. Student characteristics for heterogeneous group formation ３６ 3.2. Method ４１ 3.3. Results ５１ 3.4. Discussion ５９ 3.5. Summary ６４ Chapter 4. Real-time dashboard for adaptive feedback in face-to-face CSCL ６７ 4.1. Theoretical background ７０ 4.2. Dashboard characteristics ８１ 4.3. Evaluation of the dashboard ９４ 4.4. Discussion １０７ 4.5. Summary １１４ Chapter 5. Real-time detection of at-risk groups in face-to-face CSCL １１８ 5.1. Important learning behaviors of group in collaborative argumentation １１８ 5.2. Method １２０ 5.3. Model performance and influential features １２５ 5.4. Discussion １２９ 5.5. Summary １３２ Chapter 6. Conclusion １３４ Bibliography １４０Docto

Making Course Content Inclusive: Implementing UDL and ADA Best Practices

Learn about best practices in instructional design, organization, and engagement from two faculty who have been teaching and researching online learning for years. Come away with concrete examples you can use to improve your online or blended course. Topics will include: • Course structure and organization • Implementing UDL and ADA digitally accessible content • Using H5P to create interactive and engaging content • Creating teaching presence • Guiding learning with adaptive release • Using technology to facilitate group project

The evolution and evaluation of an online role play through design-based research

This paper presents selected findings from a 5-year design-based research case study of the evolution of an online role play that allows postgraduate students to explore the complexities inherent in land rights negotiations between indigenous peoples and others. In the context of Laurillard’s (2002) conversational framework and a design-based research methodology, diverse private and public discussion forum spaces were created for group negotiations on a learning management system (LMS) platform. Our analysis of the conversational framework structure in the evolved role play showed that all four stages – discursive, adaptive, integrative, and reflective – were evidenced, with the adaptive and integrative stages cycling through multiple times. The online role play, whilst implemented as a simple virtual world, facilitated personal, deep and socialised learning experiences focused on consultation, negotiation and decision-making. We also found that student anonymity was not necessary for full engagement in role play, and that students chose to incorporate communication technologies outside the LMS into their learning activities. This research shows that with a strong pedagogical design, and attention paid to an evidence-based iterative improvement cycle, online role plays can provide powerful collaborative learning experiences

Developing Effective Online Training Tools For Maine Adaptive Sports And Recreation

Background: Maine Adaptive Sports and Recreation (MASR) relies on volunteers to instruct their participants with disabilities to participate in a variety of adaptive sport programs. Volunteers must have a comprehensive understanding of participants’ health conditions to assist appropriately. MASR’s traditional training program lacked a formal curriculum and assessment of volunteer learning. Our purpose was to create online learning modules and determine whether implementing a massed or distributed schedule resulted in better long term retention. Methods: Two non-randomized groups of eleven adults were assigned to either an in-class, massed format (Group A) or an at-home, distributed schedule (Group B) to complete six online learning modules. Participant competence was assessed prior, immediately after, and two weeks after completion of learning modules. A global rating scale survey and satisfaction survey were also completed to determine perceived confidence in using the information learned and obtain feedback. Results: Post-hoc testing revealed both groups had significant increase in competence after reviewing the modules, in terms of both immediate recall and long-term retention scores compared to baseline. There was a significant difference between group pre-test scores, but no difference between the groups’ immediate recall or long-term retention scores. Both groups exceeded the MCIC score of 2 points for the Global Rate of Change Scale, indicating a notable increase in confidence. Participants reported the modules to be beneficial and effective in the Volunteer Satisfaction Survey. Conclusion: Our findings suggest the online learning modules were effective regardless of the applied learning schedule. Both groups increased their competence and reported improved confidence with the presented material. A small sample size and discrepancies in participant demographics between groups presented limitations which prohibit recommending a superior learning schedule

Introducing Adaptivity and Collaborative Support into a Web-Based LMS

In this paper the design and implementation of AHyCo (Adaptive Hypermedia Courseware), a web-based learning management system based on adaptive hypermedia, is described. AHyCo consists of a domain model, a student model, an adaptive model and a collaborative model. AHyCo supports interaction between students and content by using adaptive hypermedia and online tests. Particular attention is given to the design of the collaborative functionality which enables automatic grouping of students based on various criteria. Furthermore, student to student and student to teacher interaction is supported through asynchronous communication (forum). File sharing and inter-group grading and evaluation modules were introduced into the collaborative module as well enticing interaction between students across groups

Efficient Methods for Non-stationary Online Learning

Non-stationary online learning has drawn much attention in recent years. In particular, dynamic regret and adaptive regret are proposed as two principled performance measures for online convex optimization in non-stationary environments. To optimize them, a two-layer online ensemble is usually deployed due to the inherent uncertainty of the non-stationarity, in which a group of base-learners are maintained and a meta-algorithm is employed to track the best one on the fly. However, the two-layer structure raises the concern about the computational complexity -- those methods typically maintain $\mathcal{O}(\log T)$ base-learners simultaneously for a $T$-round online game and thus perform multiple projections onto the feasible domain per round, which becomes the computational bottleneck when the domain is complicated. In this paper, we present efficient methods for optimizing dynamic regret and adaptive regret, which reduce the number of projections per round from $\mathcal{O}(\log T)$ to $1$. Moreover, our obtained algorithms require only one gradient query and one function evaluation at each round. Our technique hinges on the reduction mechanism developed in parameter-free online learning and requires non-trivial twists on non-stationary online methods. Empirical studies verify our theoretical findings.Comment: preliminary conference version appeared at NeurIPS 2022; this extended version improves the paper presentation, further investigates the interval dynamic regret, and adds two applications (online non-stochastic control and online PCA

Multi-expert learning of adaptive legged locomotion

Achieving versatile robot locomotion requires motor skills which can adapt to previously unseen situations. We propose a Multi-Expert Learning Architecture (MELA) that learns to generate adaptive skills from a group of representative expert skills. During training, MELA is first initialised by a distinct set of pre-trained experts, each in a separate deep neural network (DNN). Then by learning the combination of these DNNs using a Gating Neural Network (GNN), MELA can acquire more specialised experts and transitional skills across various locomotion modes. During runtime, MELA constantly blends multiple DNNs and dynamically synthesises a new DNN to produce adaptive behaviours in response to changing situations. This approach leverages the advantages of trained expert skills and the fast online synthesis of adaptive policies to generate responsive motor skills during the changing tasks. Using a unified MELA framework, we demonstrated successful multi-skill locomotion on a real quadruped robot that performed coherent trotting, steering, and fall recovery autonomously, and showed the merit of multi-expert learning generating behaviours which can adapt to unseen scenarios

From procrastination to engagement? An experimental exploration of the effects of an adaptive virtual assistant on self-regulation in online learning

Compared to traditional classroom learning, success in online learning tends to depend more on the learner’s skill to self-regulate. Self-regulation is a complex meta-cognitive skill set that can be acquired. This study explores the effectiveness of a virtual learning assistant in terms of (a) developmental, (b) general compensatory, and (c) differential compensatory effects on learners’ self-regulatory skills in a sample of N = 157 online learners using an experimental intervention-control group design. Methods employed include behavioural trace data as well as self-reporting measures. Participants provided demographic information and responded to a 24-item self-regulation questionnaire and a 20-item personality trait questionnaire. Results indicate that the adaptive assistance did not lead to substantial developmental shifts as captured in learners’ perceived levels of self-regulation. However, various patterns of behavioural changes emerged in response to the intervention. This suggests that the virtual learning assistant has the potential to help online learners effectively compensate for deficits (in contrast to developmental shifts) in self-regulatory skills that might not yet have been developed