MOOC Learning Research Articles

Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners

Dropout is not always a failure! Exploration on the prior knowledge and learning behaviors of MOOC learners

NONLINEAR LEARNING PATH: A SYSTEMATIC REVIEW

Students benefit from a nonlinear learning path, also known as a nonsequential learning path, because it allows them to control the pace and sequence of their learning. However, the dynamics of a nonlinear learning path, particularly within an open learning environment like MOOCs, remain underexplored. The current study aims to map out various nonlinear learning paths and explore the potential for personalisation within these environments. Guided by the PRISMA 2020 Statement, we conducted a comprehensive review of 3,418 articles from three databases, focusing on 30 that were relevant to nonlinear learning paths in open learning environments. We discovered that a nonlinear learning path in MOOCs involves path selection, cyclical paths, or skipped paths, all influenced by the design of the MOOC learning materials. In classrooms utilizing an open learning environment, a nonlinear learning path is facilitated by activities such as face-to- face or online discussions, self-study materials, student-created content, project mentoring or coaching, peer feedback, and co-learning activities. Additionally, personalisation, tailored by educators or technology, is key to preventing students from becoming disoriented within these open learning environments. Our findings highlight the importance of promoting nonlinear learning paths in both classrooms and MOOCs, developing learning path recommender systems, and creating supportive MOOC learning materials. Future research should explore students’ perceptions of nonlinear learning paths within an open learning environment, particularly focusing on the integration and impact of MOOC learning materials.

A design layer to support self and social regulation processes of learning in MOOC

Open, social and networked approaches to learning pose challenges for learners, who must assume the role of actively directing their own learning, in interaction and collaboration with others, in an increasingly complex environment. In this context, concepts such as self-direction and self-regulation of learning have attracted renewed interest as umbrella terms for a skill set and provisions that allow subjects to independently guide their own learning process and assume responsibility for it. Moreover, many authors have pointed out various problems regarding MOOC design and quality from a pedagogical perspective. This raises the need for a greater and more holistic understanding of learning regulatory processes and for developing models and instruments to support learners in this regard. This research aims to analyse how to support learning regulation processes as a whole in MOOC environments. This means paying attention to both social and individual dimensions of regulation, by studying how processes of self-regulation, co-regulation and socially shared regulation can be supported and promoted in this type of learning contexts. To this end, we apply the methodology of design-based research in order to intervene directly in the pedagogical practice through an iterative cycle based on stages of design, intervention, reflection and redesign of a design layer to support learning regulation in a MOOC. In this paper we present the design of the regulation support layer proposed in the first research iteration. We begin by presenting its theoretical foundations and then describe the support layer that has been designed as well as the empirical case of an xMOOC were it has been implemented. Finally, some conclusions and recommendations for the design and practice of learning regulation in MOOC are drawn from the results obtained in the first research iteration.

MOOC learners’ perspectives of the effects of self-regulated learning strategy intervention on their self-regulation and speaking performance

MOOC learners’ perspectives of the effects of self-regulated learning strategy intervention on their self-regulation and speaking performance

Development Adaptive Instructional Scaffolding on Online Forum Discussion to Improve Personalization in MOOCs Learning Environments

Development Adaptive Instructional Scaffolding on Online Forum Discussion to Improve Personalization in MOOCs Learning Environments

The Involvement of Academic and Emotional Support for Sustainable Use of MOOCs.

MOOCs, the Massive Open Online Courses, are online educational courses that offer open access to a large number of participants globally. However, online engagement during MOOC learning remains a problem, as reflected in relatively high dropout rates. This paper involves academic and emotional support, aiming to explore whether they contribute to users' sustainable use of the MOOC platform. A total of 410 college students learning English as a foreign language (EFL) and with MOOC learning experience participated in this study. Employing the structural equation modeling (SEM) techniques, we examined the relationships among five factors in the EFL MOOC learning context: academic support (AS), emotional support (ES), perceived usefulness (PU), perceived ease of use (PEoU), and platform reputation (PR). The results indicate that academic support influences learners' perceptions of the usefulness and ease of use of the MOOC platform, as well as enhancing learners' feelings of being emotionally supported. Simultaneously, platform reputation plays a crucial role in influencing learners' perceptions of MOOC platforms. However, results suggest that emotional support does not have a statistically significant impact on the perceived usefulness and perceived ease of use of the platform in EFL MOOC learning contexts.

Effects of personal and instructor goals on MOOC continuance intention

The purpose of this study was to examine the relationships among MOOC learners’ personal goal achievement, instructor goal achievement, learning experiences measured with perceived learning and course satisfaction, and their continuance intention in a MOOC offered by a research university in the United States. A total of 203 MOOC learners voluntarily participated in the study. The results showed that personal goal achievement was a more powerful predictor than instructor goal achievement in forecasting MOOC learners’ perceived learning and course satisfaction. Conversely, instructor goal achievement negatively predicted continuance intention while perceived learning and course satisfaction positively predicted it. Learners’ personal goal achievement did not significantly predict continuance intention. Our study contributes to the current body of MOOC literature, underscoring the importance of learners’ personal goals in relation to their learning experiences mediating their continuance intention.

Understanding self-regulated learning and learner performance in MOOCs

Self-regulated learning (SRL) has been viewed as a critical skill for learner success in MOOCs, but due to methodological limitations of self-reported measures, the relationship between SRL and learner performance remains unquantified. Therefore, this study sought to investigate how SRL impacted learner performance in MOOCs by linking clickstream data about SRL to learner performance variables. Following a social cognitive perspective of SRL, learner traces were categorized into three phases including forethought, performance/volition control, and self-reflection phase. Then separate multiple linear regression analyses were performed to examine the relationship between SRL traces in each phase and learner performance variables (e.g., quizzes scores and forum participation record). The findings of this study indicated that SRL traces in performance/volition control and self-reflection phases positively predict learners’ quizzes scores. In addition, SRL traces in each of the three phases were significantly correlated to learners’ forum participation, despite a small effect of the model. Particularly, self-reflective traces negatively predicted learners’ forum participation but SRL traces in the other two phases had a positive relationship with the outcome variable. Practical implications about supporting learners in MOOCs are discussed in the end.

Effect of MOOC Learning Duration on Achievement of Learning Goals

Effect of MOOC Learning Duration on Achievement of Learning Goals

MOOC awareness and utilization among students of selected Polish universities

Abstract Purpose The main aim of the article was to examine Polish students’ awareness about MOOCs. For this purpose, the following issues were examined: the percentage of students aware of the existence of free courses, the percentage of students who have used the courses, the most popular educational platforms among Polish students, the percentage of people who did not complete a course, and the reasons for failure. Students who had completed any MOOC were also asked about their experiences and the usefulness of the course. Methodology The study was conducted in October/November 2022 in the form of an online survey. The generated link to the survey was sent by e-mail to students of the Jagiellonian University, the College of Engineering and Health in Warsaw and the Gdańsk College of Health, with a request to complete. Findings The results obtained may be surprising, especially after looking at the situation of MOOCs in other parts of the world. The knowledge about the availability of MOOC courses among Polish students is very low. Only 61 people of all respondents knew beforehand that MOOC learning was a possibility. Originality The added value of the article is examination of the motivation of students to take a MOOC course, as well as the reasons for not completing the course or not participating in it. The conclusions of the survey may be useful for designers of MOOCs. They set out some good practices in remote education and ways to keep young people interested.

Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities

Exploring MOOC Learners’ Behavioural Patterns Considering Age, Gender and Number of Course Enrolments: Insights for Improving Educational Opportunities

Early prediction of MOOC dropout in self-paced students using deep learning

ABSTRACT To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows: Firstly, the dropout rate among self-paced students in MOOCs exceeds 90%, with over 50% of students participating in online learning activities for only one day. Furthermore, the starting dates for students in the same course differ significantly. Secondly, leveraging early learning behavior and relevant background features, the proposed early prediction model accurately predicts over 98% of dropout cases and identifies over 50% of engaged students. Through training, the model's convolutional kernels capture meaningful weights for different days and activities. Lastly, background features related to students and courses have a more significant impact on dropout rates. The utilization of resources such as videos and active participation in learning activities, like asking questions, demonstrate a particularly significant influence on dropout rates. Notably, there is no fixed period that consistently affects dropout rates. These method and findings provide effective strategies for decreasing dropout rates and improving student engagement.

Beyond Boundaries: The Role of Learning Types in Shaping MOOC Learner Engagement and Progression

Beyond Boundaries: The Role of Learning Types in Shaping MOOC Learner Engagement and Progression

Exploring the impact of positive reappraisal on self-regulated learning in MOOCs: The mediating roles of control and value appraisals and positive emotion

Effective emotion regulation is important for learning. However, few studies have examined the role of strategies with typical adaptive regulatory potential (e.g., positive reappraisal) in MOOC learning. Drawing on the models aimed at integrating learners’ cognitive and emotional activities, this study investigated the relationship between positive reappraisal and self-regulated learning in MOOCs, with control/value appraisal and positive emotion as potential single and serial mediators. This investigation employed partial least squares structural equation modeling on survey data, supplemented by in-depth interviews. A total of 834 learners from four MOOCs completed a questionnaire indicating the level or extent of their positive reappraisal, control and value appraisals, positive emotion (enjoyment, hope), and self-regulated learning strategies used. Fifteen of those surveyed participated in individual interviews. The results showed that positive reappraisal was indirectly and positively related to self-regulated learning through control/value appraisal, through positive emotion, and sequentially through control/value appraisal and positive emotion. The interview data provided further insight into the mediating mechanisms identified from the survey results. Practical implications for MOOC designers and instructors are presented. This study contributes to the extant MOOC literature by demonstrating the potential of adaptive emotion regulation strategies in facilitating self-regulated learning in MOOCs.

The Influence of Presence Types on Learning Engagement in a MOOC: The Role of Autonomous Motivation and Grit.

We developed a research framework based on the community of inquiry theory to examine the relationship between three kinds of MOOC presence (teaching presence, social presence, and cognitive presence) and learning engagement. Further, we explored the mediating effect of autonomous motivation and the moderating role of grit in this relationship. The online survey included a sample of N = 794 college students (46.473% male) between the ages of 18 and 20. The COI survey instrument, short grit scale, the Utrecht work engagement scale-student scale and academic self-regulation questionnaire were used to test hypotheses. The study results showed that all three MOOC presences (teaching presence, social presence, and cognitive presence) had significant positive relationship with learning engagement. Autonomous motivation had a mediating role in them. Also, the positive relationship between social presence, cognitive presence, and autonomous motivation was stronger for students with a higher level of grit. This study enriches the literature on whether and how the presence knowledge of MOOC courses affects college students' learning engagement, and the complexity of MOOC environments determines that grit plays an indispensable role in the learning process.

Effective interactive engagement strategies for MOOC forum discussion: A self-efficacy perspective.

This mixed methods sequential explanatory study identified and explained the features of engagement strategies for MOOC forum discussion that help low-achieving students make significant progress. Triangulated data were collected from MOOC learners' (n = 335) scores in two reading assessments, their posts to the embedded online discussion forum, and their self-reflection learning journals. Based on learning progress between pre- and post-assessment tasks, MOOC learners are divided into three groups: 1) little, 2) moderate, and 3) significant progress. According to a statistical analysis of the quantified posts, surprisingly, the low-scoring students from the pre-test who demonstrated significant progress later engaged in significantly fewer peer-peer and peer-teacher interactions in the online discussion forum. Guided by self-efficacy literature, the reflective journals of these learners suggested that 1) learner-content interactions may help them advance learning and obtain new information and linguistic knowledge from the peer-made learning materials in the discussion forum; 2) they did not share and exchange ideas and answers with their peers. Instead, they prefer learning from others' discussions and wish to get quick feedback and suggestions on their contributions to the discussion forum; and 3) peer-peer and peer-teacher interactions were proposed as two solutions to regulate their online learning experience as they lack self-discipline and time-management skills. Implications include teachers' continuous support to encourage low-achieving students to learn peer-generated content and quick feedback on their contributions to the discussion forum.

Retracted: Assessing the Impact of the MOOC Learning Platform on the Comprehensive Development of English Teachers at College Level under “Double First-Rate” by Utilization of the SWOT Analysis in Hunan Province, China

Retracted: Assessing the Impact of the MOOC Learning Platform on the Comprehensive Development of English Teachers at College Level under “Double First-Rate” by Utilization of the SWOT Analysis in Hunan Province, China

Self-Regulated Learning and Active Feedback of MOOC Learners Supported by the Intervention Strategy of a Learning Analytics System

MOOCs offer great learning opportunities, but they also present several challenges for learners that hinder them from successfully completing MOOCs. To address these challenges, edX-LIMS (System for Learning Intervention and its Monitoring for edX MOOCs) was developed. It is a learning analytics system that supports an intervention strategy (based on learners’ interactions with the MOOC) to provide feedback to learners through web-based Learner Dashboards. Additionally, edX-LIMS provides a web-based Instructor Dashboard for instructors to monitor their learners. In this article, an enhanced version of the aforementioned system called edX-LIMS+ is presented. This upgrade introduces new services that enhance both the learners’ and instructors’ dashboards with a particular focus on self-regulated learning. Moreover, the system detects learners’ problems to guide them and assist instructors in better monitoring learners and providing necessary support. The results obtained from the use of this new version (through learners’ interactions and opinions about their dashboards) demonstrate that the feedback provided has been significantly improved, offering more valuable information to learners and enhancing their perception of both the dashboard and the intervention strategy supported by the system. Additionally, the majority of learners agreed with their detected problems, thereby enabling instructors to enhance interventions and support learners’ learning processes.

MOOC-BERT: Automatically Identifying Learner Cognitive Presence From MOOC Discussion Data

In a MOOC learning environment, it is essential to understand students' social knowledge constructs and critical thinking for instructors to design intervention strategies. The development of social knowledge constructs and critical thinking can be represented by cognitive presence, which is a primary component of the Community of Inquiry (CoI) model. However, identifying learners' cognitive presence is a challenging problem, and most researchers have performed this task using traditional machine learning methods that require both manual feature construction and adequate labeled data. In this paper, we present a novel variant of the Bidirectional Encoder Representations from Transformers (BERT) model for cognitive presence identification, namely MOOC-BERT, which is pre-trained on large-scale unlabeled discussion data collected from various MOOCs involving different disciplines. MOOC-BERT learned deep representations of unlabeled data and adopted Chinese characters as inputs without any feature engineering. The experimental results showed that MOOC-BERT outperformed the representative machine learning algorithms and deep learning models in the performance of identification and cross-course generalization. Then, MOOC-BERT was adopted to identify the unlabeled posts of the two courses. The empirical analysis results revealed the evolution and differences in MOOC learners' cognitive presence levels. These findings provide valuable insights into the effectiveness of pre-training on large-scale and multidiscipline discussion data in facilitating accurate cognitive presence identification, demonstrating the practical value of MOOC-BERT in learning analytics.

Self-Directed Learning and Intrinsic Motivation Levels in MOOCs

Self-Directed Learning and Intrinsic Motivation Levels in MOOCs