Massive Open Online Courses Environment Research Articles

Towards automated assessment of participation and value creation in networked learning

This paper investigates the integration of artificial intelligence (AI) in Massive Open Online Courses (MOOCs) to improve student engagement and learning outcomes. MOOCs have transformed the landscape of online education, offering wide accessibility but at the price of high dropout rates and limited ability to measure student engagement. With MOOCs' expansive user base, it has been difficult until now to move beyond conventional metrics of course completion and quiz scores, which are insufficient for understanding the multifaceted nature of student engagement. The study addresses these challenges through a novel method of applying a large language model (LLM) to develop a more complex and meaningful understanding of student engagement and experiences in MOOCs.The research is grounded in the networked learning (NL) research tradition by using the Community of Inquiry framework to explore social, cognitive, and teaching presence in online educational environments and automate the assessment of student learning participation. The study focuses on social engagement and interaction, crucial for perceived value creation. Theoretical soundness, data targeting and quality, interpretation benchmarks, and robust data sampling and analysis strategies constitute the foundational pillars of the proposed predictive system. This framework guides the study's approach to address common MOOC issues, such as learner isolation and limited interaction, which are critical factors in students' perceived value and learning.The methodology involves a dual approach of human and LLM coding, testing the ability of LLMs to replicate human coding accuracy. The study was conducted in a non-massive open online course to test system effectiveness before applying it to larger MOOCs. Analysis indicates that an initial noncomplex LLM model can match human coding performance to 60% reliability, representing a significant step forward in automating qualitative analysis in online education, with potential for enhanced results using more complex LLM modelling. Key findings show that AI can effectively measure and enhance student engagement in MOOCs. The use of LLMs in coding qualitative data offers a reliable method for understanding student participation, which is vital for improving educational outcomes in online courses. The research highlights the potential of AI in transforming online education by providing deeper insights into student engagement and learning processes.The study also provides insights into the interplay of AI-measured engagement and learners' value perception, advancing the understanding of networked learning participation and its impact on educational outcomes. Implications extend to curriculum development, pedagogical strategies, and the broader field of online education. Future work includes expanding this methodology to more extensive MOOC environments and incorporating qualitative interviews to enrich the analysis.

Unlocking teachers’ potential: MOOCLS, a visualization tool for enhancing MOOC teaching

Massive Open Online Courses (MOOCs) are revolutionizing online education and have become a popular teaching platform. However, traditional MOOCs often overlook learners' individual needs and preferences when designing learning materials and activities, resulting in suboptimal learning experiences. To address this issue, this paper proposes an approach to identify learners' preferences for different learning styles by analyzing their traces in MOOC environments. The Felder–Silverman Learning Style Model is adopted as it is one of the most widely used models in technology-enhanced learning. This research focuses on developing a reliable predictive model that can accurately identify learning styles. Based on insights gained from our model implementation, we propose MOOCLS (MOOC Learning Styles), an intuitive visualization tool. MOOCLS can help teachers and instructional designers to gain significant insight into the diversity of learning styles within their MOOCs. This will allow them to design activities and content that better support the learning styles of their learners, which can lead to higher learning engagement, improved performance, and reduction in time to learn.

Exploring the impact of chat-based collaborative activities and SRL-focused interventions on students’ self-regulation profiles, participation in collaborative activities, retention, and learning in MOOCs

AbstractDespite their potential to deliver a high-quality learning experience, massive open online courses (MOOCs) pose several issues, such as high dropout rates, difficulties in collaboration between students, low teaching involvement, and limited teacher–student interaction. Most of these issues can be attributed to the large number, diversity, and variation in self-regulated learning (SRL) skills of participants in MOOCs. Many instructional designers try to overcome these issues by incorporating collaborative activities. Others try to scaffold students’ SRL levels by making SRL-focused interventions. However, limited research combines the study of SRL-focused interventions with students’ engagement in collaborative activities, course retention, and learning outcomes of MOOC environments. We deployed a programming-oriented MOOC in which we incorporated chat-based collaborative activities, supported by a learning analytics dashboard. Students were asked to complete SRL-focused questionnaires at the beginning and the end of the course. Based on their score, we calculated an average score that forms their SRL level, creating three groups: (a) control, (b) general intervention, and (c) personalized intervention in which we provided personalized interventions. We compared the students’ learning outcomes, participation in collaborative activities, and retention in the MOOC. These comparisons provided evidence regarding the positive impact of different intervention modes on students’ engagement in collaborative activities and their learning outcomes, with respect to their various SRL profiles. Students allocated to the general and personalized intervention groups displayed increased participation in the collaborative activities and learning outcomes, as compared to students assigned to the control group. We also documented that the SRL interventions positively affected students’ course retention.

Digital twins in MOOCs: exploring ways to enhance interactivity

PurposeThe purpose of this conceptual paper is to inform the administrators, designers, trainers and scholars, and encourage conversations on the role of digital twin technologies in enhancing interactivity in the context of massive open online courses (MOOCs).Design/methodology/approachDrawing on a review of literature, this article highlights the concept of digital twins and discusses its potential applications on enhancing interactivity in MOOCs.FindingsFindings of this conceptual paper highlights the significance of the emerging industry 4.0 technologies such as digital twins, in enhancing interactivity in a MOOC environment. Implementation of modern technologies such as digital twins and AI in MOOCs could guarantee access to comprehensive and equitable quality education and could expand the scope of lifelong learning for everyone.Practical implicationsThis conceptual paper is particularly relevant to the post-pandemic context where organizations and educational institutions have both adopted a hybrid mode of learning. Inclusion of the interactive elements may provide better insights to the platform administrators, designers, trainers and to enhance levels of learner engagement in MOOC environments. Moreover, effective utilization of these technology driven learning models in future may have larger impacts in catering to meet the sustainable development goal (SDG-4) towards provision of quality education.Originality/valueWhile past studies focused more on the conceptualization of human-to-human interactivity, the facets of human-to-platform interactivity remained largely unexplored. To our understanding, this paper is one of the initial attempts to conceptually highlight the applications of digital-twin technology to enhance human to platform interactivity in a MOOC context.

Evaluating Human-Computer Interaction at Design Time: an experience report with MoLVERIC Check in a MOOC environment

The Modeling Language for Interaction as Conversation (MoLIC) is an epistemic tool inspired by Semiotic Engineering to model (human-computer) interaction through diagrams. MoLVERIC check (MCheck) is a diagram inspection technique developed for MoLIC, whose objective is to identify defects that would propagate in the modeled system's implementation and become communication interruptions. In this sense, this article is an experience report of evaluating a massive open online course (MOOC) environment through MoLIC, and predicting the quality of interaction to mitigate disruptions of metacommunication by MCheck. Although MCheck supports the inspection of MoLIC diagrams, in this article, we noticed the lack of MoLIC elements in the MCheck, indicating that this technique needs to evolve.

Modeling the Impact of Massive Open Online Courses (MOOC) Implementation Factors on Continuance Intention of Students: PLS-SEM Approach

The Engineers in Society (EIS) course is a common course unique to Universiti Teknologi PETRONAS. However, every semester, the course receives 300 to 500 students, making managing and delivering it a challenging task. The EIS course is thus in need of a suitable mode of delivery where the teaching and learning process can cater to a large number of participants from a variety of programs. The aim of this study is to address the effect of Massive Open Online Courses MOOC factors implementation on the continuance intention of students. The study employed a survey that was designed from a literature review. The survey adopted a series of questions to gather information about the problem under investigation. One hundred forty-eight responses were collected from the students in different engineering, project and operation management, quality, sustainability, and entrepreneurship programs. In addition, partial least squares regression-structural equation modelling was used to analyze data. Based on the results, there is a significant impact of MOOC implementation factors on the continuance intention of students. Nevertheless, students showed a high intention to continue studying engineering in society courses online MOOC. Therefore, the current study provides practical evidence for management and lecturers of the university to enhance MOOC factors to ensure the high quality of teaching and enhance the continuance intention of students to study in a MOOC environment.

Exploring the link between self‐regulated learning and learner behaviour in a massive open online course

AbstractBackgroundLearners in Massive Open Online Courses (MOOCs) are presented with great autonomy over their learning process. Learners must engage in self‐regulated learning (SRL) to handle this autonomy. It is assumed that learners' SRL, through monitoring and control, influences learners' behaviour within the MOOC environment (e.g., watching videos). The exact relationship between SRL and learner behaviour has however not been investigated.ObjectivesWe explored whether differences in SRL are related to differences in learner behaviour in a MOOC. As insight in this relationship could improve our understanding of the influence of SRL on behaviour, could help explain the variety in online learner behaviour, and could be useful for the development of successful SRL support for learners.MethodsMOOC learners were grouped based on their self‐reported SRL. Next, we used process mining to create process models of learners' activities. These process models were compared between groups of learners.Results and conclusionsFour clusters emerged: average regulators, help seekers, self‐regulators, and weak regulators. Learners in all clusters closely followed the designed course structure. However, the process models also showed differences which could be linked to differences in the SRL scores between clusters.TakeawaysThe study shows that SRL may explain part of the variability in online learner behaviour. Implications for the design of SRL interventions include the necessity to integrate support for weak regulators in the course structure.

Assessing Learner Facilitation in MOOC Forums: A Mixed-Methods Evaluation Study

One of the main challenges of massive open online courses (MOOCs) is the effective facilitation of learners in the course forum. The more learners participating in the forum, the more difficult it is for instructors to provide timely support. The effective intervention of teaching assistants (TAs) can play a crucial role in mitigating this issue; however, questions arise regarding the instructional approaches that TAs follow and whether they can effectively promote learning in a MOOC environment. In this article, we study the instructional approaches that TAs followed in two MOOCs of different subject matters, using mixed-methods. The goal was to evaluate the pedagogies that TAs adopted in facilitating learners, following a broadly accepted framework on MOOC instructional quality. Content analysis and interviews with the TAs provided insights into their intervention strategies, while linguistic and social network analysis enhanced the findings. The study revealed that the TAs most often were rushing to provide direct answers rather than guiding learners in finding themselves an answer. Problem-solving approaches and collaboration were not adequately promoted. The findings of this article provide design implications for the development of supportive tools that could automatically assess facilitators’ instructional approaches in the forum. Thus, timely feedback could be provided to instructors so as to refine the instructional design of their online courses and to provide guided support to their learner facilitators while courses are running.

Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners' cognitive presence and learning achievements.

The MOOCs (Massive Open Online Courses) forum carries rich discussion data that contains multi-level cognition-related behavior patterns, which brings the potential for an in-depth investigation into the development trend of the group and individual cognitive presence in discourse interaction. This paper describes a study conducted in the context of an introductory astronomy course on the Chinese MOOCs platform, examining the relationship between discussion pacings (i.e., instructor-paced or learner-paced discussion), cognitive presence, and learning achievements. Using content analysis, lag sequential analysis, logistic regression, and grouped regression approaches, the study analysed the online discussion data collected from the Astronomy Talk course involving 2603 participants who contributed 24,018 posts. The findings of the study demonstrated the significant cognitive sequential patterns, and revealed the significant differences in the distribution of cognitive presence with different discussion pacings and learning achievement groups, respectively. Moreover, we found that the high-achieving learners were mostly in the exploration, integration, and resolution phase, and learner-paced discussion had a greater moderating effect on the relationship between cognitive presence and learning achievements. Based on the findings and discussion, suggestions for improving the learners’ cognitive presence and learning achievements in the MOOC environment are discussed.

Psychometric assessment of a tool to evaluate motivation and knowledge of an energy-related topic MOOC

ABSTRACT Online education has increased over the last two decades in response to an increasing learning demand. Massive open online courses (MOOCs) seem to be an adequate tool to facilitate current educational dynamics. However, evaluating students’ motivation and learning to predict their engagement when attending a MOOC represents a challenge. This study combines characteristics of data from MOOC environments (e.g., large sample size) with well-established psychometric methodology (validity and reliability estimations) to attain a reliable resource to evaluate MOOC participants. Four independent datasets from MOOCs on energy-related topics were used to psychometrically examine the new “Initial survey of interests, motivations, and knowledge regarding MOOCs” (EIIMC-MOOC). The results indicate that the EIIMC-MOOC is a valid, reliable and stable tool to evaluate initial motivation and prior knowledge of participants attending energy-related topics.

Dynamic Identification of Learning Styles in MOOC Environment Using Ontology Based Browser Extension

With the advent of the era of big data and Web 3.0 on the horizon, different types of online deliverable resources in the pedagogical field have also become raft. Massive Open Online Courses (MOOCs) are the most important of such learning resources that provide many courses at different levels for the learners on the go. The data generated by these MOOCs, however, is often unorganized and difficult to track or is not used to the extent that allows identification of learner types to facilitate better learning. The proposed approach in this paper aims to detect the learning style of a learner, interacting with the MOOC portal, dynamically and automatically through a novel, indigenous and in-built browser extension. This extension is used to capture the usage parameters of the learner and analyze learning behavior in real-time. The usage parameters are captured and stored as a learner ontology to ease sharing and operating across different platforms. The learning style so deduced is based on the Felder Silverman Learning Style Model (FSLSM), where learner’s behavior under multiple criteria, vis-`a-vis perception, input, understanding, and processing are measured. Based on the generated ontological semantics of learner’s behavior, multiple models can be made to facilitate precise and efficient learning. The result shows that this state-of-the-art approach identifies and detects the learning styles of the learners automatically and dynamically, i.e., changing over time

Moroccan Teachers of English Acceptance and Readiness to Use MOOCs for Continuing Professional Development

MOOCs (Massive Open Online Courses) have become a modern concept in education and new meaningful learning environment for technology-enhanced learning, where huge numbers of users worldwide access free, online-based, open content generated by world-renowned institutions. To provide successful MOOC environments requires recognizing the learners' motivations to utilize MOOCs programs. Because of the scarcity of research on MOOCs' use for teacher's professional development, it makes use of information technologies to enhance the learning experience for teachers and students. The present study seeks to investigate acceptance and readiness towards MOOCs' use for continuing professional development besides the degree to which teachers benefit from MOOCs. A survey questionnaire based on TAM (Technology Acceptance Model) [8] is employed as a data collection instrument regarding Usefulness(U), Perceived Ease of Use (PEU), User Satisfaction and Attribute of Usability (USAU) of MOOCs in professional development. The population is senior high secondary school teachers of English from different regions of Morocco. The analysis of the results obtained from various statistical tests revealed that teachers accepted MOOCs programs for continuing professional development with an objective of developing, acquiring and consolidating the necessary insight, skills and competencies required in modern teaching for 21-century education. Most teachers attending MOOCs' programs for continuing professional development were satisfied with the results they obtained from their participation. They also reported that MOOCs programs had an unequivocal and effective impact on their professional and career development.

Content presentation techniques for learning experience enhancement in Massive Open Online Course (MOOC)

MOOCs (Massive Open Online Courses) are a new and innovative technique of disseminating knowledge to millions of people all over the world. Despite being a fantastic learning tool with a global reach, MOOCs have their own set of restrictions, resulting in an extraordinarily low course completion rate. Personalization and engagement are lacking in traditional material presentation strategies. Hence, MOOCs must adopt advanced learning approaches in order to create a more engaging experience for the learners. This study introduces Augmented MOOC (A-MOOC), an enriched learning MOOC environment. A-MOOC explores Active Learning, Augmented Reality, and Gamification techiques, and show how these strategies can be combined with MOOC content in both online and offline modes. It demonstrates the favourable influence of creating interactive and more engaging content in the current learning setting to increase learners’ motivation to complete a MOOC.

MOOC discussion forums: The interplay of the cognitive and the social

Massive Open Online Course (MOOC) platforms capture the digital traces of millions of learners and generate an avalanche of “numbers” on learner behavior in MOOCs. Yet little is known about the dynamics through which MOOCs can support individual learning as the cognitive and social constituents of this complex process and their interplay within this process do not clearly surface in this large mass of “numbers”. This study analyzed the content generated by learners in a MOOC discussion forum with a particular focus on the still under-explored cognitive dimension of learning in MOOCs and demonstrated how certain levels of cognitive engagement relate to learning. It further examined the interplay between the cognitive and social aspects, revealing the moderating role of the social aspect in the association between the lowest level of cognitive engagement and learning in a MOOC environment. The study concludes with discussing the theoretical and practical implications of the findings and with highlighting the need to consider the interdependencies between the cognitive and social variables and learning when designing and evaluating MOOCs.

Application of PBL Mode in a Resident-Focused Perioperative Transesophageal Echocardiography Training Program: A Perspective of MOOC Environment.

BackgroundMassive open online courses (MOOCs) is a new teaching technology based on a network platform, which can provide more students with the possibility of systematic and repeated learning. Due to the complexity of transesophageal echocardiography (TEE), the wide range of knowledge involved, and many knowledge points being difficult to understand, it is difficult to meet the teaching needs of this content with the traditional teaching mode. This study aimed to discuss the application of lecture based learning (LBL) and problem based learning (PBL) in the training of perioperative TEE in the context of MOOCs.MethodsSixty residents were randomly divided into two groups to participate in a 5-day perioperative TEE training program. The web-based PBL mode was used in the observation group and the LBL mode was used in the control group, with 30 residents in each group. The teaching effect of the web-based PBL mode was compared with the LBL mode by basic theory test, practice examination, image interpretation and questionnaire.ResultsThe image interpretation score and the total test score were higher in the observation group than in the control group with statistically significant differences (P<0.001). The results of the questionnaire show that the residents were more satisfied with the web-based PBL mode than the LBL mode (P<0.001).ConclusionThe web-based PBL method has obvious advantages over the LBL mode in the training of perioperative TEE.

Evaluando la Transferencia del Aprendizaje de MOOCs al Centro de Trabajo: Un Estudio de Caso en Educación para el Profesorado y Lanzando Innovación en Colegios

Over two iterations of a Massive Open Online Course (MOOC) for school leaders, Launching Innovation in Schools, we developed and tested design elements to support the transfer of online learning into offline action. Effective professional learning is job-embedded: learners should employ news skills and knowledge at work as part of their learning experience. This MOOC aimed to get participants to plan and actually launch new change efforts, and a subset of our most engaged participants were able and willing to do so during the course. Required assessments spurred student actions, along with instructor calls to action and modeling and exemplars provided by course elements. We found that participants led change initiatives, held stakeholder meetings, collected new data about their contexts, and shared and used course materials collaboratively. Collecting data about participant learning and behavior outside the MOOC environment is essential for researchers and designers looking to create effective online environments for professional learning.

Deconstructing self-regulated learning in MOOCs: In search of help-seeking mechanisms.

Researchers see self-regulated learning (SRL) as a fundamental skill for succeeding in massive open online courses (MOOCs). However, there is no sufficient evidence of adequate functioning of SRL dimensions such as environment structuring, goal setting, time management, help-seeking, task strategies, and self-evaluation in the MOOC environment. This study fills the gap in understanding the structure of SRL skills utilising the Online Self-Regulated Learning Questionnaire (OSLQ). The construct-related validity of the OSLQ is evaluated based on self-reported survey responses of 913 Russian MOOC learners with confirmatory factor analysis and criterion-related validity is checked with independent samples t-tests comparison. The results show that the original six-factor hierarchical model does not fit the data adequately. The evidence implies that the dimension ‘help-seeking’ is not effective in the MOOC environment. Therefore, a redefined five-factor hierarchical model of the OSLQ is suggested.

A Model and Tool to Support Pedagogical Scenario Building for Connectivist MOOC

The work presented in this paper examines part of a broader issue in the field of technology-enhanced learning and focused on Massive Open Online Courses environments (MOOC). The main goal is to provide an approach, models and tools to assist teachers in the scenario design process and the operationalization of pedagogical scenarios in a connectivist MOOC context. The major contribution of this work is the MOOC Authoring Tool (MOOCAT), which is a visual authoring tool that is intended for the design and deployment of cMOOC-oriented scenarios. MOOCAT has two main innovative features. The first feature offers a tool for designing educational scenarios in a simple way, through a graphical representation for defining learning workflow. The second feature is related to its capacity to bridge the gap between the design phase and its execution in different Learning Management Systems (LMS), by offering services that allow the automatic deployment of pedagogical scenarios to existing LMS. This paper presents the underlying model of MOOCAT, and describes the cMOOC scenario-building process. The tool was evaluated primarily from the point of view of utility and usability. A total of 40 individuals have participated in the experimentation. The findings confirm that MOOCAT can be used to design connectivist pedagogical scenarios and can provide all the necessary elements for the design of such courses. Feedback from participants emphasized the ease of use and the benefits of the proposed visual authoring tool.

Motivational effects of open badges in MOOCs. A learner perception study in OpenVM Learning Hub and MiriadaX

Open Badges have become a metadata standard that has been used to transform digital credentialing practices in learning contexts including Massive Open Online Courses (MOOCs). Badges may be designed and used in diverse ways to enhance learner motivation in MOOCs. However, empirical research about the motivational effects of the badging process has been limited to anecdotal evidence. Objective: The goal of this study was to explore the motivational effects of open badges in MOOCs. The primary research question was whether and how open badges motivate participants to: a) learn in MOOCs, b) to complete MOOCs, and c) to use Open Badges outside of the MOOC environment. Method: An exploratory learner perception study was conducted in two platforms: OpenVM and MiriadaX. Both platforms issue Open Badges to recognize learning outcomes in MOOCs. The research sample included 1788 participants with diverse backgrounds, who participated in OpenVM (n = 1412) and in MiriadaX (n = 376) courses. Learner perceptions were self-reported via two online surveys, which included an aligned set of items in English (OpenVM) and Spanish (MiriadaX). Results: The findings show that regardless of the MOOC platform, the topic of the course and the language of instruction, MOOC participants were motivated by open badges, especially by an adequate description of the skills developed in the MOOCs. A regression analysis indicated that an adequate skill description in the open badges had the greatest predictive value for motivation to learn in MOOCs and to complete the course (r2 = 56.3%). The instrument with five items showed high internal consistency (α = 0.92).

Factors Influencing Learners’ Self –Regulated Learning Skills in a Massive Open Online Course (MOOC) Environment

The importance of self-regulation in a MOOC has been extensively discussed in research studies that provide evidence about the significant relationship between self-regulated learning and success in an e-learning environment. Learners with high self-regulated learning are more independent in regulating their learning and have a greater probability of success in their online courses. This study identifies factors that influence self-regulated learning and determines relationships between these factors and self-regulated learning. A conceptual model is proposed for combining success factors for self-regulated learning in a MOOC environment. A research instrument based on the model was designed and administered to six hundred and twenty-two MOOC students enrolled in five universities. Relationships between relevant factors and self-regulated learning were examined using a Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, and the statistical findings revealed that three factors - service quality, attitude, and course quality - influence self-regulated learning in a MOOC.