Massive Open Online Courses Learners Research Articles

Extending Information System Success Model with Customer Experience: A MOOC System Evaluation

An extension of the open education resource (OER) and open course ware (OCW) movement, massive open online courses (MOOCs) have changed the education landscape. MOOCs offer free courses from reputed universities to learners across the world. Despite immense popularity and universal adoption, very little is known about what constitutes MOOC success and effectiveness. The purpose of this study was to investigate the factors influencing MOOC success by developing a model named the learner satisfaction model for MOOCs (LSMM). The model was empirically tested through a cross-sectional research design. Primary data were collected using purposive sampling from 227 respondents in India. The partial least square structural equation modelling (PLS-SEM) technique was used for testing the hypothesized relationships. The outcomes demonstrated that MOOC system usage significantly influenced the user satisfaction of MOOC learners. Also, the learner experience positively influenced MOOC system usage and satisfaction. However, it was observed that system quality influenced satisfaction but did not impact usage, and information quality had no impact on system use and satisfaction. Although the study has limited generalizability, the findings will help the MOOC platform providers, facilitators, and other stakeholders to design and implement the MOOCs in such a way to enhance their success and effectiveness.

Research on personalized recommendation of MOOC resources based on ontology

PurposeThe purpose of this study is to analyze from multiple perspectives, so as to form an effective massive open online course (MOOC)personalized recommendation method to help learners efficiently obtain MOOC resources.Design/methodology/approachThis study introduced ontology construction technology and a new semantic association algorithm to form a new MOOC resource personalized recommendation idea. On the one hand, by constructing a learner model and a MOOC resource ontology model, based on the learner’s characteristics, the learner’s MOOC resource learning preference is predicted, and a recommendation list is formed. On the other hand, the semantic association algorithm is used to calculate the correlation between the MOOC resources to be recommended and the learners’ rated resources and predict the learner’s learning preferences to form a recommendation list. Finally, the two recommendation lists were comprehensively analyzed to form the final MOOC resource personalized recommendation list.FindingsThe semantic association algorithm based on hierarchical correlation analysis and attribute correlation analysis introduced in this study can effectively analyze the semantic similarity between MOOC resources. The hybrid recommendation method that introduces ontology construction technology and performs semantic association analysis can effectively realize the personalized recommendation of MOOC resources.Originality/valueThis study has formed an effective method for personalized recommendation of MOOC resources, solved the problems existing in the personalized recommendation that is, the recommendation relies on the learner’s rating of the resource, the recommendation is specialized, and the knowledge structure of the recommended resource is static, and provides a new idea for connecting MOOC learners and resources.

Trends and Issues in MOOC Learning Analytics Empirical Research: A Systematic Literature Review (2011–2021)

Learning analytics (LA) is a growing research trend and has recently been used in research and practices in massive open online courses (MOOCs). This systematic review of 166 articles from 2011–2021 synthesizes the trends and critical issues of LA in MOOCs. The eight-step process proposed by Okoli and Schabram was used to guide this systematic review in analyzing publication outlets, research purposes and methods, stakeholders, and researchers’ geographic locations and subjects. The review showed that MOOC LA research is primarily published in top-tier journals, such as the Journal of Learning Analytics, Journal of Computer Assisted Learning, and Computers & Education, as well as conference proceedings. The review also revealed that LA in MOOCs was used more frequently for the purpose of research than for practice (i.e., learning and teaching). Approximately 60% of the reviewed studies adopted learners’ log data and achievement data as the primary data sources. Statistics, machine learning, content analysis, social network analysis, text analysis, and data visualization were the top six specific data analysis techniques used in the MOOC LA studies. Regarding collaboration, more than half of the reviewed studies involved interdisciplinary collaborations, and approximately one-third involved international collaborations. We suggested future studies on MOOC LA interventions to improve learning and teaching practices, and the active interdisciplinary collaboration to increase the rigor of the studies and the dissemination of the knowledge. More detailed discussion and implications for research and practice are presented. This research provides insights on future research and practices regarding LA use in MOOCs.

Analysis of The Characteristics of emotional Experience in MOOC Learning

With the rapid development of massive open online courses (MOOCs), researchers have begun to pay attention to the experience of teachers and students in the MOOC classroom. Select the middle school mathematics curriculum standard and textbook research course in the MOOC platform of Chinese universities and collect 66 selfreported data of instantaneous experience and long-term experience of 10 learners on the course learning within two weeks. Qualitative and quantitative analyses were carried out in 8 sub-dimensions, including device usage preferences and problems, and platform tool application. The purpose of the research is to investigate the learning experience of MOOC learning platform more comprehensively and deeply. The research results show that mobile learning has become the main way of MOOC learning. The appearance, education and economy of the tools displayed on the platform directly affect the learning experience of learners. Demonstration tools are highly dependent, but the frequency of application, types and functions of tools are limited, and there is a lack of awareness and application of tools that promote advanced learning, deep learning, and reflective learning; the overall emotional experience of learners is in a positive emotional state and shows a distinct group characteristic. The learning experience of MOOC is directly related to the appearance, education and economy of the display tools in the platform; Learners have diverse experience of platform communication and cooperation tools, and are highly dependent on learning content display tools in the platform; Learners' emotional experience is both positive and negative, but it is dominated by positive emotions and shows distinct group characteristics.

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

This research is to investigate the impact of massive open online course (MOOC) learning platforms on teacher development, promote the development and innovation of online learning models under the “Double First-Rate,” and especially expand the application of the MOOC platforms combining language learning with professional development paths. The MOOC online learning platform has a problem of a high abandonment rate. This paper first proposes a MOOC learning recommendation algorithm based on the learning sequence and similarity distance analysis as well as evaluates its accuracy. Then, the reliability test and the MOOC learning recommendation algorithm are used to evaluate the quality evaluation system of English teaching in the MOOC utilizing the structural equation model. Finally, the strengths, weaknesses, opportunities, and threats (SWOT) are determined to analyze the impact of the MOOC regarding the English teaching platform on teachers' comprehensive development. The results show that the MOOC platform-based learning recommendation algorithm has higher recommendation accuracy and efficiency, improving the learning effect with the utilization of the MOOC. Also, it can effectively reduce the abandonment rate and has a positive effect of resolving the interaction problem pertinent to characteristic differences and sequences in the learning recommendation. The quality evaluation system of online English teaching in the MOOC has higher reliability and convergent validity, which shows better stability and consistency in all dimensions. If teachers can actively learn from the resources of the MOOC platform, then they continuously update teaching concepts, improve online teaching, give full play to their language advantages, accurately locate student needs, and develop unique courses. Therefore, it will promote the overall development of their careers and improve innovation.

Massive Open Online Course Study Group: Interaction Patterns in Face-to-Face and Online (Facebook) Discussions.

Interaction has been regarded as a key design component in online and distance learning. In this study, we convened a student-led, blended mode (face-to-face and online/Facebook discussions) massive open online course (MOOC) study group to facilitate interactions for learning. Multiple data, including voice recordings, one-on-one interviews, video recordings, and artifacts were collected and analyzed to detect patterns of interaction in both face-to-face and online/Facebook settings, as well as student perceptions of the blended MOOC study group. Findings indicated that, overall, the blended mode MOOC study group was helpful for promoting communication, providing help, resolving problems, and exchanging ideas and information among group members. Moreover, face-to-face meetings and online discussions both might have exerted their unique strengths and functions in different learning situations for different learners. We recommend future studies continue to explore the tenability of the blended mode MOOC study group in different contexts, subject areas, and age groups, as well as examining group dynamics and interactions that transform MOOC learning into interactive, motivating, and fulfilling journeys among study group members.

Fostering self-directed learning in MOOCs: Motivation, learning strategies, and instruction

Given the increasing number of learners in massive open online courses (MOOCs), students’ self-directed learning (SDL) skills are necessary for their success. The purpose of this study was to explore learners’ motivation for enrolling in MOOCs and their SDL strategies, as well as instructional elements that support SDL from learners’ perspectives. This qualitative study adopted a phenomenological research design. The data source was semi-structured interviews with 15 learners from three MOOCs. The data were analyzed using thematic analysis. The researchers found that the motivation for enrolling in MOOCs included intrinsic motivation (e.g., curiosity, improving personal knowledge, and personal interest) and extrinsic motivation (e.g., supporting formal education and career development). The learning strategies used by MOOC learners were task strategies, self-monitoring, and self-management strategies. The task strategies included taking notes, reading texts or subtitles, watching videos, and conducting further research. The self-monitoring strategies included self-assessment, self-reflection, progress indicators, final projects, and authentic tasks. Learners’ self-management strategies (e.g., time management and resource management) varied depending on their diverse motivations. In addition, the instructional elements that support SDL were self-assessment and discussion forums, instructor feedback, flexibility, clearly stated learning goals, the authenticity of the content, and small learning units. The implications of the study are discussed in the paper.

Intelligent System Using Deep Learning for Answering Learner Questions in a MOOC

Despite the great success of Massive Open Online Courses (MOOCs), the success rate of learners remains very low. Therefore, a lot of research has been done to understand and solve this abandonment problem. This work is part of the same effort which aims to improve learning in MOOCs. We offer an intelligent system capable of assisting the learner by providing answers to all his questions on the subjects covered in the MOOC. The architecture of our system is based on new advances in artificial intelligence, in particular the applications of deep learning in the field of natural language processing (NLP). The results obtained are quite interesting and demonstrate the relevance of our solution.

A learning model for improving in-service teachers’ course completion in MOOCs

ABSTRACT The use of massive open online courses (MOOCs) for teacher professional development (TDP) has increased in the past decades. This study explored the key factors that influenced teachers’ online course completion as a significant indicator of their success in a TPD MOOC. Six key influencing factors (self-efficacy, interaction with curriculum content and peers, satisfaction, overall course score, and TPACK self-assessment) were identified from the perspectives of general influencing factors and teacher specific influencing factors. Employing a learning model that was constructed as a hypothesis path model based on literature review, we analysed these influencing factors’ effects on participants’ online learning completion. Results showed that participants’ TPACK self-assessment and overall course score had a strong direct effect on their course completion, while their interaction with course content and peers had a significant indirect effect on their course completion. This study also discussed the features of teachers’ learning in TPD MOOCs based on the results. Suggestions to promote the success of TPD MOOCs from the aspects of curriculum, learners, and community were put forward. Implications and prospects for research and practice are discussed.

Measuring online self-regulated learning among early-career medical doctors in a Massive Open Online Course on COVID-19

Introduction: As significant autonomy is given in a Massive Open Online Course (MOOC), online self-regulated learning (SRL) ability is crucial in such courses. We aim to measure the online SRL abilities of early-career medical doctors enrolled in a MOOC. Methods: We performed a cross-sectional study using the Self-Regulated Online Learning Questionnaire-revised version (SOL-Qr). We conducted a three-stage cross-cultural validation of the SOL-Qr, followed by Confirmatory Factor Analysis (CFA). The online SRL ability of 5,432 medical doctors enrolled in a MOOC was measured using the validated SOL-Qr. Results: The CFA of the cross-translated SOL-Qr confirmed its comparability to the original version, with excellent validity & reliability. Participants showed high levels of online SRL during their early careers. Despite high online SRL scores, MOOC completion rate was low. Male participants showed slightly better time management ability than female participants. Participants working in the primary epicentrum for COVID-19 in the country showed lower online SRL scores, while participants who graduated from higher accreditation levels showed better time management ability. Conclusion: The SOL-Qr and its subscales are suitable and valid for measuring the online SRL abilities of medical doctors in a MOOC during their early-career period. Time management ability was associated with previous experience during the medical education period, while other online SRL subscales were mostly associated with workload. However, as the scores did not correlate with the time spent for learning in MOOC, the corresponding learning effort or time spent may be beyond just the commitment to the described MOOC.

Measuring online self-regulated learning among early-career medical doctors in a Massive Open Online Course on COVID-19

Introduction: As significant autonomy is given in a Massive Open Online Course (MOOC), online self-regulated learning (SRL) ability is crucial in such courses. We aim to measure the online SRL abilities of early-career medical doctors enrolled in a MOOC. Methods: We performed a cross-sectional study using the Self-Regulated Online Learning Questionnaire-revised version (SOL-Qr). We conducted a three-stage cross-cultural validation of the SOL-Qr, followed by Confirmatory Factor Analysis (CFA). The online SRL ability of 5,432 medical doctors enrolled in a MOOC was measured using the validated SOL-Qr. Results: The CFA of the cross-translated SOL-Qr confirmed its comparability to the original version, with excellent validity & reliability. Participants showed high levels of online SRL during their early careers. Despite high online SRL scores, MOOC completion rate was low. Male participants showed slightly better time management ability than female participants. Participants working in the primary epicentrum for COVID-19 in the country showed lower online SRL scores, while participants who graduated from higher accreditation levels showed better time management ability. Conclusion: The SOL-Qr and its subscales are suitable and valid for measuring the online SRL abilities of medical doctors in a MOOC during their early-career period. Time management ability was associated with previous experience during the medical education period, while other online SRL subscales were mostly associated with workload. However, as the scores did not correlate with the time spent for learning in MOOC, the corresponding learning effort or time spent may be beyond just the commitment to the described MOOC.

Self-Regulated Learning User Interface (SRLUI) Design Principles and Experimental Findings in MOOCs

There have been many attempts to support learners in massive open online courses (MOOCs) with retention and self-regulated learning (SRL) with limited and mixed findings. This paper presents a design of integrated SRL support called SRLUI developed from a review of SRL support and empirical findings. The results from implementing SRLUI for 808 learners in eight MOOCs are also presented. The finding indicates that SRLUI has a high compliance rate (80%), and increases learning outcomes for a high-performance group. There is no direct evidence indicating SRLUI has an impact on learner persistence. Future studies are suggested to investigate the potential typologies of MOOC learners and differentiate SRL supports into subgroups.

Intelligent System for Personalised Interventions and Early Drop-out Prediction in MOOCs

In this paper, we propose an approach to early detect students at high risk of drop-out in MOOC (Massive Open Online Course); we design personalised interventions to mitigate that risk. We apply Machine Learning (ML) algorithms and data mining techniques to a dataset extracted from XuetangX MOOC learning platforms and sourced from the KDD cup 2015. Since this dataset contains only raw student log activity records, we perform a hybrid feature selection and dimensionality reduction techniques to extract relevant features, and reduce models complexity and computation time. Besides, we built two models based on: Genetic Algorithms (GA) and Deep Learning (DL) with supervised learning methods. The obtained results, according to the accuracy and the AUC (Area Under Curve)-ROC (Reciever Operator Characteristic) metrics, prove the pertinence of the extracted features and encourage the use of the hybrid features selection. They also proved that GA and DL are outperforming the baseline algorithms used in related works. To assess the generalisation of the approach used in this work, The same process is performed to a second benchmark dataset extracted from the university MOOC. Then, a single web application hosted on the university server, produces an individual weekly drop-out probability, using time series data. It also proposes an approach to personalise and prioritise interventions for at-risk students according to the drop-out patterns.

Inovasi Pembelajaran Pendidikan Agama Islam di Era Revolusi Industri 4.0 Melalui Media Massive Open Online Course (MOOC)

This article discusses Islamic Religious Education (PAI) in the digital era, Massive Open Online Course (MOOC) media as a learning innovation that is more modern, adaptive, and aligned with the independent learning curriculum. The Independent learning approach help learners broaden their horizons and improve their abilities based on their separate learning experiences. The success and quality of learning, especially Islamic Religious Education, can be supported by the course design contained in the MOOC. The advantages of flexible and collaborative MOOC learning media can be shared between universities and are expected to improve the quality of Islamic Religious Education in Indonesia. The research method uses a qualitative descriptive approach to explore Islamic Religious Education innovations in the Industrial Revolution 4.0 era through Massive Open Online Course (MOOC) media. The final result is the MOOC learning media as an innovation for Islamic Religious Education which is adaptively prepared in Islamic Higher Education and is aligned with the independent learning curriculum.

Revealing the regulation of learning strategies of MOOC retakers: A learning analytic study

Massive Open Online Courses (MOOCs) have already shown a great potential to be used as an alternative model for teacher professional development (TPD). Not only do MOOCs offer relevant content and activities, but they also provide opportunities for strengthening the skills for self-regulated learning of teachers as life-long learners. In this study, we focused on a unique sub-group of MOOC learners – retakers who take the same TPD MOOC multiple times. In the empirical study reported in this paper, we examined what learning strategies they choose when taking a TPD MOOC and the extent to which they are able to adapt their learning strategies to improve their performance in the subsequent attempts. By using learning analytic methods, we detected five learning strategies and eight major forms of strategy change adapted by MOOC retakers. We found that two strategy changes (from Content-Oriented Strategy to Intensive-Thorough Strategy or Balanced Strategy) with associated with significantly higher performance in the subsequent attempts compared to no change in strategy. Our findings have implications for MOOC instructors and providers about the ways they can support MOOC retakers. Our findings also suggest that MOOCs are not only valuable TPD resources for teachers, but they also have unique opportunities for strengthening the self-regulation skills of teacher learners as the “by-products of learning”.

Students' Technology Competency Levels for Online Learning Using MOOCs During the COVID-19 Pandemic

This study was conducted with the aim of identifying technological competencies of students who use Massive Open Online Courses (MOOC) as an online learning platform during the COVID-19 pandemic. Data were collected using online surveys with 1162 undergraduates. Descriptive analysis was performed on the data obtained. The results of the data analysis and discussion have been made and it can be concluded that the level of technical competence, social competence with instructors and classmates as well as communication proficiency among students using MOOC learning is at a high level

Development and Validation of Student’s MOOC-efficacy Scale: Exploratory Factor Analysis

Abstract: Massive Open Online Courses (MOOCs), the fast developing teaching and learning platforms of global interest, are the product of higher education's expanded usage of e-learning. MOOCs have been heralded as the potential educational breakthrough for the 21st century. A positive attitude toward self-efficacy is essential for successful MOOC implementation. However, only a few studies have been conducted to ascertain a student's MOOC efficacy level, and a very limited scale for assessing a student's MOOC efficacy has been identified in the context of Malaysia. This study aims to develop and validate a student’s MOOC-efficacy scale for higher institutions. In this study, students’ MOOC-efficacy was conceived in four ways (i.e. information searching, making queries, MOOC learning, and MOOC usability). A descriptive research design with a scale was employed to collect information from 289 students enrolled in higher institutions. The Educational and Psychological Testing Standards were adapted to develop a scale to gauge students’ MOOC-efficacy. The scale's appropriate items, as well as its validity and reliability, were determined using Exploratory Factor Analysis (EFA). Significant levels of validity and reliability for the developed scale were found. The scale comprised four dimensions, i.e., information searching (three items), making queries (seven items), MOOC learning (six items) and MOOC usability (seven items), amounting to a total of 23 items. This scale was then used to determine the efficacy of higher institution students in using MOOCs. The eigenvalues of the four MOOC efficacy dimensions were within the range of 1.39 to 8.49. The factor structures explained 63.9% of the total variance. The scores of reliability varied in the range of 0.822 to 0.890. This work produced a psychometrically sound scale to measure students’ MOOC efficacy. Students, educators, administrators, and other individuals may benefit from the application of the verified MOOC efficacy scale. The information generated from the scale can be utilized to determine the training needs of students, educators and MOOC developers.
 
 Keywords: Exploratory Factor Analysis (EFA), Massive Open Online Course (MOOC), student’s MOOC-efficacy, scale development, higher institutions.

The effects of information format on learners’ experience

ABSTRACT Positive experience can increase the likelihood of revisiting Massive Open Online Courses (MOOC) site. Our interest focuses on effects of presentation formats on learner’s experience in MOOC. In two empirical studies, participants viewed online course webpages that either contained an image of the instructor or not. We examined effects of presentation format adding social cues on learner’s attention, emotion and learning outcome using questionnaire and the physical evidence provided by an eye tracker. The results obtained allow us to conclude that the instructor’s image positively affects visual attention and learner’s emotion, especially perceived usefulness from emotion factor, and has changed learner’s browsing behaviour. Moreover, the results reveal learner’s cognition mechanism in MOOC learning. The results significantly contribute to efforts to design of MOOC webpages and also provide a valuable reference when selecting presentation format for online learning.

MOOCsChain: A blockchain-based secure storage and sharing scheme for MOOCs learning

Massive Open Online Courses (MOOCs) have become a paramount online learning approach for flexible learning methods and extensive learning courses. Different from the traditional learning method, MOOCs advocate completing the learning process through online devices. Electronic Learning Records (ELRs) are vital for learners as compelling evidence of the learning process, generally are stored in a cloud data center. However, with trustless third-party storage, the security and privacy of ELRs cannot be guaranteed. Due to such, we propose a Blockchain-based secure storage and sharing scheme for ELRs in MOOCs learning systems. Designed to take advantage of blockchain, the proposed solution can support efficient conditional anonymity, secure storage, and sharing without the need for sophisticated cryptographic calculations. The experimental results and the security analysis shows that the proposed scheme achieves legitimate security assurance and outperforms other similar works.

Emotionally Engaged Learners Are More Satisfied with Online Courses

Research on massive open online courses (MOOCs) has tended to focus on outcome indicators valued in traditional higher education settings, particularly achievement and completion. This study highlights the differences between MOOCs and credit-bearing university courses and shifts this focus to an alternative outcome indicator—learner satisfaction. In this study, engagement is identified as an important antecedent of learner satisfaction and is conceptualised and operationalised as a multidimensional construct. This study built three regression models to identify the relative importance of behavioural, cognitive, emotional, and social engagement for learner satisfaction after controlling for personal characteristics unrelated to the criteria of good teaching. The analysis showed that engagement explained approximately 20% of the variance in learner satisfaction with MOOCs. Emotional engagement was more influential for predicting learner satisfaction than cognitive engagement and behavioural engagement. Social engagement had no significant effect on learner satisfaction. Demographics (age, education level, and origin) and motivation were of limited utility in predicting learner satisfaction with MOOCs, accounting for 4% and 2% of variance, respectively. Based on research findings, the article presents the following propositions: (1) configure the MOOC teaching and learning environment in a way that enhances emotional engagement; (2) statistically adjust for age, education level, origin, and motivation when interpreting learner satisfaction results; and (3) monitor the level of emotional engagement and implement educational interventions to provide support for emotional disengagers.