Massive Open Online Courses Instructors Research Articles

Unlocking the Potential in a Gamification-Based MOOC: Assessing Autonomous Learning and Self-Directed Learning Behaviors

Massive open online courses (MOOCs) have significantly advanced online education. Yet, MOOC instructors still grapple with the ongoing challenge of low learner engagement while striving to foster autonomous and self-directed learning (SDL). Integrating gamification into these courses could be a promising solution. However, the extent to which gamification shapes autonomous and SDL behaviors within MOOCs remains underexplored. This mixed-methods sequential explanatory study investigates the interplay between MOOC, gamification, autonomous learning, and SDL behaviors to harness the potential of online education. We address this research void by delving into the intricate relationship between gamification and learners’ engagement in autonomous learning and SDL behaviors. Drawing upon self-determination theory (SDT) as a theoretical framework, we scrutinize how gamified MOOCs influence intrinsic motivation and SDL behaviors. Our study utilizes questionnaires and structured interviews to comprehensively examine the users’ experiences within a gamified MOOC. The findings suggest that thoughtfully integrated gamification elements not only enhance learner engagement but also stimulate autonomous learning and SDL behaviors. However, it is crucial to approach the integration of gamification thoughtfully, ensuring a balance that promotes intrinsic motivation without undue reliance on extrinsic motivators. These findings hold significant implications for educators, course designers, and policymakers leveraging gamification to enrich online learning experiences and cultivate self-directed educational pathways. They offer valuable insights for educators, course designers, and policymakers aiming to leverage gamification for enriching online learning experiences and nurturing self-directed educational pathways in MOOCs.

Designing MOOCs in South America towards open and equitable education

This study utilizes a qualitative research design to investigate the design experiences of massive open online courses (MOOCs) instructors in South American academic institutions. It reveals that MOOC instructors are primarily motivated to teach MOOCs from their desire to make contributions to society (e.g., providing free and accessible MOOCs on emerging topics and demanding subjects) and experience innovative teaching and learning, as well as respond to their institutions’ call for MOOCs. These courses are designed to reach audiences who face linguistic, financial, and geographical disadvantages within South American regions. The findings of this study inform the need to develop more non-English MOOCs in a variety range of subject areas to provide access to a broader population and promote educational equity. This study further addresses the ambiguity of the relationship between OER and MOOCs. The findings indicate that though MOOC instructors in South America have not yet fully perceived the benefits of OER, many of them have successfully integrated OER into their MOOCs to make them more accessible.

Scrutinizing student dropout issues in MOOCs using an intuitionistic fuzzy decision support system

Massive open online courses (MOOCs) are a recent e-learning programme that has received widespread acceptance among several colleges. Student dropout from MOOCs is a big worry in higher education and policy-making circles, as it occurs frequently in colleges that offer these types of courses. The majority of student dropouts are caused by causes beyond the institution’s control. Using an IF-DEMATEL (Intuitive Fuzzy Decision-making Trial and Evaluation Laboratory) approach, the primary factors and potential causal relationships for the high dropout rate were identified. The most effective aspects of massive open online courses (MOOCs) are identified using IF-DEMATEL and CIFCS. Moreover, it explains the interconnectedness of the various MOOC components. As an added measure, a number of DEMATEL techniques are used to conduct a side-by-side comparison of the results. Decisions made by the educational organisation could benefit from the findings. According to the research, there are a total of twelve indicators across four dimensions that are related to online course withdrawal amongst students. Then, experienced MOOC instructors from various higher education institutions were invited to assess the level of influence of these characteristics on each other. Academic skills and talents, prior experience, course design, feedback, social presence, and social support were identified as six primary characteristics that directly influenced student dropout in MOOCs. Interaction, course difficulty and length, dedication, motivation, and family/work circumstances have all been found to play a secondary part in student dropout in massive open online courses (MOOCs). The causal connections between the major and secondary factors were traced and discussed. The results of this study can help college professors and administrators come up with and implement effective ways to reduce the high number of students who drop out of massive open online courses (MOOCs).

Understanding Instructors’Motivations to Improve MOOC Sustainability

The global success of Massive Open Online Courses (MOOC) makes its analysis crucial to guarantee the quality, engagement, and best results in their implementation. Much research has been dedicated to understanding learner’s experience and institutional results, but few papers have studied the instructor’s perspective, which is essential to making MOOC a sustainable endeavor. This study has examined the perceived motivation for participating in a MOOC initiative and the impact on their career development for 79 teachers involved in a university experience that has made more than 600 editions of 115 MOOCs with 4 million enrolments. The study aims to confirm and expand previous studies’ findings and consolidate the institutional strategy regarding the resources and dynamics of MOOCs. Main research findings indicate intrinsic motivation factors are essential for MOOC instructors, and the lack of official recognition can be a relevant factor affecting MOOCs’ long-time sustainability.

Mapping of Instructional Design of MOOC’s Elements

MOOC (Massive Open Online Course) is an innovation in the field of online learning that has been popular since 2012. MOOC users during the COVID-19 pandemic increased dramatically, but the dropout rate for MOOC was also high. One of the things that causes the high dropout rate is the MOOC design. To get a good MOOC design, it is necessary to design an instructional design. Instructional design is a guide in designing instruction or learning stages to facilitate effective learning so that learning objectives can be achieved. Many examples of instructional design have been introduced in previous studies, one of which is "The 7Cs Learning Design Framework". In this research, a mapping of elements that are found in MOOC will be carried out into the category of MOOC features that are closely related because of the similarity of functions and properties with "The 7Cs Learning Design Framework". It is hoped that after this mapping has been carried out, system developers and MOOC instructors can focus more on a good MOOC design so as to maintain MOOC user retention and satisfaction.

Analyzing Log Data of Students Who Have Achieved Scores Adjacent to the Minimum Passing Grade for a K-MOOC Completion in the Context of Learning Analytics

Collecting and analyzing log data can provide students with individualized learning to maintain their motivation and engagement in learning activities and reduce dropout in Massive Open Online Courses (MOOCs). As online learning becomes more and more important, the demand for learning analytics is surging to design a variety of interventions that can achieve learning success and achieve individual learning goals and targets. In response to significant demand, we intended to derive data standards for learning analytics by specifying more the factors influencing MOOC completion suggested in previous research results. Therefore, this study aims to compare the event logs of students who have achieved scores adjacent to the minimum passing score of Korean Massive Open Online Course (K-MOOC) completion by dividing them into the completion (C) group and the non-completion (NC) group. As a result of analyzing the log data accumulated on the 60 K-MOOCs, what is interesting in the results of this study is that there was no significant difference between the C group and the NC group in video viewing, which is considered the main learning activity on the MOOC platform. On the other hand, there was a statistically significant difference between the C group and the NC group for textbook interactions in the percentage of learners who performed and the average number of logs per learner, as well as problem interactions in the average number of logs per learner. Students’ assertive activities such as textbook interaction and problem interaction might have greater value for MOOC completion than passive activities such as video watching. Therefore, MOOC instructors and developers should explore more specific design guidelines on how to provide problems with individualized hints and feedback and offer effective digital textbooks or reference materials for the large number of students. The results suggest that collecting and analyzing MOOC students’ log data on interactions, for understanding their motivation and engagement, should be investigated to create an individualized learning environment and increase their learning persistence in completing MOOCs. Future studies should focus on investigating meaningful patterns of the event logs on learning activities in massive quantitative and qualitative data sets.

Interest in Educational Data and Barriers to Data Use Among Massive Open Online Course Instructors.

Higher education instructors constantly rely on educational data to assess and evaluate the behavior of their students and to make informed decisions such as which content to focus on and how to best engage the students with it. Massive open online course (MOOC) platforms may assist in the data-driven instructional process, as they enable access to a wide range of educational data that is gathered automatically and continuously. Successful implementation of a data-driven instruction initiative depends highly on the support and acceptance of the instructors. Yet, our understanding of instructors’ perspectives regarding the process of data-driven instruction, especially with reference to MOOC teaching, is still limited. Hence, this study was set to characterize MOOC instructors’ interest in educational data and their perceived barriers to data use for decision-making. Taking a qualitative approach, data were collected via semi-structured interviews with higher education MOOC instructors from four public universities in Israel. Findings indicated that the instructors showed great interest mostly in data about social interactions between learners and about problems with the MOOC educational resources. The main reported barriers for using educational data for decision-making were lack of customized data, real-time access, data literacy, and institutional support. The results highlight the need to provide MOOC instructors with professional development opportunities for the proper use of educational data for skilled decision-making.

Understanding Learners’ Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis

Massive open online courses (MOOCs) have exploded in popularity; course reviews are important sources for exploring learners’ perceptions about different factors associated with course design and implementation. This study aims to investigate the possibility of automatic classification for the semantic content of MOOC course reviews to understand factors that can predict learners’ satisfaction and their perceptions of these factors. To do this, this study employs a quantitative research methodology based on sentiment analysis and deep learning. Learners’ review data from Class Central are analyzed to automatically identify the key factors related to course design and implementation and the learners’ perceptions of these factors. A total of 186,738 review sentences associated with 13 subject areas are analyzed, and consequently, seven course factors that learners frequently mentioned are found. These factors include: “Platforms and tools”, “Course quality”, “Learning resources”, “Instructor”, “Relationship”, “Process”, and “Assessment”. Subsequently, each factor is assigned a sentimental value using lexicon-driven methodologies, and the topics that can influence learners’ learning experiences the most are decided. In addition, learners’ perceptions across different topics and subjects are explored and discussed. The findings of this study contribute to helping MOOC instructors in tailoring course design and implementation to bring more satisfactory learning experiences for learners.

Indicators for enhancing learners’ engagement in massive open online courses: A systematic review

Massive open online courses (MOOCs) have paved a new learning path for the 21st-century world. The potential to reach a massive geographically dispersed audience is one of the major advantages of MOOCs. Moreover, they can be offered on a self-paced and self-regulated basis and have become an integral part of lifelong learning, especially in workplaces. However, one persistent problem is the lack of learners’ engagement. A harmonisation of studies providing a holistic view into aggregating indicators for enhancing learners’ engagement in MOOCs is lacking. The coronavirus pandemic has accelerated MOOC adoption, and learners’ engagement in MOOCs has become even more essential for the success of this educational innovation. We examine the existing literature to derive indicators important for enhancing learners’ engagement in MOOC learning environments. Using a systematic approach, 83 empirical studies were examined, and 10 indicators were identified as important considerations for enhancing learners’ engagement while designing MOOCs—from initiatives for individual learners to platform and instructional design perspectives. We also present a table describing these indicators and offer a structured discussion on each one. We believe the results provide guidelines for MOOC designers and instructors, educational policymakers, higher education institutions, and MOOC engagement researchers.

Impact of an Instructor's Personalized Email Intervention on Completion Rates in a Massive Open Online Course (MOOC)

Although Massive Open Online Courses (MOOCs) are increasing in popularity, they have been subject to criticism due to the high dropout rate. This study examined the impact of an instructor's personalized email intervention on the rate of completion of a nine week course, which included seven weekly quizzes, and the rate of completion of the final exam. The participants, who took an Israeli noncredit academic MOOC on negotiation management, were randomly assigned to two groups. Treatment group participants (N = 576) who did not complete the weekly quiz received a tailored reminder by email from their instructor encouraging them to complete the quiz and offering them assistance in order to deal with the past week’s contents. The control group (N = 608) that did not complete the weekly quiz did not get any emails from the instructor. The impact of the intervention was measured in three different ways: the immediate-impact, the delayed-impact and a cumulative impact. The increase in quiz completion within a week after the instructor's email was defined as an immediate-impact. The increase in the completion of the next quiz was defined as a delayed impact. The increase in the final exam completion rates was defined as a cumulative impact. The results show that the weekly intervention had an immediate impact as well as a cumulative impact on the final exam completion rate. The results suggest that an instructor's acknowledgement and interest might increase learners' commitment to learning in a MOOC. This study aimed to gain insight into learners' propensity to stay active in a MOOC and to increase completion rates. Findings of this study can be useful to MOOC designers and instructors to design and facilitate more effective MOOCs for learners by using email interventions to prevent students from dropping out of courses.

GamiTool: Supporting Instructors in the Gamification of MOOCs

Reward-based gamification strategies are proposed as a promising technique to increase student engagement in Massive Open Online Courses (MOOCs), following its success in other small-scale educational settings. However, these strategies imply a number of orchestration tasks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., design, management) that are usually carried out by instructors, and which may hinder their use and adoption. Furthermore, some MOOC distinctive features ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., scale, 24/7 availability, etc.) have considerable implications on how these gamification strategies are orchestrated, resulting in an unmanageable instructors’ workload in cases of manual operation. Therefore, an eventual adoption of gamification in MOOCs calls for automatic systems capable of decreasing the additional workload of instructors. The limitations identified in the current solutions ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., non-usable graphical interfaces, inflexible gamification designs) led us to propose and develop a new gamification system named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GamiTool</i> . An evaluation with 19 MOOC instructors and gamification designers showed the high design expressiveness, usability and potential for adoption of GamiTool. Hence, GamiTool can be used by instructors to improve students’ engagement, and also, by researchers to keep understanding the effects of gamification in MOOC settings.

Taking action to reduce dropout in MOOCs: Tested interventions

Massive Open Online Courses (MOOCs) have democratized access to higher education, but low entry and exit barriers make dropout rates in these courses much higher than in traditional in-person courses. Previous research has explored the main factors driving student dropout and proposed predictive models to identify students at risk. However, it is still unclear what type of interventions should be implemented to effectively reduce the dropout rate in MOOCs. We use findings from the literature to propose a theoretical framework to guide the design of interventions to reduce dropout in MOOCs. We design four interventions and implement them using A/B testing and natural experiment approaches in courses of the MITx MicroMasters® Program in Supply Chain Management (a MOOC-based program). Our findings reveal that ad hoc interventions communicated via email are not effective in reducing dropout, but interventions that modify course contents to make them more approachable for students show a positive impact on dropout reduction. By proposing a theoretical framework and uncovering the design features that can help to create effective interventions for dropout reduction, we provide a foundation for further research in the area of dropout interventions. This paper offers guidance to MOOC designers and instructors on how to improve student engagement and increase completion rates.

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”.

Agent-Supported Peer Collaboration in MOOCs.

While massive open online courses (MOOCs) can be effective in scaling education, orchestrating collaborative learning activities for large audiences remains a non-trivial task that introduces a series of practical challenges, such as the lack of adequate human support. Even when collaboration takes place, there is uncertainty whether meaningful interactions will occur among learners. This work presents the architecture of a prototype system called PeerTalk. The system was created to enable instructors to easily incorporate real-time collaborative learning activities into their online courses. Furthermore, PeerTalk employs a conversational agent service that aims to scaffold students’ online collaboration and provide valuable guidance, which can be configured by the course instructor. In order to investigate the user-acceptance of the system, two evaluation studies took place. The first one involved a group of experts, i.e., MOOC instructors who are expected to use such a system in their course, whereas the second study featured 44 postgraduate students. The study findings were encouraging in terms of the system efficiency and usability levels, laying the foundation for a conversational agent service, which can effectively scale the support of the teaching staff and be easily integrated in MOOC platforms, creating further opportunities for valuable social interaction among learners.

Enhancing MOOC learners’ skills for self-directed learning

ABSTRACT The management of self-directed learning is especially critical for massive open online course (MOOC) learners. This mixed-method study investigated MOOC instructors’ strategies to facilitate learners’ self-management skills for SDL. The study used three data sources: (a) an online survey with 198 MOOC instructors; (b) semi-structured interviews with 22 participants (the primary data source); and (c) document reviews of 22 MOOCs. The quantitative data were analyzed using descriptive statistics, and the qualitative data were analyzed using content analysis. The study findings indicate that MOOC instructors facilitated learners’ self-management skills from four perspectives: the enactment of learning goals (e.g., explanations and appreciation of students’ learning goals); time management (e.g., providing time frames, progress indicators, and short learning units); management of resources and support (e.g., flexible learning resources, peer assessments, and accessibilities); and navigation (e.g., clear organization and video tutorials on navigating the MOOC). The paper discusses the implications for researchers and practitioners.

Acting in secret: Interaction, knowledge construction and sequential discussion patterns of partial role-assignment in a MOOC

Forum discussions have been utilised widely as a means of facilitating learning interaction and social-knowledge construction in online learning. Much research has been conducted on the instructional interventions that benefit asynchronous discussions. Role-playing, or assigning roles to discussants, has been proven effective in promoting interactivity and knowledge construction in the context of both face-to-face and online learning. However, assigning and rotating roles to thousands of learners in massive open online courses (MOOCs) and preparing them to act properly in their roles sounds impractical to MOOC instructors due to work overload. The present study provided three types of role assignment in a MOOC during various course offerings: fall offerings with no role-assignment, spring offerings with partial role-assignment and summer offerings with full role-assignment. Through the examination of the discussion patterns and role-assignment differences among 4,239 students and 5,439 posts in 56 forums, we suggest that partial role-assignment is as effective as full-role assignment. By assigning as few as 10 students with rotating roles, MOOC instructors can leverage this effective strategy while minimising their effort in preparing the discussants and moderating the discussions. These students act behind the scenes and improve the behavioural patterns of asynchronous discussions.&#x0D; Implications for practice or policy:&#x0D; &#x0D; MOOC instructors and teaching assistants can leverage a partial role-assignment strategy to improve asynchronous discussion quality with manageable effort.&#x0D; MOOC platform leaders and instructional designers may explore work-smart teaching strategies that are viable in practice without overburdening instructors.&#x0D;

Using Phenomenological Methodology with Thematic Analysis to Examine and Reflect on Commonalities of Instructors’ Experiences in MOOCs

Instructors’ experiences in MOOCs assist their curriculum development and teaching skills as well as professional growth, which is seldom explored. The study examined and reflected on the commonalities of instructors’ experiences in Massive Open Online Courses (MOOC) based on phenomenological methodology with thematic analysis. By summarizing the commonalities in phenomenology from the implicit experiences of the instructors who were interviewed, commonalities of MOOC instructors’ experiences were found to be the following: (1) affinity—knowing how to provide MOOC material that is approachable; (2) ability to tell a story—knowing how to write and direct a video that contains a story or scenarios for classes as innovative teaching; (3) macro attitude—broadening a learner’s horizon is more important than lecturing on knowledge; (4) altruism—concerning the welfare of students rather than personal fame and fortune; and (5) learning by doing—having a passion for innovative teaching and bravely implementing it. Finally, several suggestions and inspirations were given based upon the reflections on the commonalities of MOOC instructors’ experiences.

ADDRESSING LEARNER CULTURAL DIVERSITY IN MOOC DESIGN AND DELIVERY: STRATEGIES AND PRACTICES OF EXPERTS

This mixed-methods study investigates the design and instructional practices of massive open online courses (MOOCs) instructors within the learning environment to address the cultural diversity and learner personalization needs. Leveraging a grounded theory approach, the researchers analyzed two rounds of email interviews (n1= 25; n2=19) with MOOC and open education leaders about cultural sensitivity and personalization in MOOCs. Those interviews led to the formation of a 30-item online questionnaire completed by 152 MOOC instructors. While many of the MOOC instructors within the sample did not fully grasp the complex issues of cultural diversity, most made attempts to modify their instructional practices to accommodate cultural variances. To address cultural and linguistic differences, instructors added subtitles to video content and offered transcripts for video or audio content. Additionally, instructors were careful with language use and hand gestures, used simplified language, slowed their pace of speech, made the course content easy to navigate, limited text by leveraging (multi)media, and encouraged learners to translate and localize content for their peers. Furthermore, many instructors favored collaborative, small group learning; however, instructors could not agree on best practices to establish these groups. Implications and future directions for MOOC instructors and instructional designers are offered.

Exploring behavioural differences between certificate achievers and explorers in MOOCs

ABSTRACT Massive open online courses (MOOCs) face the ongoing problem of low pass rates. A number of MOOC learners engage in most or all course activities but fail to pass the course. To improve participation and thereby increase successful completion, this study explores the behavioural differences between certificate achievers and explorers that lead to their different learning outcomes. Eleven behaviours were extracted from more than 80,000 behaviour records for 215 certificate achievers and 456 explorers, which were categorized into four types of activities: graded assessment, content-related, course-related, and interactive activities. Random forest classifier, independent-sample t-testing, and lag sequential analysis were used. The results showed six important behaviours that are highly related to certificate achievement. Certificate achievers engaged in more course-related and graded assessment activities during the course, while explorers engaged in more content-related and interactive activities, and graded assessment activities occurring at the end of the course. Compared to explorers, certificate achievers exhibited more bidirectional behaviours in terms of interactive and course-related activities and more repeated behaviours in terms of course-related and graded assessment activities. The study has significant implications for MOOC learners and instructors in improving learning and teaching performance by implementing appropriate behavioural interventions.

Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs

Massive Open Online Courses (MOOCs) have become a popular tool for worldwide learners. However, a lack of emotional interaction and support is an important reason for learners to abandon their learning and eventually results in poor learning performance. This study applied an integrative framework of achievement emotions to uncover their holistic influence on students’ learning by analyzing more than 400,000 forum posts from 13 MOOCs. Six machine-learning models were first built to automatically identify achievement emotions, including K-Nearest Neighbor, Logistic Regression, Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machines. Results showed that Random Forest performed the best with a kappa of 0.83 and an ROC_AUC of 0.97. Then, multilevel modeling with the “Stepwise Build-up” strategy was used to quantify the effect of achievement emotions on students’ academic performance. Results showed that different achievement emotions influenced students’ learning differently. These findings allow MOOC platforms and instructors to provide relevant emotional feedback to students automatically or manually, thereby improving their learning in MOOCs.