Massive Open Online Courses Learners Research Articles

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.

Exploring On-Campus Student Satisfaction on Learning Via Mooc

Satisfaction is the key to determine students’ intention to continue using a Massive Open Online Course (MOOC) and examining these students’ MOOC learning experience can provide insights into their satisfaction. Thus, this study aims to explore the MOOC learning experience of on-campus students who took up a MOOC on ICT Competency to identify aspects that are satisfying as well as dissatisfying to them. This study employed a qualitative approach in which eight students who had completed the ICT Competency MOOC were purposively chosen. The critical incident technique (CIT) was employed to collect and analyze information about significant experiences or critical occurrences of these participants during their MOOC learning. The meanings to these critical occurrences were collected to determine whether each experience inferred satisfaction towards the MOOC learning experience or otherwise. Participants were interviewed individually and the interview was guided by the eight aspects of Badrul Khan’s e-learning framework. The study reveals six satisfaction factors and another six dissatisfaction factors that point to six important lessons in designing and implementing a MOOC. The six lessons learned include the importance of providing flexibility in learning; providing a user-friendly interface and appealing as well as comprehendible learning materials; providing manageable, relevant assessments with clear assessment instructions; providing adequate instructor engagement; providing essential infrastructure and stable technological affordances; and incorporating anti-plagiarism strategies.

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.

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.

MOOC Learning Assessment in Clinical Settings: Analysis from Quality Dimensions.

Online learning is becoming a fundamental modality of learning in medical education, and can be of great help during global crisis like the current COVID-19 pandemic. The MOOC (massive open online course) mode of e-learning is increasing its penetration worldwide, as a valid teaching approach to reach large populations. A major challenge in clinical education is the assessment of medical students and residents in clinical settings, and there is substantial evidence that the current situation requires improvement. The goal of this study was to evaluate the quality dimensions of a MOOC titled “Learning assessment in clinical settings,” developed by three Mexican universities in the Coursera platform. A mixed-method study design was used to assess the quality dimensions of the MOOC in two phases: pilot and implementation. The best-rated aspects were learning resources and pedagogical perspective, and those with opportunity for improvement were collaboration and time management. Assessment might be learned through a MOOC format, especially for participants willing to engage with educational technology and self-direction.

Influence of MOOC learners discussion forum social interactions on online reviews of MOOC

Although some studies have explored massive open online courses (MOOCs) discussion forums and MOOC online reviews separately, studies of both aspects are insufficient. Based on the theory of self-determination, this paper proposes research hypotheses that MOOC learning progress has a direct impact on MOOC online reviews and an indirect influence on MOOC online reviews through social interactions in discussion forums, as well. Coursera the largest MOOC platform, is selected as the empirical research object, and data from learners who participated in the MOOC discussion forum and provided MOOC online reviews from August 2016 to December 2019 are obtained from the most popular course, “Machine Learning”. After processing, data from 4376 learners are obtained. Then, according to research hypotheses, multi regression models are constructed accordingly. The results show that the length of MOOC online review text is affected by the MOOC learning progress, the number of discussion forum posts, the number of follow, the online review sentiment and MOOC rating. This study highlights the main factors that affect MOOC online reviews. As a result, some suggestions are put forward for the construction of MOOC.

Disclosing Personal Names in Screen Names Predicts Better Final Achievement Levels in Massive Open Online Courses

The anonymity of the Internet used to be considered as an encouraging factor that helped learners engage in online learning. However, academic studies on anonymity have found that its effect on learning is context-dependent or mixed. In this research, we focused on massive open online course (MOOC) learners’ preference for personal name disclosure in their screen names as a predictor of their final achievement levels (FALs) at the end of a course including 2606 active learners. We conducted two studies, one to examine the associations between these two variables and one to demonstrate how such associations can be utilized in MOOC FAL prediction. We found that MOOC learners who included personal names in their MOOC screen names significantly outperformed other learners in their FALs (p < 0.001). We also found that screen name preference improved FAL prediction accuracy utilizing natural language processing and proper machine learning technologies. The error rate was reduced to 4.03% by a random forest algorithm with an appropriate feature combination: the personal name disclosure indicator (PNDI), quiz scores, number of replies, and exam scores. The results are potentially useful for the development of an early intervention to provide different types of help to students who prefer to disclose personal names and those who do not. The practical effects of these interventions will be examined in the future. In addition, whether the course difficulty level or course type affects the associations between personal name disclosure and FAL will also be examined.

Enhanced Clustering-based MOOC Recommendations using LinkedIn Profiles (MR-LI)

With the rapid development of massive open online courses (MOOCs), the interest of learners in MOOCs has increased significantly. MOOC platforms offer thousands of varied courses with many options. These options make it difficult for learners to choose courses that suit their needs and compatible with their interests. So, they become exposed to many courses on all topics. Therefore, there is an urgent need for personalized recommendation systems that assist learners in filtering courses according to their interests. Therefore, in this research, we target learners on the professional platform, LinkedIn, to be the basis for user modeling; the number of extracted profiles equals 5,039. Then, skill-based clustering algorithms were applied to LinkedIn users. Subsequently, we applied the similarity measurement between the vector features of the resulting clusters and the extracted course vectors. In the experiment result, four clusters were provided with the top-N course recommendations. Ultimately, the proposed approach was evaluated, and the F1-score of the approach was .81.

Evaluating On-Line Courses via Reviews Mining

Because the participants are not limited by age-, gender-, race-, or geography-related barriers, recently, massive open online courses (MOOC) have witnessed remarkable growth in number of online self-learners, courses providers and online platforms. MOOC learners usually share some learning experiences and release millions of course-related comments in discussion forum. On the one hand, these comments could reflect the learners’ attitudes toward the online courses. On the other hand, semantic knowledge hidden in these comments would assist other learners to choose the appropriate courses and help instructors to improve their courses’ attraction. Recently, few research works focus on evaluating the courses through reviews mining. Thus, this paper constructs a curriculum evaluation system based on MOOC reviews, which quantifies the curriculum from different topics. Firstly, we employ latent dirichlet allocation (LDA) to mine the reviews generated by students, and obtain a topic-word distribution matrix and a comment-topic distribution matrix which can describe the topics of the course comments. Next, the emotion values of the comments in each topic are calculated by the auto-encoder and Bi-LSTM text classification model. We utilize the emotions and the quantified scores of the courses on different topics to establish a comprehensive curriculum evaluation system. The experimental results show that there are five main indicators abstracted from students’ reviews, which are instructor, course content, course assessment, MOOC platform, and hot courses. Moreover, comment texts of each course under different evaluation indicators are objectively and accurately converted into numerical marks, which can provide the students and educators with reliable references.

Learning Analytics in MOOCs as an Instrument for Measuring Math Anxiety

In this paper, math anxiety descriptions are extracted from Massive Open Online Course (MOOC) reviews using text mining techniques. Learners’ emotional states associated with math phobia represent substantial barriers to learning mathematics and acquiring basic mathematical knowledge required for future career success. MOOC platforms accumulate big sets of educational data, learners’ feedback being of particular research interest. Thirty-eight math MOOCs on Udemy and 1,898 learners’ reviews are investigated in this study. VADER sentiment analysis, k-means clustering of content with negative sentiment, and sentence embedding based on the Bidirectional Encoder Representations from Transformers (BERT) language model allow identifying a few clusters containing descriptions of various negative emotions related to bad math experiences in the past, a cluster with descriptions of regrets about missed opportunities due to negative attitudes towards math in the past, and a cluster describing gradual overcoming of math anxiety while progressing through a math MOOC. The constructed knowledge graph makes it possible to visualize some regularities pertaining to different negative emotions experienced by math MOOC learners.

An Algorithm and a Tool for the Automatic Grading of MOOC Learners from Their Contributions in the Discussion Forum

MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners’ grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.

Assessment of cognitive, behavioral, and affective learning outcomes in massive open online courses: A systematic literature review

This systematic review on massive open online courses (MOOCs) in higher education examined the research on the assessment of learning outcomes based on 65 peer-reviewed articles published between 2017 and 2019. This study aims to investigate the learning outcomes, related instruments, and assessment characteristics of these instruments in MOOCs. Learning outcomes that were examined in the studies that were reviewed concerned cognitive, behavioral, and affective learning outcomes. Twenty-five types of assessment approaches were employed to examine these outcomes and to identify the assessment characteristics. The results indicate that a consideration of the assessment of learning outcomes at the beginning of course design could support the formulation of explicit assessment goals and, in this way, instruct learners to work toward learning outcomes. A combination of knowledge tests and skill tasks can be used to examine cognitive outcomes in a particular MOOC. Outcome-oriented feedback rubrics are beneficial to support learner essay performance and interpretations of the utilization of rubrics could better guide providers to give peer feedback. A variety of behavioral and affective outcomes reflect multiple aspects of participant learning in MOOCs, which might contribute to better understanding by teachers and the provision of learning support. Furthermore, assessment tasks throughout the course may differ in difficulty and complexity, which could align with different levels of learner motivation. The findings provide a holistic picture of learning outcomes and related assessment instruments in current MOOCs. Curriculum designers and teachers could benefit from this study to consider appropriate learning outcome variables and instruments to apply in their MOOC practices. Future research might investigate the motivation of learners to participate in a MOOC and how this changes during a MOOC. This could help MOOC designers and teachers to align how learners are motivated, what they want to learn, and what they actually do learn.

The use of Massive Open Online Courses (MOOCs) in blended learning courses and the functional value perceived by students

Massive Open Online Courses (MOOCs) are presented as an option to the traditional model of higher education institutions. MOOCs have been used as part of face-to-face regular university courses as a new form of blended learning (BL), but little is known about the best ways to design effective MOOC-based BL. Besides, studies that assess the intention to continue using MOOCs do not assess their quality and value as perceived by students. This study aims to understand how MOOCs can be better integrated into blended learning. To do so, an exploratory case study was carried out a in MOOC-based BL in the discipline Fundamentals of Administration at a Brazilian university to evaluate three aspects: (1) pedagogical approach/rationale; (2) pedagogical/instructional design for integrating the MOOC into the course; and (3) perceived quality and value by the students. The results show that the MOOC was used as a blended learning method in an introductory course, replacing part of the hours of face-to-face classes, allowing an increase of the number of students per teacher, besides making the discipline more attractive to the students. Results also show that the functional value perceived by the students is favorable, with a score of 4.53 (7-point scale) and is influenced by the perceived quality regarding the MOOC (r = 0.29) and blended learning process (r = 0.22). This study extends previous research by demonstrating two different designs of MOOC-based BL that contribute to the improvement of the pedagogical process, reduce costs, and improve the quality problems of MOOCs. In addition, this study developed a model that associates perceived quality and perceived value with the intention to continue MOOCs within the context of the MOOC-based BL. This model may be used to evaluate which strategies for MOOC-based BL generate the highest perceived quality by the students.

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.

The Relationship between Students’ MOOC-efficacy and Meaningful Learning

This research explored two important constructs in 21st century e-education—students’ MOOC-efficacy and meaningful learning among undergraduate students in selected public universities in Malaysia. Its main objective was to examine the causal relationship of students’ MOOC-efficacy on meaningful learning. The study conceptualized students’ MOOC-efficacy in four dimensions (i.e. information searching, making queries, MOOC learning, and MOOC usability), while conceiving meaningful learning as having five dimensions (i.e. cooperative learning, active learning, authentic learning, constructive learning, and intentional learning). This research applied cross-sectional survey design. Data were collected with a 52-item questionnaire whose reliability indexes ranged from 0.822 to 0.890 for the dimensions. The study's population was identified as university students who have had some experience with MOOCs and who willingly volunteered to participate in the research. A sample of 603 respondents was drawn through simple random sampling. The full-fledged Structural Equation Modeling (SEM) was adopted for data analysis. The finding indicated that students’ MOOC-efficacy was positively associated with meaningful learning. The results show that students’ MOOC-efficacy explains 67% of the variance in meaningful learning. The fit indices indicate an adequate fit: RMSEA = 0.041, CFI = 0.923 and χ2/df = 2.067. The finding provides further insights into what works in an open online environment. The insights may be used to fulfill learners’ needs and preferences. MOOC-efficacy interventions are crucial in order to encourage students’ meaningful learning in the e-learning platform.&#x0D; Keywords: Massive Open Online Course (MOOC), meaningful learning, students’ MOOC-efficacy

Education for Sustainable Development—The Case of Massive Open Online Courses

Education for Sustainable Development (ESD) is a key instrument in transforming societies towards sustainable development, which can play a crucial role in the achievement of Agenda 2030 Sustainable Development Goals. The main purpose of this paper is to explore the current status and problems of implementation of Education for Sustainable Development by examining the sustainability-related knowledge content included in the higher education curriculum, with a special focus on the Massive Open Online Courses (MOOCs). We used both qualitative and quantitative data collection and analysis methods, including keyword analysis of selected MOOCs’ syllabuses, to capture the nature of the studied phenomenon and enable assessment of the degree to which the sustainable development goals are integrated into the learning content. We found that most of 17 SDGs are reflected in the MOOCs learning content, but in case of some learning topics, such as “Zero hunger” and “Peace, Justice and Strong Institutions”, a bigger effort should be made to include them more broadly in the curricula. The research results can support educators’ efforts in identifying and incorporating challenging issues within the processes of curriculum development, indicating the need for a holistic and interdisciplinary approach, in which sustainability should be mainstreamed into all aspects of education.

How Do Mixed Teaching Teams Influence Learning in IT MOOCs?

The impact of mixed teaching teams on learning quality and the outcomes of diverse target groups in a massive open online learning context in the workplace is scarcely analyzed. Insights from gender studies on female role models, research on team teaching in computer science as well as digitally supported learning do not transcend these separate areas and are therefore of restricted explanatory power. Accordingly, this session will answer the research question on how male-only and mixed teaching teams influence IT MOOCs (Massive Open Online Courses) in regard to the proportion of female learners, participation in the discussion forum, and success rates. This study will be backed by the learning data gathered in the time of running two courses about blockchain with 14,000 learners on our MOOC platform openHPI with a mixed teaching team and a male teacher. The courses are conducted entirely online and are open and free of charge. All the material provided can be accessed from any device connected to the Internet. As our MOOCs in the enterprise context address not only employees, but also partners, students and other interested (lifelong) learn-ers we will include a closer look at learners’ socio-demographic background, such as career status, highest degree, professional life, and position. Additionally, we statistically analyze accompanying survey data using research methods in social sciences. Finally, our session will highlight and discuss challenges of mixed teaching teams in IT MOOCs and possible solutions to support gender diverse online learners in the workplace.

Persepsi Peserta Atas Penyelenggaraan Massive Open Online Course (MOOC) Audit Berbasis Risiko

This study is aimed at identifying and measuring the critical success factors in the implementation of the Massive Open Online Course (MOOC) in technical training for auditor within the Badan Pengawasan Keuangan dan Pembangunan. The study is based on the perceptions of training participants obtained from the answers to the questions in the questionnaire that were filled out when completing the learning session. The questions are divided into five groups of questions which according to the literature are critical success factors including the provision of training, training materials and learning designs, online lecturers / instructors, training infrastructure, and support services. The sample in the study was taken from the MOOC learning session which included 165 responses. The results showed that the training participants perceived that the learning infrastructure factor was the most important determinant of success and was followed by the online instructor / instructor factor. While the training material and learning design factors were perceived by the training participants as the least influencing factors on the successful implementation of the MOOC. &#x0D; &#x0D; &#x0D;

Improving learner engagement in MOOCs using a learning intervention system: A research study in engineering education

AbstractThe development of web applications is an essential and critical skill for Computer Science Engineers. Nowadays, this is a core subject in the Computer Science Engineering Studies and an increasingly demanded subject in other Engineering Studies. With the aim to facilitate introductory know‐how in this area, the University Autónoma of Madrid offers at edX Platform the Massive Open Online Course (MOOC): “Introduction to Development of Web Applications”. Several studies have emphasised the positive impact of methods, such as the use of continuous feedback, to improve and promote learning persistence and engagement in online courses. We propose a system for periodically providing MOOC learners information about their progress and suggestions about how can they can improve their learner performance based on their achievements and interactions in MOOCs. This system is called Learning Intervention System for edX MOOCs (edX‐LIS). To test the intervention strategy on learners' behaviour supported by edX‐LIS, a research study was conducted with the learners of the mentioned MOOC. The results obtained from this study have demonstrated that the intervention strategy supported by the edX‐LIS has a positive impact on the motivation, persistence and engagement of the learners in the MOOC.

Profiling MOOCs from Viewing Perspective

Course assessments inextricably connect to learning behaviors, which are different from traditional courses to Massive open on line courses (MOOCs). The emerged high course dropout and low exam participation make viewing be the mostly presentive behavior of MOOC learning, and make us call for new assessment indexes. Based on the viewing behavior data provided by course platform Icourse, we provide a method to recognize potential all-rounders, and indexes to measure course attractions and order correlations between teaching and learning. For example, we treat the video label of viewing events as a random variable, adopt its information to measure viewing width, and then use the geometric mean of the information and the relative length of viewing time comparing with the length of videos to measure course attractions. The index can measure the diminishing marginal utility for learners as well as the information increment by viewing new videos. However, the unclear relationship between viewing and learning makes our results could not be used to assess course quality positively. Nevertheless, these can be used to detect which aspects of a course need to improve.