Design Principles and Impact of a Learning Analytics Dashboard: Evidence from a Randomized MOOC Experiment

This chapter analyzes the instructional quality and learning design of different categories of online courses and their history, with a special focus on massive open online courses (MOOCs). Online courses have a long tradition that has gained public attention, broad interest, and huge numbers of participants thanks to the introduction of free MOOCs accessible online for all interested learners worldwide. In this chapter, we first define MOOCs, their characteristics, and history. Afterward, theoretical frameworks and practical instruments and tools based on scientific research are presented. From the beginning, the quality of MOOCs (and of online courses in general) has been debated. That led to discussions about the learning design and outcomes of MOOCs, which we introduce in the next section. Key research findings and practical validated instruments for designing and evaluating MOOCs (and online courses in general) are presented. Then following, the key benefits of MOOCs and the main arguments and scenarios for their usage are summarized. Based on our analysis of the research results, practices, and standards, a framework for categories and types of (massive open) online courses is proposed, called the typologies of online courses (TOC) framework. As part of the global community for open educational resources (OER) and in combination with the UNESCO recommendation on OER, MOOCs can play a significant role in achieving the SDG4 of the United Nations: inclusive and equitable quality education for all. This is true in particular during times of public lockdowns, such as during the ongoing COVID-19 pandemic.

A massive open online course on the laws of thermodynamics has been designed using the 5Es (Engage, Explore, Explain, Elaborate, Evaluate) lesson planning methodology and delivered using everyday engineering examples to provide familiar contexts for both a global cohort of more than 3000 learners ranging in age from teenagers to septuagenarians in 137 different countries plus a large class of 355 university undergraduates. In addition to the structured pedagogy, the massive open online course included innovations in the use of practical exercises, that could be performed in the learner’s kitchen, and worked examples to enhance the learning experience. The massive open online course was provided in parallel with an introductory undergraduate course and as an additional learning resource for the undergraduate students. A little less than one-fifth of the undergraduates participated in the massive open online course and just over half of these responded that it would be an acceptable substitute for traditional lectures. The percentage completing the massive open online course was relatively high at 28% compared to 21% for other massive open online courses on the same platform, which is attributed to the pedagogy and strong support for social learning based on a survey of fully participating learners in the last week of the five-week course.

Massive Open Online Courses (MOOCs) have been integrated into face-to-face language modules to enhance the educational experience of students. MOOCs appear to offer language learning opportunities as well as challenging the self-regulated learning (SRL) behaviour of students. This paper presents a work in progress with regards to the literature review of my PhD, which investigates SRL in face-to-face language modules using MOOCs. It discusses SRL as a requirement for engaging with these courses and analyses the way studies have integrated MOOCs into instructional language modules. This paper concludes by highlighting the need for understanding students’ SRL behaviour when engaging with MOOCs as part of their language modules.

Nowadays, the Web has revolutionized our vision as to how deliver courses in a radically transformed and enhanced way. Boosted by Cloud computing, the use of the Web in education has revealed new challenges and looks forward to new aspirations such as MOOCs (Massive Open Online Courses) as a technology-led revolution ushering in a new generation of learning environments. Expected to deliver effective education strategies, pedagogies and practices, which lead to student success, the massive open online courses, considered as the "linux of education", are increasingly developed by elite US institutions such MIT, Harvard and Stanford by supplying open/distance learning for large online community without paying any fees, MOOCs have the potential to enable free university-level education on an enormous scale. Nevertheless, a concern often is raised about MOOCs is that a very small proportion of learners complete the course while thousands enrol for courses. In this paper, we present LASyM, a learning analytics system for massive open online courses. The system is a Hadoop based one whose main objective is to assure Learning Analytics for MOOCs' communities as a mean to help them investigate massive raw data, generated by MOOC platforms around learning outcomes and assessments, and reveal any useful information to be used in designing learning-optimized MOOCs. To evaluate the effectiveness of the proposed system we developed a method to identify, with low latency, online learners more likely to drop out

The running security and stability of massive open online course's teaching network directly affect the implementation process of massive open online course's teaching tasks. In order to provide valuable reference data for the management and maintenance of the network, this paper puts forward an analysis method of abnormal traffic of higher vocational massive open online course's teaching network based on deep convolution neural network. According to the structure of the teaching network of massive open online course in higher vocational colleges, a network model is built. Under this model, the flow data is collected, and the preprocessing of the initial flow data is completed through data cleaning, standardized conversion and clustering. The deep convolution neural network is established, and the characteristics of network traffic data are extracted through back propagation iteration. After discrete detection and feature matching, the abnormal traffic in the teaching network of higher vocational massive open online course is detected, and the visual analysis results are obtained. Compared with the traditional network abnormal traffic analysis method, it is found that the detection error and missed detection rate of the optimized design method are reduced by 3.75MB and 1.02% respectively, the accuracy of traffic abnormal type analysis is increased by 1%, and the analysis speed is obviously improved.KeywordsDeep Convolution Neural NetworkTeaching in Massive Open Online CourseTeaching NetworkAbnormal Flow Analysis

<span>The United Nations adopted the sustainable development goal of “quality education” as one of its objectives. In emergency teaching and learning amid the outbreak in 2019, the emphasis has been placed on providing a versatile and easily accessible lifelong learning experience to ensure high-quality education. One type of online e-learning course is the massive open online course (MOOC). It provides a free course that may be taken whenever and anywhere. However, difficulties have come up regarding student performance, course completion, and dropping out as a result of quality assurance of e-learning platforms like MOOC. In order to keep students interested in the course until the end, this study will review and recommend MOOC strategies. The strategies in enhancing for MOOC engagement reveals that they include development, collaboration between educators and students, and evaluation. Self-regulation learning is a crucial motivation in retention from dropping out of the MOOC facilitates participation through the utilization of innovative pedagogies, as well as the interaction between students and educators on both the MOOC platforms and social media platforms according to research. Course information and instructional design are also found to attract learners to complete the course. Giving prizes for completing MOOC assignments and tests is an extra choice in retaining from dropping out the course. The contribution of study is MOOC online learning engagement strategies are introduced. By developing a good course design, collaboration between students and educators and evaluation performance based can enhance students’ engagement to completion of the course.</span>

The current shift from traditional classrooms to online learning in higher education calls for more attention to self-regulated learning. This research is motivated by the growing interest in potential of using learning analytics dashboard (LAD) to increase individuals' self-regulation by creating visibility into their performance in various applications. This study explores how data visualization can be integrated with online learning to improve learners' performance through enhancing their skills in planning and organization. We are working on the design of a comprehensive LAD, focusing on micro-level of learning analytics to support learning activities of students. The LAD includes the following two features to enhance students' self-regulation in online learning: (1) a function to track students' progress compared to other students' over time; (2) reminders to help students with upcoming deadlines and auto-generating to do lists. The hypothesis is that the LAD will increase students' engagement, motivation, and self-regulation in an online learning environment. This study is significant because it contributes to the body of knowledge by exploring how student-generated data can be used to improve self-regulated learning. The practical contribution of this study is to create a personalized LAD for students based on the learner-generated data to benefit students' organization skill, planning skill, and motivation.

Massive Open Online Courses (MOOCs) have been often described as a major innovation Higher Education, but their application in the teaching of clinical disciplines is still very limited, while there is a lack of scientific evaluations in this domain. The aim of this study was to investigate learners' behaviours and correlate patterns of Self-Regulated Learning (SRL) with performance and achievement during a MOOC in Implant Dentistry. The clickstream data of learners from the first run of the MOOC Implant dentistry by The University of Hong Kong were modelled and quantified based on Zimmerman's SRL model. The data were quantitatively analysed by means of k-means clustering for evidence of five SRL behavioural indicators of student activity. The clusters identified were then correlated with student engagement and learning performance. A total of 7608 individuals enrolled, 5014 engaged (active learners 65.90%), 1277 of them (25.47%) completed the course and 1232 purchased a certificate. Two major groups of learners emerged: Attentive (n=1433) who were more likely to follow the prescribed pathway in the MOOC and Auditors (n=3581) who accessed content selectively. There was significantly higher engagement, achievement and completion rates among Attentive than Auditors. Both groups included subcategories (Browser, Digger, Test-driven, Sampler, Persistent) which might reflect different SRL strategies. A MOOC in a clinical discipline can achieve high enrolment and completion rates as compared to current benchmarks. There appears to be a wide diversity of learning behaviours among learners, with two however dominant patterns. Learners with a linear learning pathway achieved significantly higher grades and completion rates than those who accessed content irregularly and selectively. Such differences however might be influenced by the demographic and professional background of the learner, as well as their motivation to attending the MOOC. Certain learning behaviours, in particular how learners access content in relation to assessments might be closer related to SRL.

MOOC(Massive Open Online Course)란 대단위로 누구나 무료로 수강할 수 있는 온라인 강좌이다. 이러한 MOOC가 기존 대학체계를 무너뜨리고, 교육의 새 지평을 열 것이라는 전망이 있었다. 그러나 MOOC는 기존의 고등교육기관을 위협할 것이라는 최초의 전망과는 다른 양상으로 진행되고 있다. 그래서 본 연구는 MOOC의 허와 실을 드러냄으로써 MOOC를 우리 고등교육의 실정에 맞게 제작하고 활용할 수 있는 논의의 시발점을 마련하고자 한다. MOOC는 기본적으로 온라인 교육이므로 기존 온라인 교육의 본질적인 문제점인 '상호작용, 감독 및 평가'의 문제를 그대로 계승할 수밖에 없다. 그리고 새로이 추가된 'Massive' 개념은 교육 현장에서 가장 민감한 부분인 강좌당 학생 수 문제를 불러일으키며, 'Open' 개념은 MOOC가 불특정 다수를 염두에 둔 강좌이므로 맞춤형 교육에 역행된다. 결론적으로 MOOC는 자기 주도적 학습 능력이 매우 탁월한 학습자들과 전통적인 온라인 고등교육기관에도 접근할 수 없는 사람들을 위한 교육 서비스로 자리매김 되고, 대학에서는 Flipped Learning의 수단으로 사용될 가능성이 매우 높다. 그러므로 향후 MOOC에서는 이 점이 고려되어 강좌의 대상에 맞게 차별화되어 개발될 필요가 있다. MOOC (Massive Open Online Course) is an online course aimed at limited participation and open access via the web. There is even the prospect that MOOC may break down the existing university system and open a new horizon of education. However, MOOC is going differently from the first prospect that it would threat the existing higher education institutions. So this study wishes to provide a starting point of discussion that we can produce and utilize MOOC to fit our situation of higher education by revealing the inherent problems of MOOC. MOOC is a basically online education, so that it cannot help inheriting the essential weakness of the existing one: the interaction, supervision and evaluation. The newly added concept 'massive' raises the problem of class size which is the most sensitive part in the field of education. The concept 'open' reverses a customized education because MOOC is a kind of video clips of a lecture for unspecific massive learners. As a conclusion, we predicts that MOOC will be positioned as a higher education service for very prominent learners in self-led learning ability and people who cannot access even traditional online higher education institutions. Also MOOC is highly likely to be used as a means of Flipped Learning in universities. Therefore, considering these points in the future, we need to develop MOOC to suit these targets.

BackgroundMassive open online courses (MOOCs) have become immensely popular in a short span of time. However, there is very little research exploring MOOCs in the discipline of health and medicine.ObjectiveWe aim to provide a review of MOOCs related to health and medicine offered by various MOOC platforms in 2013, by analyzing and comparing the various offerings, their target audience, typical length of course, and credentials offered. We also discuss opportunities and challenges presented by MOOCs in health and medicine.MethodsHealth and medicine–related MOOCs were gathered using several methods to ensure the richness and completeness of data. Identified MOOC platform websites were used to gather the lists of offerings. In parallel, these MOOC platforms were contacted to access official data on their offerings. Two MOOC aggregator sites (Class Central and MOOC List) were also consulted to gather data on MOOC offerings. Eligibility criteria were defined to concentrate on the courses that were offered in 2013 and primarily on the subject of health and medicine. All language translations in this paper were done using Google Translate.ResultsThe search identified 225 courses, of which 98 were eligible for the review. Over half (58%, 57/98) of the MOOCs considered were offered on the Coursera platform, and 94% (92/98) of all the MOOCs were offered in English. Universities offered 90 MOOCs, and the John Hopkins University offered the largest number of MOOCs (12/90). Only three MOOCs were offered by developing countries (China, West Indies, and Saudi Arabia). The duration of MOOCs varied from 3-20 weeks with an average length of 6.7 weeks. On average, MOOCs expected a participant to work on the material for 4.2 hours a week. Verified certificates were offered by 14 MOOCs, while three others offered other professional recognition.ConclusionsThe review presents evidence to suggest that MOOCs can be used as a way to provide continuous medical education. It also shows the potential of MOOCs as a means of increasing health literacy among the public.

Problem and purpose. The article analyzes the development of massive open online courses (MOOC), their structure and application in the practice of higher education, the introduction of new experience and strategy of distance learning at the Department of Physical Culture of the Siberian Federal University. The purpose of the article is to analyze the stages of the emergence and development of one of the areas of distance learning – massive open online courses (MOOC) and to analyze their application in the educational process of the University. The methodology of the research is based on the analysis of theoretical materials and research works of foreign and domestic scientists on the development of massive open online courses and the experience in the implementation of massive open online courses. Results. The article is devoted to the theory and experience of using MOОС in the world and in the educational process of the University. The authors describe the main intermediate results of the massive open online courses introduction in the educational process at the Department of Physical Culture of the Siberian Federal University. The problems, errors in the work with the MOОС and the aspects correcting the errors in future are analyzed. Conclusion. The results obtained so far allow us to conclude that the possible option of using distance learning technologies in the form of MOОС in teaching the discipline of „Physical Culture” is effective but requires additional research and further considerations.

In this paper we propose an overview of Learning Analytics approaches in the context of Open education in its most recent developments related to Massive Open Online Courses (MOOCs) and Linked Data (LD), both strictly interconnected to the Open Educational Resources (OERs) concept. The MOOCs have developed as a seamless integration of different solutions and services which have appeared on the Web in recent years to enable the opening of courses to hundreds or thousands of participants on the Internet (Siemens 2013). Actually MOOCs combine, amongst others, the networking potentials offered by the social Web (developed around the Web 2.0 portals), the ease of access to OERs, the informal learning opportunities which characterize the learners' autonomy in identifying their own patterns to knowledge, thus changing the way learning occurs. The huge amount of data produced during a MOOC suggest that Learning Analytics techniques could be extremely useful to support the learning and teaching processes during the course. Nevertheless, there is not yet a substantial body of literature on the Learning Analytics of MOOCs (Clow, 2013). D'Aquin in (d'Aquin, 2012) has pointed out how the Linked Data can be exploited in the educational field. In fact, LD offers educational benefits for teaching and learning by providing integration mechanisms of heterogeneous resources from diverse repositories and sources and by supporting automatic search techniques that provide learners with educational resources semantically related to a specific knowledge field. As a consequence, the huge amount of datasets of LD can be exploited for educational purposes, thus becoming an alternative source of Open Educational resources. The recent evolution of LD and its datasets has had a remarkable influence on Learning Analytics as well (Gasevic, 2012), so that Learning Analytics is not limited to numeric data, but it has to take into account data and their semantic representation. The aim of this paper is therefore to highlight - through the analysis of the recent literature - how Learning Analytics can enhance learning experiences in Open Education settings based on MOOCs and LD.

Massive Open Online Courses (MOOCs) have gained a lot of attention in the last years as a new technology enhanced learning (TEL) approach in higher education. MOOCs provide more educational opportunities to a massive number of learners to attend free online courses around the globe. Discussions around MOOCs have been focusing on the potential, social, institutional, technological, relevance, and marketing issues and less on the quality design of MOOC environments. Several studies have reported a high drop-out rate in average of 95% of course participants and other pedagogical problems concerning assessment and feedback. Thus, the quality of MOOCs design is worth additional investigation. Although several studies identified a large set of criteria to the successful design of TEL systems in general, not all of them can be used in the MOOC context, due to some unique features of MOOCs. This study is a first step towards identifying specific criteria that need to be considered when designing and implementing MOOCs. The results of this empirical study are based on a large survey targeting learners as well as professors, both with MOOC experience. As a result, we identified and rated 74 indicators classified into our two main dimensions of pedagogical and technological criteria distributed over six categories. From these, the learning analytics and assessment categories were found to be the key features for effective MOOCs.

Self-regulated learning (SRL) is a cyclical process through which individuals plan their objectives, execute them and self-evaluate their own behavior so as to obtain their proposed goals. SRL has been investigated by different authors such as Zimmerman, Boekaerts, Winne and Hadwin, Pintrich, Efklides and Hadwin, Jarvela and Miller and it's being applied in learning environments. This systematic review describes the current state of the art in terms of the support for SRL in Massive Online Open courses (MOOC) using technologies based on psychological models. 66 studies conducted between 2010 and 2020 were analyzed by searching three multidisciplinary databases: Scopus, Web of Science and Google Scholar. The review methodology steps were the review planning, the search, literature analysis and the results report. Results show SRL in MOOCs is an emerging study area incentivized by the high dropout rate of the participants in MOOC. Regarding models of SRL, the most representative author reported was identified as Zimmerman. The most prominent self-regulation strategies used by MOOCs participants are: Goal setting, Help Seeking, Time management, Self-evaluation and Strategic planning. The platforms with research on SRL in MOOCs that stand out are Coursera, Edx, Open Edx and Moodle. We identified tools which have been developed to support SRL in MOOC and a set of good practices useful to support SLR that can be used by MOOC designers and tutors. Finally, a series of open problems and challenges that could lead to new research on the topic of SRL in MOOCs have been identified.

The dominant direction in the development of online education, actively discussed by the pedagogical community, is training using massive open online courses (MOOCs). The key factor in the effectiveness of the implementation of MOOCs in the practice of educational organizations is primarily the availability of elite education. In modern conditions, which determine the priority of lifelong education (lifelong learning), it is particularly relevant to form schoolchildrens readiness for self-education, which can be facilitated by training on MOOC platforms. The broad discussion of MOOC implementation in educational practice is more concerned with university education, additional education and the system of advanced training. The use of MOOCs in school education in the publications of teachers-researchers is not considered fully enough and is mostly related to natural sciences. The paper analyzes the MOOC on the example of humanities and the features of the implementation of mixed learning models (inverted learning, autonomous group, individual trajectory) based on the use of online courses. This analysis suggests that there should be a need for more in-depth research in this area and the need to introduce elements of learning into school practice through massive open online courses. This will resolve the contradiction between the need for differentiation and individualization of training and the potential of existing unused massive open online courses.