{"title":"The Influence of Grades on Learning Behavior in MOOCs: Certification vs, Continued Participation","authors":"Li Wang, Erik Hemberg, Una-May O’Reilly","doi":"10.1109/lwmoocs47620.2019.8939614","DOIUrl":"https://doi.org/10.1109/lwmoocs47620.2019.8939614","url":null,"abstract":"MOOCs (massive open online courses) often use comprehensive exams and homework problem sets to assess students in their overall understanding of course material. The grades students receive on these tests and assignments determine whether they complete or become certified in course material. However, beyond receiving a certification, how do grades impact the learning behavior in students? Do students who receive poor grades actively change their overall activity to improve their grades? To better understand the impact of grades, we observe overall student activity on two MITx MOOCs for certified students and students who continuously participate in MOOC assignments. We use click stream data to compile the overall activity of a student and we use points earned divided by total possible points to calculate the students’ grades. We observe that students with the highest levels of activity have some of the highest grades. We also observe that the difference in activity before and after the finalization of a grade (delta-activity) have greater variation as grade increases. Finally, we observe very little changes in grades (delta-grade) for certified students when visualized against delta-activity and that continuously participating students have greater grade changes compared to certified students.","PeriodicalId":336528,"journal":{"name":"2019 IEEE Learning With MOOCS (LWMOOCS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124734339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Cross, Nopphon Keerativoranan, M. Carlon, Yong Hong Tan, Zarina Rakhimberdina, Hideki Mori
{"title":"Improving MOOC quality using learning analytics and tools","authors":"J. Cross, Nopphon Keerativoranan, M. Carlon, Yong Hong Tan, Zarina Rakhimberdina, Hideki Mori","doi":"10.1109/lwmoocs47620.2019.8939617","DOIUrl":"https://doi.org/10.1109/lwmoocs47620.2019.8939617","url":null,"abstract":"Assessing the quality of MOOCs is an important issue for learners since learners are paying fees for accessing the content (e.g. graded assignments), certificates of completion and for course credit. One of the unique advantages of online courses is that all the content can be assessed and analyzed even before the courses are released using various learning analytical and natural language processing tools. However, to date there are few studies in the literature published on the analysis of MOOC content. Furthermore, MOOC providers expect the course developers to periodically revise their MOOCs. Various types of analysis that can be done on the course text, video transcripts and assessments such as readability, listenability, videolytics, and text analysis. By analyzing the course content before its release, the content can be adjusted to target various learners. Subsequently, the same techniques can be used to analyze the discussion board posts and post-course survey to identify areas in a course that need to be modified in to order to improve the course quality for subsequent release. In this paper natural language processing and MOOC analytics were applied to several MOOCs to identify areas for revision to enhance their quality.","PeriodicalId":336528,"journal":{"name":"2019 IEEE Learning With MOOCS (LWMOOCS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132959966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualising Learning Pathways in MOOCs","authors":"Karsten Øster Lundqvist, S. Warburton","doi":"10.1109/lwmoocs47620.2019.8939659","DOIUrl":"https://doi.org/10.1109/lwmoocs47620.2019.8939659","url":null,"abstract":"It is difficult to get an understanding of learner behaviour within MOOCs. Commonly used metrics are not well suited for this. This paper describes a visualisation tool based on modified Sankey diagrams to explore learner paths through edX MOOCs. The tool visualises learners’ progression through the learning material and their interaction with discussion forums. These diagrams can be filtered based on user demographies and behaviours. Automated clustering of users based on behaviour metrics is available to provide suggestions of different learner behaviours. Statistical tests are also included to allow hypothesis exploration by researchers. The tool was developed using a devops development method to improve the usability of the tool for MOOC learning designers and researchers.","PeriodicalId":336528,"journal":{"name":"2019 IEEE Learning With MOOCS (LWMOOCS)","volume":"164 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127303333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enrique Alvarez Vazquez, M. Pearson, Lauren Singelmann, R. Striker, Ellen Swartz
{"title":"Federal Funding Opportunity Announcements as a Catalyst of Students’ Projects in MOOC Environments","authors":"Enrique Alvarez Vazquez, M. Pearson, Lauren Singelmann, R. Striker, Ellen Swartz","doi":"10.1109/lwmoocs47620.2019.8939657","DOIUrl":"https://doi.org/10.1109/lwmoocs47620.2019.8939657","url":null,"abstract":"Our research group is currently engaged in an enhanced learning environment, within higher education, using Massive Open Online Courses (MOOCs) in combination with Innovation-Based Learning (IBL). Both approaches to learning when arranged correctly, improve students learning and engagement towards the course. When using IBL, one of the main problems that students experience is the selection of their class project. We propose a methodology based on funding opportunity announcements (FOA) to aid in the selection process. Student groups are able to quickly compare and match their skills and interests to market demand as expressed by groups such as NIH or NSF. The students in the class experience a blended approach; they collaborate online and in-class, and they apply knowledge acquired from online content to their main project. In this blended approach, FOAs act as a catalyst to students’ learning; it both focuses them at the beginning and motivates them throughout the course. Students in this MOOC-enabled learning ecosystem grow in their intrinsic motivation and engagement. Furthermore, FOAs project selection solves scalability issues when dealing with inter-disciplinary projects and availability of proper experts/mentors for students. This tailored combination improves efficiency and targets project’s relevance, which makes it very attractive to higher education administrators.","PeriodicalId":336528,"journal":{"name":"2019 IEEE Learning With MOOCS (LWMOOCS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125328189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evolution, Education and Massive Open Online Courses: A Multiverse Proposal","authors":"A. D. Rosa, Mario Pistolese","doi":"10.1109/lwmoocs47620.2019.8939620","DOIUrl":"https://doi.org/10.1109/lwmoocs47620.2019.8939620","url":null,"abstract":"The 21st century has given rise to an economic and social system based on learning and, therefore, on the capacity to create and connect new and existing knowledge through people, rather than on the abundance of content. This change, compared to the industrial model, calls for a serious analysis of the need for flexible, fluid and transversal training in subjects or, rather, in competencies, from different fields. Learning systems have begun to address this challenge with massive open online courses. Nonetheless, MOOCs often replicate classical models of learning. They follow traditional learning paths based on the concept of “cycles”, with analysis of topics within a single subject area. We need to develop learning objects that can be (re)articulated and developed according to the binary logic of decomposition-aggregation, fragmentation-recomposition. In this way, we can propose a sort of multi-skilled knowledge and skills set that is agile and capable of selecting and communicating on different levels, able to deal convincingly with diverse subject areas, and that can be constantly updated. This is the only way for lifelong learning to take place, within a constant process of regeneration and multiplication, rather than through cycles. Digital learning can play a decisive role in responding to this challenge and, in the long term, will be indispensible. It is a model that follows and, in some way, supports or partially replaces the traditional University offering. But, more than that, digital learning provides new “granular” access to knowledge in response to the contingent needs of those people who “have to” access that knowledge.","PeriodicalId":336528,"journal":{"name":"2019 IEEE Learning With MOOCS (LWMOOCS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123524868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Solving Diversity Issues in University Staff Training with UNIPS Pedagogical Online Courses","authors":"Samuli Laato, Heidi Salmento, Neea Heinonen, Emilia Lipponen, Henna Vilppu, Henna Virtanen, M. Murtonen","doi":"10.1109/lwmoocs47620.2019.8939634","DOIUrl":"https://doi.org/10.1109/lwmoocs47620.2019.8939634","url":null,"abstract":"In Finland and globally, many university teachers are teaching without pedagogical training. Employee training courses on pedagogy are offered via contact teaching, thus excluding potential students who are too busy to attend sessions at a specific time and place. In addition, majority of teaching is in Finnish, even though, for example, in the University of Turku, 10% of all employees are international. Due to limited teaching resources, university pedagogical studies used to be only available for university staff members who have teaching duties, excluding the majority of doctoral students from the courses. The UNIPS learning platform, developed by eight Finnish universities, was created to solve these problems. The current study investigates the impact UNIPS solution has on the above mentioned issues by looking quantitatively (N=590) at (1) which departments participants come from? (2) Are participants’ doctoral students or university employees and (3) what are the age and gender distributions of participants? In addition, participants’ perceptions of UNIPS studies are analyzed qualitatively. Based on the findings, UNIPS courses and similar MOOCs seem a promising way to support teachers’ pedagogical training. They can not only increase the diversity of offered studies, but also help create a more inclusive environment at universities.","PeriodicalId":336528,"journal":{"name":"2019 IEEE Learning With MOOCS (LWMOOCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129536182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
James J. Lohse, Christine A. McManus, David A. Joyner
{"title":"Surveying the MOOC Data Set Universe","authors":"James J. Lohse, Christine A. McManus, David A. Joyner","doi":"10.1109/lwmoocs47620.2019.8939594","DOIUrl":"https://doi.org/10.1109/lwmoocs47620.2019.8939594","url":null,"abstract":"This paper is a survey of the availability of open data sets generated from Massively Open Online Courses (MOOCs). This log data allows researchers to analyze and predict student performance. Often, the goal of the analysis is to focus on at-risk students who are not likely to finish a course. There is a growing gap between the average researcher (who does not have access to proprietary data) and the ready availability of data sets for analysis. Most research papers studying and predicting student performance in MOOCs are done on proprietary data sets that are not anonymized (de-identified) or released for general study. There are no standardized tools that provide a gateway to access usable data sets; instead, the researcher must navigate a maze of sites with different data structures and varying data access policies. To our knowledge, no open data sets are being produced, and have not been since 2016. The authors survey the history of MOOC data sharing, identify the few available open data sets, and discuss a path forward to increase the reproducibility of MOOC research.","PeriodicalId":336528,"journal":{"name":"2019 IEEE Learning With MOOCS (LWMOOCS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127263884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}